experiments/mackey-glass.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.recurrent import LSTM, RNN\n",
    "from keras.initializers import RandomUniform\n",
    "from keras.models import Sequential\n",
    "from sklearn.metrics import mean_squared_error\n",
    "# import tensorflow as tf; tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
    "\n",
    "import nengo\n",
    "from lmu import LMUCell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "\n",
    "def mackey_glass(sample_len=1000, tau=17, delta_t=10, seed=None, n_samples=1):\n",
    "    # Adapted from https://github.com/mila-iqia/summerschool2015/blob/master/rnn_tutorial/synthetic.py\n",
    "    '''\n",
    "    mackey_glass(sample_len=1000, tau=17, seed = None, n_samples = 1) -> input\n",
    "    Generate the Mackey Glass time-series. Parameters are:\n",
    "        - sample_len: length of the time-series in timesteps. Default is 1000.\n",
    "        - tau: delay of the MG - system. Commonly used values are tau=17 (mild \n",
    "          chaos) and tau=30 (moderate chaos). Default is 17.\n",
    "        - seed: to seed the random generator, can be used to generate the same\n",
    "          timeseries at each invocation.\n",
    "        - n_samples : number of samples to generate\n",
    "    '''\n",
    "    history_len = tau * delta_t \n",
    "    # Initial conditions for the history of the system\n",
    "    timeseries = 1.2\n",
    "    \n",
    "    if seed is not None:\n",
    "        np.random.seed(seed)\n",
    "\n",
    "    samples = []\n",
    "\n",
    "    for _ in range(n_samples):\n",
    "        history = collections.deque(1.2 * np.ones(history_len) + 0.2 * \\\n",
    "                                    (np.random.rand(history_len) - 0.5))\n",
    "        # Preallocate the array for the time-series\n",
    "        inp = np.zeros((sample_len,1))\n",
    "        \n",
    "        for timestep in range(sample_len):\n",
    "            for _ in range(delta_t):\n",
    "                xtau = history.popleft()\n",
    "                history.append(timeseries)\n",
    "                timeseries = history[-1] + (0.2 * xtau / (1.0 + xtau ** 10) - \\\n",
    "                             0.1 * history[-1]) / delta_t\n",
    "            inp[timestep] = timeseries\n",
    "        \n",
    "        # Squash timeseries through tanh\n",
    "        inp = np.tanh(inp - 1)\n",
    "        samples.append(inp)\n",
    "    return samples\n",
    "\n",
    "\n",
    "def generate_data(n_batches, length, split=0.5, seed=0,\n",
    "                  predict_length=15, tau=17, washout=100, delta_t=1,\n",
    "                  center=True):\n",
    "    X = np.asarray(mackey_glass(\n",
    "        sample_len=length+predict_length+washout, tau=tau,\n",
    "        seed=seed, n_samples=n_batches))\n",
    "    X = X[:, washout:, :]\n",
    "    cutoff = int(split*n_batches)\n",
    "    if center:\n",
    "        X -= np.mean(X)  # global mean over all batches, approx -0.066\n",
    "    Y = X[:, :-predict_length, :]\n",
    "    X = X[:, predict_length:, :]\n",
    "    assert X.shape == Y.shape\n",
    "    return ((X[:cutoff], Y[:cutoff]),\n",
    "            (X[cutoff:], Y[cutoff:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 5000, 1) (64, 5000, 1)\n"
     ]
    }
   ],
   "source": [
    "(train_X, train_Y), (test_X, test_Y) = generate_data(128, 5000)\n",
    "print(train_X.shape, test_X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cool_plot(X, Y, title=\"\"):\n",
    "    plt.figure(figsize=(14, 8))\n",
    "    plt.title(title)\n",
    "    plt.scatter(X[:, 0], Y[:, 0] - X[:, 0], s=8, alpha=0.7,\n",
    "                c=np.arange(X.shape[0]), cmap=sns.cubehelix_palette(as_cmap=True))\n",
    "    plt.plot(X[:, 0], Y[:, 0] - X[:, 0], c='black', alpha=0.2)\n",
    "    plt.xlabel(\"$x(t)$\")\n",
    "    plt.ylabel(\"$y(t) - x(t)$\")\n",
    "    sns.despine(offset=15)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "cool_plot(train_X[0], train_Y[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_19 (LSTM)               (None, 5000, 25)          2700      \n",
      "_________________________________________________________________\n",
      "lstm_20 (LSTM)               (None, 5000, 25)          5100      \n",
      "_________________________________________________________________\n",
      "lstm_21 (LSTM)               (None, 5000, 25)          5100      \n",
      "_________________________________________________________________\n",
      "lstm_22 (LSTM)               (None, 5000, 25)          5100      \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 5000, 1)           26        \n",
      "=================================================================\n",
      "Total params: 18,026\n",
      "Trainable params: 18,026\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "rnn_19 (RNN)                 (None, 5000, 49)          2720      \n",
      "_________________________________________________________________\n",
      "rnn_20 (RNN)                 (None, 5000, 49)          5120      \n",
      "_________________________________________________________________\n",
      "rnn_21 (RNN)                 (None, 5000, 49)          5120      \n",
      "_________________________________________________________________\n",
      "rnn_22 (RNN)                 (None, 5000, 49)          5120      \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 5000, 1)           50        \n",
      "=================================================================\n",
      "Total params: 18,130\n",
      "Trainable params: 18,050\n",
      "Non-trainable params: 80\n",
      "_________________________________________________________________\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "rnn_23 (RNN)                 (None, 5000, 40)          1865      \n",
      "_________________________________________________________________\n",
      "lstm_23 (LSTM)               (None, 5000, 25)          6600      \n",
      "_________________________________________________________________\n",
      "rnn_24 (RNN)                 (None, 5000, 40)          2849      \n",
      "_________________________________________________________________\n",
      "lstm_24 (LSTM)               (None, 5000, 25)          6600      \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 5000, 1)           26        \n",
      "=================================================================\n",
      "Total params: 17,940\n",
      "Trainable params: 17,900\n",
      "Non-trainable params: 40\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def make_lstm(units, layers):\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units,\n",
    "                   input_shape=(train_X.shape[1], 1),  # (timesteps, input_dims)\n",
    "                   return_sequences=True))  # continuously outputs per timestep\n",
    "    for _ in range(layers-1):\n",
    "        model.add(LSTM(units, return_sequences=True))\n",
    "    model.add(Dense(train_X.shape[-1], activation='tanh'))\n",
    "    model.compile(loss=\"mse\", optimizer=\"adam\")\n",
    "    model.summary()\n",
    "    return model\n",
    "\n",
    "\n",
    "def delay_layer(units, **kwargs):\n",
    "    return RNN(LMUCell(units=units,\n",
    "                       order=4,\n",
    "                       theta=4,\n",
    "                      ),\n",
    "               return_sequences=True,\n",
    "               **kwargs)\n",
    "\n",
    "\n",
    "def make_lmu(units, layers):\n",
    "    model = Sequential()\n",
    "    model.add(delay_layer(units,\n",
    "                          input_shape=(train_X.shape[1], 1)))  # (timesteps, input_dims)\n",
    "    for _ in range(layers-1):\n",
    "        model.add(delay_layer(units))\n",
    "    model.add(Dense(train_X.shape[-1], activation='linear'))\n",
    "    model.compile(loss=\"mse\", optimizer=\"adam\")\n",
    "    model.summary()\n",
    "    \n",
    "    return model\n",
    "\n",
    "\n",
    "def make_hybrid(units_lstm, units_lmu, layers):\n",
    "    assert layers == 4, \"unsupported\"\n",
    "    model = Sequential()\n",
    "    model.add(delay_layer(units=units_lmu,input_shape=(train_X.shape[1], 1)))\n",
    "    model.add(LSTM(units=units_lstm, return_sequences=True))\n",
    "    model.add(delay_layer(units=units_lmu))\n",
    "    model.add(LSTM(units=units_lstm, return_sequences=True))\n",
    "    model.add(Dense(train_X.shape[-1], activation='tanh'))\n",
    "    model.compile(loss=\"mse\", optimizer=\"adam\")\n",
    "    model.summary()\n",
    "    return model\n",
    "\n",
    "\n",
    "layers = 4\n",
    "lstm_model = make_lstm(units=25, layers=layers)\n",
    "lmu_model = make_lmu(units=49, layers=layers) \n",
    "hybrid_model = make_hybrid(units_lstm=25, units_lmu=40, layers=layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "#from tempfile import mktemp\n",
    "\n",
    "import time                     \n",
    "\n",
    "def fit(model, fname):\n",
    "    #_tname = mktemp(suffix=\".hdf5\")\n",
    "    callbacks = [#EarlyStopping(monitor='val_loss', min_delta=1e-6, patience=20),\n",
    "                 ModelCheckpoint(filepath=fname, verbose=1, save_best_only=True)]\n",
    "    \n",
    "    start = time.time()\n",
    "    epochs = 500\n",
    "    result = model.fit(\n",
    "        train_X, train_Y, batch_size=32,\n",
    "        epochs=epochs, callbacks=callbacks, validation_split=0.5)\n",
    "    model.load_weights(fname)  # load best weights\n",
    "    return result, (time.time() - start) / epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 32 samples, validate on 32 samples\n",
      "Epoch 1/500\n",
      "32/32 [==============================] - 17s 543ms/step - loss: 0.3979 - val_loss: 0.1118\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.11182, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 2/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 0.1120 - val_loss: 0.1192\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.11182\n",
      "Epoch 3/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 0.1195 - val_loss: 0.0851\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.11182 to 0.08511, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 4/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 0.0853 - val_loss: 0.0706\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.08511 to 0.07064, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 5/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 0.0708 - val_loss: 0.0603\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.07064 to 0.06027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 6/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 0.0606 - val_loss: 0.0386\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.06027 to 0.03863, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 7/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 0.0389 - val_loss: 0.0258\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.03863 to 0.02583, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 8/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 0.0260 - val_loss: 0.0292\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.02583\n",
      "Epoch 9/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0293 - val_loss: 0.0311\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.02583\n",
      "Epoch 10/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 0.0312 - val_loss: 0.0232\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.02583 to 0.02316, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 11/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 0.0232 - val_loss: 0.0160\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.02316 to 0.01602, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 12/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0161 - val_loss: 0.0146\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.01602 to 0.01462, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 13/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0147 - val_loss: 0.0157\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.01462\n",
      "Epoch 14/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 0.0158 - val_loss: 0.0142\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.01462 to 0.01418, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 15/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 0.0142 - val_loss: 0.0109\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.01418 to 0.01085, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 16/500\n",
      "32/32 [==============================] - 13s 409ms/step - loss: 0.0109 - val_loss: 0.0089\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.01085 to 0.00889, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 17/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 0.0089 - val_loss: 0.0091\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.00889\n",
      "Epoch 18/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 0.0091 - val_loss: 0.0097\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.00889\n",
      "Epoch 19/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 0.0097 - val_loss: 0.0087\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.00889 to 0.00869, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 20/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 0.0087 - val_loss: 0.0067\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.00869 to 0.00671, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 21/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0067 - val_loss: 0.0058\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.00671 to 0.00579, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 22/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 0.0058 - val_loss: 0.0066\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.00579\n",
      "Epoch 23/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 0.0066 - val_loss: 0.0075\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.00579\n",
      "Epoch 24/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 0.0075 - val_loss: 0.0071\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.00579\n",
      "Epoch 25/500\n",
      "32/32 [==============================] - 13s 417ms/step - loss: 0.0071 - val_loss: 0.0058\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.00579 to 0.00576, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 26/500\n",
      "32/32 [==============================] - 12s 391ms/step - loss: 0.0058 - val_loss: 0.0051\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.00576 to 0.00506, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 27/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0050 - val_loss: 0.0051\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.00506\n",
      "Epoch 28/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 0.0051 - val_loss: 0.0050\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.00506 to 0.00503, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 29/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 0.0050 - val_loss: 0.0045\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.00503 to 0.00451, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 30/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0045 - val_loss: 0.0040\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.00451 to 0.00403, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 31/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 0.0040 - val_loss: 0.0040\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.00403\n",
      "Epoch 32/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 0.0040 - val_loss: 0.0042\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.00403\n",
      "Epoch 33/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 0.0042 - val_loss: 0.0039\n",
      "\n",
      "Epoch 00033: val_loss improved from 0.00403 to 0.00385, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 34/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 0.0038 - val_loss: 0.0031\n",
      "\n",
      "Epoch 00034: val_loss improved from 0.00385 to 0.00311, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 35/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 0.0031 - val_loss: 0.0026\n",
      "\n",
      "Epoch 00035: val_loss improved from 0.00311 to 0.00260, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 36/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 0.0026 - val_loss: 0.0026\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.00260\n",
      "Epoch 37/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0026 - val_loss: 0.0028\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.00260\n",
      "Epoch 38/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 0.0027 - val_loss: 0.0027\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 0.00260\n",
      "Epoch 39/500\n",
      "32/32 [==============================] - 12s 387ms/step - loss: 0.0027 - val_loss: 0.0025\n",
      "\n",
      "Epoch 00039: val_loss improved from 0.00260 to 0.00248, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 40/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 0.0025 - val_loss: 0.0025\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.00248 to 0.00246, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 41/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 0.0024 - val_loss: 0.0024\n",
      "\n",
      "Epoch 00041: val_loss improved from 0.00246 to 0.00239, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 42/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 0.0024 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00042: val_loss improved from 0.00239 to 0.00217, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 43/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 0.0022 - val_loss: 0.0020\n",
      "\n",
      "Epoch 00043: val_loss improved from 0.00217 to 0.00198, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 44/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 13s 404ms/step - loss: 0.0020 - val_loss: 0.0019\n",
      "\n",
      "Epoch 00044: val_loss improved from 0.00198 to 0.00194, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 45/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 0.0019 - val_loss: 0.0020\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 0.00194\n",
      "Epoch 46/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 0.0020 - val_loss: 0.0021\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.00194\n",
      "Epoch 47/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 0.0020 - val_loss: 0.0019\n",
      "\n",
      "Epoch 00047: val_loss improved from 0.00194 to 0.00194, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 48/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 0.0019 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00048: val_loss improved from 0.00194 to 0.00177, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 49/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 0.0017 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00049: val_loss improved from 0.00177 to 0.00169, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 50/500\n",
      "32/32 [==============================] - 13s 420ms/step - loss: 0.0017 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00050: val_loss improved from 0.00169 to 0.00163, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 51/500\n",
      "32/32 [==============================] - 14s 424ms/step - loss: 0.0016 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00051: val_loss improved from 0.00163 to 0.00153, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 52/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00052: val_loss improved from 0.00153 to 0.00146, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 53/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 0.0014 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.00146\n",
      "Epoch 54/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.00146\n",
      "Epoch 55/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0015 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00055: val_loss improved from 0.00146 to 0.00141, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 56/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 0.0014 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00056: val_loss improved from 0.00141 to 0.00133, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 57/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00057: val_loss improved from 0.00133 to 0.00129, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 58/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00058: val_loss improved from 0.00129 to 0.00128, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 59/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00059: val_loss improved from 0.00128 to 0.00126, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 60/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00060: val_loss improved from 0.00126 to 0.00124, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 61/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.00124\n",
      "Epoch 62/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00062: val_loss improved from 0.00124 to 0.00124, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 63/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00063: val_loss improved from 0.00124 to 0.00119, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 64/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 0.0012 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00064: val_loss improved from 0.00119 to 0.00115, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 65/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00065: val_loss improved from 0.00115 to 0.00113, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 66/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00066: val_loss improved from 0.00113 to 0.00113, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 67/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00067: val_loss improved from 0.00113 to 0.00111, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 68/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00068: val_loss improved from 0.00111 to 0.00109, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 69/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00069: val_loss improved from 0.00109 to 0.00108, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 70/500\n",
      "32/32 [==============================] - 12s 388ms/step - loss: 0.0010 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00070: val_loss improved from 0.00108 to 0.00106, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 71/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 0.0010 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00071: val_loss improved from 0.00106 to 0.00102, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 72/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 9.8671e-04 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00072: val_loss improved from 0.00102 to 0.00101, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 73/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 9.7264e-04 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00073: val_loss improved from 0.00101 to 0.00100, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 74/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 9.6649e-04 - val_loss: 9.9083e-04\n",
      "\n",
      "Epoch 00074: val_loss improved from 0.00100 to 0.00099, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 75/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 9.5525e-04 - val_loss: 9.7643e-04\n",
      "\n",
      "Epoch 00075: val_loss improved from 0.00099 to 0.00098, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 76/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 9.3740e-04 - val_loss: 9.5381e-04\n",
      "\n",
      "Epoch 00076: val_loss improved from 0.00098 to 0.00095, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 77/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 9.1637e-04 - val_loss: 9.4382e-04\n",
      "\n",
      "Epoch 00077: val_loss improved from 0.00095 to 0.00094, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 78/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 9.0785e-04 - val_loss: 9.2971e-04\n",
      "\n",
      "Epoch 00078: val_loss improved from 0.00094 to 0.00093, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 79/500\n",
      "32/32 [==============================] - 14s 425ms/step - loss: 8.9525e-04 - val_loss: 9.0923e-04\n",
      "\n",
      "Epoch 00079: val_loss improved from 0.00093 to 0.00091, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 80/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 8.7740e-04 - val_loss: 9.0304e-04\n",
      "\n",
      "Epoch 00080: val_loss improved from 0.00091 to 0.00090, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 81/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 8.7307e-04 - val_loss: 8.9215e-04\n",
      "\n",
      "Epoch 00081: val_loss improved from 0.00090 to 0.00089, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 82/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 8.5815e-04 - val_loss: 8.7760e-04\n",
      "\n",
      "Epoch 00082: val_loss improved from 0.00089 to 0.00088, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 83/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 8.4675e-04 - val_loss: 8.6820e-04\n",
      "\n",
      "Epoch 00083: val_loss improved from 0.00088 to 0.00087, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 84/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 8.3013e-04 - val_loss: 8.5488e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00084: val_loss improved from 0.00087 to 0.00085, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 85/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 8.1451e-04 - val_loss: 8.4622e-04\n",
      "\n",
      "Epoch 00085: val_loss improved from 0.00085 to 0.00085, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 86/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 8.0991e-04 - val_loss: 8.3673e-04\n",
      "\n",
      "Epoch 00086: val_loss improved from 0.00085 to 0.00084, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 87/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 7.9972e-04 - val_loss: 8.2237e-04\n",
      "\n",
      "Epoch 00087: val_loss improved from 0.00084 to 0.00082, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 88/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 7.8315e-04 - val_loss: 8.2133e-04\n",
      "\n",
      "Epoch 00088: val_loss improved from 0.00082 to 0.00082, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 89/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 7.7907e-04 - val_loss: 8.0247e-04\n",
      "\n",
      "Epoch 00089: val_loss improved from 0.00082 to 0.00080, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 90/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 7.6169e-04 - val_loss: 7.9651e-04\n",
      "\n",
      "Epoch 00090: val_loss improved from 0.00080 to 0.00080, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 91/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 7.5517e-04 - val_loss: 7.8892e-04\n",
      "\n",
      "Epoch 00091: val_loss improved from 0.00080 to 0.00079, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 92/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 7.4561e-04 - val_loss: 7.7663e-04\n",
      "\n",
      "Epoch 00092: val_loss improved from 0.00079 to 0.00078, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 93/500\n",
      "32/32 [==============================] - 12s 391ms/step - loss: 7.3679e-04 - val_loss: 7.7058e-04\n",
      "\n",
      "Epoch 00093: val_loss improved from 0.00078 to 0.00077, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 94/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 7.2866e-04 - val_loss: 7.5627e-04\n",
      "\n",
      "Epoch 00094: val_loss improved from 0.00077 to 0.00076, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 95/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 7.1544e-04 - val_loss: 7.5172e-04\n",
      "\n",
      "Epoch 00095: val_loss improved from 0.00076 to 0.00075, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 96/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 7.0586e-04 - val_loss: 7.3997e-04\n",
      "\n",
      "Epoch 00096: val_loss improved from 0.00075 to 0.00074, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 97/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 6.9596e-04 - val_loss: 7.3407e-04\n",
      "\n",
      "Epoch 00097: val_loss improved from 0.00074 to 0.00073, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 98/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 6.8937e-04 - val_loss: 7.2689e-04\n",
      "\n",
      "Epoch 00098: val_loss improved from 0.00073 to 0.00073, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 99/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 6.8207e-04 - val_loss: 7.1762e-04\n",
      "\n",
      "Epoch 00099: val_loss improved from 0.00073 to 0.00072, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 100/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 6.7203e-04 - val_loss: 7.1057e-04\n",
      "\n",
      "Epoch 00100: val_loss improved from 0.00072 to 0.00071, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 101/500\n",
      "32/32 [==============================] - 13s 415ms/step - loss: 6.6337e-04 - val_loss: 6.9938e-04\n",
      "\n",
      "Epoch 00101: val_loss improved from 0.00071 to 0.00070, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 102/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 6.5317e-04 - val_loss: 6.9276e-04\n",
      "\n",
      "Epoch 00102: val_loss improved from 0.00070 to 0.00069, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 103/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 6.4497e-04 - val_loss: 6.8502e-04\n",
      "\n",
      "Epoch 00103: val_loss improved from 0.00069 to 0.00069, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 104/500\n",
      "32/32 [==============================] - 12s 389ms/step - loss: 6.3828e-04 - val_loss: 6.7747e-04\n",
      "\n",
      "Epoch 00104: val_loss improved from 0.00069 to 0.00068, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 105/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 6.2982e-04 - val_loss: 6.7087e-04\n",
      "\n",
      "Epoch 00105: val_loss improved from 0.00068 to 0.00067, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 106/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 6.2147e-04 - val_loss: 6.6346e-04\n",
      "\n",
      "Epoch 00106: val_loss improved from 0.00067 to 0.00066, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 107/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 6.1545e-04 - val_loss: 6.5506e-04\n",
      "\n",
      "Epoch 00107: val_loss improved from 0.00066 to 0.00066, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 108/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 6.0627e-04 - val_loss: 6.4762e-04\n",
      "\n",
      "Epoch 00108: val_loss improved from 0.00066 to 0.00065, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 109/500\n",
      "32/32 [==============================] - 12s 388ms/step - loss: 6.0195e-04 - val_loss: 6.4124e-04\n",
      "\n",
      "Epoch 00109: val_loss improved from 0.00065 to 0.00064, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 110/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 5.9496e-04 - val_loss: 6.3617e-04\n",
      "\n",
      "Epoch 00110: val_loss improved from 0.00064 to 0.00064, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 111/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 5.8772e-04 - val_loss: 6.3324e-04\n",
      "\n",
      "Epoch 00111: val_loss improved from 0.00064 to 0.00063, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 112/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 5.8221e-04 - val_loss: 6.2569e-04\n",
      "\n",
      "Epoch 00112: val_loss improved from 0.00063 to 0.00063, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 113/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 5.7531e-04 - val_loss: 6.2112e-04\n",
      "\n",
      "Epoch 00113: val_loss improved from 0.00063 to 0.00062, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 114/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 5.6942e-04 - val_loss: 6.1203e-04\n",
      "\n",
      "Epoch 00114: val_loss improved from 0.00062 to 0.00061, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 115/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 5.6169e-04 - val_loss: 6.0675e-04\n",
      "\n",
      "Epoch 00115: val_loss improved from 0.00061 to 0.00061, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 116/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 5.5659e-04 - val_loss: 5.9993e-04\n",
      "\n",
      "Epoch 00116: val_loss improved from 0.00061 to 0.00060, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 117/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 5.4879e-04 - val_loss: 5.9554e-04\n",
      "\n",
      "Epoch 00117: val_loss improved from 0.00060 to 0.00060, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 118/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 5.4717e-04 - val_loss: 5.9205e-04\n",
      "\n",
      "Epoch 00118: val_loss improved from 0.00060 to 0.00059, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 119/500\n",
      "32/32 [==============================] - 13s 414ms/step - loss: 5.4064e-04 - val_loss: 5.8617e-04\n",
      "\n",
      "Epoch 00119: val_loss improved from 0.00059 to 0.00059, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 120/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 5.3518e-04 - val_loss: 5.8096e-04\n",
      "\n",
      "Epoch 00120: val_loss improved from 0.00059 to 0.00058, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 121/500\n",
      "32/32 [==============================] - 12s 387ms/step - loss: 5.2917e-04 - val_loss: 5.7311e-04\n",
      "\n",
      "Epoch 00121: val_loss improved from 0.00058 to 0.00057, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 122/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 5.2205e-04 - val_loss: 5.7136e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00122: val_loss improved from 0.00057 to 0.00057, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 123/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 5.2109e-04 - val_loss: 5.6333e-04\n",
      "\n",
      "Epoch 00123: val_loss improved from 0.00057 to 0.00056, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 124/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 5.1286e-04 - val_loss: 5.6191e-04\n",
      "\n",
      "Epoch 00124: val_loss improved from 0.00056 to 0.00056, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 125/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 5.0923e-04 - val_loss: 5.5311e-04\n",
      "\n",
      "Epoch 00125: val_loss improved from 0.00056 to 0.00055, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 126/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 5.0110e-04 - val_loss: 5.4851e-04\n",
      "\n",
      "Epoch 00126: val_loss improved from 0.00055 to 0.00055, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 127/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 5.0091e-04 - val_loss: 5.4345e-04\n",
      "\n",
      "Epoch 00127: val_loss improved from 0.00055 to 0.00054, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 128/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 4.9356e-04 - val_loss: 5.4342e-04\n",
      "\n",
      "Epoch 00128: val_loss improved from 0.00054 to 0.00054, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 129/500\n",
      "32/32 [==============================] - 13s 415ms/step - loss: 4.8852e-04 - val_loss: 5.3757e-04\n",
      "\n",
      "Epoch 00129: val_loss improved from 0.00054 to 0.00054, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 130/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 4.8556e-04 - val_loss: 5.3353e-04\n",
      "\n",
      "Epoch 00130: val_loss improved from 0.00054 to 0.00053, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 131/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 4.8080e-04 - val_loss: 5.2979e-04\n",
      "\n",
      "Epoch 00131: val_loss improved from 0.00053 to 0.00053, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 132/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 4.7848e-04 - val_loss: 5.2615e-04\n",
      "\n",
      "Epoch 00132: val_loss improved from 0.00053 to 0.00053, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 133/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 4.7325e-04 - val_loss: 5.2054e-04\n",
      "\n",
      "Epoch 00133: val_loss improved from 0.00053 to 0.00052, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 134/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 4.6793e-04 - val_loss: 5.1514e-04\n",
      "\n",
      "Epoch 00134: val_loss improved from 0.00052 to 0.00052, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 135/500\n",
      "32/32 [==============================] - 13s 419ms/step - loss: 4.6286e-04 - val_loss: 5.1227e-04\n",
      "\n",
      "Epoch 00135: val_loss improved from 0.00052 to 0.00051, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 136/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 4.6242e-04 - val_loss: 5.0859e-04\n",
      "\n",
      "Epoch 00136: val_loss improved from 0.00051 to 0.00051, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 137/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 4.5663e-04 - val_loss: 5.0806e-04\n",
      "\n",
      "Epoch 00137: val_loss improved from 0.00051 to 0.00051, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 138/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 4.5439e-04 - val_loss: 5.0158e-04\n",
      "\n",
      "Epoch 00138: val_loss improved from 0.00051 to 0.00050, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 139/500\n",
      "32/32 [==============================] - 13s 416ms/step - loss: 4.4874e-04 - val_loss: 5.0054e-04\n",
      "\n",
      "Epoch 00139: val_loss improved from 0.00050 to 0.00050, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 140/500\n",
      "32/32 [==============================] - 12s 386ms/step - loss: 4.4832e-04 - val_loss: 4.9488e-04\n",
      "\n",
      "Epoch 00140: val_loss improved from 0.00050 to 0.00049, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 141/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 4.4203e-04 - val_loss: 4.9274e-04\n",
      "\n",
      "Epoch 00141: val_loss improved from 0.00049 to 0.00049, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 142/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 4.3966e-04 - val_loss: 4.8680e-04\n",
      "\n",
      "Epoch 00142: val_loss improved from 0.00049 to 0.00049, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 143/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 4.3463e-04 - val_loss: 4.8455e-04\n",
      "\n",
      "Epoch 00143: val_loss improved from 0.00049 to 0.00048, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 144/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 4.3217e-04 - val_loss: 4.7962e-04\n",
      "\n",
      "Epoch 00144: val_loss improved from 0.00048 to 0.00048, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 145/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 4.2629e-04 - val_loss: 4.7749e-04\n",
      "\n",
      "Epoch 00145: val_loss improved from 0.00048 to 0.00048, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 146/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 4.2436e-04 - val_loss: 4.7246e-04\n",
      "\n",
      "Epoch 00146: val_loss improved from 0.00048 to 0.00047, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 147/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 4.1916e-04 - val_loss: 4.7021e-04\n",
      "\n",
      "Epoch 00147: val_loss improved from 0.00047 to 0.00047, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 148/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 4.1689e-04 - val_loss: 4.6555e-04\n",
      "\n",
      "Epoch 00148: val_loss improved from 0.00047 to 0.00047, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 149/500\n",
      "32/32 [==============================] - 12s 391ms/step - loss: 4.1192e-04 - val_loss: 4.6365e-04\n",
      "\n",
      "Epoch 00149: val_loss improved from 0.00047 to 0.00046, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 150/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 4.0977e-04 - val_loss: 4.5989e-04\n",
      "\n",
      "Epoch 00150: val_loss improved from 0.00046 to 0.00046, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 151/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 4.0597e-04 - val_loss: 4.5730e-04\n",
      "\n",
      "Epoch 00151: val_loss improved from 0.00046 to 0.00046, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 152/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 4.0311e-04 - val_loss: 4.5334e-04\n",
      "\n",
      "Epoch 00152: val_loss improved from 0.00046 to 0.00045, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 153/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 3.9952e-04 - val_loss: 4.5023e-04\n",
      "\n",
      "Epoch 00153: val_loss improved from 0.00045 to 0.00045, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 154/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 3.9636e-04 - val_loss: 4.4740e-04\n",
      "\n",
      "Epoch 00154: val_loss improved from 0.00045 to 0.00045, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 155/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 3.9300e-04 - val_loss: 4.4428e-04\n",
      "\n",
      "Epoch 00155: val_loss improved from 0.00045 to 0.00044, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 156/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 3.8978e-04 - val_loss: 4.4163e-04\n",
      "\n",
      "Epoch 00156: val_loss improved from 0.00044 to 0.00044, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 157/500\n",
      "32/32 [==============================] - 13s 415ms/step - loss: 3.8713e-04 - val_loss: 4.3846e-04\n",
      "\n",
      "Epoch 00157: val_loss improved from 0.00044 to 0.00044, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 158/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 3.8390e-04 - val_loss: 4.3609e-04\n",
      "\n",
      "Epoch 00158: val_loss improved from 0.00044 to 0.00044, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 159/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 3.8139e-04 - val_loss: 4.3284e-04\n",
      "\n",
      "Epoch 00159: val_loss improved from 0.00044 to 0.00043, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 160/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 3.7811e-04 - val_loss: 4.3057e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00160: val_loss improved from 0.00043 to 0.00043, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 161/500\n",
      "32/32 [==============================] - 13s 416ms/step - loss: 3.7577e-04 - val_loss: 4.2760e-04\n",
      "\n",
      "Epoch 00161: val_loss improved from 0.00043 to 0.00043, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 162/500\n",
      "32/32 [==============================] - 13s 409ms/step - loss: 3.7285e-04 - val_loss: 4.2512e-04\n",
      "\n",
      "Epoch 00162: val_loss improved from 0.00043 to 0.00043, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 163/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 3.7029e-04 - val_loss: 4.2234e-04\n",
      "\n",
      "Epoch 00163: val_loss improved from 0.00043 to 0.00042, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 164/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 3.6768e-04 - val_loss: 4.1993e-04\n",
      "\n",
      "Epoch 00164: val_loss improved from 0.00042 to 0.00042, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 165/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 3.6499e-04 - val_loss: 4.1759e-04\n",
      "\n",
      "Epoch 00165: val_loss improved from 0.00042 to 0.00042, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 166/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 3.6276e-04 - val_loss: 4.1528e-04\n",
      "\n",
      "Epoch 00166: val_loss improved from 0.00042 to 0.00042, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 167/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 3.6020e-04 - val_loss: 4.1254e-04\n",
      "\n",
      "Epoch 00167: val_loss improved from 0.00042 to 0.00041, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 168/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 3.5777e-04 - val_loss: 4.1050e-04\n",
      "\n",
      "Epoch 00168: val_loss improved from 0.00041 to 0.00041, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 169/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 3.5511e-04 - val_loss: 4.0818e-04\n",
      "\n",
      "Epoch 00169: val_loss improved from 0.00041 to 0.00041, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 170/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 3.5288e-04 - val_loss: 4.0559e-04\n",
      "\n",
      "Epoch 00170: val_loss improved from 0.00041 to 0.00041, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 171/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 3.5077e-04 - val_loss: 4.0368e-04\n",
      "\n",
      "Epoch 00171: val_loss improved from 0.00041 to 0.00040, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 172/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 3.4824e-04 - val_loss: 4.0123e-04\n",
      "\n",
      "Epoch 00172: val_loss improved from 0.00040 to 0.00040, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 173/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 3.4582e-04 - val_loss: 3.9905e-04\n",
      "\n",
      "Epoch 00173: val_loss improved from 0.00040 to 0.00040, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 174/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 3.4375e-04 - val_loss: 3.9694e-04\n",
      "\n",
      "Epoch 00174: val_loss improved from 0.00040 to 0.00040, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 175/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 3.4153e-04 - val_loss: 3.9435e-04\n",
      "\n",
      "Epoch 00175: val_loss improved from 0.00040 to 0.00039, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 176/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 3.3950e-04 - val_loss: 3.9247e-04\n",
      "\n",
      "Epoch 00176: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 177/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 3.3708e-04 - val_loss: 3.9051e-04\n",
      "\n",
      "Epoch 00177: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 178/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 3.3534e-04 - val_loss: 3.8850e-04\n",
      "\n",
      "Epoch 00178: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 179/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 3.3321e-04 - val_loss: 3.8606e-04\n",
      "\n",
      "Epoch 00179: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 180/500\n",
      "32/32 [==============================] - 13s 421ms/step - loss: 3.3125e-04 - val_loss: 3.8438e-04\n",
      "\n",
      "Epoch 00180: val_loss improved from 0.00039 to 0.00038, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 181/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 3.2902e-04 - val_loss: 3.8248e-04\n",
      "\n",
      "Epoch 00181: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 182/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 3.2726e-04 - val_loss: 3.8060e-04\n",
      "\n",
      "Epoch 00182: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 183/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 3.2544e-04 - val_loss: 3.7862e-04\n",
      "\n",
      "Epoch 00183: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 184/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 3.2327e-04 - val_loss: 3.7664e-04\n",
      "\n",
      "Epoch 00184: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 185/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 3.2168e-04 - val_loss: 3.7504e-04\n",
      "\n",
      "Epoch 00185: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 186/500\n",
      "32/32 [==============================] - 13s 414ms/step - loss: 3.1968e-04 - val_loss: 3.7307e-04\n",
      "\n",
      "Epoch 00186: val_loss improved from 0.00038 to 0.00037, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 187/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 3.1774e-04 - val_loss: 3.7157e-04\n",
      "\n",
      "Epoch 00187: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 188/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 3.1624e-04 - val_loss: 3.6933e-04\n",
      "\n",
      "Epoch 00188: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 189/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 3.1399e-04 - val_loss: 3.6750e-04\n",
      "\n",
      "Epoch 00189: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 190/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 3.1232e-04 - val_loss: 3.6547e-04\n",
      "\n",
      "Epoch 00190: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 191/500\n",
      "32/32 [==============================] - 13s 421ms/step - loss: 3.1079e-04 - val_loss: 3.6373e-04\n",
      "\n",
      "Epoch 00191: val_loss improved from 0.00037 to 0.00036, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 192/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 3.0887e-04 - val_loss: 3.6243e-04\n",
      "\n",
      "Epoch 00192: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 193/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 3.0721e-04 - val_loss: 3.6023e-04\n",
      "\n",
      "Epoch 00193: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 194/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 3.0538e-04 - val_loss: 3.5876e-04\n",
      "\n",
      "Epoch 00194: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 195/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 3.0399e-04 - val_loss: 3.5714e-04\n",
      "\n",
      "Epoch 00195: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 196/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 3.0171e-04 - val_loss: 3.5574e-04\n",
      "\n",
      "Epoch 00196: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 197/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 3.0033e-04 - val_loss: 3.5360e-04\n",
      "\n",
      "Epoch 00197: val_loss improved from 0.00036 to 0.00035, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 198/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 2.9850e-04 - val_loss: 3.5216e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00198: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 199/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 2.9699e-04 - val_loss: 3.5069e-04\n",
      "\n",
      "Epoch 00199: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 200/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 2.9533e-04 - val_loss: 3.4901e-04\n",
      "\n",
      "Epoch 00200: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 201/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 2.9367e-04 - val_loss: 3.4714e-04\n",
      "\n",
      "Epoch 00201: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 202/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 2.9226e-04 - val_loss: 3.4558e-04\n",
      "\n",
      "Epoch 00202: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 203/500\n",
      "32/32 [==============================] - 13s 415ms/step - loss: 2.9047e-04 - val_loss: 3.4437e-04\n",
      "\n",
      "Epoch 00203: val_loss improved from 0.00035 to 0.00034, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 204/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 2.8921e-04 - val_loss: 3.4265e-04\n",
      "\n",
      "Epoch 00204: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 205/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 2.8740e-04 - val_loss: 3.4093e-04\n",
      "\n",
      "Epoch 00205: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 206/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 2.8622e-04 - val_loss: 3.3959e-04\n",
      "\n",
      "Epoch 00206: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 207/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 2.8442e-04 - val_loss: 3.3832e-04\n",
      "\n",
      "Epoch 00207: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 208/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 2.8329e-04 - val_loss: 3.3663e-04\n",
      "\n",
      "Epoch 00208: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 209/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 2.8150e-04 - val_loss: 3.3499e-04\n",
      "\n",
      "Epoch 00209: val_loss improved from 0.00034 to 0.00033, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 210/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 2.8031e-04 - val_loss: 3.3364e-04\n",
      "\n",
      "Epoch 00210: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 211/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 2.7866e-04 - val_loss: 3.3244e-04\n",
      "\n",
      "Epoch 00211: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 212/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 2.7757e-04 - val_loss: 3.3086e-04\n",
      "\n",
      "Epoch 00212: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 213/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 2.7588e-04 - val_loss: 3.2915e-04\n",
      "\n",
      "Epoch 00213: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 214/500\n",
      "32/32 [==============================] - 13s 411ms/step - loss: 2.7480e-04 - val_loss: 3.2795e-04\n",
      "\n",
      "Epoch 00214: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 215/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 2.7315e-04 - val_loss: 3.2674e-04\n",
      "\n",
      "Epoch 00215: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 216/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 2.7211e-04 - val_loss: 3.2534e-04\n",
      "\n",
      "Epoch 00216: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 217/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 2.7079e-04 - val_loss: 3.2403e-04\n",
      "\n",
      "Epoch 00217: val_loss improved from 0.00033 to 0.00032, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 218/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 2.6946e-04 - val_loss: 3.2260e-04\n",
      "\n",
      "Epoch 00218: val_loss improved from 0.00032 to 0.00032, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 219/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 2.6803e-04 - val_loss: 3.2128e-04\n",
      "\n",
      "Epoch 00219: val_loss improved from 0.00032 to 0.00032, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 220/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 2.6679e-04 - val_loss: 3.1999e-04\n",
      "\n",
      "Epoch 00220: val_loss improved from 0.00032 to 0.00032, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 221/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 2.6545e-04 - val_loss: 3.1873e-04\n",
      "\n",
      "Epoch 00221: val_loss improved from 0.00032 to 0.00032, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 222/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 2.6424e-04 - val_loss: 3.1730e-04\n",
      "\n",
      "Epoch 00222: val_loss improved from 0.00032 to 0.00032, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 223/500\n",
      "32/32 [==============================] - 13s 420ms/step - loss: 2.6296e-04 - val_loss: 3.1605e-04\n",
      "\n",
      "Epoch 00223: val_loss improved from 0.00032 to 0.00032, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 224/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 2.6172e-04 - val_loss: 3.1481e-04\n",
      "\n",
      "Epoch 00224: val_loss improved from 0.00032 to 0.00031, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 225/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 2.6050e-04 - val_loss: 3.1345e-04\n",
      "\n",
      "Epoch 00225: val_loss improved from 0.00031 to 0.00031, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 226/500\n",
      "32/32 [==============================] - 13s 409ms/step - loss: 2.5927e-04 - val_loss: 3.1232e-04\n",
      "\n",
      "Epoch 00226: val_loss improved from 0.00031 to 0.00031, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 227/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 2.5813e-04 - val_loss: 3.1089e-04\n",
      "\n",
      "Epoch 00227: val_loss improved from 0.00031 to 0.00031, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 228/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 2.5689e-04 - val_loss: 3.0969e-04\n",
      "\n",
      "Epoch 00228: val_loss improved from 0.00031 to 0.00031, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 229/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 2.5565e-04 - val_loss: 3.0854e-04\n",
      "\n",
      "Epoch 00229: val_loss improved from 0.00031 to 0.00031, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 230/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 2.5460e-04 - val_loss: 3.0718e-04\n",
      "\n",
      "Epoch 00230: val_loss improved from 0.00031 to 0.00031, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 231/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 2.5336e-04 - val_loss: 3.0596e-04\n",
      "\n",
      "Epoch 00231: val_loss improved from 0.00031 to 0.00031, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 232/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 2.5216e-04 - val_loss: 3.0487e-04\n",
      "\n",
      "Epoch 00232: val_loss improved from 0.00031 to 0.00030, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 233/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 2.5112e-04 - val_loss: 3.0357e-04\n",
      "\n",
      "Epoch 00233: val_loss improved from 0.00030 to 0.00030, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 234/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 2.4999e-04 - val_loss: 3.0235e-04\n",
      "\n",
      "Epoch 00234: val_loss improved from 0.00030 to 0.00030, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 235/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 2.4877e-04 - val_loss: 3.0129e-04\n",
      "\n",
      "Epoch 00235: val_loss improved from 0.00030 to 0.00030, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 236/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 2.4777e-04 - val_loss: 3.0009e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00236: val_loss improved from 0.00030 to 0.00030, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 237/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 2.4673e-04 - val_loss: 2.9886e-04\n",
      "\n",
      "Epoch 00237: val_loss improved from 0.00030 to 0.00030, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 238/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 2.4548e-04 - val_loss: 2.9780e-04\n",
      "\n",
      "Epoch 00238: val_loss improved from 0.00030 to 0.00030, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 239/500\n",
      "32/32 [==============================] - 13s 416ms/step - loss: 2.4449e-04 - val_loss: 2.9663e-04\n",
      "\n",
      "Epoch 00239: val_loss improved from 0.00030 to 0.00030, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 240/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 2.4359e-04 - val_loss: 2.9532e-04\n",
      "\n",
      "Epoch 00240: val_loss improved from 0.00030 to 0.00030, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 241/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 2.4233e-04 - val_loss: 2.9435e-04\n",
      "\n",
      "Epoch 00241: val_loss improved from 0.00030 to 0.00029, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 242/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 2.4130e-04 - val_loss: 2.9336e-04\n",
      "\n",
      "Epoch 00242: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 243/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 2.4035e-04 - val_loss: 2.9205e-04\n",
      "\n",
      "Epoch 00243: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 244/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 2.3912e-04 - val_loss: 2.9096e-04\n",
      "\n",
      "Epoch 00244: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 245/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 2.3828e-04 - val_loss: 2.8966e-04\n",
      "\n",
      "Epoch 00245: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 246/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 2.3717e-04 - val_loss: 2.8870e-04\n",
      "\n",
      "Epoch 00246: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 247/500\n",
      "32/32 [==============================] - 13s 409ms/step - loss: 2.3609e-04 - val_loss: 2.8776e-04\n",
      "\n",
      "Epoch 00247: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 248/500\n",
      "32/32 [==============================] - 12s 389ms/step - loss: 2.3531e-04 - val_loss: 2.8662e-04\n",
      "\n",
      "Epoch 00248: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 249/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 2.3414e-04 - val_loss: 2.8512e-04\n",
      "\n",
      "Epoch 00249: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 250/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 2.3334e-04 - val_loss: 2.8415e-04\n",
      "\n",
      "Epoch 00250: val_loss improved from 0.00029 to 0.00028, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 251/500\n",
      "32/32 [==============================] - 12s 387ms/step - loss: 2.3218e-04 - val_loss: 2.8278e-04\n",
      "\n",
      "Epoch 00251: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 252/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 2.3108e-04 - val_loss: 2.8205e-04\n",
      "\n",
      "Epoch 00252: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 253/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 2.3018e-04 - val_loss: 2.8102e-04\n",
      "\n",
      "Epoch 00253: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 254/500\n",
      "32/32 [==============================] - 13s 409ms/step - loss: 2.2923e-04 - val_loss: 2.8051e-04\n",
      "\n",
      "Epoch 00254: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 255/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 2.2805e-04 - val_loss: 2.7947e-04\n",
      "\n",
      "Epoch 00255: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 256/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 2.2703e-04 - val_loss: 2.7792e-04\n",
      "\n",
      "Epoch 00256: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 257/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 2.2660e-04 - val_loss: 2.7764e-04\n",
      "\n",
      "Epoch 00257: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 258/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 2.2615e-04 - val_loss: 2.7652e-04\n",
      "\n",
      "Epoch 00258: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 259/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 2.2504e-04 - val_loss: 2.7483e-04\n",
      "\n",
      "Epoch 00259: val_loss improved from 0.00028 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 260/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 2.2365e-04 - val_loss: 2.7450e-04\n",
      "\n",
      "Epoch 00260: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 261/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 2.2265e-04 - val_loss: 2.7405e-04\n",
      "\n",
      "Epoch 00261: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 262/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 2.2270e-04 - val_loss: 2.7287e-04\n",
      "\n",
      "Epoch 00262: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 263/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 2.2179e-04 - val_loss: 2.7150e-04\n",
      "\n",
      "Epoch 00263: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 264/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 2.2053e-04 - val_loss: 2.7133e-04\n",
      "\n",
      "Epoch 00264: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 265/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 2.2060e-04 - val_loss: 2.7024e-04\n",
      "\n",
      "Epoch 00265: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 266/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 2.1965e-04 - val_loss: 2.6937e-04\n",
      "\n",
      "Epoch 00266: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 267/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 2.1818e-04 - val_loss: 2.6711e-04\n",
      "\n",
      "Epoch 00267: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 268/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 2.1830e-04 - val_loss: 2.6671e-04\n",
      "\n",
      "Epoch 00268: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 269/500\n",
      "32/32 [==============================] - 12s 389ms/step - loss: 2.1663e-04 - val_loss: 2.6597e-04\n",
      "\n",
      "Epoch 00269: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 270/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 2.1537e-04 - val_loss: 2.6376e-04\n",
      "\n",
      "Epoch 00270: val_loss improved from 0.00027 to 0.00026, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 271/500\n",
      "32/32 [==============================] - 13s 416ms/step - loss: 2.1414e-04 - val_loss: 2.6200e-04\n",
      "\n",
      "Epoch 00271: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 272/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 2.1261e-04 - val_loss: 2.6051e-04\n",
      "\n",
      "Epoch 00272: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 273/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 2.1111e-04 - val_loss: 2.6177e-04\n",
      "\n",
      "Epoch 00273: val_loss did not improve from 0.00026\n",
      "Epoch 274/500\n",
      "32/32 [==============================] - 13s 420ms/step - loss: 2.0950e-04 - val_loss: 2.5974e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00274: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 275/500\n",
      "32/32 [==============================] - 13s 411ms/step - loss: 2.0876e-04 - val_loss: 2.6013e-04\n",
      "\n",
      "Epoch 00275: val_loss did not improve from 0.00026\n",
      "Epoch 276/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 2.1065e-04 - val_loss: 2.5847e-04\n",
      "\n",
      "Epoch 00276: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 277/500\n",
      "32/32 [==============================] - 14s 422ms/step - loss: 2.0897e-04 - val_loss: 2.5831e-04\n",
      "\n",
      "Epoch 00277: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 278/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 2.0930e-04 - val_loss: 2.5769e-04\n",
      "\n",
      "Epoch 00278: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 279/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 2.0822e-04 - val_loss: 2.5626e-04\n",
      "\n",
      "Epoch 00279: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 280/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 2.0817e-04 - val_loss: 2.5544e-04\n",
      "\n",
      "Epoch 00280: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 281/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 2.0733e-04 - val_loss: 2.5472e-04\n",
      "\n",
      "Epoch 00281: val_loss improved from 0.00026 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 282/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 2.0634e-04 - val_loss: 2.5375e-04\n",
      "\n",
      "Epoch 00282: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 283/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 2.0648e-04 - val_loss: 2.5340e-04\n",
      "\n",
      "Epoch 00283: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 284/500\n",
      "32/32 [==============================] - 13s 409ms/step - loss: 2.0572e-04 - val_loss: 2.5240e-04\n",
      "\n",
      "Epoch 00284: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 285/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 2.0372e-04 - val_loss: 2.5198e-04\n",
      "\n",
      "Epoch 00285: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 286/500\n",
      "32/32 [==============================] - 12s 388ms/step - loss: 2.0324e-04 - val_loss: 2.5124e-04\n",
      "\n",
      "Epoch 00286: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 287/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 2.0239e-04 - val_loss: 2.5013e-04\n",
      "\n",
      "Epoch 00287: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 288/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 2.0125e-04 - val_loss: 2.4951e-04\n",
      "\n",
      "Epoch 00288: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 289/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 2.0052e-04 - val_loss: 2.4892e-04\n",
      "\n",
      "Epoch 00289: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 290/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.9978e-04 - val_loss: 2.4747e-04\n",
      "\n",
      "Epoch 00290: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 291/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 1.9886e-04 - val_loss: 2.4667e-04\n",
      "\n",
      "Epoch 00291: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 292/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 1.9827e-04 - val_loss: 2.4589e-04\n",
      "\n",
      "Epoch 00292: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 293/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.9796e-04 - val_loss: 2.4433e-04\n",
      "\n",
      "Epoch 00293: val_loss improved from 0.00025 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 294/500\n",
      "32/32 [==============================] - 14s 426ms/step - loss: 1.9693e-04 - val_loss: 2.4328e-04\n",
      "\n",
      "Epoch 00294: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 295/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 1.9600e-04 - val_loss: 2.4287e-04\n",
      "\n",
      "Epoch 00295: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 296/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 1.9549e-04 - val_loss: 2.4206e-04\n",
      "\n",
      "Epoch 00296: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 297/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.9492e-04 - val_loss: 2.4158e-04\n",
      "\n",
      "Epoch 00297: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 298/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.9396e-04 - val_loss: 2.4043e-04\n",
      "\n",
      "Epoch 00298: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 299/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 1.9305e-04 - val_loss: 2.3997e-04\n",
      "\n",
      "Epoch 00299: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 300/500\n",
      "32/32 [==============================] - 13s 419ms/step - loss: 1.9260e-04 - val_loss: 2.3915e-04\n",
      "\n",
      "Epoch 00300: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 301/500\n",
      "32/32 [==============================] - 12s 389ms/step - loss: 1.9181e-04 - val_loss: 2.3834e-04\n",
      "\n",
      "Epoch 00301: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 302/500\n",
      "32/32 [==============================] - 13s 409ms/step - loss: 1.9097e-04 - val_loss: 2.3785e-04\n",
      "\n",
      "Epoch 00302: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 303/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.9049e-04 - val_loss: 2.3711e-04\n",
      "\n",
      "Epoch 00303: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 304/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.8984e-04 - val_loss: 2.3630e-04\n",
      "\n",
      "Epoch 00304: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 305/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 1.8887e-04 - val_loss: 2.3561e-04\n",
      "\n",
      "Epoch 00305: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 306/500\n",
      "32/32 [==============================] - 12s 391ms/step - loss: 1.8843e-04 - val_loss: 2.3528e-04\n",
      "\n",
      "Epoch 00306: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 307/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.8805e-04 - val_loss: 2.3385e-04\n",
      "\n",
      "Epoch 00307: val_loss improved from 0.00024 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 308/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 1.8710e-04 - val_loss: 2.3324e-04\n",
      "\n",
      "Epoch 00308: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 309/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 1.8619e-04 - val_loss: 2.3251e-04\n",
      "\n",
      "Epoch 00309: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 310/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.8598e-04 - val_loss: 2.3246e-04\n",
      "\n",
      "Epoch 00310: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 311/500\n",
      "32/32 [==============================] - 13s 417ms/step - loss: 1.8585e-04 - val_loss: 2.3074e-04\n",
      "\n",
      "Epoch 00311: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 312/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 1.8483e-04 - val_loss: 2.2973e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00312: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 313/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.8408e-04 - val_loss: 2.2921e-04\n",
      "\n",
      "Epoch 00313: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 314/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 1.8364e-04 - val_loss: 2.2896e-04\n",
      "\n",
      "Epoch 00314: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 315/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 1.8318e-04 - val_loss: 2.2791e-04\n",
      "\n",
      "Epoch 00315: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 316/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 1.8247e-04 - val_loss: 2.2714e-04\n",
      "\n",
      "Epoch 00316: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 317/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.8155e-04 - val_loss: 2.2623e-04\n",
      "\n",
      "Epoch 00317: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 318/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.8099e-04 - val_loss: 2.2568e-04\n",
      "\n",
      "Epoch 00318: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 319/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 1.8052e-04 - val_loss: 2.2498e-04\n",
      "\n",
      "Epoch 00319: val_loss improved from 0.00023 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 320/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.7977e-04 - val_loss: 2.2486e-04\n",
      "\n",
      "Epoch 00320: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 321/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 1.7920e-04 - val_loss: 2.2401e-04\n",
      "\n",
      "Epoch 00321: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 322/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.7850e-04 - val_loss: 2.2287e-04\n",
      "\n",
      "Epoch 00322: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 323/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.7860e-04 - val_loss: 2.2300e-04\n",
      "\n",
      "Epoch 00323: val_loss did not improve from 0.00022\n",
      "Epoch 324/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.7773e-04 - val_loss: 2.2229e-04\n",
      "\n",
      "Epoch 00324: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 325/500\n",
      "32/32 [==============================] - 13s 411ms/step - loss: 1.7703e-04 - val_loss: 2.2123e-04\n",
      "\n",
      "Epoch 00325: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 326/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 1.7670e-04 - val_loss: 2.2079e-04\n",
      "\n",
      "Epoch 00326: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 327/500\n",
      "32/32 [==============================] - 13s 417ms/step - loss: 1.7640e-04 - val_loss: 2.1999e-04\n",
      "\n",
      "Epoch 00327: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 328/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.7542e-04 - val_loss: 2.1946e-04\n",
      "\n",
      "Epoch 00328: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 329/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 1.7489e-04 - val_loss: 2.1841e-04\n",
      "\n",
      "Epoch 00329: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 330/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.7424e-04 - val_loss: 2.1804e-04\n",
      "\n",
      "Epoch 00330: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 331/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.7393e-04 - val_loss: 2.1775e-04\n",
      "\n",
      "Epoch 00331: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 332/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 1.7346e-04 - val_loss: 2.1655e-04\n",
      "\n",
      "Epoch 00332: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 333/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 1.7247e-04 - val_loss: 2.1602e-04\n",
      "\n",
      "Epoch 00333: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 334/500\n",
      "32/32 [==============================] - 12s 387ms/step - loss: 1.7221e-04 - val_loss: 2.1552e-04\n",
      "\n",
      "Epoch 00334: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 335/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.7175e-04 - val_loss: 2.1545e-04\n",
      "\n",
      "Epoch 00335: val_loss improved from 0.00022 to 0.00022, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 336/500\n",
      "32/32 [==============================] - 13s 413ms/step - loss: 1.7115e-04 - val_loss: 2.1456e-04\n",
      "\n",
      "Epoch 00336: val_loss improved from 0.00022 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 337/500\n",
      "32/32 [==============================] - 13s 419ms/step - loss: 1.7038e-04 - val_loss: 2.1343e-04\n",
      "\n",
      "Epoch 00337: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 338/500\n",
      "32/32 [==============================] - 13s 413ms/step - loss: 1.7041e-04 - val_loss: 2.1291e-04\n",
      "\n",
      "Epoch 00338: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 339/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 1.6991e-04 - val_loss: 2.1230e-04\n",
      "\n",
      "Epoch 00339: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 340/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 1.6932e-04 - val_loss: 2.1100e-04\n",
      "\n",
      "Epoch 00340: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 341/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 1.6830e-04 - val_loss: 2.1047e-04\n",
      "\n",
      "Epoch 00341: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 342/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 1.6784e-04 - val_loss: 2.1000e-04\n",
      "\n",
      "Epoch 00342: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 343/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.6739e-04 - val_loss: 2.0968e-04\n",
      "\n",
      "Epoch 00343: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 344/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.6703e-04 - val_loss: 2.0919e-04\n",
      "\n",
      "Epoch 00344: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 345/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.6603e-04 - val_loss: 2.0897e-04\n",
      "\n",
      "Epoch 00345: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 346/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 1.6576e-04 - val_loss: 2.0902e-04\n",
      "\n",
      "Epoch 00346: val_loss did not improve from 0.00021\n",
      "Epoch 347/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.6511e-04 - val_loss: 2.0773e-04\n",
      "\n",
      "Epoch 00347: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 348/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 1.6480e-04 - val_loss: 2.0719e-04\n",
      "\n",
      "Epoch 00348: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 349/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.6447e-04 - val_loss: 2.0713e-04\n",
      "\n",
      "Epoch 00349: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 350/500\n",
      "32/32 [==============================] - 13s 411ms/step - loss: 1.6474e-04 - val_loss: 2.0609e-04\n",
      "\n",
      "Epoch 00350: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 351/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 13s 411ms/step - loss: 1.6398e-04 - val_loss: 2.0542e-04\n",
      "\n",
      "Epoch 00351: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 352/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 1.6313e-04 - val_loss: 2.0523e-04\n",
      "\n",
      "Epoch 00352: val_loss improved from 0.00021 to 0.00021, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 353/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.6298e-04 - val_loss: 2.0472e-04\n",
      "\n",
      "Epoch 00353: val_loss improved from 0.00021 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 354/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 1.6258e-04 - val_loss: 2.0405e-04\n",
      "\n",
      "Epoch 00354: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 355/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.6185e-04 - val_loss: 2.0375e-04\n",
      "\n",
      "Epoch 00355: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 356/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.6145e-04 - val_loss: 2.0375e-04\n",
      "\n",
      "Epoch 00356: val_loss did not improve from 0.00020\n",
      "Epoch 357/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.6099e-04 - val_loss: 2.0283e-04\n",
      "\n",
      "Epoch 00357: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 358/500\n",
      "32/32 [==============================] - 12s 387ms/step - loss: 1.6016e-04 - val_loss: 2.0265e-04\n",
      "\n",
      "Epoch 00358: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 359/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 1.6045e-04 - val_loss: 2.0257e-04\n",
      "\n",
      "Epoch 00359: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 360/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.6011e-04 - val_loss: 2.0182e-04\n",
      "\n",
      "Epoch 00360: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 361/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.5973e-04 - val_loss: 1.9976e-04\n",
      "\n",
      "Epoch 00361: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 362/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.5823e-04 - val_loss: 2.0041e-04\n",
      "\n",
      "Epoch 00362: val_loss did not improve from 0.00020\n",
      "Epoch 363/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.5906e-04 - val_loss: 2.0099e-04\n",
      "\n",
      "Epoch 00363: val_loss did not improve from 0.00020\n",
      "Epoch 364/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.5879e-04 - val_loss: 1.9923e-04\n",
      "\n",
      "Epoch 00364: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 365/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 1.5704e-04 - val_loss: 1.9829e-04\n",
      "\n",
      "Epoch 00365: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 366/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 1.5666e-04 - val_loss: 1.9807e-04\n",
      "\n",
      "Epoch 00366: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 367/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 1.5570e-04 - val_loss: 1.9805e-04\n",
      "\n",
      "Epoch 00367: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 368/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.5566e-04 - val_loss: 1.9838e-04\n",
      "\n",
      "Epoch 00368: val_loss did not improve from 0.00020\n",
      "Epoch 369/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 1.5620e-04 - val_loss: 1.9745e-04\n",
      "\n",
      "Epoch 00369: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 370/500\n",
      "32/32 [==============================] - 13s 411ms/step - loss: 1.5504e-04 - val_loss: 1.9606e-04\n",
      "\n",
      "Epoch 00370: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 371/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 1.5377e-04 - val_loss: 1.9526e-04\n",
      "\n",
      "Epoch 00371: val_loss improved from 0.00020 to 0.00020, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 372/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.5335e-04 - val_loss: 1.9535e-04\n",
      "\n",
      "Epoch 00372: val_loss did not improve from 0.00020\n",
      "Epoch 373/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 1.5335e-04 - val_loss: 1.9492e-04\n",
      "\n",
      "Epoch 00373: val_loss improved from 0.00020 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 374/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 1.5430e-04 - val_loss: 1.9343e-04\n",
      "\n",
      "Epoch 00374: val_loss improved from 0.00019 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 375/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 1.5239e-04 - val_loss: 1.9337e-04\n",
      "\n",
      "Epoch 00375: val_loss improved from 0.00019 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 376/500\n",
      "32/32 [==============================] - 13s 411ms/step - loss: 1.5169e-04 - val_loss: 1.9389e-04\n",
      "\n",
      "Epoch 00376: val_loss did not improve from 0.00019\n",
      "Epoch 377/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.5349e-04 - val_loss: 1.9394e-04\n",
      "\n",
      "Epoch 00377: val_loss did not improve from 0.00019\n",
      "Epoch 378/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 1.5377e-04 - val_loss: 1.9178e-04\n",
      "\n",
      "Epoch 00378: val_loss improved from 0.00019 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 379/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 1.5181e-04 - val_loss: 1.9039e-04\n",
      "\n",
      "Epoch 00379: val_loss improved from 0.00019 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 380/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 1.5042e-04 - val_loss: 1.9076e-04\n",
      "\n",
      "Epoch 00380: val_loss did not improve from 0.00019\n",
      "Epoch 381/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 1.5045e-04 - val_loss: 1.9084e-04\n",
      "\n",
      "Epoch 00381: val_loss did not improve from 0.00019\n",
      "Epoch 382/500\n",
      "32/32 [==============================] - 13s 411ms/step - loss: 1.5068e-04 - val_loss: 1.9056e-04\n",
      "\n",
      "Epoch 00382: val_loss did not improve from 0.00019\n",
      "Epoch 383/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.5021e-04 - val_loss: 1.8944e-04\n",
      "\n",
      "Epoch 00383: val_loss improved from 0.00019 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 384/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 1.4942e-04 - val_loss: 1.8896e-04\n",
      "\n",
      "Epoch 00384: val_loss improved from 0.00019 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 385/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.4884e-04 - val_loss: 1.8859e-04\n",
      "\n",
      "Epoch 00385: val_loss improved from 0.00019 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 386/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.4852e-04 - val_loss: 1.8761e-04\n",
      "\n",
      "Epoch 00386: val_loss improved from 0.00019 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 387/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.4761e-04 - val_loss: 1.8829e-04\n",
      "\n",
      "Epoch 00387: val_loss did not improve from 0.00019\n",
      "Epoch 388/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 1.4705e-04 - val_loss: 1.8823e-04\n",
      "\n",
      "Epoch 00388: val_loss did not improve from 0.00019\n",
      "Epoch 389/500\n",
      "32/32 [==============================] - 13s 415ms/step - loss: 1.4730e-04 - val_loss: 1.8619e-04\n",
      "\n",
      "Epoch 00389: val_loss improved from 0.00019 to 0.00019, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 390/500\n",
      "32/32 [==============================] - 12s 384ms/step - loss: 1.4758e-04 - val_loss: 1.8714e-04\n",
      "\n",
      "Epoch 00390: val_loss did not improve from 0.00019\n",
      "Epoch 391/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 1.4769e-04 - val_loss: 1.8863e-04\n",
      "\n",
      "Epoch 00391: val_loss did not improve from 0.00019\n",
      "Epoch 392/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 1.4862e-04 - val_loss: 1.8753e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00392: val_loss did not improve from 0.00019\n",
      "Epoch 393/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.4771e-04 - val_loss: 1.8684e-04\n",
      "\n",
      "Epoch 00393: val_loss did not improve from 0.00019\n",
      "Epoch 394/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.4786e-04 - val_loss: 1.8992e-04\n",
      "\n",
      "Epoch 00394: val_loss did not improve from 0.00019\n",
      "Epoch 395/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.4933e-04 - val_loss: 1.8943e-04\n",
      "\n",
      "Epoch 00395: val_loss did not improve from 0.00019\n",
      "Epoch 396/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 1.4950e-04 - val_loss: 1.8669e-04\n",
      "\n",
      "Epoch 00396: val_loss did not improve from 0.00019\n",
      "Epoch 397/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 1.4705e-04 - val_loss: 1.8420e-04\n",
      "\n",
      "Epoch 00397: val_loss improved from 0.00019 to 0.00018, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 398/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 1.4432e-04 - val_loss: 1.8422e-04\n",
      "\n",
      "Epoch 00398: val_loss did not improve from 0.00018\n",
      "Epoch 399/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.4461e-04 - val_loss: 1.8525e-04\n",
      "\n",
      "Epoch 00399: val_loss did not improve from 0.00018\n",
      "Epoch 400/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 1.4599e-04 - val_loss: 1.8359e-04\n",
      "\n",
      "Epoch 00400: val_loss improved from 0.00018 to 0.00018, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 401/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.4519e-04 - val_loss: 1.8367e-04\n",
      "\n",
      "Epoch 00401: val_loss did not improve from 0.00018\n",
      "Epoch 402/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 1.4365e-04 - val_loss: 1.8293e-04\n",
      "\n",
      "Epoch 00402: val_loss improved from 0.00018 to 0.00018, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 403/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 1.4349e-04 - val_loss: 1.8142e-04\n",
      "\n",
      "Epoch 00403: val_loss improved from 0.00018 to 0.00018, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 404/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.4194e-04 - val_loss: 1.8008e-04\n",
      "\n",
      "Epoch 00404: val_loss improved from 0.00018 to 0.00018, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 405/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.4067e-04 - val_loss: 1.7954e-04\n",
      "\n",
      "Epoch 00405: val_loss improved from 0.00018 to 0.00018, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 406/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 1.3983e-04 - val_loss: 1.7910e-04\n",
      "\n",
      "Epoch 00406: val_loss improved from 0.00018 to 0.00018, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 407/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 1.3988e-04 - val_loss: 1.7916e-04\n",
      "\n",
      "Epoch 00407: val_loss did not improve from 0.00018\n",
      "Epoch 408/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.3987e-04 - val_loss: 1.7865e-04\n",
      "\n",
      "Epoch 00408: val_loss improved from 0.00018 to 0.00018, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 409/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.4009e-04 - val_loss: 1.7899e-04\n",
      "\n",
      "Epoch 00409: val_loss did not improve from 0.00018\n",
      "Epoch 410/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.3998e-04 - val_loss: 1.8088e-04\n",
      "\n",
      "Epoch 00410: val_loss did not improve from 0.00018\n",
      "Epoch 411/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 1.4140e-04 - val_loss: 1.7961e-04\n",
      "\n",
      "Epoch 00411: val_loss did not improve from 0.00018\n",
      "Epoch 412/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 1.4030e-04 - val_loss: 1.7696e-04\n",
      "\n",
      "Epoch 00412: val_loss improved from 0.00018 to 0.00018, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 413/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 1.3845e-04 - val_loss: 1.7486e-04\n",
      "\n",
      "Epoch 00413: val_loss improved from 0.00018 to 0.00017, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 414/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.3784e-04 - val_loss: 1.7536e-04\n",
      "\n",
      "Epoch 00414: val_loss did not improve from 0.00017\n",
      "Epoch 415/500\n",
      "32/32 [==============================] - 12s 389ms/step - loss: 1.3775e-04 - val_loss: 1.7598e-04\n",
      "\n",
      "Epoch 00415: val_loss did not improve from 0.00017\n",
      "Epoch 416/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 1.3831e-04 - val_loss: 1.7553e-04\n",
      "\n",
      "Epoch 00416: val_loss did not improve from 0.00017\n",
      "Epoch 417/500\n",
      "32/32 [==============================] - 12s 391ms/step - loss: 1.3864e-04 - val_loss: 1.7526e-04\n",
      "\n",
      "Epoch 00417: val_loss did not improve from 0.00017\n",
      "Epoch 418/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 1.3717e-04 - val_loss: 1.7397e-04\n",
      "\n",
      "Epoch 00418: val_loss improved from 0.00017 to 0.00017, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 419/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 1.3606e-04 - val_loss: 1.7240e-04\n",
      "\n",
      "Epoch 00419: val_loss improved from 0.00017 to 0.00017, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 420/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.3573e-04 - val_loss: 1.7271e-04\n",
      "\n",
      "Epoch 00420: val_loss did not improve from 0.00017\n",
      "Epoch 421/500\n",
      "32/32 [==============================] - 13s 408ms/step - loss: 1.3533e-04 - val_loss: 1.7363e-04\n",
      "\n",
      "Epoch 00421: val_loss did not improve from 0.00017\n",
      "Epoch 422/500\n",
      "32/32 [==============================] - 13s 399ms/step - loss: 1.3628e-04 - val_loss: 1.7421e-04\n",
      "\n",
      "Epoch 00422: val_loss did not improve from 0.00017\n",
      "Epoch 423/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 1.3663e-04 - val_loss: 1.7303e-04\n",
      "\n",
      "Epoch 00423: val_loss did not improve from 0.00017\n",
      "Epoch 424/500\n",
      "32/32 [==============================] - 13s 395ms/step - loss: 1.3612e-04 - val_loss: 1.7231e-04\n",
      "\n",
      "Epoch 00424: val_loss improved from 0.00017 to 0.00017, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 425/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.3575e-04 - val_loss: 1.7361e-04\n",
      "\n",
      "Epoch 00425: val_loss did not improve from 0.00017\n",
      "Epoch 426/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.3592e-04 - val_loss: 1.7258e-04\n",
      "\n",
      "Epoch 00426: val_loss did not improve from 0.00017\n",
      "Epoch 427/500\n",
      "32/32 [==============================] - 13s 412ms/step - loss: 1.3609e-04 - val_loss: 1.7202e-04\n",
      "\n",
      "Epoch 00427: val_loss improved from 0.00017 to 0.00017, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 428/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.3507e-04 - val_loss: 1.7200e-04\n",
      "\n",
      "Epoch 00428: val_loss improved from 0.00017 to 0.00017, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 429/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.3522e-04 - val_loss: 1.7117e-04\n",
      "\n",
      "Epoch 00429: val_loss improved from 0.00017 to 0.00017, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 430/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.3403e-04 - val_loss: 1.7528e-04\n",
      "\n",
      "Epoch 00430: val_loss did not improve from 0.00017\n",
      "Epoch 431/500\n",
      "32/32 [==============================] - 12s 391ms/step - loss: 1.3845e-04 - val_loss: 1.8464e-04\n",
      "\n",
      "Epoch 00431: val_loss did not improve from 0.00017\n",
      "Epoch 432/500\n",
      "32/32 [==============================] - 12s 386ms/step - loss: 1.4730e-04 - val_loss: 1.9627e-04\n",
      "\n",
      "Epoch 00432: val_loss did not improve from 0.00017\n",
      "Epoch 433/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.5983e-04 - val_loss: 2.0624e-04\n",
      "\n",
      "Epoch 00433: val_loss did not improve from 0.00017\n",
      "Epoch 434/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.6881e-04 - val_loss: 2.0843e-04\n",
      "\n",
      "Epoch 00434: val_loss did not improve from 0.00017\n",
      "Epoch 435/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.7061e-04 - val_loss: 2.0463e-04\n",
      "\n",
      "Epoch 00435: val_loss did not improve from 0.00017\n",
      "Epoch 436/500\n",
      "32/32 [==============================] - 13s 413ms/step - loss: 1.6798e-04 - val_loss: 1.9618e-04\n",
      "\n",
      "Epoch 00436: val_loss did not improve from 0.00017\n",
      "Epoch 437/500\n",
      "32/32 [==============================] - 13s 407ms/step - loss: 1.5837e-04 - val_loss: 1.8573e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00437: val_loss did not improve from 0.00017\n",
      "Epoch 438/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.4875e-04 - val_loss: 1.7723e-04\n",
      "\n",
      "Epoch 00438: val_loss did not improve from 0.00017\n",
      "Epoch 439/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.4077e-04 - val_loss: 1.7205e-04\n",
      "\n",
      "Epoch 00439: val_loss did not improve from 0.00017\n",
      "Epoch 440/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 1.3506e-04 - val_loss: 1.7255e-04\n",
      "\n",
      "Epoch 00440: val_loss did not improve from 0.00017\n",
      "Epoch 441/500\n",
      "32/32 [==============================] - 12s 388ms/step - loss: 1.3636e-04 - val_loss: 1.7783e-04\n",
      "\n",
      "Epoch 00441: val_loss did not improve from 0.00017\n",
      "Epoch 442/500\n",
      "32/32 [==============================] - 13s 415ms/step - loss: 1.4071e-04 - val_loss: 1.8255e-04\n",
      "\n",
      "Epoch 00442: val_loss did not improve from 0.00017\n",
      "Epoch 443/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.4598e-04 - val_loss: 1.8915e-04\n",
      "\n",
      "Epoch 00443: val_loss did not improve from 0.00017\n",
      "Epoch 444/500\n",
      "32/32 [==============================] - 13s 409ms/step - loss: 1.5231e-04 - val_loss: 1.9345e-04\n",
      "\n",
      "Epoch 00444: val_loss did not improve from 0.00017\n",
      "Epoch 445/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.5705e-04 - val_loss: 1.9587e-04\n",
      "\n",
      "Epoch 00445: val_loss did not improve from 0.00017\n",
      "Epoch 446/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.5892e-04 - val_loss: 1.9474e-04\n",
      "\n",
      "Epoch 00446: val_loss did not improve from 0.00017\n",
      "Epoch 447/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.5710e-04 - val_loss: 1.8856e-04\n",
      "\n",
      "Epoch 00447: val_loss did not improve from 0.00017\n",
      "Epoch 448/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.5236e-04 - val_loss: 1.7741e-04\n",
      "\n",
      "Epoch 00448: val_loss did not improve from 0.00017\n",
      "Epoch 449/500\n",
      "32/32 [==============================] - 13s 393ms/step - loss: 1.4166e-04 - val_loss: 1.7255e-04\n",
      "\n",
      "Epoch 00449: val_loss did not improve from 0.00017\n",
      "Epoch 450/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.3531e-04 - val_loss: 1.7006e-04\n",
      "\n",
      "Epoch 00450: val_loss improved from 0.00017 to 0.00017, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 451/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.3359e-04 - val_loss: 1.7082e-04\n",
      "\n",
      "Epoch 00451: val_loss did not improve from 0.00017\n",
      "Epoch 452/500\n",
      "32/32 [==============================] - 12s 386ms/step - loss: 1.3432e-04 - val_loss: 1.7417e-04\n",
      "\n",
      "Epoch 00452: val_loss did not improve from 0.00017\n",
      "Epoch 453/500\n",
      "32/32 [==============================] - 13s 411ms/step - loss: 1.3775e-04 - val_loss: 1.7896e-04\n",
      "\n",
      "Epoch 00453: val_loss did not improve from 0.00017\n",
      "Epoch 454/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 1.4288e-04 - val_loss: 1.8515e-04\n",
      "\n",
      "Epoch 00454: val_loss did not improve from 0.00017\n",
      "Epoch 455/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 1.4850e-04 - val_loss: 1.9175e-04\n",
      "\n",
      "Epoch 00455: val_loss did not improve from 0.00017\n",
      "Epoch 456/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.5549e-04 - val_loss: 1.9387e-04\n",
      "\n",
      "Epoch 00456: val_loss did not improve from 0.00017\n",
      "Epoch 457/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 1.5776e-04 - val_loss: 1.9852e-04\n",
      "\n",
      "Epoch 00457: val_loss did not improve from 0.00017\n",
      "Epoch 458/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.6235e-04 - val_loss: 2.0227e-04\n",
      "\n",
      "Epoch 00458: val_loss did not improve from 0.00017\n",
      "Epoch 459/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.6591e-04 - val_loss: 2.0321e-04\n",
      "\n",
      "Epoch 00459: val_loss did not improve from 0.00017\n",
      "Epoch 460/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 1.6720e-04 - val_loss: 2.0304e-04\n",
      "\n",
      "Epoch 00460: val_loss did not improve from 0.00017\n",
      "Epoch 461/500\n",
      "32/32 [==============================] - 13s 410ms/step - loss: 1.6707e-04 - val_loss: 2.0256e-04\n",
      "\n",
      "Epoch 00461: val_loss did not improve from 0.00017\n",
      "Epoch 462/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.6688e-04 - val_loss: 2.0236e-04\n",
      "\n",
      "Epoch 00462: val_loss did not improve from 0.00017\n",
      "Epoch 463/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 1.6660e-04 - val_loss: 2.0468e-04\n",
      "\n",
      "Epoch 00463: val_loss did not improve from 0.00017\n",
      "Epoch 464/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.6767e-04 - val_loss: 2.0782e-04\n",
      "\n",
      "Epoch 00464: val_loss did not improve from 0.00017\n",
      "Epoch 465/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.7149e-04 - val_loss: 2.1610e-04\n",
      "\n",
      "Epoch 00465: val_loss did not improve from 0.00017\n",
      "Epoch 466/500\n",
      "32/32 [==============================] - 13s 402ms/step - loss: 1.8051e-04 - val_loss: 2.2438e-04\n",
      "\n",
      "Epoch 00466: val_loss did not improve from 0.00017\n",
      "Epoch 467/500\n",
      "32/32 [==============================] - 13s 405ms/step - loss: 1.8932e-04 - val_loss: 2.3513e-04\n",
      "\n",
      "Epoch 00467: val_loss did not improve from 0.00017\n",
      "Epoch 468/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 1.9930e-04 - val_loss: 2.4372e-04\n",
      "\n",
      "Epoch 00468: val_loss did not improve from 0.00017\n",
      "Epoch 469/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 2.0805e-04 - val_loss: 2.6475e-04\n",
      "\n",
      "Epoch 00469: val_loss did not improve from 0.00017\n",
      "Epoch 470/500\n",
      "32/32 [==============================] - 13s 417ms/step - loss: 2.3012e-04 - val_loss: 2.9437e-04\n",
      "\n",
      "Epoch 00470: val_loss did not improve from 0.00017\n",
      "Epoch 471/500\n",
      "32/32 [==============================] - 13s 391ms/step - loss: 2.6062e-04 - val_loss: 3.3608e-04\n",
      "\n",
      "Epoch 00471: val_loss did not improve from 0.00017\n",
      "Epoch 472/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 3.0143e-04 - val_loss: 3.7705e-04\n",
      "\n",
      "Epoch 00472: val_loss did not improve from 0.00017\n",
      "Epoch 473/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 3.4188e-04 - val_loss: 4.2166e-04\n",
      "\n",
      "Epoch 00473: val_loss did not improve from 0.00017\n",
      "Epoch 474/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 3.8779e-04 - val_loss: 4.5591e-04\n",
      "\n",
      "Epoch 00474: val_loss did not improve from 0.00017\n",
      "Epoch 475/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 4.2195e-04 - val_loss: 4.6502e-04\n",
      "\n",
      "Epoch 00475: val_loss did not improve from 0.00017\n",
      "Epoch 476/500\n",
      "32/32 [==============================] - 13s 411ms/step - loss: 4.3079e-04 - val_loss: 4.5306e-04\n",
      "\n",
      "Epoch 00476: val_loss did not improve from 0.00017\n",
      "Epoch 477/500\n",
      "32/32 [==============================] - 13s 394ms/step - loss: 4.1779e-04 - val_loss: 3.9211e-04\n",
      "\n",
      "Epoch 00477: val_loss did not improve from 0.00017\n",
      "Epoch 478/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 3.5740e-04 - val_loss: 3.2291e-04\n",
      "\n",
      "Epoch 00478: val_loss did not improve from 0.00017\n",
      "Epoch 479/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 2.8778e-04 - val_loss: 2.4432e-04\n",
      "\n",
      "Epoch 00479: val_loss did not improve from 0.00017\n",
      "Epoch 480/500\n",
      "32/32 [==============================] - 13s 397ms/step - loss: 2.1065e-04 - val_loss: 1.8255e-04\n",
      "\n",
      "Epoch 00480: val_loss did not improve from 0.00017\n",
      "Epoch 481/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.4793e-04 - val_loss: 1.5424e-04\n",
      "\n",
      "Epoch 00481: val_loss improved from 0.00017 to 0.00015, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 482/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.1944e-04 - val_loss: 1.5943e-04\n",
      "\n",
      "Epoch 00482: val_loss did not improve from 0.00015\n",
      "Epoch 483/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 1.2537e-04 - val_loss: 1.8389e-04\n",
      "\n",
      "Epoch 00483: val_loss did not improve from 0.00015\n",
      "Epoch 484/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.4983e-04 - val_loss: 2.0523e-04\n",
      "\n",
      "Epoch 00484: val_loss did not improve from 0.00015\n",
      "Epoch 485/500\n",
      "32/32 [==============================] - 13s 413ms/step - loss: 1.7155e-04 - val_loss: 2.1321e-04\n",
      "\n",
      "Epoch 00485: val_loss did not improve from 0.00015\n",
      "Epoch 486/500\n",
      "32/32 [==============================] - 13s 417ms/step - loss: 1.7947e-04 - val_loss: 1.9797e-04\n",
      "\n",
      "Epoch 00486: val_loss did not improve from 0.00015\n",
      "Epoch 487/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 13s 400ms/step - loss: 1.6455e-04 - val_loss: 1.8095e-04\n",
      "\n",
      "Epoch 00487: val_loss did not improve from 0.00015\n",
      "Epoch 488/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 1.4665e-04 - val_loss: 1.6122e-04\n",
      "\n",
      "Epoch 00488: val_loss did not improve from 0.00015\n",
      "Epoch 489/500\n",
      "32/32 [==============================] - 12s 388ms/step - loss: 1.2676e-04 - val_loss: 1.5078e-04\n",
      "\n",
      "Epoch 00489: val_loss improved from 0.00015 to 0.00015, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 490/500\n",
      "32/32 [==============================] - 13s 396ms/step - loss: 1.1726e-04 - val_loss: 1.5874e-04\n",
      "\n",
      "Epoch 00490: val_loss did not improve from 0.00015\n",
      "Epoch 491/500\n",
      "32/32 [==============================] - 13s 400ms/step - loss: 1.2465e-04 - val_loss: 1.7703e-04\n",
      "\n",
      "Epoch 00491: val_loss did not improve from 0.00015\n",
      "Epoch 492/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.4269e-04 - val_loss: 1.8512e-04\n",
      "\n",
      "Epoch 00492: val_loss did not improve from 0.00015\n",
      "Epoch 493/500\n",
      "32/32 [==============================] - 13s 401ms/step - loss: 1.5148e-04 - val_loss: 1.8100e-04\n",
      "\n",
      "Epoch 00493: val_loss did not improve from 0.00015\n",
      "Epoch 494/500\n",
      "32/32 [==============================] - 13s 404ms/step - loss: 1.4707e-04 - val_loss: 1.6778e-04\n",
      "\n",
      "Epoch 00494: val_loss did not improve from 0.00015\n",
      "Epoch 495/500\n",
      "32/32 [==============================] - 12s 390ms/step - loss: 1.3461e-04 - val_loss: 1.5477e-04\n",
      "\n",
      "Epoch 00495: val_loss did not improve from 0.00015\n",
      "Epoch 496/500\n",
      "32/32 [==============================] - 14s 425ms/step - loss: 1.2163e-04 - val_loss: 1.4875e-04\n",
      "\n",
      "Epoch 00496: val_loss improved from 0.00015 to 0.00015, saving model to ../models/lmu-mackey-glass.hdf5\n",
      "Epoch 497/500\n",
      "32/32 [==============================] - 13s 392ms/step - loss: 1.1539e-04 - val_loss: 1.5181e-04\n",
      "\n",
      "Epoch 00497: val_loss did not improve from 0.00015\n",
      "Epoch 498/500\n",
      "32/32 [==============================] - 13s 406ms/step - loss: 1.1888e-04 - val_loss: 1.6102e-04\n",
      "\n",
      "Epoch 00498: val_loss did not improve from 0.00015\n",
      "Epoch 499/500\n",
      "32/32 [==============================] - 13s 403ms/step - loss: 1.2761e-04 - val_loss: 1.6500e-04\n",
      "\n",
      "Epoch 00499: val_loss did not improve from 0.00015\n",
      "Epoch 500/500\n",
      "32/32 [==============================] - 13s 398ms/step - loss: 1.3045e-04 - val_loss: 1.6120e-04\n",
      "\n",
      "Epoch 00500: val_loss did not improve from 0.00015\n"
     ]
    }
   ],
   "source": [
    "lmu_history, lmu_time = fit(\n",
    "    lmu_model, \"../models/lmu-mackey-glass.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 32 samples, validate on 32 samples\n",
      "Epoch 1/500\n",
      "32/32 [==============================] - 26s 809ms/step - loss: 0.0477 - val_loss: 0.0463\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.04634, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 2/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 0.0464 - val_loss: 0.0451\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.04634 to 0.04513, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 3/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0452 - val_loss: 0.0440\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.04513 to 0.04397, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 4/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0440 - val_loss: 0.0428\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.04397 to 0.04279, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 5/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0428 - val_loss: 0.0416\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.04279 to 0.04155, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 6/500\n",
      "32/32 [==============================] - 20s 621ms/step - loss: 0.0416 - val_loss: 0.0402\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.04155 to 0.04022, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 7/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0403 - val_loss: 0.0387\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.04022 to 0.03871, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 8/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0388 - val_loss: 0.0370\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.03871 to 0.03701, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 9/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0371 - val_loss: 0.0351\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.03701 to 0.03507, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 10/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0351 - val_loss: 0.0328\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.03507 to 0.03282, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 11/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 0.0329 - val_loss: 0.0302\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.03282 to 0.03023, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 12/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 0.0303 - val_loss: 0.0273\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.03023 to 0.02728, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 13/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0273 - val_loss: 0.0239\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.02728 to 0.02393, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 14/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0240 - val_loss: 0.0202\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.02393 to 0.02020, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 15/500\n",
      "32/32 [==============================] - 20s 624ms/step - loss: 0.0202 - val_loss: 0.0162\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.02020 to 0.01617, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 16/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0162 - val_loss: 0.0120\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.01617 to 0.01203, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 17/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 0.0121 - val_loss: 0.0081\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.01203 to 0.00813, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 18/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0082 - val_loss: 0.0051\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.00813 to 0.00508, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 19/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0051 - val_loss: 0.0037\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.00508 to 0.00368, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 20/500\n",
      "32/32 [==============================] - 20s 623ms/step - loss: 0.0037 - val_loss: 0.0045\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.00368\n",
      "Epoch 21/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0046 - val_loss: 0.0067\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.00368\n",
      "Epoch 22/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0067 - val_loss: 0.0081\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.00368\n",
      "Epoch 23/500\n",
      "32/32 [==============================] - 20s 620ms/step - loss: 0.0082 - val_loss: 0.0079\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.00368\n",
      "Epoch 24/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 0.0079 - val_loss: 0.0066\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.00368\n",
      "Epoch 25/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 0.0066 - val_loss: 0.0050\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.00368\n",
      "Epoch 26/500\n",
      "32/32 [==============================] - 20s 625ms/step - loss: 0.0051 - val_loss: 0.0039\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.00368\n",
      "Epoch 27/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0039 - val_loss: 0.0033\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.00368 to 0.00332, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 28/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0033 - val_loss: 0.0034\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.00332\n",
      "Epoch 29/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 0.0034 - val_loss: 0.0037\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.00332\n",
      "Epoch 30/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0037 - val_loss: 0.0041\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.00332\n",
      "Epoch 31/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0042 - val_loss: 0.0045\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.00332\n",
      "Epoch 32/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0045 - val_loss: 0.0047\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.00332\n",
      "Epoch 33/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0047 - val_loss: 0.0047\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.00332\n",
      "Epoch 34/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 0.0047 - val_loss: 0.0044\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.00332\n",
      "Epoch 35/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0045 - val_loss: 0.0041\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.00332\n",
      "Epoch 36/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 0.0041 - val_loss: 0.0037\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.00332\n",
      "Epoch 37/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0037 - val_loss: 0.0032\n",
      "\n",
      "Epoch 00037: val_loss improved from 0.00332 to 0.00324, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 38/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0033 - val_loss: 0.0029\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.00324 to 0.00287, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 39/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0029 - val_loss: 0.0026\n",
      "\n",
      "Epoch 00039: val_loss improved from 0.00287 to 0.00263, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 40/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0027 - val_loss: 0.0025\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.00263 to 0.00255, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 41/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0026 - val_loss: 0.0026\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.00255\n",
      "Epoch 42/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 0.0026 - val_loss: 0.0028\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.00255\n",
      "Epoch 43/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 0.0028 - val_loss: 0.0029\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.00255\n",
      "Epoch 44/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0030 - val_loss: 0.0030\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.00255\n",
      "Epoch 45/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 0.0031 - val_loss: 0.0030\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00045: val_loss did not improve from 0.00255\n",
      "Epoch 46/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0031 - val_loss: 0.0030\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.00255\n",
      "Epoch 47/500\n",
      "32/32 [==============================] - 20s 625ms/step - loss: 0.0030 - val_loss: 0.0028\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 0.00255\n",
      "Epoch 48/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 0.0028 - val_loss: 0.0026\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.00255\n",
      "Epoch 49/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 0.0027 - val_loss: 0.0025\n",
      "\n",
      "Epoch 00049: val_loss improved from 0.00255 to 0.00252, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 50/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 0.0025 - val_loss: 0.0025\n",
      "\n",
      "Epoch 00050: val_loss improved from 0.00252 to 0.00245, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 51/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0025 - val_loss: 0.0024\n",
      "\n",
      "Epoch 00051: val_loss improved from 0.00245 to 0.00243, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 52/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0025 - val_loss: 0.0024\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.00243\n",
      "Epoch 53/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0025 - val_loss: 0.0025\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.00243\n",
      "Epoch 54/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 0.0025 - val_loss: 0.0025\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.00243\n",
      "Epoch 55/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 0.0025 - val_loss: 0.0025\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.00243\n",
      "Epoch 56/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0025 - val_loss: 0.0024\n",
      "\n",
      "Epoch 00056: val_loss improved from 0.00243 to 0.00242, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 57/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 0.0024 - val_loss: 0.0024\n",
      "\n",
      "Epoch 00057: val_loss improved from 0.00242 to 0.00237, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 58/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 0.0024 - val_loss: 0.0023\n",
      "\n",
      "Epoch 00058: val_loss improved from 0.00237 to 0.00231, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 59/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 0.0023 - val_loss: 0.0023\n",
      "\n",
      "Epoch 00059: val_loss improved from 0.00231 to 0.00226, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 60/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0023 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00060: val_loss improved from 0.00226 to 0.00223, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 61/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0022 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00061: val_loss improved from 0.00223 to 0.00221, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 62/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0022 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00062: val_loss improved from 0.00221 to 0.00221, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 63/500\n",
      "32/32 [==============================] - 20s 623ms/step - loss: 0.0022 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.00221\n",
      "Epoch 64/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0022 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00064: val_loss improved from 0.00221 to 0.00221, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 65/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0022 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00065: val_loss improved from 0.00221 to 0.00220, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 66/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0022 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00066: val_loss improved from 0.00220 to 0.00217, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 67/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0022 - val_loss: 0.0021\n",
      "\n",
      "Epoch 00067: val_loss improved from 0.00217 to 0.00213, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 68/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0021 - val_loss: 0.0021\n",
      "\n",
      "Epoch 00068: val_loss improved from 0.00213 to 0.00209, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 69/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0021 - val_loss: 0.0021\n",
      "\n",
      "Epoch 00069: val_loss improved from 0.00209 to 0.00206, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 70/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0021 - val_loss: 0.0020\n",
      "\n",
      "Epoch 00070: val_loss improved from 0.00206 to 0.00203, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 71/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0021 - val_loss: 0.0020\n",
      "\n",
      "Epoch 00071: val_loss improved from 0.00203 to 0.00202, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 72/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0020 - val_loss: 0.0020\n",
      "\n",
      "Epoch 00072: val_loss improved from 0.00202 to 0.00201, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 73/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 0.0020 - val_loss: 0.0020\n",
      "\n",
      "Epoch 00073: val_loss improved from 0.00201 to 0.00201, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 74/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0020 - val_loss: 0.0020\n",
      "\n",
      "Epoch 00074: val_loss improved from 0.00201 to 0.00200, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 75/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0020 - val_loss: 0.0020\n",
      "\n",
      "Epoch 00075: val_loss improved from 0.00200 to 0.00198, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 76/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 0.0020 - val_loss: 0.0020\n",
      "\n",
      "Epoch 00076: val_loss improved from 0.00198 to 0.00196, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 77/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0020 - val_loss: 0.0019\n",
      "\n",
      "Epoch 00077: val_loss improved from 0.00196 to 0.00194, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 78/500\n",
      "32/32 [==============================] - 20s 624ms/step - loss: 0.0020 - val_loss: 0.0019\n",
      "\n",
      "Epoch 00078: val_loss improved from 0.00194 to 0.00192, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 79/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 0.0019 - val_loss: 0.0019\n",
      "\n",
      "Epoch 00079: val_loss improved from 0.00192 to 0.00189, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 80/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0019 - val_loss: 0.0019\n",
      "\n",
      "Epoch 00080: val_loss improved from 0.00189 to 0.00188, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 81/500\n",
      "32/32 [==============================] - 20s 624ms/step - loss: 0.0019 - val_loss: 0.0019\n",
      "\n",
      "Epoch 00081: val_loss improved from 0.00188 to 0.00186, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 82/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0019 - val_loss: 0.0019\n",
      "\n",
      "Epoch 00082: val_loss improved from 0.00186 to 0.00185, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 83/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0019 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00083: val_loss improved from 0.00185 to 0.00184, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 84/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0019 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00084: val_loss improved from 0.00184 to 0.00182, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 85/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0018 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00085: val_loss improved from 0.00182 to 0.00181, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 86/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 0.0018 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00086: val_loss improved from 0.00181 to 0.00179, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 87/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 20s 627ms/step - loss: 0.0018 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00087: val_loss improved from 0.00179 to 0.00178, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 88/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0018 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00088: val_loss improved from 0.00178 to 0.00176, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 89/500\n",
      "32/32 [==============================] - 20s 623ms/step - loss: 0.0018 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00089: val_loss improved from 0.00176 to 0.00175, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 90/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 0.0018 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00090: val_loss improved from 0.00175 to 0.00174, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 91/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0017 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00091: val_loss improved from 0.00174 to 0.00172, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 92/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 0.0017 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00092: val_loss improved from 0.00172 to 0.00171, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 93/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0017 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00093: val_loss improved from 0.00171 to 0.00170, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 94/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0017 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00094: val_loss improved from 0.00170 to 0.00168, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 95/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0017 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00095: val_loss improved from 0.00168 to 0.00167, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 96/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0017 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00096: val_loss improved from 0.00167 to 0.00166, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 97/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0017 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00097: val_loss improved from 0.00166 to 0.00164, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 98/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0017 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00098: val_loss improved from 0.00164 to 0.00163, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 99/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 0.0016 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00099: val_loss improved from 0.00163 to 0.00162, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 100/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0016 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00100: val_loss improved from 0.00162 to 0.00161, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 101/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0016 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00101: val_loss improved from 0.00161 to 0.00160, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 102/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0016 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00102: val_loss improved from 0.00160 to 0.00159, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 103/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0016 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00103: val_loss improved from 0.00159 to 0.00158, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 104/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0016 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00104: val_loss improved from 0.00158 to 0.00156, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 105/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0016 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00105: val_loss improved from 0.00156 to 0.00155, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 106/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 0.0016 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00106: val_loss improved from 0.00155 to 0.00154, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 107/500\n",
      "32/32 [==============================] - 21s 646ms/step - loss: 0.0016 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00107: val_loss improved from 0.00154 to 0.00153, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 108/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00108: val_loss improved from 0.00153 to 0.00152, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 109/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00109: val_loss improved from 0.00152 to 0.00151, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 110/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00110: val_loss improved from 0.00151 to 0.00150, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 111/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00111: val_loss improved from 0.00150 to 0.00149, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 112/500\n",
      "32/32 [==============================] - 20s 623ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00112: val_loss improved from 0.00149 to 0.00148, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 113/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00113: val_loss improved from 0.00148 to 0.00147, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 114/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00114: val_loss improved from 0.00147 to 0.00146, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 115/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0015 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00115: val_loss improved from 0.00146 to 0.00146, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 116/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0015 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00116: val_loss improved from 0.00146 to 0.00145, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 117/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0015 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00117: val_loss improved from 0.00145 to 0.00144, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 118/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 0.0014 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00118: val_loss improved from 0.00144 to 0.00143, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 119/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0014 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00119: val_loss improved from 0.00143 to 0.00142, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 120/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0014 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00120: val_loss improved from 0.00142 to 0.00141, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 121/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0014 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00121: val_loss improved from 0.00141 to 0.00140, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 122/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0014 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00122: val_loss improved from 0.00140 to 0.00139, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 123/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0014 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00123: val_loss improved from 0.00139 to 0.00139, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 124/500\n",
      "32/32 [==============================] - 20s 622ms/step - loss: 0.0014 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00124: val_loss improved from 0.00139 to 0.00138, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 125/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0014 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00125: val_loss improved from 0.00138 to 0.00137, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 126/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0014 - val_loss: 0.0014\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00126: val_loss improved from 0.00137 to 0.00136, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 127/500\n",
      "32/32 [==============================] - 20s 621ms/step - loss: 0.0014 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00127: val_loss improved from 0.00136 to 0.00135, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 128/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0014 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00128: val_loss improved from 0.00135 to 0.00135, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 129/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0014 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00129: val_loss improved from 0.00135 to 0.00134, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 130/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00130: val_loss improved from 0.00134 to 0.00133, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 131/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00131: val_loss improved from 0.00133 to 0.00132, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 132/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00132: val_loss improved from 0.00132 to 0.00131, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 133/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00133: val_loss improved from 0.00131 to 0.00131, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 134/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00134: val_loss improved from 0.00131 to 0.00130, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 135/500\n",
      "32/32 [==============================] - 20s 622ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00135: val_loss improved from 0.00130 to 0.00129, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 136/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00136: val_loss improved from 0.00129 to 0.00129, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 137/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00137: val_loss improved from 0.00129 to 0.00128, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 138/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00138: val_loss improved from 0.00128 to 0.00127, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 139/500\n",
      "32/32 [==============================] - 20s 621ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00139: val_loss improved from 0.00127 to 0.00126, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 140/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00140: val_loss improved from 0.00126 to 0.00126, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 141/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00141: val_loss improved from 0.00126 to 0.00125, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 142/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0013 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00142: val_loss improved from 0.00125 to 0.00124, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 143/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 0.0013 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00143: val_loss improved from 0.00124 to 0.00124, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 144/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00144: val_loss improved from 0.00124 to 0.00123, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 145/500\n",
      "32/32 [==============================] - 20s 625ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00145: val_loss improved from 0.00123 to 0.00122, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 146/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00146: val_loss improved from 0.00122 to 0.00122, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 147/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00147: val_loss improved from 0.00122 to 0.00121, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 148/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00148: val_loss improved from 0.00121 to 0.00120, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 149/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00149: val_loss improved from 0.00120 to 0.00120, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 150/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00150: val_loss improved from 0.00120 to 0.00119, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 151/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00151: val_loss improved from 0.00119 to 0.00118, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 152/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00152: val_loss improved from 0.00118 to 0.00118, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 153/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00153: val_loss improved from 0.00118 to 0.00117, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 154/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00154: val_loss improved from 0.00117 to 0.00116, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 155/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00155: val_loss improved from 0.00116 to 0.00116, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 156/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00156: val_loss improved from 0.00116 to 0.00115, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 157/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0012 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00157: val_loss improved from 0.00115 to 0.00115, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 158/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0012 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00158: val_loss improved from 0.00115 to 0.00114, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 159/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00159: val_loss improved from 0.00114 to 0.00113, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 160/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00160: val_loss improved from 0.00113 to 0.00113, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 161/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00161: val_loss improved from 0.00113 to 0.00112, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 162/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00162: val_loss improved from 0.00112 to 0.00111, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 163/500\n",
      "32/32 [==============================] - 20s 625ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00163: val_loss improved from 0.00111 to 0.00111, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 164/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00164: val_loss improved from 0.00111 to 0.00110, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 165/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00165: val_loss improved from 0.00110 to 0.00109, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 166/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 20s 624ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00166: val_loss improved from 0.00109 to 0.00109, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 167/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00167: val_loss improved from 0.00109 to 0.00108, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 168/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00168: val_loss improved from 0.00108 to 0.00108, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 169/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00169: val_loss improved from 0.00108 to 0.00107, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 170/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00170: val_loss improved from 0.00107 to 0.00106, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 171/500\n",
      "32/32 [==============================] - 21s 650ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00171: val_loss improved from 0.00106 to 0.00106, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 172/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00172: val_loss improved from 0.00106 to 0.00105, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 173/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 0.0011 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00173: val_loss improved from 0.00105 to 0.00104, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 174/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 0.0010 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00174: val_loss improved from 0.00104 to 0.00104, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 175/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 0.0010 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00175: val_loss improved from 0.00104 to 0.00103, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 176/500\n",
      "32/32 [==============================] - 20s 624ms/step - loss: 0.0010 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00176: val_loss improved from 0.00103 to 0.00103, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 177/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 0.0010 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00177: val_loss improved from 0.00103 to 0.00102, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 178/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 0.0010 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00178: val_loss improved from 0.00102 to 0.00101, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 179/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 0.0010 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00179: val_loss improved from 0.00101 to 0.00101, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 180/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 0.0010 - val_loss: 9.9956e-04\n",
      "\n",
      "Epoch 00180: val_loss improved from 0.00101 to 0.00100, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 181/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 0.0010 - val_loss: 9.9316e-04\n",
      "\n",
      "Epoch 00181: val_loss improved from 0.00100 to 0.00099, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 182/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 9.9714e-04 - val_loss: 9.8676e-04\n",
      "\n",
      "Epoch 00182: val_loss improved from 0.00099 to 0.00099, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 183/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 9.9059e-04 - val_loss: 9.8035e-04\n",
      "\n",
      "Epoch 00183: val_loss improved from 0.00099 to 0.00098, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 184/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 9.8403e-04 - val_loss: 9.7393e-04\n",
      "\n",
      "Epoch 00184: val_loss improved from 0.00098 to 0.00097, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 185/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 9.7746e-04 - val_loss: 9.6750e-04\n",
      "\n",
      "Epoch 00185: val_loss improved from 0.00097 to 0.00097, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 186/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 9.7088e-04 - val_loss: 9.6107e-04\n",
      "\n",
      "Epoch 00186: val_loss improved from 0.00097 to 0.00096, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 187/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 9.6430e-04 - val_loss: 9.5462e-04\n",
      "\n",
      "Epoch 00187: val_loss improved from 0.00096 to 0.00095, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 188/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 9.5771e-04 - val_loss: 9.4818e-04\n",
      "\n",
      "Epoch 00188: val_loss improved from 0.00095 to 0.00095, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 189/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 9.5112e-04 - val_loss: 9.4172e-04\n",
      "\n",
      "Epoch 00189: val_loss improved from 0.00095 to 0.00094, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 190/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 9.4451e-04 - val_loss: 9.3526e-04\n",
      "\n",
      "Epoch 00190: val_loss improved from 0.00094 to 0.00094, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 191/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 9.3791e-04 - val_loss: 9.2880e-04\n",
      "\n",
      "Epoch 00191: val_loss improved from 0.00094 to 0.00093, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 192/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 9.3129e-04 - val_loss: 9.2234e-04\n",
      "\n",
      "Epoch 00192: val_loss improved from 0.00093 to 0.00092, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 193/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 9.2468e-04 - val_loss: 9.1587e-04\n",
      "\n",
      "Epoch 00193: val_loss improved from 0.00092 to 0.00092, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 194/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 9.1806e-04 - val_loss: 9.0940e-04\n",
      "\n",
      "Epoch 00194: val_loss improved from 0.00092 to 0.00091, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 195/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 9.1144e-04 - val_loss: 9.0292e-04\n",
      "\n",
      "Epoch 00195: val_loss improved from 0.00091 to 0.00090, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 196/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 9.0481e-04 - val_loss: 8.9645e-04\n",
      "\n",
      "Epoch 00196: val_loss improved from 0.00090 to 0.00090, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 197/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 8.9819e-04 - val_loss: 8.8997e-04\n",
      "\n",
      "Epoch 00197: val_loss improved from 0.00090 to 0.00089, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 198/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 8.9156e-04 - val_loss: 8.8350e-04\n",
      "\n",
      "Epoch 00198: val_loss improved from 0.00089 to 0.00088, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 199/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 8.8494e-04 - val_loss: 8.7702e-04\n",
      "\n",
      "Epoch 00199: val_loss improved from 0.00088 to 0.00088, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 200/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 8.7832e-04 - val_loss: 8.7055e-04\n",
      "\n",
      "Epoch 00200: val_loss improved from 0.00088 to 0.00087, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 201/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 8.7170e-04 - val_loss: 8.6408e-04\n",
      "\n",
      "Epoch 00201: val_loss improved from 0.00087 to 0.00086, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 202/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 8.6508e-04 - val_loss: 8.5762e-04\n",
      "\n",
      "Epoch 00202: val_loss improved from 0.00086 to 0.00086, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 203/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 8.5847e-04 - val_loss: 8.5116e-04\n",
      "\n",
      "Epoch 00203: val_loss improved from 0.00086 to 0.00085, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 204/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 8.5186e-04 - val_loss: 8.4471e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00204: val_loss improved from 0.00085 to 0.00084, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 205/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 8.4526e-04 - val_loss: 8.3827e-04\n",
      "\n",
      "Epoch 00205: val_loss improved from 0.00084 to 0.00084, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 206/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 8.3867e-04 - val_loss: 8.3184e-04\n",
      "\n",
      "Epoch 00206: val_loss improved from 0.00084 to 0.00083, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 207/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 8.3209e-04 - val_loss: 8.2542e-04\n",
      "\n",
      "Epoch 00207: val_loss improved from 0.00083 to 0.00083, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 208/500\n",
      "32/32 [==============================] - 21s 644ms/step - loss: 8.2552e-04 - val_loss: 8.1901e-04\n",
      "\n",
      "Epoch 00208: val_loss improved from 0.00083 to 0.00082, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 209/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 8.1897e-04 - val_loss: 8.1261e-04\n",
      "\n",
      "Epoch 00209: val_loss improved from 0.00082 to 0.00081, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 210/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 8.1242e-04 - val_loss: 8.0623e-04\n",
      "\n",
      "Epoch 00210: val_loss improved from 0.00081 to 0.00081, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 211/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 8.0589e-04 - val_loss: 7.9986e-04\n",
      "\n",
      "Epoch 00211: val_loss improved from 0.00081 to 0.00080, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 212/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 7.9938e-04 - val_loss: 7.9351e-04\n",
      "\n",
      "Epoch 00212: val_loss improved from 0.00080 to 0.00079, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 213/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 7.9288e-04 - val_loss: 7.8718e-04\n",
      "\n",
      "Epoch 00213: val_loss improved from 0.00079 to 0.00079, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 214/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 7.8641e-04 - val_loss: 7.8088e-04\n",
      "\n",
      "Epoch 00214: val_loss improved from 0.00079 to 0.00078, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 215/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 7.7996e-04 - val_loss: 7.7459e-04\n",
      "\n",
      "Epoch 00215: val_loss improved from 0.00078 to 0.00077, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 216/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 7.7352e-04 - val_loss: 7.6833e-04\n",
      "\n",
      "Epoch 00216: val_loss improved from 0.00077 to 0.00077, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 217/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 7.6712e-04 - val_loss: 7.6209e-04\n",
      "\n",
      "Epoch 00217: val_loss improved from 0.00077 to 0.00076, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 218/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 7.6074e-04 - val_loss: 7.5588e-04\n",
      "\n",
      "Epoch 00218: val_loss improved from 0.00076 to 0.00076, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 219/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 7.5439e-04 - val_loss: 7.4970e-04\n",
      "\n",
      "Epoch 00219: val_loss improved from 0.00076 to 0.00075, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 220/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 7.4806e-04 - val_loss: 7.4355e-04\n",
      "\n",
      "Epoch 00220: val_loss improved from 0.00075 to 0.00074, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 221/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 7.4177e-04 - val_loss: 7.3743e-04\n",
      "\n",
      "Epoch 00221: val_loss improved from 0.00074 to 0.00074, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 222/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 7.3551e-04 - val_loss: 7.3135e-04\n",
      "\n",
      "Epoch 00222: val_loss improved from 0.00074 to 0.00073, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 223/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 7.2929e-04 - val_loss: 7.2530e-04\n",
      "\n",
      "Epoch 00223: val_loss improved from 0.00073 to 0.00073, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 224/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 7.2311e-04 - val_loss: 7.1930e-04\n",
      "\n",
      "Epoch 00224: val_loss improved from 0.00073 to 0.00072, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 225/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 7.1696e-04 - val_loss: 7.1333e-04\n",
      "\n",
      "Epoch 00225: val_loss improved from 0.00072 to 0.00071, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 226/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 7.1086e-04 - val_loss: 7.0740e-04\n",
      "\n",
      "Epoch 00226: val_loss improved from 0.00071 to 0.00071, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 227/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 7.0479e-04 - val_loss: 7.0152e-04\n",
      "\n",
      "Epoch 00227: val_loss improved from 0.00071 to 0.00070, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 228/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 6.9878e-04 - val_loss: 6.9569e-04\n",
      "\n",
      "Epoch 00228: val_loss improved from 0.00070 to 0.00070, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 229/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 6.9281e-04 - val_loss: 6.8990e-04\n",
      "\n",
      "Epoch 00229: val_loss improved from 0.00070 to 0.00069, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 230/500\n",
      "32/32 [==============================] - 21s 644ms/step - loss: 6.8689e-04 - val_loss: 6.8416e-04\n",
      "\n",
      "Epoch 00230: val_loss improved from 0.00069 to 0.00068, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 231/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 6.8102e-04 - val_loss: 6.7848e-04\n",
      "\n",
      "Epoch 00231: val_loss improved from 0.00068 to 0.00068, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 232/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 6.7521e-04 - val_loss: 6.7285e-04\n",
      "\n",
      "Epoch 00232: val_loss improved from 0.00068 to 0.00067, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 233/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 6.6945e-04 - val_loss: 6.6728e-04\n",
      "\n",
      "Epoch 00233: val_loss improved from 0.00067 to 0.00067, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 234/500\n",
      "32/32 [==============================] - 21s 646ms/step - loss: 6.6375e-04 - val_loss: 6.6176e-04\n",
      "\n",
      "Epoch 00234: val_loss improved from 0.00067 to 0.00066, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 235/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 6.5810e-04 - val_loss: 6.5631e-04\n",
      "\n",
      "Epoch 00235: val_loss improved from 0.00066 to 0.00066, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 236/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 6.5253e-04 - val_loss: 6.5092e-04\n",
      "\n",
      "Epoch 00236: val_loss improved from 0.00066 to 0.00065, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 237/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 6.4701e-04 - val_loss: 6.4559e-04\n",
      "\n",
      "Epoch 00237: val_loss improved from 0.00065 to 0.00065, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 238/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 6.4156e-04 - val_loss: 6.4034e-04\n",
      "\n",
      "Epoch 00238: val_loss improved from 0.00065 to 0.00064, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 239/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 6.3618e-04 - val_loss: 6.3515e-04\n",
      "\n",
      "Epoch 00239: val_loss improved from 0.00064 to 0.00064, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 240/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 6.3087e-04 - val_loss: 6.3003e-04\n",
      "\n",
      "Epoch 00240: val_loss improved from 0.00064 to 0.00063, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 241/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 6.2564e-04 - val_loss: 6.2498e-04\n",
      "\n",
      "Epoch 00241: val_loss improved from 0.00063 to 0.00062, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 242/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 6.2047e-04 - val_loss: 6.2001e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00242: val_loss improved from 0.00062 to 0.00062, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 243/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 6.1539e-04 - val_loss: 6.1512e-04\n",
      "\n",
      "Epoch 00243: val_loss improved from 0.00062 to 0.00062, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 244/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 6.1038e-04 - val_loss: 6.1031e-04\n",
      "\n",
      "Epoch 00244: val_loss improved from 0.00062 to 0.00061, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 245/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 6.0545e-04 - val_loss: 6.0557e-04\n",
      "\n",
      "Epoch 00245: val_loss improved from 0.00061 to 0.00061, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 246/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 6.0061e-04 - val_loss: 6.0092e-04\n",
      "\n",
      "Epoch 00246: val_loss improved from 0.00061 to 0.00060, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 247/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 5.9585e-04 - val_loss: 5.9635e-04\n",
      "\n",
      "Epoch 00247: val_loss improved from 0.00060 to 0.00060, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 248/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 5.9117e-04 - val_loss: 5.9187e-04\n",
      "\n",
      "Epoch 00248: val_loss improved from 0.00060 to 0.00059, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 249/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 5.8659e-04 - val_loss: 5.8748e-04\n",
      "\n",
      "Epoch 00249: val_loss improved from 0.00059 to 0.00059, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 250/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 5.8208e-04 - val_loss: 5.8317e-04\n",
      "\n",
      "Epoch 00250: val_loss improved from 0.00059 to 0.00058, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 251/500\n",
      "32/32 [==============================] - 20s 624ms/step - loss: 5.7767e-04 - val_loss: 5.7895e-04\n",
      "\n",
      "Epoch 00251: val_loss improved from 0.00058 to 0.00058, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 252/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 5.7335e-04 - val_loss: 5.7482e-04\n",
      "\n",
      "Epoch 00252: val_loss improved from 0.00058 to 0.00057, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 253/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 5.6912e-04 - val_loss: 5.7077e-04\n",
      "\n",
      "Epoch 00253: val_loss improved from 0.00057 to 0.00057, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 254/500\n",
      "32/32 [==============================] - 21s 645ms/step - loss: 5.6498e-04 - val_loss: 5.6682e-04\n",
      "\n",
      "Epoch 00254: val_loss improved from 0.00057 to 0.00057, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 255/500\n",
      "32/32 [==============================] - 21s 644ms/step - loss: 5.6094e-04 - val_loss: 5.6296e-04\n",
      "\n",
      "Epoch 00255: val_loss improved from 0.00057 to 0.00056, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 256/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 5.5698e-04 - val_loss: 5.5919e-04\n",
      "\n",
      "Epoch 00256: val_loss improved from 0.00056 to 0.00056, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 257/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 5.5312e-04 - val_loss: 5.5551e-04\n",
      "\n",
      "Epoch 00257: val_loss improved from 0.00056 to 0.00056, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 258/500\n",
      "32/32 [==============================] - 21s 644ms/step - loss: 5.4935e-04 - val_loss: 5.5192e-04\n",
      "\n",
      "Epoch 00258: val_loss improved from 0.00056 to 0.00055, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 259/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 5.4567e-04 - val_loss: 5.4842e-04\n",
      "\n",
      "Epoch 00259: val_loss improved from 0.00055 to 0.00055, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 260/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 5.4209e-04 - val_loss: 5.4501e-04\n",
      "\n",
      "Epoch 00260: val_loss improved from 0.00055 to 0.00055, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 261/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 5.3859e-04 - val_loss: 5.4168e-04\n",
      "\n",
      "Epoch 00261: val_loss improved from 0.00055 to 0.00054, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 262/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 5.3518e-04 - val_loss: 5.3844e-04\n",
      "\n",
      "Epoch 00262: val_loss improved from 0.00054 to 0.00054, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 263/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 5.3186e-04 - val_loss: 5.3529e-04\n",
      "\n",
      "Epoch 00263: val_loss improved from 0.00054 to 0.00054, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 264/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 5.2862e-04 - val_loss: 5.3221e-04\n",
      "\n",
      "Epoch 00264: val_loss improved from 0.00054 to 0.00053, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 265/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 5.2547e-04 - val_loss: 5.2922e-04\n",
      "\n",
      "Epoch 00265: val_loss improved from 0.00053 to 0.00053, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 266/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 5.2240e-04 - val_loss: 5.2630e-04\n",
      "\n",
      "Epoch 00266: val_loss improved from 0.00053 to 0.00053, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 267/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 5.1941e-04 - val_loss: 5.2346e-04\n",
      "\n",
      "Epoch 00267: val_loss improved from 0.00053 to 0.00052, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 268/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 5.1649e-04 - val_loss: 5.2069e-04\n",
      "\n",
      "Epoch 00268: val_loss improved from 0.00052 to 0.00052, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 269/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 5.1365e-04 - val_loss: 5.1798e-04\n",
      "\n",
      "Epoch 00269: val_loss improved from 0.00052 to 0.00052, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 270/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 5.1088e-04 - val_loss: 5.1535e-04\n",
      "\n",
      "Epoch 00270: val_loss improved from 0.00052 to 0.00052, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 271/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 5.0817e-04 - val_loss: 5.1277e-04\n",
      "\n",
      "Epoch 00271: val_loss improved from 0.00052 to 0.00051, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 272/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 5.0553e-04 - val_loss: 5.1026e-04\n",
      "\n",
      "Epoch 00272: val_loss improved from 0.00051 to 0.00051, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 273/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 5.0295e-04 - val_loss: 5.0780e-04\n",
      "\n",
      "Epoch 00273: val_loss improved from 0.00051 to 0.00051, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 274/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 5.0042e-04 - val_loss: 5.0539e-04\n",
      "\n",
      "Epoch 00274: val_loss improved from 0.00051 to 0.00051, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 275/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 4.9795e-04 - val_loss: 5.0303e-04\n",
      "\n",
      "Epoch 00275: val_loss improved from 0.00051 to 0.00050, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 276/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 4.9553e-04 - val_loss: 5.0071e-04\n",
      "\n",
      "Epoch 00276: val_loss improved from 0.00050 to 0.00050, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 277/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 4.9316e-04 - val_loss: 4.9844e-04\n",
      "\n",
      "Epoch 00277: val_loss improved from 0.00050 to 0.00050, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 278/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 4.9083e-04 - val_loss: 4.9621e-04\n",
      "\n",
      "Epoch 00278: val_loss improved from 0.00050 to 0.00050, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 279/500\n",
      "32/32 [==============================] - 21s 649ms/step - loss: 4.8853e-04 - val_loss: 4.9400e-04\n",
      "\n",
      "Epoch 00279: val_loss improved from 0.00050 to 0.00049, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 280/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 4.8628e-04 - val_loss: 4.9184e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00280: val_loss improved from 0.00049 to 0.00049, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 281/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 4.8405e-04 - val_loss: 4.8969e-04\n",
      "\n",
      "Epoch 00281: val_loss improved from 0.00049 to 0.00049, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 282/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 4.8186e-04 - val_loss: 4.8758e-04\n",
      "\n",
      "Epoch 00282: val_loss improved from 0.00049 to 0.00049, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 283/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 4.7969e-04 - val_loss: 4.8549e-04\n",
      "\n",
      "Epoch 00283: val_loss improved from 0.00049 to 0.00049, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 284/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 4.7755e-04 - val_loss: 4.8342e-04\n",
      "\n",
      "Epoch 00284: val_loss improved from 0.00049 to 0.00048, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 285/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 4.7542e-04 - val_loss: 4.8136e-04\n",
      "\n",
      "Epoch 00285: val_loss improved from 0.00048 to 0.00048, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 286/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 4.7332e-04 - val_loss: 4.7932e-04\n",
      "\n",
      "Epoch 00286: val_loss improved from 0.00048 to 0.00048, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 287/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 4.7123e-04 - val_loss: 4.7730e-04\n",
      "\n",
      "Epoch 00287: val_loss improved from 0.00048 to 0.00048, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 288/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 4.6916e-04 - val_loss: 4.7528e-04\n",
      "\n",
      "Epoch 00288: val_loss improved from 0.00048 to 0.00048, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 289/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 4.6710e-04 - val_loss: 4.7328e-04\n",
      "\n",
      "Epoch 00289: val_loss improved from 0.00048 to 0.00047, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 290/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 4.6505e-04 - val_loss: 4.7128e-04\n",
      "\n",
      "Epoch 00290: val_loss improved from 0.00047 to 0.00047, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 291/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 4.6301e-04 - val_loss: 4.6929e-04\n",
      "\n",
      "Epoch 00291: val_loss improved from 0.00047 to 0.00047, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 292/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 4.6098e-04 - val_loss: 4.6731e-04\n",
      "\n",
      "Epoch 00292: val_loss improved from 0.00047 to 0.00047, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 293/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 4.5895e-04 - val_loss: 4.6532e-04\n",
      "\n",
      "Epoch 00293: val_loss improved from 0.00047 to 0.00047, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 294/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 4.5693e-04 - val_loss: 4.6335e-04\n",
      "\n",
      "Epoch 00294: val_loss improved from 0.00047 to 0.00046, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 295/500\n",
      "32/32 [==============================] - 20s 618ms/step - loss: 4.5492e-04 - val_loss: 4.6137e-04\n",
      "\n",
      "Epoch 00295: val_loss improved from 0.00046 to 0.00046, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 296/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 4.5290e-04 - val_loss: 4.5940e-04\n",
      "\n",
      "Epoch 00296: val_loss improved from 0.00046 to 0.00046, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 297/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 4.5089e-04 - val_loss: 4.5742e-04\n",
      "\n",
      "Epoch 00297: val_loss improved from 0.00046 to 0.00046, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 298/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 4.4889e-04 - val_loss: 4.5547e-04\n",
      "\n",
      "Epoch 00298: val_loss improved from 0.00046 to 0.00046, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 299/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 4.4689e-04 - val_loss: 4.5350e-04\n",
      "\n",
      "Epoch 00299: val_loss improved from 0.00046 to 0.00045, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 300/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 4.4492e-04 - val_loss: 4.5176e-04\n",
      "\n",
      "Epoch 00300: val_loss improved from 0.00045 to 0.00045, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 301/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 4.4308e-04 - val_loss: 4.5038e-04\n",
      "\n",
      "Epoch 00301: val_loss improved from 0.00045 to 0.00045, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 302/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 4.4180e-04 - val_loss: 4.5196e-04\n",
      "\n",
      "Epoch 00302: val_loss did not improve from 0.00045\n",
      "Epoch 303/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 4.4310e-04 - val_loss: 4.6454e-04\n",
      "\n",
      "Epoch 00303: val_loss did not improve from 0.00045\n",
      "Epoch 304/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 4.5618e-04 - val_loss: 5.2514e-04\n",
      "\n",
      "Epoch 00304: val_loss did not improve from 0.00045\n",
      "Epoch 305/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 5.1567e-04 - val_loss: 6.6354e-04\n",
      "\n",
      "Epoch 00305: val_loss did not improve from 0.00045\n",
      "Epoch 306/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 6.5598e-04 - val_loss: 7.0986e-04\n",
      "\n",
      "Epoch 00306: val_loss did not improve from 0.00045\n",
      "Epoch 307/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 6.9988e-04 - val_loss: 4.6042e-04\n",
      "\n",
      "Epoch 00307: val_loss did not improve from 0.00045\n",
      "Epoch 308/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 4.5213e-04 - val_loss: 5.4826e-04\n",
      "\n",
      "Epoch 00308: val_loss did not improve from 0.00045\n",
      "Epoch 309/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 5.4046e-04 - val_loss: 6.0659e-04\n",
      "\n",
      "Epoch 00309: val_loss did not improve from 0.00045\n",
      "Epoch 310/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 5.9710e-04 - val_loss: 4.3768e-04\n",
      "\n",
      "Epoch 00310: val_loss improved from 0.00045 to 0.00044, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 311/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 4.2914e-04 - val_loss: 5.8296e-04\n",
      "\n",
      "Epoch 00311: val_loss did not improve from 0.00044\n",
      "Epoch 312/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 5.7535e-04 - val_loss: 4.8587e-04\n",
      "\n",
      "Epoch 00312: val_loss did not improve from 0.00044\n",
      "Epoch 313/500\n",
      "32/32 [==============================] - 21s 652ms/step - loss: 4.7693e-04 - val_loss: 4.8731e-04\n",
      "\n",
      "Epoch 00313: val_loss did not improve from 0.00044\n",
      "Epoch 314/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 4.7838e-04 - val_loss: 5.2536e-04\n",
      "\n",
      "Epoch 00314: val_loss did not improve from 0.00044\n",
      "Epoch 315/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 5.1760e-04 - val_loss: 4.3556e-04\n",
      "\n",
      "Epoch 00315: val_loss improved from 0.00044 to 0.00044, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 316/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 4.2730e-04 - val_loss: 5.1864e-04\n",
      "\n",
      "Epoch 00316: val_loss did not improve from 0.00044\n",
      "Epoch 317/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 5.0961e-04 - val_loss: 4.3262e-04\n",
      "\n",
      "Epoch 00317: val_loss improved from 0.00044 to 0.00043, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 318/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 4.2435e-04 - val_loss: 4.9054e-04\n",
      "\n",
      "Epoch 00318: val_loss did not improve from 0.00043\n",
      "Epoch 319/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 4.8270e-04 - val_loss: 4.4781e-04\n",
      "\n",
      "Epoch 00319: val_loss did not improve from 0.00043\n",
      "Epoch 320/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 4.3915e-04 - val_loss: 4.5885e-04\n",
      "\n",
      "Epoch 00320: val_loss did not improve from 0.00043\n",
      "Epoch 321/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 4.5010e-04 - val_loss: 4.5841e-04\n",
      "\n",
      "Epoch 00321: val_loss did not improve from 0.00043\n",
      "Epoch 322/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 4.5042e-04 - val_loss: 4.3449e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00322: val_loss did not improve from 0.00043\n",
      "Epoch 323/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 4.2632e-04 - val_loss: 4.6212e-04\n",
      "\n",
      "Epoch 00323: val_loss did not improve from 0.00043\n",
      "Epoch 324/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 4.5335e-04 - val_loss: 4.2431e-04\n",
      "\n",
      "Epoch 00324: val_loss improved from 0.00043 to 0.00042, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 325/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 4.1588e-04 - val_loss: 4.5625e-04\n",
      "\n",
      "Epoch 00325: val_loss did not improve from 0.00042\n",
      "Epoch 326/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 4.4827e-04 - val_loss: 4.2230e-04\n",
      "\n",
      "Epoch 00326: val_loss improved from 0.00042 to 0.00042, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 327/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 4.1391e-04 - val_loss: 4.4502e-04\n",
      "\n",
      "Epoch 00327: val_loss did not improve from 0.00042\n",
      "Epoch 328/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 4.3640e-04 - val_loss: 4.2418e-04\n",
      "\n",
      "Epoch 00328: val_loss did not improve from 0.00042\n",
      "Epoch 329/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 4.1605e-04 - val_loss: 4.3219e-04\n",
      "\n",
      "Epoch 00329: val_loss did not improve from 0.00042\n",
      "Epoch 330/500\n",
      "32/32 [==============================] - 21s 644ms/step - loss: 4.2418e-04 - val_loss: 4.2759e-04\n",
      "\n",
      "Epoch 00330: val_loss did not improve from 0.00042\n",
      "Epoch 331/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 4.1915e-04 - val_loss: 4.2227e-04\n",
      "\n",
      "Epoch 00331: val_loss improved from 0.00042 to 0.00042, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 332/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 4.1389e-04 - val_loss: 4.2804e-04\n",
      "\n",
      "Epoch 00332: val_loss did not improve from 0.00042\n",
      "Epoch 333/500\n",
      "32/32 [==============================] - 21s 644ms/step - loss: 4.2003e-04 - val_loss: 4.1501e-04\n",
      "\n",
      "Epoch 00333: val_loss improved from 0.00042 to 0.00042, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 334/500\n",
      "32/32 [==============================] - 20s 641ms/step - loss: 4.0685e-04 - val_loss: 4.2619e-04\n",
      "\n",
      "Epoch 00334: val_loss did not improve from 0.00042\n",
      "Epoch 335/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 4.1775e-04 - val_loss: 4.1186e-04\n",
      "\n",
      "Epoch 00335: val_loss improved from 0.00042 to 0.00041, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 336/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 4.0364e-04 - val_loss: 4.2114e-04\n",
      "\n",
      "Epoch 00336: val_loss did not improve from 0.00041\n",
      "Epoch 337/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 4.1310e-04 - val_loss: 4.1131e-04\n",
      "\n",
      "Epoch 00337: val_loss improved from 0.00041 to 0.00041, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 338/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 4.0301e-04 - val_loss: 4.1517e-04\n",
      "\n",
      "Epoch 00338: val_loss did not improve from 0.00041\n",
      "Epoch 339/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 4.0679e-04 - val_loss: 4.1137e-04\n",
      "\n",
      "Epoch 00339: val_loss did not improve from 0.00041\n",
      "Epoch 340/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 4.0323e-04 - val_loss: 4.0895e-04\n",
      "\n",
      "Epoch 00340: val_loss improved from 0.00041 to 0.00041, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 341/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 4.0076e-04 - val_loss: 4.1124e-04\n",
      "\n",
      "Epoch 00341: val_loss did not improve from 0.00041\n",
      "Epoch 342/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 4.0280e-04 - val_loss: 4.0482e-04\n",
      "\n",
      "Epoch 00342: val_loss improved from 0.00041 to 0.00040, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 343/500\n",
      "32/32 [==============================] - 20s 624ms/step - loss: 3.9647e-04 - val_loss: 4.0886e-04\n",
      "\n",
      "Epoch 00343: val_loss did not improve from 0.00040\n",
      "Epoch 344/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 4.0063e-04 - val_loss: 4.0270e-04\n",
      "\n",
      "Epoch 00344: val_loss improved from 0.00040 to 0.00040, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 345/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 3.9429e-04 - val_loss: 4.0514e-04\n",
      "\n",
      "Epoch 00345: val_loss did not improve from 0.00040\n",
      "Epoch 346/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 3.9663e-04 - val_loss: 4.0185e-04\n",
      "\n",
      "Epoch 00346: val_loss improved from 0.00040 to 0.00040, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 347/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 3.9349e-04 - val_loss: 4.0054e-04\n",
      "\n",
      "Epoch 00347: val_loss improved from 0.00040 to 0.00040, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 348/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 3.9216e-04 - val_loss: 4.0108e-04\n",
      "\n",
      "Epoch 00348: val_loss did not improve from 0.00040\n",
      "Epoch 349/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 3.9253e-04 - val_loss: 3.9718e-04\n",
      "\n",
      "Epoch 00349: val_loss improved from 0.00040 to 0.00040, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 350/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 3.8868e-04 - val_loss: 3.9867e-04\n",
      "\n",
      "Epoch 00350: val_loss did not improve from 0.00040\n",
      "Epoch 351/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 3.9025e-04 - val_loss: 3.9530e-04\n",
      "\n",
      "Epoch 00351: val_loss improved from 0.00040 to 0.00040, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 352/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 3.8674e-04 - val_loss: 3.9533e-04\n",
      "\n",
      "Epoch 00352: val_loss did not improve from 0.00040\n",
      "Epoch 353/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 3.8672e-04 - val_loss: 3.9405e-04\n",
      "\n",
      "Epoch 00353: val_loss improved from 0.00040 to 0.00039, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 354/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 3.8552e-04 - val_loss: 3.9185e-04\n",
      "\n",
      "Epoch 00354: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 355/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 3.8326e-04 - val_loss: 3.9233e-04\n",
      "\n",
      "Epoch 00355: val_loss did not improve from 0.00039\n",
      "Epoch 356/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 3.8363e-04 - val_loss: 3.8956e-04\n",
      "\n",
      "Epoch 00356: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 357/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 3.8092e-04 - val_loss: 3.8929e-04\n",
      "\n",
      "Epoch 00357: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 358/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 3.8065e-04 - val_loss: 3.8811e-04\n",
      "\n",
      "Epoch 00358: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 359/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 3.7935e-04 - val_loss: 3.8629e-04\n",
      "\n",
      "Epoch 00359: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 360/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 3.7751e-04 - val_loss: 3.8607e-04\n",
      "\n",
      "Epoch 00360: val_loss improved from 0.00039 to 0.00039, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 361/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 3.7732e-04 - val_loss: 3.8410e-04\n",
      "\n",
      "Epoch 00361: val_loss improved from 0.00039 to 0.00038, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 362/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 3.7525e-04 - val_loss: 3.8332e-04\n",
      "\n",
      "Epoch 00362: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 363/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 3.7442e-04 - val_loss: 3.8234e-04\n",
      "\n",
      "Epoch 00363: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 364/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 3.7346e-04 - val_loss: 3.8059e-04\n",
      "\n",
      "Epoch 00364: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 365/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 20s 629ms/step - loss: 3.7164e-04 - val_loss: 3.8007e-04\n",
      "\n",
      "Epoch 00365: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 366/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 3.7105e-04 - val_loss: 3.7859e-04\n",
      "\n",
      "Epoch 00366: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 367/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 3.6959e-04 - val_loss: 3.7724e-04\n",
      "\n",
      "Epoch 00367: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 368/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 3.6820e-04 - val_loss: 3.7657e-04\n",
      "\n",
      "Epoch 00368: val_loss improved from 0.00038 to 0.00038, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 369/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 3.6744e-04 - val_loss: 3.7499e-04\n",
      "\n",
      "Epoch 00369: val_loss improved from 0.00038 to 0.00037, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 370/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 3.6587e-04 - val_loss: 3.7385e-04\n",
      "\n",
      "Epoch 00370: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 371/500\n",
      "32/32 [==============================] - 20s 625ms/step - loss: 3.6469e-04 - val_loss: 3.7297e-04\n",
      "\n",
      "Epoch 00371: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 372/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 3.6374e-04 - val_loss: 3.7144e-04\n",
      "\n",
      "Epoch 00372: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 373/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 3.6221e-04 - val_loss: 3.7035e-04\n",
      "\n",
      "Epoch 00373: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 374/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 3.6108e-04 - val_loss: 3.6936e-04\n",
      "\n",
      "Epoch 00374: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 375/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 3.6002e-04 - val_loss: 3.6789e-04\n",
      "\n",
      "Epoch 00375: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 376/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 3.5854e-04 - val_loss: 3.6676e-04\n",
      "\n",
      "Epoch 00376: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 377/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 3.5737e-04 - val_loss: 3.6572e-04\n",
      "\n",
      "Epoch 00377: val_loss improved from 0.00037 to 0.00037, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 378/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 3.5627e-04 - val_loss: 3.6431e-04\n",
      "\n",
      "Epoch 00378: val_loss improved from 0.00037 to 0.00036, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 379/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 3.5484e-04 - val_loss: 3.6311e-04\n",
      "\n",
      "Epoch 00379: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 380/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 3.5359e-04 - val_loss: 3.6204e-04\n",
      "\n",
      "Epoch 00380: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 381/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 3.5246e-04 - val_loss: 3.6069e-04\n",
      "\n",
      "Epoch 00381: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 382/500\n",
      "32/32 [==============================] - 21s 645ms/step - loss: 3.5110e-04 - val_loss: 3.5940e-04\n",
      "\n",
      "Epoch 00382: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 383/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 3.4975e-04 - val_loss: 3.5828e-04\n",
      "\n",
      "Epoch 00383: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 384/500\n",
      "32/32 [==============================] - 21s 644ms/step - loss: 3.4857e-04 - val_loss: 3.5701e-04\n",
      "\n",
      "Epoch 00384: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 385/500\n",
      "32/32 [==============================] - 20s 622ms/step - loss: 3.4728e-04 - val_loss: 3.5567e-04\n",
      "\n",
      "Epoch 00385: val_loss improved from 0.00036 to 0.00036, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 386/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 3.4589e-04 - val_loss: 3.5443e-04\n",
      "\n",
      "Epoch 00386: val_loss improved from 0.00036 to 0.00035, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 387/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 3.4459e-04 - val_loss: 3.5321e-04\n",
      "\n",
      "Epoch 00387: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 388/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 3.4334e-04 - val_loss: 3.5191e-04\n",
      "\n",
      "Epoch 00388: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 389/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 3.4198e-04 - val_loss: 3.5055e-04\n",
      "\n",
      "Epoch 00389: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 390/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 3.4058e-04 - val_loss: 3.4926e-04\n",
      "\n",
      "Epoch 00390: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 391/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 3.3925e-04 - val_loss: 3.4801e-04\n",
      "\n",
      "Epoch 00391: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 392/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 3.3794e-04 - val_loss: 3.4666e-04\n",
      "\n",
      "Epoch 00392: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 393/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 3.3656e-04 - val_loss: 3.4529e-04\n",
      "\n",
      "Epoch 00393: val_loss improved from 0.00035 to 0.00035, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 394/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 3.3513e-04 - val_loss: 3.4393e-04\n",
      "\n",
      "Epoch 00394: val_loss improved from 0.00035 to 0.00034, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 395/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 3.3372e-04 - val_loss: 3.4259e-04\n",
      "\n",
      "Epoch 00395: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 396/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 3.3233e-04 - val_loss: 3.4126e-04\n",
      "\n",
      "Epoch 00396: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 397/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 3.3094e-04 - val_loss: 3.3987e-04\n",
      "\n",
      "Epoch 00397: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 398/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 3.2951e-04 - val_loss: 3.3846e-04\n",
      "\n",
      "Epoch 00398: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 399/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 3.2805e-04 - val_loss: 3.3702e-04\n",
      "\n",
      "Epoch 00399: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 400/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 3.2656e-04 - val_loss: 3.3559e-04\n",
      "\n",
      "Epoch 00400: val_loss improved from 0.00034 to 0.00034, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 401/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 3.2507e-04 - val_loss: 3.3416e-04\n",
      "\n",
      "Epoch 00401: val_loss improved from 0.00034 to 0.00033, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 402/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 3.2359e-04 - val_loss: 3.3272e-04\n",
      "\n",
      "Epoch 00402: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 403/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 20s 630ms/step - loss: 3.2210e-04 - val_loss: 3.3129e-04\n",
      "\n",
      "Epoch 00403: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 404/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 3.2060e-04 - val_loss: 3.2983e-04\n",
      "\n",
      "Epoch 00404: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 405/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 3.1909e-04 - val_loss: 3.2840e-04\n",
      "\n",
      "Epoch 00405: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 406/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 3.1759e-04 - val_loss: 3.2697e-04\n",
      "\n",
      "Epoch 00406: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 407/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 3.1613e-04 - val_loss: 3.2571e-04\n",
      "\n",
      "Epoch 00407: val_loss improved from 0.00033 to 0.00033, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 408/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 3.1478e-04 - val_loss: 3.2472e-04\n",
      "\n",
      "Epoch 00408: val_loss improved from 0.00033 to 0.00032, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 409/500\n",
      "32/32 [==============================] - 20s 624ms/step - loss: 3.1378e-04 - val_loss: 3.2491e-04\n",
      "\n",
      "Epoch 00409: val_loss did not improve from 0.00032\n",
      "Epoch 410/500\n",
      "32/32 [==============================] - 20s 625ms/step - loss: 3.1385e-04 - val_loss: 3.2833e-04\n",
      "\n",
      "Epoch 00410: val_loss did not improve from 0.00032\n",
      "Epoch 411/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 3.1730e-04 - val_loss: 3.4333e-04\n",
      "\n",
      "Epoch 00411: val_loss did not improve from 0.00032\n",
      "Epoch 412/500\n",
      "32/32 [==============================] - 21s 644ms/step - loss: 3.3209e-04 - val_loss: 3.9274e-04\n",
      "\n",
      "Epoch 00412: val_loss did not improve from 0.00032\n",
      "Epoch 413/500\n",
      "32/32 [==============================] - 20s 628ms/step - loss: 3.8170e-04 - val_loss: 5.2866e-04\n",
      "\n",
      "Epoch 00413: val_loss did not improve from 0.00032\n",
      "Epoch 414/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 5.1711e-04 - val_loss: 6.8729e-04\n",
      "\n",
      "Epoch 00414: val_loss did not improve from 0.00032\n",
      "Epoch 415/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 6.7645e-04 - val_loss: 6.1335e-04\n",
      "\n",
      "Epoch 00415: val_loss did not improve from 0.00032\n",
      "Epoch 416/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 6.0174e-04 - val_loss: 3.2548e-04\n",
      "\n",
      "Epoch 00416: val_loss did not improve from 0.00032\n",
      "Epoch 417/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 3.1446e-04 - val_loss: 4.6173e-04\n",
      "\n",
      "Epoch 00417: val_loss did not improve from 0.00032\n",
      "Epoch 418/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 4.5091e-04 - val_loss: 5.2602e-04\n",
      "\n",
      "Epoch 00418: val_loss did not improve from 0.00032\n",
      "Epoch 419/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 5.1450e-04 - val_loss: 3.1583e-04\n",
      "\n",
      "Epoch 00419: val_loss improved from 0.00032 to 0.00032, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 420/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 3.0470e-04 - val_loss: 4.6765e-04\n",
      "\n",
      "Epoch 00420: val_loss did not improve from 0.00032\n",
      "Epoch 421/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 4.5675e-04 - val_loss: 4.1061e-04\n",
      "\n",
      "Epoch 00421: val_loss did not improve from 0.00032\n",
      "Epoch 422/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 3.9913e-04 - val_loss: 3.4073e-04\n",
      "\n",
      "Epoch 00422: val_loss did not improve from 0.00032\n",
      "Epoch 423/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 3.2939e-04 - val_loss: 4.4839e-04\n",
      "\n",
      "Epoch 00423: val_loss did not improve from 0.00032\n",
      "Epoch 424/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 4.3749e-04 - val_loss: 3.1819e-04\n",
      "\n",
      "Epoch 00424: val_loss did not improve from 0.00032\n",
      "Epoch 425/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 3.0695e-04 - val_loss: 3.9948e-04\n",
      "\n",
      "Epoch 00425: val_loss did not improve from 0.00032\n",
      "Epoch 426/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 3.8802e-04 - val_loss: 3.4602e-04\n",
      "\n",
      "Epoch 00426: val_loss did not improve from 0.00032\n",
      "Epoch 427/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 3.3494e-04 - val_loss: 3.4431e-04\n",
      "\n",
      "Epoch 00427: val_loss did not improve from 0.00032\n",
      "Epoch 428/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 3.3319e-04 - val_loss: 3.6962e-04\n",
      "\n",
      "Epoch 00428: val_loss did not improve from 0.00032\n",
      "Epoch 429/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 3.5806e-04 - val_loss: 3.1486e-04\n",
      "\n",
      "Epoch 00429: val_loss improved from 0.00032 to 0.00031, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 430/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 3.0341e-04 - val_loss: 3.7003e-04\n",
      "\n",
      "Epoch 00430: val_loss did not improve from 0.00031\n",
      "Epoch 431/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 3.5882e-04 - val_loss: 3.1019e-04\n",
      "\n",
      "Epoch 00431: val_loss improved from 0.00031 to 0.00031, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 432/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 2.9873e-04 - val_loss: 3.4905e-04\n",
      "\n",
      "Epoch 00432: val_loss did not improve from 0.00031\n",
      "Epoch 433/500\n",
      "32/32 [==============================] - 20s 625ms/step - loss: 3.3745e-04 - val_loss: 3.1897e-04\n",
      "\n",
      "Epoch 00433: val_loss did not improve from 0.00031\n",
      "Epoch 434/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 3.0767e-04 - val_loss: 3.2444e-04\n",
      "\n",
      "Epoch 00434: val_loss did not improve from 0.00031\n",
      "Epoch 435/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 3.1317e-04 - val_loss: 3.2907e-04\n",
      "\n",
      "Epoch 00435: val_loss did not improve from 0.00031\n",
      "Epoch 436/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 3.1755e-04 - val_loss: 3.0760e-04\n",
      "\n",
      "Epoch 00436: val_loss improved from 0.00031 to 0.00031, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 437/500\n",
      "32/32 [==============================] - 21s 642ms/step - loss: 2.9616e-04 - val_loss: 3.3043e-04\n",
      "\n",
      "Epoch 00437: val_loss did not improve from 0.00031\n",
      "Epoch 438/500\n",
      "32/32 [==============================] - 20s 640ms/step - loss: 3.1917e-04 - val_loss: 3.0109e-04\n",
      "\n",
      "Epoch 00438: val_loss improved from 0.00031 to 0.00030, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 439/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 2.8967e-04 - val_loss: 3.2204e-04\n",
      "\n",
      "Epoch 00439: val_loss did not improve from 0.00030\n",
      "Epoch 440/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 3.1050e-04 - val_loss: 3.0367e-04\n",
      "\n",
      "Epoch 00440: val_loss did not improve from 0.00030\n",
      "Epoch 441/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 2.9230e-04 - val_loss: 3.0917e-04\n",
      "\n",
      "Epoch 00441: val_loss did not improve from 0.00030\n",
      "Epoch 442/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 2.9783e-04 - val_loss: 3.0897e-04\n",
      "\n",
      "Epoch 00442: val_loss did not improve from 0.00030\n",
      "Epoch 443/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 2.9745e-04 - val_loss: 2.9875e-04\n",
      "\n",
      "Epoch 00443: val_loss improved from 0.00030 to 0.00030, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 444/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 2.8726e-04 - val_loss: 3.0964e-04\n",
      "\n",
      "Epoch 00444: val_loss did not improve from 0.00030\n",
      "Epoch 445/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 2.9825e-04 - val_loss: 2.9469e-04\n",
      "\n",
      "Epoch 00445: val_loss improved from 0.00030 to 0.00029, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 446/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 2.8317e-04 - val_loss: 3.0395e-04\n",
      "\n",
      "Epoch 00446: val_loss did not improve from 0.00029\n",
      "Epoch 447/500\n",
      "32/32 [==============================] - 20s 623ms/step - loss: 2.9230e-04 - val_loss: 2.9610e-04\n",
      "\n",
      "Epoch 00447: val_loss did not improve from 0.00029\n",
      "Epoch 448/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 2.8453e-04 - val_loss: 2.9525e-04\n",
      "\n",
      "Epoch 00448: val_loss did not improve from 0.00029\n",
      "Epoch 449/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 20s 636ms/step - loss: 2.8361e-04 - val_loss: 2.9803e-04\n",
      "\n",
      "Epoch 00449: val_loss did not improve from 0.00029\n",
      "Epoch 450/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 2.8623e-04 - val_loss: 2.8950e-04\n",
      "\n",
      "Epoch 00450: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 451/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 2.7770e-04 - val_loss: 2.9536e-04\n",
      "\n",
      "Epoch 00451: val_loss did not improve from 0.00029\n",
      "Epoch 452/500\n",
      "32/32 [==============================] - 20s 629ms/step - loss: 2.8357e-04 - val_loss: 2.8871e-04\n",
      "\n",
      "Epoch 00452: val_loss improved from 0.00029 to 0.00029, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 453/500\n",
      "32/32 [==============================] - 20s 626ms/step - loss: 2.7677e-04 - val_loss: 2.8902e-04\n",
      "\n",
      "Epoch 00453: val_loss did not improve from 0.00029\n",
      "Epoch 454/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 2.7702e-04 - val_loss: 2.8937e-04\n",
      "\n",
      "Epoch 00454: val_loss did not improve from 0.00029\n",
      "Epoch 455/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 2.7739e-04 - val_loss: 2.8386e-04\n",
      "\n",
      "Epoch 00455: val_loss improved from 0.00029 to 0.00028, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 456/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 2.7178e-04 - val_loss: 2.8699e-04\n",
      "\n",
      "Epoch 00456: val_loss did not improve from 0.00028\n",
      "Epoch 457/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 2.7479e-04 - val_loss: 2.8266e-04\n",
      "\n",
      "Epoch 00457: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 458/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 2.7047e-04 - val_loss: 2.8165e-04\n",
      "\n",
      "Epoch 00458: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 459/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 2.6940e-04 - val_loss: 2.8236e-04\n",
      "\n",
      "Epoch 00459: val_loss did not improve from 0.00028\n",
      "Epoch 460/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 2.7000e-04 - val_loss: 2.7801e-04\n",
      "\n",
      "Epoch 00460: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 461/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 2.6564e-04 - val_loss: 2.7895e-04\n",
      "\n",
      "Epoch 00461: val_loss did not improve from 0.00028\n",
      "Epoch 462/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 2.6656e-04 - val_loss: 2.7722e-04\n",
      "\n",
      "Epoch 00462: val_loss improved from 0.00028 to 0.00028, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 463/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 2.6471e-04 - val_loss: 2.7453e-04\n",
      "\n",
      "Epoch 00463: val_loss improved from 0.00028 to 0.00027, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 464/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 2.6199e-04 - val_loss: 2.7524e-04\n",
      "\n",
      "Epoch 00464: val_loss did not improve from 0.00027\n",
      "Epoch 465/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 2.6268e-04 - val_loss: 2.7264e-04\n",
      "\n",
      "Epoch 00465: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 466/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 2.5998e-04 - val_loss: 2.7110e-04\n",
      "\n",
      "Epoch 00466: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 467/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 2.5838e-04 - val_loss: 2.7111e-04\n",
      "\n",
      "Epoch 00467: val_loss did not improve from 0.00027\n",
      "Epoch 468/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 2.5837e-04 - val_loss: 2.6856e-04\n",
      "\n",
      "Epoch 00468: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 469/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 2.5573e-04 - val_loss: 2.6738e-04\n",
      "\n",
      "Epoch 00469: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 470/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 2.5448e-04 - val_loss: 2.6699e-04\n",
      "\n",
      "Epoch 00470: val_loss improved from 0.00027 to 0.00027, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 471/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 2.5407e-04 - val_loss: 2.6475e-04\n",
      "\n",
      "Epoch 00471: val_loss improved from 0.00027 to 0.00026, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 472/500\n",
      "32/32 [==============================] - 20s 634ms/step - loss: 2.5174e-04 - val_loss: 2.6345e-04\n",
      "\n",
      "Epoch 00472: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 473/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 2.5037e-04 - val_loss: 2.6294e-04\n",
      "\n",
      "Epoch 00473: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 474/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 2.4983e-04 - val_loss: 2.6112e-04\n",
      "\n",
      "Epoch 00474: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 475/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 2.4792e-04 - val_loss: 2.5950e-04\n",
      "\n",
      "Epoch 00475: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 476/500\n",
      "32/32 [==============================] - 20s 631ms/step - loss: 2.4624e-04 - val_loss: 2.5886e-04\n",
      "\n",
      "Epoch 00476: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 477/500\n",
      "32/32 [==============================] - 20s 627ms/step - loss: 2.4555e-04 - val_loss: 2.5758e-04\n",
      "\n",
      "Epoch 00477: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 478/500\n",
      "32/32 [==============================] - 20s 636ms/step - loss: 2.4418e-04 - val_loss: 2.5575e-04\n",
      "\n",
      "Epoch 00478: val_loss improved from 0.00026 to 0.00026, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 479/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 2.4230e-04 - val_loss: 2.5468e-04\n",
      "\n",
      "Epoch 00479: val_loss improved from 0.00026 to 0.00025, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 480/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 2.4117e-04 - val_loss: 2.5384e-04\n",
      "\n",
      "Epoch 00480: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 481/500\n",
      "32/32 [==============================] - 21s 641ms/step - loss: 2.4025e-04 - val_loss: 2.5233e-04\n",
      "\n",
      "Epoch 00481: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 482/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 2.3869e-04 - val_loss: 2.5076e-04\n",
      "\n",
      "Epoch 00482: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 483/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 2.3705e-04 - val_loss: 2.4969e-04\n",
      "\n",
      "Epoch 00483: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 484/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 2.3590e-04 - val_loss: 2.4872e-04\n",
      "\n",
      "Epoch 00484: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 485/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 2.3488e-04 - val_loss: 2.4744e-04\n",
      "\n",
      "Epoch 00485: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 486/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 2.3352e-04 - val_loss: 2.4594e-04\n",
      "\n",
      "Epoch 00486: val_loss improved from 0.00025 to 0.00025, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 487/500\n",
      "32/32 [==============================] - 20s 635ms/step - loss: 2.3196e-04 - val_loss: 2.4467e-04\n",
      "\n",
      "Epoch 00487: val_loss improved from 0.00025 to 0.00024, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 488/500\n",
      "32/32 [==============================] - 20s 625ms/step - loss: 2.3062e-04 - val_loss: 2.4366e-04\n",
      "\n",
      "Epoch 00488: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 489/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 20s 637ms/step - loss: 2.2953e-04 - val_loss: 2.4262e-04\n",
      "\n",
      "Epoch 00489: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 490/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 2.2843e-04 - val_loss: 2.4143e-04\n",
      "\n",
      "Epoch 00490: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 491/500\n",
      "32/32 [==============================] - 21s 643ms/step - loss: 2.2717e-04 - val_loss: 2.4010e-04\n",
      "\n",
      "Epoch 00491: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 492/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 2.2578e-04 - val_loss: 2.3882e-04\n",
      "\n",
      "Epoch 00492: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 493/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 2.2442e-04 - val_loss: 2.3764e-04\n",
      "\n",
      "Epoch 00493: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 494/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 2.2317e-04 - val_loss: 2.3656e-04\n",
      "\n",
      "Epoch 00494: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 495/500\n",
      "32/32 [==============================] - 20s 638ms/step - loss: 2.2202e-04 - val_loss: 2.3555e-04\n",
      "\n",
      "Epoch 00495: val_loss improved from 0.00024 to 0.00024, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 496/500\n",
      "32/32 [==============================] - 20s 630ms/step - loss: 2.2093e-04 - val_loss: 2.3454e-04\n",
      "\n",
      "Epoch 00496: val_loss improved from 0.00024 to 0.00023, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 497/500\n",
      "32/32 [==============================] - 20s 632ms/step - loss: 2.1986e-04 - val_loss: 2.3358e-04\n",
      "\n",
      "Epoch 00497: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 498/500\n",
      "32/32 [==============================] - 20s 639ms/step - loss: 2.1881e-04 - val_loss: 2.3262e-04\n",
      "\n",
      "Epoch 00498: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 499/500\n",
      "32/32 [==============================] - 20s 637ms/step - loss: 2.1780e-04 - val_loss: 2.3181e-04\n",
      "\n",
      "Epoch 00499: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lstm-mackey-glass.hdf5\n",
      "Epoch 500/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 2.1690e-04 - val_loss: 2.3117e-04\n",
      "\n",
      "Epoch 00500: val_loss improved from 0.00023 to 0.00023, saving model to ../models/lstm-mackey-glass.hdf5\n"
     ]
    }
   ],
   "source": [
    "lstm_history, lstm_time = fit(\n",
    "    lstm_model, \"../models/lstm-mackey-glass.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 32 samples, validate on 32 samples\n",
      "Epoch 1/500\n",
      "32/32 [==============================] - 20s 633ms/step - loss: 0.0137 - val_loss: 0.0245\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.02448, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 2/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 0.0246 - val_loss: 0.0121\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.02448 to 0.01212, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 3/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 0.0122 - val_loss: 0.0048\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.01212 to 0.00481, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 4/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 0.0048 - val_loss: 0.0084\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.00481\n",
      "Epoch 5/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 0.0084 - val_loss: 0.0068\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.00481\n",
      "Epoch 6/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 0.0068 - val_loss: 0.0046\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.00481 to 0.00460, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 7/500\n",
      "32/32 [==============================] - 16s 514ms/step - loss: 0.0046 - val_loss: 0.0047\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.00460\n",
      "Epoch 8/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 0.0047 - val_loss: 0.0048\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.00460\n",
      "Epoch 9/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 0.0048 - val_loss: 0.0039\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.00460 to 0.00391, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 10/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 0.0039 - val_loss: 0.0027\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.00391 to 0.00274, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 11/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 0.0027 - val_loss: 0.0024\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.00274 to 0.00235, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 12/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 0.0024 - val_loss: 0.0029\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.00235\n",
      "Epoch 13/500\n",
      "32/32 [==============================] - 16s 485ms/step - loss: 0.0029 - val_loss: 0.0034\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.00235\n",
      "Epoch 14/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 0.0034 - val_loss: 0.0031\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.00235\n",
      "Epoch 15/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 0.0031 - val_loss: 0.0024\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.00235\n",
      "Epoch 16/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 0.0024 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.00235 to 0.00176, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 17/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 0.0018 - val_loss: 0.0017\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.00176 to 0.00165, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 18/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 0.0017 - val_loss: 0.0019\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.00165\n",
      "Epoch 19/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 0.0019 - val_loss: 0.0023\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.00165\n",
      "Epoch 20/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 0.0023 - val_loss: 0.0024\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.00165\n",
      "Epoch 21/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 0.0024 - val_loss: 0.0022\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.00165\n",
      "Epoch 22/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 0.0022 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.00165\n",
      "Epoch 23/500\n",
      "32/32 [==============================] - 16s 487ms/step - loss: 0.0018 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.00165 to 0.00146, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 24/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 0.0015 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.00146 to 0.00126, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 25/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.00126\n",
      "Epoch 26/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 0.0013 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.00126\n",
      "Epoch 27/500\n",
      "32/32 [==============================] - 16s 492ms/step - loss: 0.0015 - val_loss: 0.0016\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.00126\n",
      "Epoch 28/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 0.0016 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.00126\n",
      "Epoch 29/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 0.0015 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.00126\n",
      "Epoch 30/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 0.0013 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.00126 to 0.00114, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 31/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00031: val_loss improved from 0.00114 to 0.00108, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 32/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00032: val_loss improved from 0.00108 to 0.00107, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 33/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.00107\n",
      "Epoch 34/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 0.0011 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00034: val_loss improved from 0.00107 to 0.00105, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 35/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 0.0011 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00035: val_loss improved from 0.00105 to 0.00102, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 36/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 0.0010 - val_loss: 9.7627e-04\n",
      "\n",
      "Epoch 00036: val_loss improved from 0.00102 to 0.00098, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 37/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 9.7643e-04 - val_loss: 9.4780e-04\n",
      "\n",
      "Epoch 00037: val_loss improved from 0.00098 to 0.00095, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 38/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 9.4755e-04 - val_loss: 9.1959e-04\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.00095 to 0.00092, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 39/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 9.1969e-04 - val_loss: 8.7363e-04\n",
      "\n",
      "Epoch 00039: val_loss improved from 0.00092 to 0.00087, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 40/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 8.7481e-04 - val_loss: 8.1346e-04\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.00087 to 0.00081, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 41/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 8.1625e-04 - val_loss: 7.6790e-04\n",
      "\n",
      "Epoch 00041: val_loss improved from 0.00081 to 0.00077, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 42/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 7.7250e-04 - val_loss: 7.5904e-04\n",
      "\n",
      "Epoch 00042: val_loss improved from 0.00077 to 0.00076, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 43/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 7.6532e-04 - val_loss: 7.7185e-04\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.00076\n",
      "Epoch 44/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 7.7936e-04 - val_loss: 7.6668e-04\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.00076\n",
      "Epoch 45/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 16s 511ms/step - loss: 7.7479e-04 - val_loss: 7.2388e-04\n",
      "\n",
      "Epoch 00045: val_loss improved from 0.00076 to 0.00072, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 46/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 7.3193e-04 - val_loss: 6.6515e-04\n",
      "\n",
      "Epoch 00046: val_loss improved from 0.00072 to 0.00067, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 47/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 6.7258e-04 - val_loss: 6.2686e-04\n",
      "\n",
      "Epoch 00047: val_loss improved from 0.00067 to 0.00063, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 48/500\n",
      "32/32 [==============================] - 17s 516ms/step - loss: 6.3335e-04 - val_loss: 6.2165e-04\n",
      "\n",
      "Epoch 00048: val_loss improved from 0.00063 to 0.00062, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 49/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 6.2717e-04 - val_loss: 6.3107e-04\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.00062\n",
      "Epoch 50/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 6.3588e-04 - val_loss: 6.2932e-04\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.00062\n",
      "Epoch 51/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 6.3394e-04 - val_loss: 6.0803e-04\n",
      "\n",
      "Epoch 00051: val_loss improved from 0.00062 to 0.00061, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 52/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 6.1305e-04 - val_loss: 5.7897e-04\n",
      "\n",
      "Epoch 00052: val_loss improved from 0.00061 to 0.00058, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 53/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 5.8487e-04 - val_loss: 5.5781e-04\n",
      "\n",
      "Epoch 00053: val_loss improved from 0.00058 to 0.00056, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 54/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 5.6486e-04 - val_loss: 5.4871e-04\n",
      "\n",
      "Epoch 00054: val_loss improved from 0.00056 to 0.00055, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 55/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 5.5685e-04 - val_loss: 5.4391e-04\n",
      "\n",
      "Epoch 00055: val_loss improved from 0.00055 to 0.00054, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 56/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 5.5279e-04 - val_loss: 5.3542e-04\n",
      "\n",
      "Epoch 00056: val_loss improved from 0.00054 to 0.00054, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 57/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 5.4455e-04 - val_loss: 5.2412e-04\n",
      "\n",
      "Epoch 00057: val_loss improved from 0.00054 to 0.00052, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 58/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 5.3298e-04 - val_loss: 5.1561e-04\n",
      "\n",
      "Epoch 00058: val_loss improved from 0.00052 to 0.00052, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 59/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 5.2387e-04 - val_loss: 5.0992e-04\n",
      "\n",
      "Epoch 00059: val_loss improved from 0.00052 to 0.00051, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 60/500\n",
      "32/32 [==============================] - 17s 518ms/step - loss: 5.1749e-04 - val_loss: 5.0121e-04\n",
      "\n",
      "Epoch 00060: val_loss improved from 0.00051 to 0.00050, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 61/500\n",
      "32/32 [==============================] - 16s 512ms/step - loss: 5.0822e-04 - val_loss: 4.8818e-04\n",
      "\n",
      "Epoch 00061: val_loss improved from 0.00050 to 0.00049, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 62/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 4.9488e-04 - val_loss: 4.7736e-04\n",
      "\n",
      "Epoch 00062: val_loss improved from 0.00049 to 0.00048, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 63/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 4.8399e-04 - val_loss: 4.7347e-04\n",
      "\n",
      "Epoch 00063: val_loss improved from 0.00048 to 0.00047, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 64/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 4.8012e-04 - val_loss: 4.7251e-04\n",
      "\n",
      "Epoch 00064: val_loss improved from 0.00047 to 0.00047, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 65/500\n",
      "32/32 [==============================] - 17s 520ms/step - loss: 4.7914e-04 - val_loss: 4.6765e-04\n",
      "\n",
      "Epoch 00065: val_loss improved from 0.00047 to 0.00047, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 66/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 4.7412e-04 - val_loss: 4.5782e-04\n",
      "\n",
      "Epoch 00066: val_loss improved from 0.00047 to 0.00046, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 67/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 4.6400e-04 - val_loss: 4.4790e-04\n",
      "\n",
      "Epoch 00067: val_loss improved from 0.00046 to 0.00045, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 68/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 4.5372e-04 - val_loss: 4.4227e-04\n",
      "\n",
      "Epoch 00068: val_loss improved from 0.00045 to 0.00044, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 69/500\n",
      "32/32 [==============================] - 16s 512ms/step - loss: 4.4777e-04 - val_loss: 4.4029e-04\n",
      "\n",
      "Epoch 00069: val_loss improved from 0.00044 to 0.00044, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 70/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 4.4557e-04 - val_loss: 4.3788e-04\n",
      "\n",
      "Epoch 00070: val_loss improved from 0.00044 to 0.00044, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 71/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 4.4305e-04 - val_loss: 4.3257e-04\n",
      "\n",
      "Epoch 00071: val_loss improved from 0.00044 to 0.00043, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 72/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 4.3769e-04 - val_loss: 4.2606e-04\n",
      "\n",
      "Epoch 00072: val_loss improved from 0.00043 to 0.00043, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 73/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 4.3112e-04 - val_loss: 4.2120e-04\n",
      "\n",
      "Epoch 00073: val_loss improved from 0.00043 to 0.00042, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 74/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 4.2609e-04 - val_loss: 4.1789e-04\n",
      "\n",
      "Epoch 00074: val_loss improved from 0.00042 to 0.00042, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 75/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 4.2250e-04 - val_loss: 4.1425e-04\n",
      "\n",
      "Epoch 00075: val_loss improved from 0.00042 to 0.00041, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 76/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 4.1849e-04 - val_loss: 4.1020e-04\n",
      "\n",
      "Epoch 00076: val_loss improved from 0.00041 to 0.00041, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 77/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 4.1401e-04 - val_loss: 4.0705e-04\n",
      "\n",
      "Epoch 00077: val_loss improved from 0.00041 to 0.00041, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 78/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 4.1044e-04 - val_loss: 4.0453e-04\n",
      "\n",
      "Epoch 00078: val_loss improved from 0.00041 to 0.00040, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 79/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 4.0758e-04 - val_loss: 4.0111e-04\n",
      "\n",
      "Epoch 00079: val_loss improved from 0.00040 to 0.00040, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 80/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 4.0396e-04 - val_loss: 3.9660e-04\n",
      "\n",
      "Epoch 00080: val_loss improved from 0.00040 to 0.00040, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 81/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 3.9939e-04 - val_loss: 3.9242e-04\n",
      "\n",
      "Epoch 00081: val_loss improved from 0.00040 to 0.00039, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 82/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 3.9526e-04 - val_loss: 3.8954e-04\n",
      "\n",
      "Epoch 00082: val_loss improved from 0.00039 to 0.00039, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 83/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 16s 509ms/step - loss: 3.9246e-04 - val_loss: 3.8737e-04\n",
      "\n",
      "Epoch 00083: val_loss improved from 0.00039 to 0.00039, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 84/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 3.9028e-04 - val_loss: 3.8466e-04\n",
      "\n",
      "Epoch 00084: val_loss improved from 0.00039 to 0.00038, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 85/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 3.8743e-04 - val_loss: 3.8123e-04\n",
      "\n",
      "Epoch 00085: val_loss improved from 0.00038 to 0.00038, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 86/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 3.8372e-04 - val_loss: 3.7809e-04\n",
      "\n",
      "Epoch 00086: val_loss improved from 0.00038 to 0.00038, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 87/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 3.8020e-04 - val_loss: 3.7579e-04\n",
      "\n",
      "Epoch 00087: val_loss improved from 0.00038 to 0.00038, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 88/500\n",
      "32/32 [==============================] - 16s 516ms/step - loss: 3.7753e-04 - val_loss: 3.7360e-04\n",
      "\n",
      "Epoch 00088: val_loss improved from 0.00038 to 0.00037, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 89/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 3.7507e-04 - val_loss: 3.7094e-04\n",
      "\n",
      "Epoch 00089: val_loss improved from 0.00037 to 0.00037, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 90/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 3.7225e-04 - val_loss: 3.6814e-04\n",
      "\n",
      "Epoch 00090: val_loss improved from 0.00037 to 0.00037, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 91/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 3.6937e-04 - val_loss: 3.6556e-04\n",
      "\n",
      "Epoch 00091: val_loss improved from 0.00037 to 0.00037, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 92/500\n",
      "32/32 [==============================] - 16s 493ms/step - loss: 3.6674e-04 - val_loss: 3.6298e-04\n",
      "\n",
      "Epoch 00092: val_loss improved from 0.00037 to 0.00036, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 93/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 3.6406e-04 - val_loss: 3.6027e-04\n",
      "\n",
      "Epoch 00093: val_loss improved from 0.00036 to 0.00036, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 94/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 3.6120e-04 - val_loss: 3.5771e-04\n",
      "\n",
      "Epoch 00094: val_loss improved from 0.00036 to 0.00036, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 95/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 3.5844e-04 - val_loss: 3.5548e-04\n",
      "\n",
      "Epoch 00095: val_loss improved from 0.00036 to 0.00036, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 96/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 3.5600e-04 - val_loss: 3.5333e-04\n",
      "\n",
      "Epoch 00096: val_loss improved from 0.00036 to 0.00035, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 97/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 3.5365e-04 - val_loss: 3.5089e-04\n",
      "\n",
      "Epoch 00097: val_loss improved from 0.00035 to 0.00035, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 98/500\n",
      "32/32 [==============================] - 16s 492ms/step - loss: 3.5104e-04 - val_loss: 3.4825e-04\n",
      "\n",
      "Epoch 00098: val_loss improved from 0.00035 to 0.00035, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 99/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 3.4826e-04 - val_loss: 3.4584e-04\n",
      "\n",
      "Epoch 00099: val_loss improved from 0.00035 to 0.00035, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 100/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 3.4570e-04 - val_loss: 3.4370e-04\n",
      "\n",
      "Epoch 00100: val_loss improved from 0.00035 to 0.00034, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 101/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 3.4339e-04 - val_loss: 3.4151e-04\n",
      "\n",
      "Epoch 00101: val_loss improved from 0.00034 to 0.00034, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 102/500\n",
      "32/32 [==============================] - 16s 493ms/step - loss: 3.4102e-04 - val_loss: 3.3921e-04\n",
      "\n",
      "Epoch 00102: val_loss improved from 0.00034 to 0.00034, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 103/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 3.3852e-04 - val_loss: 3.3697e-04\n",
      "\n",
      "Epoch 00103: val_loss improved from 0.00034 to 0.00034, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 104/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 3.3609e-04 - val_loss: 3.3477e-04\n",
      "\n",
      "Epoch 00104: val_loss improved from 0.00034 to 0.00033, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 105/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 3.3373e-04 - val_loss: 3.3252e-04\n",
      "\n",
      "Epoch 00105: val_loss improved from 0.00033 to 0.00033, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 106/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 3.3135e-04 - val_loss: 3.3025e-04\n",
      "\n",
      "Epoch 00106: val_loss improved from 0.00033 to 0.00033, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 107/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 3.2898e-04 - val_loss: 3.2805e-04\n",
      "\n",
      "Epoch 00107: val_loss improved from 0.00033 to 0.00033, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 108/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 3.2671e-04 - val_loss: 3.2591e-04\n",
      "\n",
      "Epoch 00108: val_loss improved from 0.00033 to 0.00033, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 109/500\n",
      "32/32 [==============================] - 15s 484ms/step - loss: 3.2447e-04 - val_loss: 3.2376e-04\n",
      "\n",
      "Epoch 00109: val_loss improved from 0.00033 to 0.00032, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 110/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 3.2216e-04 - val_loss: 3.2161e-04\n",
      "\n",
      "Epoch 00110: val_loss improved from 0.00032 to 0.00032, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 111/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 3.1982e-04 - val_loss: 3.1956e-04\n",
      "\n",
      "Epoch 00111: val_loss improved from 0.00032 to 0.00032, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 112/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 3.1757e-04 - val_loss: 3.1757e-04\n",
      "\n",
      "Epoch 00112: val_loss improved from 0.00032 to 0.00032, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 113/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 3.1540e-04 - val_loss: 3.1551e-04\n",
      "\n",
      "Epoch 00113: val_loss improved from 0.00032 to 0.00032, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 114/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 3.1320e-04 - val_loss: 3.1338e-04\n",
      "\n",
      "Epoch 00114: val_loss improved from 0.00032 to 0.00031, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 115/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 3.1096e-04 - val_loss: 3.1128e-04\n",
      "\n",
      "Epoch 00115: val_loss improved from 0.00031 to 0.00031, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 116/500\n",
      "32/32 [==============================] - 16s 492ms/step - loss: 3.0877e-04 - val_loss: 3.0923e-04\n",
      "\n",
      "Epoch 00116: val_loss improved from 0.00031 to 0.00031, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 117/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 3.0660e-04 - val_loss: 3.0721e-04\n",
      "\n",
      "Epoch 00117: val_loss improved from 0.00031 to 0.00031, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 118/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 3.0444e-04 - val_loss: 3.0523e-04\n",
      "\n",
      "Epoch 00118: val_loss improved from 0.00031 to 0.00031, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 119/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 3.0230e-04 - val_loss: 3.0329e-04\n",
      "\n",
      "Epoch 00119: val_loss improved from 0.00031 to 0.00030, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 120/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 3.0018e-04 - val_loss: 3.0134e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00120: val_loss improved from 0.00030 to 0.00030, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 121/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 2.9807e-04 - val_loss: 2.9937e-04\n",
      "\n",
      "Epoch 00121: val_loss improved from 0.00030 to 0.00030, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 122/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 2.9595e-04 - val_loss: 2.9742e-04\n",
      "\n",
      "Epoch 00122: val_loss improved from 0.00030 to 0.00030, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 123/500\n",
      "32/32 [==============================] - 16s 512ms/step - loss: 2.9386e-04 - val_loss: 2.9552e-04\n",
      "\n",
      "Epoch 00123: val_loss improved from 0.00030 to 0.00030, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 124/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 2.9181e-04 - val_loss: 2.9364e-04\n",
      "\n",
      "Epoch 00124: val_loss improved from 0.00030 to 0.00029, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 125/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 2.8976e-04 - val_loss: 2.9176e-04\n",
      "\n",
      "Epoch 00125: val_loss improved from 0.00029 to 0.00029, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 126/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 2.8770e-04 - val_loss: 2.8990e-04\n",
      "\n",
      "Epoch 00126: val_loss improved from 0.00029 to 0.00029, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 127/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 2.8567e-04 - val_loss: 2.8807e-04\n",
      "\n",
      "Epoch 00127: val_loss improved from 0.00029 to 0.00029, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 128/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 2.8366e-04 - val_loss: 2.8622e-04\n",
      "\n",
      "Epoch 00128: val_loss improved from 0.00029 to 0.00029, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 129/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 2.8166e-04 - val_loss: 2.8439e-04\n",
      "\n",
      "Epoch 00129: val_loss improved from 0.00029 to 0.00028, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 130/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 2.7968e-04 - val_loss: 2.8258e-04\n",
      "\n",
      "Epoch 00130: val_loss improved from 0.00028 to 0.00028, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 131/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 2.7771e-04 - val_loss: 2.8080e-04\n",
      "\n",
      "Epoch 00131: val_loss improved from 0.00028 to 0.00028, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 132/500\n",
      "32/32 [==============================] - 17s 522ms/step - loss: 2.7575e-04 - val_loss: 2.7904e-04\n",
      "\n",
      "Epoch 00132: val_loss improved from 0.00028 to 0.00028, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 133/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 2.7380e-04 - val_loss: 2.7731e-04\n",
      "\n",
      "Epoch 00133: val_loss improved from 0.00028 to 0.00028, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 134/500\n",
      "32/32 [==============================] - 17s 518ms/step - loss: 2.7187e-04 - val_loss: 2.7559e-04\n",
      "\n",
      "Epoch 00134: val_loss improved from 0.00028 to 0.00028, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 135/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 2.6995e-04 - val_loss: 2.7386e-04\n",
      "\n",
      "Epoch 00135: val_loss improved from 0.00028 to 0.00027, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 136/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 2.6805e-04 - val_loss: 2.7213e-04\n",
      "\n",
      "Epoch 00136: val_loss improved from 0.00027 to 0.00027, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 137/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 2.6615e-04 - val_loss: 2.7042e-04\n",
      "\n",
      "Epoch 00137: val_loss improved from 0.00027 to 0.00027, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 138/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 2.6427e-04 - val_loss: 2.6874e-04\n",
      "\n",
      "Epoch 00138: val_loss improved from 0.00027 to 0.00027, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 139/500\n",
      "32/32 [==============================] - 16s 514ms/step - loss: 2.6240e-04 - val_loss: 2.6707e-04\n",
      "\n",
      "Epoch 00139: val_loss improved from 0.00027 to 0.00027, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 140/500\n",
      "32/32 [==============================] - 16s 515ms/step - loss: 2.6055e-04 - val_loss: 2.6543e-04\n",
      "\n",
      "Epoch 00140: val_loss improved from 0.00027 to 0.00027, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 141/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 2.5870e-04 - val_loss: 2.6380e-04\n",
      "\n",
      "Epoch 00141: val_loss improved from 0.00027 to 0.00026, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 142/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 2.5687e-04 - val_loss: 2.6217e-04\n",
      "\n",
      "Epoch 00142: val_loss improved from 0.00026 to 0.00026, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 143/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 2.5505e-04 - val_loss: 2.6055e-04\n",
      "\n",
      "Epoch 00143: val_loss improved from 0.00026 to 0.00026, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 144/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 2.5324e-04 - val_loss: 2.5896e-04\n",
      "\n",
      "Epoch 00144: val_loss improved from 0.00026 to 0.00026, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 145/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 2.5145e-04 - val_loss: 2.5738e-04\n",
      "\n",
      "Epoch 00145: val_loss improved from 0.00026 to 0.00026, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 146/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 2.4966e-04 - val_loss: 2.5581e-04\n",
      "\n",
      "Epoch 00146: val_loss improved from 0.00026 to 0.00026, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 147/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 2.4789e-04 - val_loss: 2.5426e-04\n",
      "\n",
      "Epoch 00147: val_loss improved from 0.00026 to 0.00025, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 148/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 2.4613e-04 - val_loss: 2.5272e-04\n",
      "\n",
      "Epoch 00148: val_loss improved from 0.00025 to 0.00025, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 149/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 2.4438e-04 - val_loss: 2.5118e-04\n",
      "\n",
      "Epoch 00149: val_loss improved from 0.00025 to 0.00025, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 150/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 2.4264e-04 - val_loss: 2.4967e-04\n",
      "\n",
      "Epoch 00150: val_loss improved from 0.00025 to 0.00025, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 151/500\n",
      "32/32 [==============================] - 17s 516ms/step - loss: 2.4092e-04 - val_loss: 2.4817e-04\n",
      "\n",
      "Epoch 00151: val_loss improved from 0.00025 to 0.00025, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 152/500\n",
      "32/32 [==============================] - 17s 524ms/step - loss: 2.3921e-04 - val_loss: 2.4669e-04\n",
      "\n",
      "Epoch 00152: val_loss improved from 0.00025 to 0.00025, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 153/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 2.3750e-04 - val_loss: 2.4523e-04\n",
      "\n",
      "Epoch 00153: val_loss improved from 0.00025 to 0.00025, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 154/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 2.3581e-04 - val_loss: 2.4377e-04\n",
      "\n",
      "Epoch 00154: val_loss improved from 0.00025 to 0.00024, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 155/500\n",
      "32/32 [==============================] - 17s 541ms/step - loss: 2.3414e-04 - val_loss: 2.4233e-04\n",
      "\n",
      "Epoch 00155: val_loss improved from 0.00024 to 0.00024, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 156/500\n",
      "32/32 [==============================] - 17s 541ms/step - loss: 2.3247e-04 - val_loss: 2.4089e-04\n",
      "\n",
      "Epoch 00156: val_loss improved from 0.00024 to 0.00024, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 157/500\n",
      "32/32 [==============================] - 17s 539ms/step - loss: 2.3082e-04 - val_loss: 2.3947e-04\n",
      "\n",
      "Epoch 00157: val_loss improved from 0.00024 to 0.00024, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 158/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 17s 546ms/step - loss: 2.2918e-04 - val_loss: 2.3807e-04\n",
      "\n",
      "Epoch 00158: val_loss improved from 0.00024 to 0.00024, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 159/500\n",
      "32/32 [==============================] - 17s 524ms/step - loss: 2.2755e-04 - val_loss: 2.3668e-04\n",
      "\n",
      "Epoch 00159: val_loss improved from 0.00024 to 0.00024, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 160/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 2.2593e-04 - val_loss: 2.3531e-04\n",
      "\n",
      "Epoch 00160: val_loss improved from 0.00024 to 0.00024, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 161/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 2.2432e-04 - val_loss: 2.3394e-04\n",
      "\n",
      "Epoch 00161: val_loss improved from 0.00024 to 0.00023, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 162/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 2.2273e-04 - val_loss: 2.3259e-04\n",
      "\n",
      "Epoch 00162: val_loss improved from 0.00023 to 0.00023, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 163/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 2.2115e-04 - val_loss: 2.3125e-04\n",
      "\n",
      "Epoch 00163: val_loss improved from 0.00023 to 0.00023, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 164/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 2.1958e-04 - val_loss: 2.2992e-04\n",
      "\n",
      "Epoch 00164: val_loss improved from 0.00023 to 0.00023, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 165/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 2.1802e-04 - val_loss: 2.2861e-04\n",
      "\n",
      "Epoch 00165: val_loss improved from 0.00023 to 0.00023, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 166/500\n",
      "32/32 [==============================] - 17s 523ms/step - loss: 2.1648e-04 - val_loss: 2.2731e-04\n",
      "\n",
      "Epoch 00166: val_loss improved from 0.00023 to 0.00023, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 167/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 2.1494e-04 - val_loss: 2.2602e-04\n",
      "\n",
      "Epoch 00167: val_loss improved from 0.00023 to 0.00023, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 168/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 2.1342e-04 - val_loss: 2.2474e-04\n",
      "\n",
      "Epoch 00168: val_loss improved from 0.00023 to 0.00022, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 169/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 2.1191e-04 - val_loss: 2.2348e-04\n",
      "\n",
      "Epoch 00169: val_loss improved from 0.00022 to 0.00022, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 170/500\n",
      "32/32 [==============================] - 17s 524ms/step - loss: 2.1041e-04 - val_loss: 2.2223e-04\n",
      "\n",
      "Epoch 00170: val_loss improved from 0.00022 to 0.00022, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 171/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 2.0893e-04 - val_loss: 2.2099e-04\n",
      "\n",
      "Epoch 00171: val_loss improved from 0.00022 to 0.00022, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 172/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 2.0746e-04 - val_loss: 2.1976e-04\n",
      "\n",
      "Epoch 00172: val_loss improved from 0.00022 to 0.00022, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 173/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 2.0599e-04 - val_loss: 2.1855e-04\n",
      "\n",
      "Epoch 00173: val_loss improved from 0.00022 to 0.00022, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 174/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 2.0454e-04 - val_loss: 2.1735e-04\n",
      "\n",
      "Epoch 00174: val_loss improved from 0.00022 to 0.00022, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 175/500\n",
      "32/32 [==============================] - 16s 490ms/step - loss: 2.0311e-04 - val_loss: 2.1615e-04\n",
      "\n",
      "Epoch 00175: val_loss improved from 0.00022 to 0.00022, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 176/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 2.0168e-04 - val_loss: 2.1497e-04\n",
      "\n",
      "Epoch 00176: val_loss improved from 0.00022 to 0.00021, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 177/500\n",
      "32/32 [==============================] - 16s 495ms/step - loss: 2.0027e-04 - val_loss: 2.1381e-04\n",
      "\n",
      "Epoch 00177: val_loss improved from 0.00021 to 0.00021, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 178/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 1.9887e-04 - val_loss: 2.1265e-04\n",
      "\n",
      "Epoch 00178: val_loss improved from 0.00021 to 0.00021, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 179/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.9748e-04 - val_loss: 2.1151e-04\n",
      "\n",
      "Epoch 00179: val_loss improved from 0.00021 to 0.00021, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 180/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 1.9610e-04 - val_loss: 2.1038e-04\n",
      "\n",
      "Epoch 00180: val_loss improved from 0.00021 to 0.00021, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 181/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.9473e-04 - val_loss: 2.0926e-04\n",
      "\n",
      "Epoch 00181: val_loss improved from 0.00021 to 0.00021, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 182/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 1.9338e-04 - val_loss: 2.0815e-04\n",
      "\n",
      "Epoch 00182: val_loss improved from 0.00021 to 0.00021, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 183/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 1.9203e-04 - val_loss: 2.0705e-04\n",
      "\n",
      "Epoch 00183: val_loss improved from 0.00021 to 0.00021, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 184/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.9070e-04 - val_loss: 2.0597e-04\n",
      "\n",
      "Epoch 00184: val_loss improved from 0.00021 to 0.00021, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 185/500\n",
      "32/32 [==============================] - 16s 495ms/step - loss: 1.8938e-04 - val_loss: 2.0489e-04\n",
      "\n",
      "Epoch 00185: val_loss improved from 0.00021 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 186/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.8808e-04 - val_loss: 2.0383e-04\n",
      "\n",
      "Epoch 00186: val_loss improved from 0.00020 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 187/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 1.8678e-04 - val_loss: 2.0278e-04\n",
      "\n",
      "Epoch 00187: val_loss improved from 0.00020 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 188/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.8550e-04 - val_loss: 2.0174e-04\n",
      "\n",
      "Epoch 00188: val_loss improved from 0.00020 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 189/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 1.8422e-04 - val_loss: 2.0071e-04\n",
      "\n",
      "Epoch 00189: val_loss improved from 0.00020 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 190/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 1.8296e-04 - val_loss: 1.9969e-04\n",
      "\n",
      "Epoch 00190: val_loss improved from 0.00020 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 191/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.8171e-04 - val_loss: 1.9868e-04\n",
      "\n",
      "Epoch 00191: val_loss improved from 0.00020 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 192/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.8047e-04 - val_loss: 1.9768e-04\n",
      "\n",
      "Epoch 00192: val_loss improved from 0.00020 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 193/500\n",
      "32/32 [==============================] - 17s 521ms/step - loss: 1.7925e-04 - val_loss: 1.9669e-04\n",
      "\n",
      "Epoch 00193: val_loss improved from 0.00020 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 194/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.7803e-04 - val_loss: 1.9572e-04\n",
      "\n",
      "Epoch 00194: val_loss improved from 0.00020 to 0.00020, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 195/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.7683e-04 - val_loss: 1.9475e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00195: val_loss improved from 0.00020 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 196/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 1.7563e-04 - val_loss: 1.9380e-04\n",
      "\n",
      "Epoch 00196: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 197/500\n",
      "32/32 [==============================] - 17s 517ms/step - loss: 1.7445e-04 - val_loss: 1.9285e-04\n",
      "\n",
      "Epoch 00197: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 198/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.7328e-04 - val_loss: 1.9191e-04\n",
      "\n",
      "Epoch 00198: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 199/500\n",
      "32/32 [==============================] - 16s 493ms/step - loss: 1.7212e-04 - val_loss: 1.9099e-04\n",
      "\n",
      "Epoch 00199: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 200/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 1.7097e-04 - val_loss: 1.9007e-04\n",
      "\n",
      "Epoch 00200: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 201/500\n",
      "32/32 [==============================] - 16s 491ms/step - loss: 1.6983e-04 - val_loss: 1.8917e-04\n",
      "\n",
      "Epoch 00201: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 202/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 1.6870e-04 - val_loss: 1.8827e-04\n",
      "\n",
      "Epoch 00202: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 203/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 1.6759e-04 - val_loss: 1.8739e-04\n",
      "\n",
      "Epoch 00203: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 204/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 1.6648e-04 - val_loss: 1.8651e-04\n",
      "\n",
      "Epoch 00204: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 205/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.6539e-04 - val_loss: 1.8564e-04\n",
      "\n",
      "Epoch 00205: val_loss improved from 0.00019 to 0.00019, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 206/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.6430e-04 - val_loss: 1.8479e-04\n",
      "\n",
      "Epoch 00206: val_loss improved from 0.00019 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 207/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.6323e-04 - val_loss: 1.8394e-04\n",
      "\n",
      "Epoch 00207: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 208/500\n",
      "32/32 [==============================] - 16s 512ms/step - loss: 1.6216e-04 - val_loss: 1.8310e-04\n",
      "\n",
      "Epoch 00208: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 209/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 1.6111e-04 - val_loss: 1.8227e-04\n",
      "\n",
      "Epoch 00209: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 210/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 1.6007e-04 - val_loss: 1.8145e-04\n",
      "\n",
      "Epoch 00210: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 211/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 1.5904e-04 - val_loss: 1.8064e-04\n",
      "\n",
      "Epoch 00211: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 212/500\n",
      "32/32 [==============================] - 16s 514ms/step - loss: 1.5801e-04 - val_loss: 1.7984e-04\n",
      "\n",
      "Epoch 00212: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 213/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 1.5700e-04 - val_loss: 1.7905e-04\n",
      "\n",
      "Epoch 00213: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 214/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 1.5600e-04 - val_loss: 1.7826e-04\n",
      "\n",
      "Epoch 00214: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 215/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.5501e-04 - val_loss: 1.7749e-04\n",
      "\n",
      "Epoch 00215: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 216/500\n",
      "32/32 [==============================] - 16s 490ms/step - loss: 1.5403e-04 - val_loss: 1.7672e-04\n",
      "\n",
      "Epoch 00216: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 217/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 1.5305e-04 - val_loss: 1.7596e-04\n",
      "\n",
      "Epoch 00217: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 218/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 1.5209e-04 - val_loss: 1.7521e-04\n",
      "\n",
      "Epoch 00218: val_loss improved from 0.00018 to 0.00018, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 219/500\n",
      "32/32 [==============================] - 16s 487ms/step - loss: 1.5114e-04 - val_loss: 1.7447e-04\n",
      "\n",
      "Epoch 00219: val_loss improved from 0.00018 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 220/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 1.5020e-04 - val_loss: 1.7374e-04\n",
      "\n",
      "Epoch 00220: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 221/500\n",
      "32/32 [==============================] - 15s 484ms/step - loss: 1.4927e-04 - val_loss: 1.7301e-04\n",
      "\n",
      "Epoch 00221: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 222/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 1.4834e-04 - val_loss: 1.7230e-04\n",
      "\n",
      "Epoch 00222: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 223/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.4743e-04 - val_loss: 1.7159e-04\n",
      "\n",
      "Epoch 00223: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 224/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 1.4653e-04 - val_loss: 1.7089e-04\n",
      "\n",
      "Epoch 00224: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 225/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.4564e-04 - val_loss: 1.7020e-04\n",
      "\n",
      "Epoch 00225: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 226/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 1.4475e-04 - val_loss: 1.6951e-04\n",
      "\n",
      "Epoch 00226: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 227/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 1.4388e-04 - val_loss: 1.6884e-04\n",
      "\n",
      "Epoch 00227: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 228/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 1.4301e-04 - val_loss: 1.6817e-04\n",
      "\n",
      "Epoch 00228: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 229/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 1.4216e-04 - val_loss: 1.6751e-04\n",
      "\n",
      "Epoch 00229: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 230/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 1.4131e-04 - val_loss: 1.6686e-04\n",
      "\n",
      "Epoch 00230: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 231/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 1.4048e-04 - val_loss: 1.6622e-04\n",
      "\n",
      "Epoch 00231: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 232/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.3965e-04 - val_loss: 1.6558e-04\n",
      "\n",
      "Epoch 00232: val_loss improved from 0.00017 to 0.00017, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 233/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 16s 500ms/step - loss: 1.3883e-04 - val_loss: 1.6495e-04\n",
      "\n",
      "Epoch 00233: val_loss improved from 0.00017 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 234/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 1.3802e-04 - val_loss: 1.6433e-04\n",
      "\n",
      "Epoch 00234: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 235/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.3722e-04 - val_loss: 1.6372e-04\n",
      "\n",
      "Epoch 00235: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 236/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.3643e-04 - val_loss: 1.6311e-04\n",
      "\n",
      "Epoch 00236: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 237/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 1.3565e-04 - val_loss: 1.6252e-04\n",
      "\n",
      "Epoch 00237: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 238/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.3488e-04 - val_loss: 1.6193e-04\n",
      "\n",
      "Epoch 00238: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 239/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.3412e-04 - val_loss: 1.6134e-04\n",
      "\n",
      "Epoch 00239: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 240/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 1.3336e-04 - val_loss: 1.6077e-04\n",
      "\n",
      "Epoch 00240: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 241/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 1.3262e-04 - val_loss: 1.6020e-04\n",
      "\n",
      "Epoch 00241: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 242/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.3188e-04 - val_loss: 1.5964e-04\n",
      "\n",
      "Epoch 00242: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 243/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 1.3115e-04 - val_loss: 1.5909e-04\n",
      "\n",
      "Epoch 00243: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 244/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.3043e-04 - val_loss: 1.5854e-04\n",
      "\n",
      "Epoch 00244: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 245/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 1.2973e-04 - val_loss: 1.5801e-04\n",
      "\n",
      "Epoch 00245: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 246/500\n",
      "32/32 [==============================] - 16s 514ms/step - loss: 1.2902e-04 - val_loss: 1.5748e-04\n",
      "\n",
      "Epoch 00246: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 247/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 1.2833e-04 - val_loss: 1.5695e-04\n",
      "\n",
      "Epoch 00247: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 248/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 1.2765e-04 - val_loss: 1.5644e-04\n",
      "\n",
      "Epoch 00248: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 249/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.2697e-04 - val_loss: 1.5593e-04\n",
      "\n",
      "Epoch 00249: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 250/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.2631e-04 - val_loss: 1.5543e-04\n",
      "\n",
      "Epoch 00250: val_loss improved from 0.00016 to 0.00016, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 251/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.2565e-04 - val_loss: 1.5493e-04\n",
      "\n",
      "Epoch 00251: val_loss improved from 0.00016 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 252/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 1.2500e-04 - val_loss: 1.5444e-04\n",
      "\n",
      "Epoch 00252: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 253/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.2436e-04 - val_loss: 1.5396e-04\n",
      "\n",
      "Epoch 00253: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 254/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.2373e-04 - val_loss: 1.5349e-04\n",
      "\n",
      "Epoch 00254: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 255/500\n",
      "32/32 [==============================] - 16s 490ms/step - loss: 1.2310e-04 - val_loss: 1.5303e-04\n",
      "\n",
      "Epoch 00255: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 256/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 1.2249e-04 - val_loss: 1.5257e-04\n",
      "\n",
      "Epoch 00256: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 257/500\n",
      "32/32 [==============================] - 16s 493ms/step - loss: 1.2188e-04 - val_loss: 1.5211e-04\n",
      "\n",
      "Epoch 00257: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 258/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.2128e-04 - val_loss: 1.5167e-04\n",
      "\n",
      "Epoch 00258: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 259/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 1.2069e-04 - val_loss: 1.5123e-04\n",
      "\n",
      "Epoch 00259: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 260/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.2010e-04 - val_loss: 1.5080e-04\n",
      "\n",
      "Epoch 00260: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 261/500\n",
      "32/32 [==============================] - 16s 512ms/step - loss: 1.1953e-04 - val_loss: 1.5037e-04\n",
      "\n",
      "Epoch 00261: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 262/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 1.1896e-04 - val_loss: 1.4996e-04\n",
      "\n",
      "Epoch 00262: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 263/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 1.1840e-04 - val_loss: 1.4954e-04\n",
      "\n",
      "Epoch 00263: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 264/500\n",
      "32/32 [==============================] - 17s 518ms/step - loss: 1.1785e-04 - val_loss: 1.4914e-04\n",
      "\n",
      "Epoch 00264: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 265/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 1.1730e-04 - val_loss: 1.4874e-04\n",
      "\n",
      "Epoch 00265: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 266/500\n",
      "32/32 [==============================] - 16s 488ms/step - loss: 1.1676e-04 - val_loss: 1.4835e-04\n",
      "\n",
      "Epoch 00266: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 267/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 1.1623e-04 - val_loss: 1.4796e-04\n",
      "\n",
      "Epoch 00267: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 268/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.1571e-04 - val_loss: 1.4758e-04\n",
      "\n",
      "Epoch 00268: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 269/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.1520e-04 - val_loss: 1.4721e-04\n",
      "\n",
      "Epoch 00269: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 270/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.1469e-04 - val_loss: 1.4684e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00270: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 271/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 1.1419e-04 - val_loss: 1.4648e-04\n",
      "\n",
      "Epoch 00271: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 272/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 1.1369e-04 - val_loss: 1.4613e-04\n",
      "\n",
      "Epoch 00272: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 273/500\n",
      "32/32 [==============================] - 16s 514ms/step - loss: 1.1321e-04 - val_loss: 1.4578e-04\n",
      "\n",
      "Epoch 00273: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 274/500\n",
      "32/32 [==============================] - 16s 515ms/step - loss: 1.1273e-04 - val_loss: 1.4543e-04\n",
      "\n",
      "Epoch 00274: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 275/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 1.1226e-04 - val_loss: 1.4510e-04\n",
      "\n",
      "Epoch 00275: val_loss improved from 0.00015 to 0.00015, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 276/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 1.1179e-04 - val_loss: 1.4477e-04\n",
      "\n",
      "Epoch 00276: val_loss improved from 0.00015 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 277/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.1133e-04 - val_loss: 1.4444e-04\n",
      "\n",
      "Epoch 00277: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 278/500\n",
      "32/32 [==============================] - 17s 521ms/step - loss: 1.1088e-04 - val_loss: 1.4412e-04\n",
      "\n",
      "Epoch 00278: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 279/500\n",
      "32/32 [==============================] - 17s 516ms/step - loss: 1.1043e-04 - val_loss: 1.4380e-04\n",
      "\n",
      "Epoch 00279: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 280/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 1.0999e-04 - val_loss: 1.4350e-04\n",
      "\n",
      "Epoch 00280: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 281/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 1.0956e-04 - val_loss: 1.4319e-04\n",
      "\n",
      "Epoch 00281: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 282/500\n",
      "32/32 [==============================] - 16s 495ms/step - loss: 1.0913e-04 - val_loss: 1.4289e-04\n",
      "\n",
      "Epoch 00282: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 283/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.0871e-04 - val_loss: 1.4260e-04\n",
      "\n",
      "Epoch 00283: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 284/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.0830e-04 - val_loss: 1.4231e-04\n",
      "\n",
      "Epoch 00284: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 285/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.0789e-04 - val_loss: 1.4203e-04\n",
      "\n",
      "Epoch 00285: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 286/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 1.0748e-04 - val_loss: 1.4175e-04\n",
      "\n",
      "Epoch 00286: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 287/500\n",
      "32/32 [==============================] - 16s 493ms/step - loss: 1.0709e-04 - val_loss: 1.4148e-04\n",
      "\n",
      "Epoch 00287: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 288/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.0669e-04 - val_loss: 1.4121e-04\n",
      "\n",
      "Epoch 00288: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 289/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 1.0631e-04 - val_loss: 1.4094e-04\n",
      "\n",
      "Epoch 00289: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 290/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.0593e-04 - val_loss: 1.4069e-04\n",
      "\n",
      "Epoch 00290: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 291/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 1.0555e-04 - val_loss: 1.4043e-04\n",
      "\n",
      "Epoch 00291: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 292/500\n",
      "32/32 [==============================] - 17s 519ms/step - loss: 1.0518e-04 - val_loss: 1.4018e-04\n",
      "\n",
      "Epoch 00292: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 293/500\n",
      "32/32 [==============================] - 16s 512ms/step - loss: 1.0482e-04 - val_loss: 1.3993e-04\n",
      "\n",
      "Epoch 00293: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 294/500\n",
      "32/32 [==============================] - 16s 515ms/step - loss: 1.0446e-04 - val_loss: 1.3969e-04\n",
      "\n",
      "Epoch 00294: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 295/500\n",
      "32/32 [==============================] - 17s 517ms/step - loss: 1.0411e-04 - val_loss: 1.3946e-04\n",
      "\n",
      "Epoch 00295: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 296/500\n",
      "32/32 [==============================] - 16s 495ms/step - loss: 1.0376e-04 - val_loss: 1.3922e-04\n",
      "\n",
      "Epoch 00296: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 297/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 1.0341e-04 - val_loss: 1.3899e-04\n",
      "\n",
      "Epoch 00297: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 298/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 1.0307e-04 - val_loss: 1.3877e-04\n",
      "\n",
      "Epoch 00298: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 299/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 1.0274e-04 - val_loss: 1.3855e-04\n",
      "\n",
      "Epoch 00299: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 300/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 1.0241e-04 - val_loss: 1.3833e-04\n",
      "\n",
      "Epoch 00300: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 301/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.0208e-04 - val_loss: 1.3812e-04\n",
      "\n",
      "Epoch 00301: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 302/500\n",
      "32/32 [==============================] - 17s 516ms/step - loss: 1.0176e-04 - val_loss: 1.3791e-04\n",
      "\n",
      "Epoch 00302: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 303/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.0145e-04 - val_loss: 1.3771e-04\n",
      "\n",
      "Epoch 00303: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 304/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 1.0114e-04 - val_loss: 1.3752e-04\n",
      "\n",
      "Epoch 00304: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 305/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 1.0085e-04 - val_loss: 1.3737e-04\n",
      "\n",
      "Epoch 00305: val_loss improved from 0.00014 to 0.00014, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 306/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 1.0061e-04 - val_loss: 1.3737e-04\n",
      "\n",
      "Epoch 00306: val_loss did not improve from 0.00014\n",
      "Epoch 307/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.0049e-04 - val_loss: 1.3777e-04\n",
      "\n",
      "Epoch 00307: val_loss did not improve from 0.00014\n",
      "Epoch 308/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 1.0081e-04 - val_loss: 1.3974e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00308: val_loss did not improve from 0.00014\n",
      "Epoch 309/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 1.0262e-04 - val_loss: 1.4631e-04\n",
      "\n",
      "Epoch 00309: val_loss did not improve from 0.00014\n",
      "Epoch 310/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 1.0920e-04 - val_loss: 1.6564e-04\n",
      "\n",
      "Epoch 00310: val_loss did not improve from 0.00014\n",
      "Epoch 311/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.2826e-04 - val_loss: 2.0610e-04\n",
      "\n",
      "Epoch 00311: val_loss did not improve from 0.00014\n",
      "Epoch 312/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 1.6891e-04 - val_loss: 2.4698e-04\n",
      "\n",
      "Epoch 00312: val_loss did not improve from 0.00014\n",
      "Epoch 313/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 2.0936e-04 - val_loss: 2.3056e-04\n",
      "\n",
      "Epoch 00313: val_loss did not improve from 0.00014\n",
      "Epoch 314/500\n",
      "32/32 [==============================] - 17s 523ms/step - loss: 1.9335e-04 - val_loss: 1.5772e-04\n",
      "\n",
      "Epoch 00314: val_loss did not improve from 0.00014\n",
      "Epoch 315/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 1.2017e-04 - val_loss: 1.4093e-04\n",
      "\n",
      "Epoch 00315: val_loss did not improve from 0.00014\n",
      "Epoch 316/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 1.0350e-04 - val_loss: 1.8771e-04\n",
      "\n",
      "Epoch 00316: val_loss did not improve from 0.00014\n",
      "Epoch 317/500\n",
      "32/32 [==============================] - 16s 495ms/step - loss: 1.5052e-04 - val_loss: 1.8523e-04\n",
      "\n",
      "Epoch 00317: val_loss did not improve from 0.00014\n",
      "Epoch 318/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 1.4769e-04 - val_loss: 1.3932e-04\n",
      "\n",
      "Epoch 00318: val_loss did not improve from 0.00014\n",
      "Epoch 319/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.0201e-04 - val_loss: 1.4946e-04\n",
      "\n",
      "Epoch 00319: val_loss did not improve from 0.00014\n",
      "Epoch 320/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.1221e-04 - val_loss: 1.7356e-04\n",
      "\n",
      "Epoch 00320: val_loss did not improve from 0.00014\n",
      "Epoch 321/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 1.3607e-04 - val_loss: 1.4598e-04\n",
      "\n",
      "Epoch 00321: val_loss did not improve from 0.00014\n",
      "Epoch 322/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.0875e-04 - val_loss: 1.3776e-04\n",
      "\n",
      "Epoch 00322: val_loss did not improve from 0.00014\n",
      "Epoch 323/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 1.0048e-04 - val_loss: 1.5994e-04\n",
      "\n",
      "Epoch 00323: val_loss did not improve from 0.00014\n",
      "Epoch 324/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 1.2249e-04 - val_loss: 1.4600e-04\n",
      "\n",
      "Epoch 00324: val_loss did not improve from 0.00014\n",
      "Epoch 325/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 1.0878e-04 - val_loss: 1.3468e-04\n",
      "\n",
      "Epoch 00325: val_loss improved from 0.00014 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 326/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 9.7348e-05 - val_loss: 1.5106e-04\n",
      "\n",
      "Epoch 00326: val_loss did not improve from 0.00013\n",
      "Epoch 327/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 1.1356e-04 - val_loss: 1.4305e-04\n",
      "\n",
      "Epoch 00327: val_loss did not improve from 0.00013\n",
      "Epoch 328/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 1.0574e-04 - val_loss: 1.3368e-04\n",
      "\n",
      "Epoch 00328: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 329/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 9.6274e-05 - val_loss: 1.4560e-04\n",
      "\n",
      "Epoch 00329: val_loss did not improve from 0.00013\n",
      "Epoch 330/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 1.0801e-04 - val_loss: 1.3984e-04\n",
      "\n",
      "Epoch 00330: val_loss did not improve from 0.00013\n",
      "Epoch 331/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 1.0238e-04 - val_loss: 1.3322e-04\n",
      "\n",
      "Epoch 00331: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 332/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 9.5655e-05 - val_loss: 1.4195e-04\n",
      "\n",
      "Epoch 00332: val_loss did not improve from 0.00013\n",
      "Epoch 333/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 1.0421e-04 - val_loss: 1.3709e-04\n",
      "\n",
      "Epoch 00333: val_loss did not improve from 0.00013\n",
      "Epoch 334/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 9.9441e-05 - val_loss: 1.3293e-04\n",
      "\n",
      "Epoch 00334: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 335/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 9.5170e-05 - val_loss: 1.3930e-04\n",
      "\n",
      "Epoch 00335: val_loss did not improve from 0.00013\n",
      "Epoch 336/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 1.0138e-04 - val_loss: 1.3506e-04\n",
      "\n",
      "Epoch 00336: val_loss did not improve from 0.00013\n",
      "Epoch 337/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 9.7203e-05 - val_loss: 1.3265e-04\n",
      "\n",
      "Epoch 00337: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 338/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 9.4678e-05 - val_loss: 1.3723e-04\n",
      "\n",
      "Epoch 00338: val_loss did not improve from 0.00013\n",
      "Epoch 339/500\n",
      "32/32 [==============================] - 17s 520ms/step - loss: 9.9086e-05 - val_loss: 1.3362e-04\n",
      "\n",
      "Epoch 00339: val_loss did not improve from 0.00013\n",
      "Epoch 340/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 9.5526e-05 - val_loss: 1.3231e-04\n",
      "\n",
      "Epoch 00340: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 341/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 9.4118e-05 - val_loss: 1.3556e-04\n",
      "\n",
      "Epoch 00341: val_loss did not improve from 0.00013\n",
      "Epoch 342/500\n",
      "32/32 [==============================] - 16s 512ms/step - loss: 9.7197e-05 - val_loss: 1.3264e-04\n",
      "\n",
      "Epoch 00342: val_loss did not improve from 0.00013\n",
      "Epoch 343/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 9.4299e-05 - val_loss: 1.3191e-04\n",
      "\n",
      "Epoch 00343: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 344/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 9.3480e-05 - val_loss: 1.3422e-04\n",
      "\n",
      "Epoch 00344: val_loss did not improve from 0.00013\n",
      "Epoch 345/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 9.5624e-05 - val_loss: 1.3196e-04\n",
      "\n",
      "Epoch 00345: val_loss did not improve from 0.00013\n",
      "Epoch 346/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 9.3366e-05 - val_loss: 1.3148e-04\n",
      "\n",
      "Epoch 00346: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 347/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 9.2797e-05 - val_loss: 1.3315e-04\n",
      "\n",
      "Epoch 00347: val_loss did not improve from 0.00013\n",
      "Epoch 348/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 9.4318e-05 - val_loss: 1.3147e-04\n",
      "\n",
      "Epoch 00348: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 349/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 9.2623e-05 - val_loss: 1.3105e-04\n",
      "\n",
      "Epoch 00349: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 350/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 9.2100e-05 - val_loss: 1.3230e-04\n",
      "\n",
      "Epoch 00350: val_loss did not improve from 0.00013\n",
      "Epoch 351/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 9.3205e-05 - val_loss: 1.3111e-04\n",
      "\n",
      "Epoch 00351: val_loss did not improve from 0.00013\n",
      "Epoch 352/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 9.1992e-05 - val_loss: 1.3064e-04\n",
      "\n",
      "Epoch 00352: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 353/500\n",
      "32/32 [==============================] - 16s 488ms/step - loss: 9.1427e-05 - val_loss: 1.3159e-04\n",
      "\n",
      "Epoch 00353: val_loss did not improve from 0.00013\n",
      "Epoch 354/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 9.2240e-05 - val_loss: 1.3082e-04\n",
      "\n",
      "Epoch 00354: val_loss did not improve from 0.00013\n",
      "Epoch 355/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 16s 497ms/step - loss: 9.1433e-05 - val_loss: 1.3029e-04\n",
      "\n",
      "Epoch 00355: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 356/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 9.0804e-05 - val_loss: 1.3099e-04\n",
      "\n",
      "Epoch 00356: val_loss did not improve from 0.00013\n",
      "Epoch 357/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 9.1373e-05 - val_loss: 1.3056e-04\n",
      "\n",
      "Epoch 00357: val_loss did not improve from 0.00013\n",
      "Epoch 358/500\n",
      "32/32 [==============================] - 17s 522ms/step - loss: 9.0911e-05 - val_loss: 1.3000e-04\n",
      "\n",
      "Epoch 00358: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 359/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 9.0246e-05 - val_loss: 1.3044e-04\n",
      "\n",
      "Epoch 00359: val_loss did not improve from 0.00013\n",
      "Epoch 360/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 9.0579e-05 - val_loss: 1.3030e-04\n",
      "\n",
      "Epoch 00360: val_loss did not improve from 0.00013\n",
      "Epoch 361/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 9.0397e-05 - val_loss: 1.2977e-04\n",
      "\n",
      "Epoch 00361: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 362/500\n",
      "32/32 [==============================] - 17s 518ms/step - loss: 8.9762e-05 - val_loss: 1.2995e-04\n",
      "\n",
      "Epoch 00362: val_loss did not improve from 0.00013\n",
      "Epoch 363/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 8.9851e-05 - val_loss: 1.3001e-04\n",
      "\n",
      "Epoch 00363: val_loss did not improve from 0.00013\n",
      "Epoch 364/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 8.9865e-05 - val_loss: 1.2960e-04\n",
      "\n",
      "Epoch 00364: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 365/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 8.9345e-05 - val_loss: 1.2954e-04\n",
      "\n",
      "Epoch 00365: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 366/500\n",
      "32/32 [==============================] - 17s 517ms/step - loss: 8.9207e-05 - val_loss: 1.2968e-04\n",
      "\n",
      "Epoch 00366: val_loss did not improve from 0.00013\n",
      "Epoch 367/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 8.9296e-05 - val_loss: 1.2946e-04\n",
      "\n",
      "Epoch 00367: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 368/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 8.8962e-05 - val_loss: 1.2923e-04\n",
      "\n",
      "Epoch 00368: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 369/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 8.8667e-05 - val_loss: 1.2932e-04\n",
      "\n",
      "Epoch 00369: val_loss did not improve from 0.00013\n",
      "Epoch 370/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 8.8703e-05 - val_loss: 1.2928e-04\n",
      "\n",
      "Epoch 00370: val_loss did not improve from 0.00013\n",
      "Epoch 371/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 8.8561e-05 - val_loss: 1.2901e-04\n",
      "\n",
      "Epoch 00371: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 372/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 8.8235e-05 - val_loss: 1.2897e-04\n",
      "\n",
      "Epoch 00372: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 373/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 8.8135e-05 - val_loss: 1.2903e-04\n",
      "\n",
      "Epoch 00373: val_loss did not improve from 0.00013\n",
      "Epoch 374/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 8.8101e-05 - val_loss: 1.2884e-04\n",
      "\n",
      "Epoch 00374: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 375/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 8.7866e-05 - val_loss: 1.2870e-04\n",
      "\n",
      "Epoch 00375: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 376/500\n",
      "32/32 [==============================] - 17s 517ms/step - loss: 8.7650e-05 - val_loss: 1.2872e-04\n",
      "\n",
      "Epoch 00376: val_loss did not improve from 0.00013\n",
      "Epoch 377/500\n",
      "32/32 [==============================] - 17s 523ms/step - loss: 8.7595e-05 - val_loss: 1.2866e-04\n",
      "\n",
      "Epoch 00377: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 378/500\n",
      "32/32 [==============================] - 16s 495ms/step - loss: 8.7484e-05 - val_loss: 1.2851e-04\n",
      "\n",
      "Epoch 00378: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 379/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 8.7260e-05 - val_loss: 1.2842e-04\n",
      "\n",
      "Epoch 00379: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 380/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 8.7113e-05 - val_loss: 1.2840e-04\n",
      "\n",
      "Epoch 00380: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 381/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 8.7043e-05 - val_loss: 1.2834e-04\n",
      "\n",
      "Epoch 00381: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 382/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 8.6902e-05 - val_loss: 1.2820e-04\n",
      "\n",
      "Epoch 00382: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 383/500\n",
      "32/32 [==============================] - 16s 495ms/step - loss: 8.6713e-05 - val_loss: 1.2813e-04\n",
      "\n",
      "Epoch 00383: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 384/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 8.6589e-05 - val_loss: 1.2811e-04\n",
      "\n",
      "Epoch 00384: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 385/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 8.6502e-05 - val_loss: 1.2802e-04\n",
      "\n",
      "Epoch 00385: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 386/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 8.6363e-05 - val_loss: 1.2792e-04\n",
      "\n",
      "Epoch 00386: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 387/500\n",
      "32/32 [==============================] - 16s 512ms/step - loss: 8.6198e-05 - val_loss: 1.2785e-04\n",
      "\n",
      "Epoch 00387: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 388/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 8.6079e-05 - val_loss: 1.2780e-04\n",
      "\n",
      "Epoch 00388: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 389/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 8.5983e-05 - val_loss: 1.2774e-04\n",
      "\n",
      "Epoch 00389: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 390/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 8.5854e-05 - val_loss: 1.2764e-04\n",
      "\n",
      "Epoch 00390: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 391/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 8.5707e-05 - val_loss: 1.2757e-04\n",
      "\n",
      "Epoch 00391: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 392/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 8.5586e-05 - val_loss: 1.2752e-04\n",
      "\n",
      "Epoch 00392: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 393/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 8.5485e-05 - val_loss: 1.2745e-04\n",
      "\n",
      "Epoch 00393: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 394/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 8.5369e-05 - val_loss: 1.2737e-04\n",
      "\n",
      "Epoch 00394: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 395/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 8.5235e-05 - val_loss: 1.2729e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00395: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 396/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 8.5112e-05 - val_loss: 1.2723e-04\n",
      "\n",
      "Epoch 00396: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 397/500\n",
      "32/32 [==============================] - 16s 495ms/step - loss: 8.5007e-05 - val_loss: 1.2718e-04\n",
      "\n",
      "Epoch 00397: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 398/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 8.4900e-05 - val_loss: 1.2710e-04\n",
      "\n",
      "Epoch 00398: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 399/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 8.4780e-05 - val_loss: 1.2703e-04\n",
      "\n",
      "Epoch 00399: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 400/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 8.4659e-05 - val_loss: 1.2696e-04\n",
      "\n",
      "Epoch 00400: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 401/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 8.4548e-05 - val_loss: 1.2690e-04\n",
      "\n",
      "Epoch 00401: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 402/500\n",
      "32/32 [==============================] - 16s 493ms/step - loss: 8.4445e-05 - val_loss: 1.2684e-04\n",
      "\n",
      "Epoch 00402: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 403/500\n",
      "32/32 [==============================] - 17s 521ms/step - loss: 8.4338e-05 - val_loss: 1.2677e-04\n",
      "\n",
      "Epoch 00403: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 404/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 8.4224e-05 - val_loss: 1.2670e-04\n",
      "\n",
      "Epoch 00404: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 405/500\n",
      "32/32 [==============================] - 17s 522ms/step - loss: 8.4111e-05 - val_loss: 1.2664e-04\n",
      "\n",
      "Epoch 00405: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 406/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 8.4006e-05 - val_loss: 1.2658e-04\n",
      "\n",
      "Epoch 00406: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 407/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 8.3904e-05 - val_loss: 1.2652e-04\n",
      "\n",
      "Epoch 00407: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 408/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 8.3801e-05 - val_loss: 1.2645e-04\n",
      "\n",
      "Epoch 00408: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 409/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 8.3694e-05 - val_loss: 1.2638e-04\n",
      "\n",
      "Epoch 00409: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 410/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 8.3587e-05 - val_loss: 1.2632e-04\n",
      "\n",
      "Epoch 00410: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 411/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 8.3484e-05 - val_loss: 1.2625e-04\n",
      "\n",
      "Epoch 00411: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 412/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 8.3385e-05 - val_loss: 1.2620e-04\n",
      "\n",
      "Epoch 00412: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 413/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 8.3285e-05 - val_loss: 1.2613e-04\n",
      "\n",
      "Epoch 00413: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 414/500\n",
      "32/32 [==============================] - 17s 523ms/step - loss: 8.3185e-05 - val_loss: 1.2607e-04\n",
      "\n",
      "Epoch 00414: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 415/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 8.3083e-05 - val_loss: 1.2600e-04\n",
      "\n",
      "Epoch 00415: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 416/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 8.2983e-05 - val_loss: 1.2594e-04\n",
      "\n",
      "Epoch 00416: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 417/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 8.2885e-05 - val_loss: 1.2588e-04\n",
      "\n",
      "Epoch 00417: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 418/500\n",
      "32/32 [==============================] - 17s 517ms/step - loss: 8.2789e-05 - val_loss: 1.2582e-04\n",
      "\n",
      "Epoch 00418: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 419/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 8.2693e-05 - val_loss: 1.2576e-04\n",
      "\n",
      "Epoch 00419: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 420/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 8.2597e-05 - val_loss: 1.2569e-04\n",
      "\n",
      "Epoch 00420: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 421/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 8.2501e-05 - val_loss: 1.2563e-04\n",
      "\n",
      "Epoch 00421: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 422/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 8.2405e-05 - val_loss: 1.2557e-04\n",
      "\n",
      "Epoch 00422: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 423/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 8.2311e-05 - val_loss: 1.2551e-04\n",
      "\n",
      "Epoch 00423: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 424/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 8.2218e-05 - val_loss: 1.2545e-04\n",
      "\n",
      "Epoch 00424: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 425/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 8.2126e-05 - val_loss: 1.2539e-04\n",
      "\n",
      "Epoch 00425: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 426/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 8.2035e-05 - val_loss: 1.2533e-04\n",
      "\n",
      "Epoch 00426: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 427/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 8.1943e-05 - val_loss: 1.2527e-04\n",
      "\n",
      "Epoch 00427: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 428/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 8.1852e-05 - val_loss: 1.2521e-04\n",
      "\n",
      "Epoch 00428: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 429/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 8.1762e-05 - val_loss: 1.2515e-04\n",
      "\n",
      "Epoch 00429: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 430/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 8.1671e-05 - val_loss: 1.2509e-04\n",
      "\n",
      "Epoch 00430: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 431/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 8.1582e-05 - val_loss: 1.2503e-04\n",
      "\n",
      "Epoch 00431: val_loss improved from 0.00013 to 0.00013, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 432/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 8.1494e-05 - val_loss: 1.2497e-04\n",
      "\n",
      "Epoch 00432: val_loss improved from 0.00013 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 433/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 16s 513ms/step - loss: 8.1406e-05 - val_loss: 1.2491e-04\n",
      "\n",
      "Epoch 00433: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 434/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 8.1319e-05 - val_loss: 1.2485e-04\n",
      "\n",
      "Epoch 00434: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 435/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 8.1232e-05 - val_loss: 1.2479e-04\n",
      "\n",
      "Epoch 00435: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 436/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 8.1146e-05 - val_loss: 1.2473e-04\n",
      "\n",
      "Epoch 00436: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 437/500\n",
      "32/32 [==============================] - 16s 492ms/step - loss: 8.1060e-05 - val_loss: 1.2468e-04\n",
      "\n",
      "Epoch 00437: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 438/500\n",
      "32/32 [==============================] - 16s 496ms/step - loss: 8.0975e-05 - val_loss: 1.2462e-04\n",
      "\n",
      "Epoch 00438: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 439/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 8.0890e-05 - val_loss: 1.2456e-04\n",
      "\n",
      "Epoch 00439: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 440/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 8.0806e-05 - val_loss: 1.2450e-04\n",
      "\n",
      "Epoch 00440: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 441/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 8.0722e-05 - val_loss: 1.2444e-04\n",
      "\n",
      "Epoch 00441: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 442/500\n",
      "32/32 [==============================] - 16s 488ms/step - loss: 8.0639e-05 - val_loss: 1.2438e-04\n",
      "\n",
      "Epoch 00442: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 443/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 8.0556e-05 - val_loss: 1.2433e-04\n",
      "\n",
      "Epoch 00443: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 444/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 8.0473e-05 - val_loss: 1.2427e-04\n",
      "\n",
      "Epoch 00444: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 445/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 8.0392e-05 - val_loss: 1.2421e-04\n",
      "\n",
      "Epoch 00445: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 446/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 8.0310e-05 - val_loss: 1.2415e-04\n",
      "\n",
      "Epoch 00446: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 447/500\n",
      "32/32 [==============================] - 16s 493ms/step - loss: 8.0229e-05 - val_loss: 1.2410e-04\n",
      "\n",
      "Epoch 00447: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 448/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 8.0149e-05 - val_loss: 1.2404e-04\n",
      "\n",
      "Epoch 00448: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 449/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 8.0070e-05 - val_loss: 1.2398e-04\n",
      "\n",
      "Epoch 00449: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 450/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 7.9991e-05 - val_loss: 1.2392e-04\n",
      "\n",
      "Epoch 00450: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 451/500\n",
      "32/32 [==============================] - 16s 500ms/step - loss: 7.9914e-05 - val_loss: 1.2387e-04\n",
      "\n",
      "Epoch 00451: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 452/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 7.9838e-05 - val_loss: 1.2382e-04\n",
      "\n",
      "Epoch 00452: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 453/500\n",
      "32/32 [==============================] - 16s 515ms/step - loss: 7.9766e-05 - val_loss: 1.2378e-04\n",
      "\n",
      "Epoch 00453: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 454/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 7.9699e-05 - val_loss: 1.2374e-04\n",
      "\n",
      "Epoch 00454: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 455/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 7.9643e-05 - val_loss: 1.2373e-04\n",
      "\n",
      "Epoch 00455: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 456/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 7.9608e-05 - val_loss: 1.2375e-04\n",
      "\n",
      "Epoch 00456: val_loss did not improve from 0.00012\n",
      "Epoch 457/500\n",
      "32/32 [==============================] - 16s 493ms/step - loss: 7.9617e-05 - val_loss: 1.2389e-04\n",
      "\n",
      "Epoch 00457: val_loss did not improve from 0.00012\n",
      "Epoch 458/500\n",
      "32/32 [==============================] - 17s 523ms/step - loss: 7.9717e-05 - val_loss: 1.2417e-04\n",
      "\n",
      "Epoch 00458: val_loss did not improve from 0.00012\n",
      "Epoch 459/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 8.0013e-05 - val_loss: 1.2496e-04\n",
      "\n",
      "Epoch 00459: val_loss did not improve from 0.00012\n",
      "Epoch 460/500\n",
      "32/32 [==============================] - 16s 497ms/step - loss: 8.0737e-05 - val_loss: 1.2659e-04\n",
      "\n",
      "Epoch 00460: val_loss did not improve from 0.00012\n",
      "Epoch 461/500\n",
      "32/32 [==============================] - 16s 502ms/step - loss: 8.2415e-05 - val_loss: 1.3050e-04\n",
      "\n",
      "Epoch 00461: val_loss did not improve from 0.00012\n",
      "Epoch 462/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 8.6213e-05 - val_loss: 1.3896e-04\n",
      "\n",
      "Epoch 00462: val_loss did not improve from 0.00012\n",
      "Epoch 463/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 9.4794e-05 - val_loss: 1.5784e-04\n",
      "\n",
      "Epoch 00463: val_loss did not improve from 0.00012\n",
      "Epoch 464/500\n",
      "32/32 [==============================] - 16s 493ms/step - loss: 1.1345e-04 - val_loss: 1.9607e-04\n",
      "\n",
      "Epoch 00464: val_loss did not improve from 0.00012\n",
      "Epoch 465/500\n",
      "32/32 [==============================] - 16s 512ms/step - loss: 1.5200e-04 - val_loss: 2.6119e-04\n",
      "\n",
      "Epoch 00465: val_loss did not improve from 0.00012\n",
      "Epoch 466/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 2.1669e-04 - val_loss: 3.3386e-04\n",
      "\n",
      "Epoch 00466: val_loss did not improve from 0.00012\n",
      "Epoch 467/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 2.9006e-04 - val_loss: 3.2984e-04\n",
      "\n",
      "Epoch 00467: val_loss did not improve from 0.00012\n",
      "Epoch 468/500\n",
      "32/32 [==============================] - 17s 519ms/step - loss: 2.8540e-04 - val_loss: 2.1443e-04\n",
      "\n",
      "Epoch 00468: val_loss did not improve from 0.00012\n",
      "Epoch 469/500\n",
      "32/32 [==============================] - 16s 501ms/step - loss: 1.7104e-04 - val_loss: 1.2474e-04\n",
      "\n",
      "Epoch 00469: val_loss did not improve from 0.00012\n",
      "Epoch 470/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 8.1033e-05 - val_loss: 1.7642e-04\n",
      "\n",
      "Epoch 00470: val_loss did not improve from 0.00012\n",
      "Epoch 471/500\n",
      "32/32 [==============================] - 16s 508ms/step - loss: 1.3286e-04 - val_loss: 2.2142e-04\n",
      "\n",
      "Epoch 00471: val_loss did not improve from 0.00012\n",
      "Epoch 472/500\n",
      "32/32 [==============================] - 17s 521ms/step - loss: 1.7874e-04 - val_loss: 1.5264e-04\n",
      "\n",
      "Epoch 00472: val_loss did not improve from 0.00012\n",
      "Epoch 473/500\n",
      "32/32 [==============================] - 17s 517ms/step - loss: 1.0925e-04 - val_loss: 1.3115e-04\n",
      "\n",
      "Epoch 00473: val_loss did not improve from 0.00012\n",
      "Epoch 474/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 8.8330e-05 - val_loss: 1.8002e-04\n",
      "\n",
      "Epoch 00474: val_loss did not improve from 0.00012\n",
      "Epoch 475/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 1.3782e-04 - val_loss: 1.5178e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00475: val_loss did not improve from 0.00012\n",
      "Epoch 476/500\n",
      "32/32 [==============================] - 16s 510ms/step - loss: 1.0887e-04 - val_loss: 1.2547e-04\n",
      "\n",
      "Epoch 00476: val_loss did not improve from 0.00012\n",
      "Epoch 477/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 8.3207e-05 - val_loss: 1.5779e-04\n",
      "\n",
      "Epoch 00477: val_loss did not improve from 0.00012\n",
      "Epoch 478/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 1.1595e-04 - val_loss: 1.4123e-04\n",
      "\n",
      "Epoch 00478: val_loss did not improve from 0.00012\n",
      "Epoch 479/500\n",
      "32/32 [==============================] - 17s 516ms/step - loss: 9.8674e-05 - val_loss: 1.2577e-04\n",
      "\n",
      "Epoch 00479: val_loss did not improve from 0.00012\n",
      "Epoch 480/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 8.3794e-05 - val_loss: 1.4596e-04\n",
      "\n",
      "Epoch 00480: val_loss did not improve from 0.00012\n",
      "Epoch 481/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 1.0441e-04 - val_loss: 1.3238e-04\n",
      "\n",
      "Epoch 00481: val_loss did not improve from 0.00012\n",
      "Epoch 482/500\n",
      "32/32 [==============================] - 16s 492ms/step - loss: 9.0118e-05 - val_loss: 1.2644e-04\n",
      "\n",
      "Epoch 00482: val_loss did not improve from 0.00012\n",
      "Epoch 483/500\n",
      "32/32 [==============================] - 17s 517ms/step - loss: 8.4576e-05 - val_loss: 1.3757e-04\n",
      "\n",
      "Epoch 00483: val_loss did not improve from 0.00012\n",
      "Epoch 484/500\n",
      "32/32 [==============================] - 16s 511ms/step - loss: 9.6140e-05 - val_loss: 1.2711e-04\n",
      "\n",
      "Epoch 00484: val_loss did not improve from 0.00012\n",
      "Epoch 485/500\n",
      "32/32 [==============================] - 16s 513ms/step - loss: 8.5002e-05 - val_loss: 1.2634e-04\n",
      "\n",
      "Epoch 00485: val_loss did not improve from 0.00012\n",
      "Epoch 486/500\n",
      "32/32 [==============================] - 16s 498ms/step - loss: 8.4467e-05 - val_loss: 1.3204e-04\n",
      "\n",
      "Epoch 00486: val_loss did not improve from 0.00012\n",
      "Epoch 487/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 9.0680e-05 - val_loss: 1.2424e-04\n",
      "\n",
      "Epoch 00487: val_loss did not improve from 0.00012\n",
      "Epoch 488/500\n",
      "32/32 [==============================] - 16s 504ms/step - loss: 8.2258e-05 - val_loss: 1.2576e-04\n",
      "\n",
      "Epoch 00488: val_loss did not improve from 0.00012\n",
      "Epoch 489/500\n",
      "32/32 [==============================] - 16s 507ms/step - loss: 8.3774e-05 - val_loss: 1.2873e-04\n",
      "\n",
      "Epoch 00489: val_loss did not improve from 0.00012\n",
      "Epoch 490/500\n",
      "32/32 [==============================] - 16s 505ms/step - loss: 8.7315e-05 - val_loss: 1.2201e-04\n",
      "\n",
      "Epoch 00490: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 491/500\n",
      "32/32 [==============================] - 16s 495ms/step - loss: 8.0104e-05 - val_loss: 1.2563e-04\n",
      "\n",
      "Epoch 00491: val_loss did not improve from 0.00012\n",
      "Epoch 492/500\n",
      "32/32 [==============================] - 16s 503ms/step - loss: 8.3540e-05 - val_loss: 1.2602e-04\n",
      "\n",
      "Epoch 00492: val_loss did not improve from 0.00012\n",
      "Epoch 493/500\n",
      "32/32 [==============================] - 16s 514ms/step - loss: 8.4487e-05 - val_loss: 1.2060e-04\n",
      "\n",
      "Epoch 00493: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 494/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 7.8695e-05 - val_loss: 1.2574e-04\n",
      "\n",
      "Epoch 00494: val_loss did not improve from 0.00012\n",
      "Epoch 495/500\n",
      "32/32 [==============================] - 16s 494ms/step - loss: 8.3504e-05 - val_loss: 1.2327e-04\n",
      "\n",
      "Epoch 00495: val_loss did not improve from 0.00012\n",
      "Epoch 496/500\n",
      "32/32 [==============================] - 16s 499ms/step - loss: 8.1529e-05 - val_loss: 1.2048e-04\n",
      "\n",
      "Epoch 00496: val_loss improved from 0.00012 to 0.00012, saving model to ../models/hybrid-mackey-glass.hdf5\n",
      "Epoch 497/500\n",
      "32/32 [==============================] - 16s 516ms/step - loss: 7.8546e-05 - val_loss: 1.2507e-04\n",
      "\n",
      "Epoch 00497: val_loss did not improve from 0.00012\n",
      "Epoch 498/500\n",
      "32/32 [==============================] - 16s 509ms/step - loss: 8.2706e-05 - val_loss: 1.2127e-04\n",
      "\n",
      "Epoch 00498: val_loss did not improve from 0.00012\n",
      "Epoch 499/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 7.9266e-05 - val_loss: 1.2104e-04\n",
      "\n",
      "Epoch 00499: val_loss did not improve from 0.00012\n",
      "Epoch 500/500\n",
      "32/32 [==============================] - 16s 506ms/step - loss: 7.8958e-05 - val_loss: 1.2364e-04\n",
      "\n",
      "Epoch 00500: val_loss did not improve from 0.00012\n"
     ]
    }
   ],
   "source": [
    "hybrid_history, hybrid_time = fit(\n",
    "    hybrid_model, \"../models/hybrid-mackey-glass.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = sns.color_palette(None, 3)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(lstm_history.history['loss'], c=colors[0], label=\"LSTM Training\")\n",
    "plt.plot(lstm_history.history['val_loss'], c=colors[0], label=\"LSTM Validation\", linestyle='--')\n",
    "\n",
    "plt.plot(lmu_history.history['loss'], c=colors[1], label=\"LMU Training\")\n",
    "plt.plot(lmu_history.history['val_loss'], c=colors[1], label=\"LMU Validation\", linestyle='--')\n",
    "\n",
    "plt.plot(hybrid_history.history['loss'], c=colors[2], label=\"Hybrid Training\")\n",
    "plt.plot(hybrid_history.history['val_loss'], c=colors[2], label=\"Hybrid Validation\", linestyle='--')\n",
    "\n",
    "plt.ylabel('Loss (MSE)')\n",
    "plt.xlabel('Epoch')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "\n",
    "sns.despine(offset=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.3430646276474 12.889788649082185 16.20919619178772\n"
     ]
    }
   ],
   "source": [
    "print(lstm_time, lmu_time, hybrid_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_Y = lstm_model.predict(test_X)\n",
    "lmu_Y = lmu_model.predict(test_X)\n",
    "hybrid_Y = hybrid_model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM 0.00029430085647073694\n",
      "LMU 0.0001380439872248116\n",
      "Hybrid 0.00011902449916338007\n"
     ]
    }
   ],
   "source": [
    "assert train_Y.shape[-1] == test_Y.shape[-1] == 1\n",
    "print(\"LSTM\", mean_squared_error(lstm_Y[:, :, 0], test_Y[:, :, 0]))\n",
    "print(\"LMU\", mean_squared_error(lmu_Y[:, :, 0], test_Y[:, :, 0]))\n",
    "print(\"Hybrid\", mean_squared_error(hybrid_Y[:, :, 0], test_Y[:, :, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM 7.940\n",
      "LMU 5.438\n",
      "Hybrid 5.049\n",
      "Baseline 162.274\n"
     ]
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "from collections import defaultdict\n",
    "from nengolib.signal import nrmse\n",
    "\n",
    "e_label = r'$\\left(\\hat{y}(t) - y(t)\\right)^2$'\n",
    "\n",
    "errors = defaultdict(list)\n",
    "for name, pred_Y in ((\"LSTM\", lstm_Y),\n",
    "                     (\"LMU\", lmu_Y),\n",
    "                     (\"Hybrid\", hybrid_Y),\n",
    "                     (\"Baseline\", test_X)):\n",
    "    print(name, \"%.3f\" % (100*nrmse(pred_Y.flatten(), target=test_Y.flatten())))\n",
    "    \n",
    "    for i in range(test_Y.shape[0]):\n",
    "        for j in range(test_Y.shape[1]):\n",
    "            errors[e_label].append((pred_Y[i, j, 0] - test_Y[i, j, 0])**2)\n",
    "            errors['Time-step'].append(i)\n",
    "            errors['Test'].append(j)\n",
    "            errors['Method'].append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.lineplot(data=DataFrame(errors), x=\"Time-step\", y=e_label, hue=\"Method\")\n",
    "#plt.xscale('log')\n",
    "#plt.yscale('log')\n",
    "sns.despine(offset=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}