MILNET Attention-lr_0.001.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Which weights to choose? 1:normal 2:lle(15,40)_(1000,20,300) 3:iso(15,40)_(1000,20,300) 4:lle(15,40)_(1000,20,150) 5:iso(15,40)_(1000,20,150)5\n",
      "[0 0 0 ... 1 1 1]\n",
      "data loaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/owner/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n",
      "/home/owner/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import sys, os\n",
    "sys.path.append(os.pardir)\n",
    "\n",
    "os.environ[\"KERAS_BACKEND\"]='tensorflow'\n",
    "#import glob\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "weight_choice = input('Which weights to choose? 1:normal 2:lle(15,40)_(1000,20,300) 3:iso(15,40)_(1000,20,300) 4:lle(15,40)_(1000,20,150) 5:iso(15,40)_(1000,20,150)')\n",
    "\n",
    "\n",
    "\n",
    "#from multiprocessing import Pool\n",
    "#import multiprocessing as multi\n",
    "#from data.func import load_npy, padding_mat\n",
    "#sys.path.append('C:\\\\ProgramData\\\\Anaconda3\\\\pkgs\\\\pydot-1.2.3-py36hd4f83f9_0\\\\Lib\\\\site-packages')\n",
    "#sys.path\n",
    "\n",
    "\n",
    "path_to_15_40 = 'milnet+edu/data/Preprocessed/15words+40sent/'\n",
    "path_to_20_50 = 'milnet+edu/data/Preprocessed/20words+50sent/'\n",
    "\n",
    "path =path_to_15_40\n",
    "\n",
    "x_train = np.load('/home/owner/デスクトップ/'+path+'train_data_features_sorted.npy')#np.load('/home/owner/デスクトップ/PythonFile/imdb/x_train_sort.npy')\n",
    "y_train = np.load('/home/owner/デスクトップ/'+path+'train_data_scores_binary.npy')#np.load('/home/owner/デスクトップ/PythonFile/imdb/x_test_sort.npy')\n",
    "#print(x_test)\n",
    "\n",
    "x_test = np.load('/home/owner/デスクトップ/'+path+'test_data_features_sorted.npy')#np.load('/home/owner/デスクトップ/PythonFile/imdb/t_train.npy')\n",
    "y_test = np.load('/home/owner/デスクトップ/'+path+'test_data_scores_binary.npy')#np.load('/home/owner/デスクトップ/PythonFile/imdb/t_test.npy')\n",
    "\n",
    "x_valid = np.load('/home/owner/デスクトップ/'+path+'validation_data_features_sorted.npy')\n",
    "y_valid = np.load('/home/owner/デスクトップ/'+path+'validation_data_scores_binary.npy')\n",
    "\n",
    "#train_idx = np.load('/home/owner/デスクトップ/'+path+'train_data_idx_binary_only.npy')\n",
    "#test_idx = np.load('/home/owner/デスクトップ/'+path+'test_data_idx_binary_only.npy')\n",
    "#valid_idx = np.load('/home/owner/デスクトップ/'+path+'validation_data_idx_binary_only.npy')\n",
    "\n",
    "#print(len(train_idx))\n",
    "#x_train = x_train[train_idx]\n",
    "#y_train = y_train[train_idx]\n",
    "#print(len(x_train))\n",
    "#print(len(y_train))\n",
    "\n",
    "#x_test = x_test[test_idx]\n",
    "#y_test = y_test[test_idx]\n",
    "\n",
    "#x_valid = x_valid[valid_idx]\n",
    "#y_valid = y_valid[valid_idx]\n",
    "\n",
    "#print(x_valid)\n",
    "\n",
    "#word_idx=np.load( '/home/owner/デスクトップ/'+ path +'vocab_idx.npy')\n",
    "#print(idx)\n",
    "#mani_choice = input('Which weights to choose? 1:normal 2:lle 3:iso ')\n",
    "if (int(weight_choice)==1):\n",
    "    embWeights=np.load( '/home/owner/デスクトップ/'+ path +'weights.npy')#np.load('/home/owner/デスクトップ/PythonFile/imdb/weights.npy')\n",
    "elif (int(weight_choice)==2):\n",
    "    embWeights=np.load( '/home/owner/デスクトップ/milnet+edu/data/manifold_weights/' + 'weights_LLE_(15,40)_(1000,20,300).npy')#np.load('/home/owner/デスクトップ/PythonFile/imdb/weights.npy')\n",
    "elif (int(weight_choice)==3):\n",
    "     embWeights=np.load( '/home/owner/デスクトップ/milnet+edu/data/manifold_weights/' + 'weights_Isomap_(15,40)_(1000,20,300).npy')\n",
    "elif (int(weight_choice)==4):\n",
    "    embWeights=np.load( '/home/owner/デスクトップ/milnet+edu/data/manifold_weights/' + 'weights_LLE20_1000_20_150.npy')#np.load('/home/owner/デスクトップ/PythonFile/imdb/weights.npy')\n",
    "elif (int(weight_choice)==5):\n",
    "     embWeights=np.load( '/home/owner/デスクトップ/milnet+edu/data/manifold_weights/' + 'weights_Isomap_20_1000_20_150.npy')\n",
    "\n",
    "        \n",
    "        \n",
    "#print(len(word_idx))\n",
    "print(y_test)\n",
    "\n",
    "print('data loaded')\n",
    "\n",
    "\n",
    "\n",
    "import keras\n",
    "from keras.layers import Input, merge\n",
    "from keras.models import Model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import LSTM\n",
    "import keras.backend as K\n",
    "from keras.layers import Lambda, regularizers, Average\n",
    "from keras.layers import RepeatVector, Permute, Multiply\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Conv2D, Conv1D, MaxPooling2D, GlobalMaxPooling2D, GlobalMaxPooling1D, MaxPooling1D\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers.recurrent import GRU\n",
    "from keras.layers.wrappers import Bidirectional, TimeDistributed\n",
    "from keras.layers.core import Dropout, Dense, Lambda, Masking\n",
    "from keras.layers import merge, Layer, Activation, Dot, Concatenate, Flatten, Lambda\n",
    "\n",
    "from keras.initializers import Identity,glorot_normal\n",
    "from keras import regularizers\n",
    "\n",
    "from keras import metrics\n",
    "\n",
    "from keras.utils import plot_model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "li = [x_train.tolist(), x_test.tolist(), x_valid.tolist()]\n",
    "for idx in range(3):\n",
    "    features = li[idx]\n",
    "    print(len(features))\n",
    "    for i in range(len(features)):\n",
    "        #print(len(features[i]))\n",
    "        if len(features[i])!=40:\n",
    "                print('i:'+str(i)+'   ;;;;;;;;;;;;;;;;;;;;;')\n",
    "                print(len(features[i]))\n",
    "                li[idx][i].append(li[idx][i][-1])\n",
    "        for j in range(len(features[i])):\n",
    "\n",
    "            #print(len(features[i][j]))\n",
    "            if len(features[i][j])!= 15:\n",
    "                print('i:'+str(i)+'j:'+str(j)+'   *************************')\n",
    "\n",
    "x_train=np.asarray(li[0])\n",
    "x_test=np.asarray(li[1])\n",
    "x_valid=np.asarray(li[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(210642, 40, 15) (25510, 40, 15) (25206, 40, 15)\n",
      "(210642,) (25510,) (25206,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, x_test.shape, x_valid.shape)\n",
    "print(y_train.shape, y_test.shape, y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40 15\n",
      "61560 150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/owner/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:126: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=[<tf.Tenso..., outputs=[<tf.Tenso...)`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attention Model Build Complete\n"
     ]
    }
   ],
   "source": [
    "numSentencesPerDoc, numWordsPerSentence = x_train[0].shape[0], x_train[0].shape[1]\n",
    "print(numSentencesPerDoc, numWordsPerSentence)\n",
    "#print(x_train[0])\n",
    "\n",
    "vocabSize, embeddingSize = embWeights.shape[0], embWeights.shape[1]\n",
    "print(vocabSize, embeddingSize)\n",
    "\n",
    "dropWordEmb = 0.25\n",
    "recursiveClass = GRU\n",
    "\n",
    "filters = 100 #embeddingSize*2\n",
    "windowMin = 3\n",
    "windowMax = 7# dimOfSentimentMetrics = 5\n",
    "batch_size = 200\n",
    "#epochs = 25\n",
    "dimGRU = 50\n",
    "numDensePool=10\n",
    "eta = 1e-4\n",
    "dr = 0.5\n",
    "\n",
    "##\n",
    "\n",
    "#wordsInputs = Input(shape=(numWordsPerSentence,1), batch_shape=(numSentencesPerDoc,numWordsPerSentence,), dtype='int32', name='words_input')\n",
    "\n",
    "x_in = Input( shape = ( numSentencesPerDoc, numWordsPerSentence ) , name='Input' )\n",
    "#x_pop = Lambda( lambda x: x, output_shape=(numWordsPerSentence, ) , name='convert_shape' )( x_in )\n",
    "    \n",
    "#Layer functionの定義\n",
    "embLayer = Embedding( input_dim=embWeights.shape[0], output_dim=embWeights.shape[1], weights=[embWeights]\n",
    "                      ,mask_zero=False , trainable=False, embeddings_regularizer=regularizers.l2(0.0000001)\n",
    "                      , input_length=numWordsPerSentence, name='Embedding' )\n",
    "\n",
    "maxPooledPerDoc = []\n",
    "convNets = []\n",
    "maxPools = []\n",
    "scoresForSentences = []\n",
    "\n",
    "\n",
    "extraDimLayer = Lambda(lambda x: K.expand_dims(x), name='extraDimForConvo')\n",
    "squeezeThirdLayer = Lambda(lambda x: K.squeeze(x, 3), name='squeezeThirdLayer')\n",
    "\n",
    "for windowSize in range(windowMin,windowMax):\n",
    "    name='word_mat_convo_win_size_'+str(windowSize)\n",
    "    #convNet = Conv2D(filters, kernel_size=(windowSize,embeddingSize), padding='valid', activation='relu'\n",
    "    #                 ,strides=1, use_bias=True, input_shape=(numWordsPerSentence, embeddingSize, 1), data_format=\"channels_last\",kernel_initializer=glorot_normal()\n",
    "    #                 ,bias_regularizer=regularizers.l2(eta), kernel_regularizer=regularizers.l2(eta),name=name)\n",
    "    convNet = Conv1D(filters=filters, kernel_size=windowSize, padding='valid', activation='relu', strides=1,use_bias=False,\n",
    "                    kernel_regularizer=regularizers.l2(eta), name = name)\n",
    "    convNets.append(convNet)\n",
    "    name='word_mat_max_pool_win_size_'+str(windowSize)\n",
    "    maxPool = MaxPooling1D(pool_size = int(numWordsPerSentence-windowSize-1), padding='valid')\n",
    "    maxPools.append(maxPool)\n",
    "    \n",
    "    \n",
    "for i in range(numSentencesPerDoc):\n",
    "    maxPooledPerSentence = []\n",
    "    x_pop = Lambda(lambda x: x[:,i], output_shape=(numWordsPerSentence, ) , name='convert_shape_'+'sentence'+str(i+1))( x_in )\n",
    "\n",
    "    for j in range(windowMax-windowMin):   \n",
    "        emb = embLayer(x_pop)\n",
    "        #emb = Dropout(dr,name='DropEmb'+str(i)+str(j))(emb)\n",
    "        #reshaped = extraDimLayer(emb)#Lambda(lambda x: K.expand_dims(x), name='extraDimForConvo_'+str(j)+'_sentence_'+str(i))(emb)\n",
    "        #name='word_mat_convo_win_size_'+str(j)+'_sentence_'+str(i)\n",
    "        #wordsCNN = Conv2D(filters, kernel_size=(windowSize,embeddingSize), padding='valid', \n",
    "        #                    activation='relu', strides=1, use_bias=True, input_shape=(numWordsPerSentence, embeddingSize, 1), data_format=\"channels_last\",\n",
    "        #                    kernel_initializer=glorot_normal(),kernel_regularizer=regularizers.l2(),name=name)(reshaped)\n",
    "        wordsCNN  = convNets[j](emb)\n",
    "        wordsCNN = Dropout(dr,name='DropCNN'+str(i)+str(j))(wordsCNN)\n",
    "        #squeezed = squeezeThirdLayer(wordsCNN)#Lambda(lambda x: K.squeeze(x, 3), name='squeezeThirdLayer_'+str(j)+'_sentence_'+str(i))(wordsCNN)\n",
    "        # newShape = (-1, int(squeezed.shape[1])*int(squeezed.shape[2]))\n",
    "        # squeezed = Lambda(lambda x: K.reshape(x,shape=newShape), name ='squeezeDimForMaxPool'+str(i)+str(j))(squeezed)\n",
    "        #wordsCNNPooled=GlobalMaxPooling1D()(squeezed)\n",
    "        #wordsCNNPooled= MaxPooling1D(pool_size = int(squeezed.shape[1]), padding='valid')(squeezed)\n",
    "        wordsCNNPooled = MaxPooling1D(pool_size=(numWordsPerSentence-(j+windowMin)+1))(wordsCNN)\n",
    "        flattened = Lambda(lambda x: K.squeeze(x, 1))(wordsCNNPooled)\n",
    "        maxPooledPerSentence.append(flattened)\n",
    "        \n",
    "    mergedPoolForSentence = Concatenate(axis = 1)(maxPooledPerSentence)\n",
    "    newShape=(-1,1,int(mergedPoolForSentence.shape[1]))\n",
    "    reshapedPoolForSentence = Lambda(lambda x: K.reshape(x,shape=newShape), name ='switch_axis_'+'sentence'+str(i+1)+'winSize'+str(j+windowMin))(mergedPoolForSentence)\n",
    "    maxPooledPerDoc.append(reshapedPoolForSentence)\n",
    "    \n",
    "    densePoolForSentence = Dense(numDensePool, bias_regularizer=regularizers.l2(eta),\n",
    "                                 kernel_regularizer=regularizers.l2(eta), activation='softmax', use_bias=True)(reshapedPoolForSentence)\n",
    "\n",
    "    densePoolForSentence = Dropout(dr,name='DropDense'+str(i))(densePoolForSentence)\n",
    "    scoresForSentences.append(densePoolForSentence)\n",
    "    \n",
    "#Naive Approach\n",
    "averaged = Average()(scoresForSentences) \n",
    "averaged = Lambda(lambda x:K.reshape(x,shape=(-1,int(averaged.shape[1])*int(averaged.shape[2]))), name ='attend_output')(averaged)\n",
    "out_avg = Dense(1, activation='sigmoid', use_bias=True)(averaged) \n",
    "    \n",
    "#Apply Attention \n",
    "mergedPoolPerDoc = Concatenate(axis = 1)(maxPooledPerDoc)\n",
    "biRnn_ = Bidirectional(GRU(dimGRU,  return_sequences=True), merge_mode='concat')(mergedPoolPerDoc)\n",
    "newShape = (-1, int(mergedPoolPerDoc.shape[1]), int(biRnn_.shape[2]))\n",
    "biRnn = Lambda(lambda x: K.reshape(x,shape=newShape), name ='biRnn_TF_Reminder1')(biRnn_)\n",
    "#biRnn2 = Lambda(lambda x: K.reshape(x,shape=newShape), name ='biRnn_TF_Reminder2')(biRnn_[1])\n",
    "\n",
    "#biRnn_cat = Concatenate(axis = 2)([biRnn1, biRnn2])\n",
    "\n",
    "CONTEXT_DIM = 100\n",
    "\n",
    "#eij = Dense(CONTEXT_DIM, use_bias=True, activation='tanh')(biRnn)\n",
    "#eij = Dense(1, use_bias=False, activation='softmax')(eij)\n",
    "\n",
    "#weighted_input = Dot(axes = 1)([eij, mergedPoolPerDoc])\n",
    "#weighted_input = Lambda(lambda x: K.reshape(x,shape=(-1,int(weighted_input_.shape[1])*int(weighted_input_.shape[2]))), name ='attend_output')(weighted_input_)\n",
    "eij_ = Dense(CONTEXT_DIM, use_bias=True, activation='tanh')(biRnn)\n",
    "eij = Dense(1, use_bias=False, name = 'attention_weights',bias_regularizer=regularizers.l2(eta),\n",
    "                                 kernel_regularizer=regularizers.l2(eta))(eij_)\n",
    "eij_permuted = Permute((2,1))(eij)\n",
    "eij_normalized = Activation('softmax',name = 'attention_softmax')(eij_permuted)\n",
    "eij_normalized = Permute((2,1))(eij_normalized)\n",
    "\n",
    "scoresPerDoc = Concatenate(axis = 1)(scoresForSentences)\n",
    "weighted_input = Dot(axes = 1)([eij, scoresPerDoc])\n",
    "#weighted_input = Lambda(lambda x: K.reshape(x,shape=(-1,int(weighted_input_.shape[1])*int(weighted_input_.shape[2]))), name ='attend_output')(weighted_input_)\n",
    "weighted_input = Lambda(lambda x: K.squeeze(x, 1), name='squeezeOutput')(weighted_input)\n",
    "\n",
    "out = Dense(1, activation='sigmoid', use_bias=True)(weighted_input)\n",
    "\n",
    "##\n",
    "\n",
    "model = Model(input=[x_in], output=[out])#,weighted_input,eij_normalized])\n",
    "#adadelta = keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)\n",
    "#model.compile(loss='binary_crossentropy',\n",
    "#              optimizer=adadelta,\n",
    "#              metrics=['accuracy'])\n",
    "         \n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "print(\"Attention Model Build Complete\")\n",
    "##\n",
    "#model_avg = Model(inputs=[x_in], outputs=[out_avg])\n",
    "#model_avg.compile(loss='binary_crossentropy',\n",
    "#              optimizer=keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0),\n",
    "#              metrics=['accuracy'])\n",
    "\n",
    "#print(\"Average Model Build Complete\")\n",
    "##\n",
    "#save model to png file\n",
    "#from keras.utils import plot_model\n",
    "#plot_model( model, to_file='model.png' )\n",
    "\n",
    "#モデルを保存せず直接可視化\n",
    "#from IPython.display import SVG\n",
    "#from keras.utils.vis_utils import model_to_dot\n",
    "#SVG( model_to_dot( model ).create( prog='dot', format='svg' ) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61560, 300)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embWeights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_model_path = '/home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso_150.hdf5'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#name = './params_milnet_adam_0720_3_adjCNN_fixed_weights.hdf5'\n",
    "save_model = keras.callbacks.ModelCheckpoint(save_model_path, monitor='val_loss', verbose=1\n",
    "                                             , save_best_only=True, save_weights_only=True, mode='min', period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 210642 samples, validate on 25206 samples\n",
      "Epoch 1/10\n",
      "210642/210642 [==============================] - 371s 2ms/step - loss: 0.5124 - acc: 0.8433 - val_loss: 0.4127 - val_acc: 0.8733\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.41273, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_normal.hdf5\n",
      "Epoch 2/10\n",
      "210642/210642 [==============================] - 358s 2ms/step - loss: 0.4039 - acc: 0.8746 - val_loss: 0.3810 - val_acc: 0.8817\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.41273 to 0.38096, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_normal.hdf5\n",
      "Epoch 3/10\n",
      "210642/210642 [==============================] - 357s 2ms/step - loss: 0.3815 - acc: 0.8828 - val_loss: 0.3772 - val_acc: 0.8836\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.38096 to 0.37716, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_normal.hdf5\n",
      "Epoch 4/10\n",
      "210642/210642 [==============================] - 352s 2ms/step - loss: 0.3709 - acc: 0.8882 - val_loss: 0.3679 - val_acc: 0.8893\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.37716 to 0.36792, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_normal.hdf5\n",
      "Epoch 5/10\n",
      "210642/210642 [==============================] - 360s 2ms/step - loss: 0.3657 - acc: 0.8919 - val_loss: 0.3901 - val_acc: 0.8754\n",
      "\n",
      "Epoch 00005: val_loss did not improve\n",
      "Epoch 6/10\n",
      "210642/210642 [==============================] - 359s 2ms/step - loss: 0.3615 - acc: 0.8952 - val_loss: 0.3917 - val_acc: 0.8783\n",
      "\n",
      "Epoch 00006: val_loss did not improve\n",
      "Epoch 7/10\n",
      "210642/210642 [==============================] - 360s 2ms/step - loss: 0.3591 - acc: 0.8971 - val_loss: 0.3736 - val_acc: 0.8894\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      "Epoch 8/10\n",
      "210642/210642 [==============================] - 359s 2ms/step - loss: 0.3552 - acc: 0.8996 - val_loss: 0.3801 - val_acc: 0.8869\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      "Epoch 9/10\n",
      "210642/210642 [==============================] - 358s 2ms/step - loss: 0.3536 - acc: 0.9014 - val_loss: 0.3689 - val_acc: 0.8930\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      "Epoch 10/10\n",
      "210642/210642 [==============================] - 357s 2ms/step - loss: 0.3519 - acc: 0.9028 - val_loss: 0.4062 - val_acc: 0.8767\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n"
     ]
    }
   ],
   "source": [
    "history_normal = model.fit(x_train, y_train, batch_size = batch_size, verbose=1, epochs=10 #epochs\n",
    "                        ,validation_data=(x_valid,y_valid), shuffle=True, callbacks=[save_model])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 210642 samples, validate on 25206 samples\n",
      "Epoch 1/10\n",
      "210642/210642 [==============================] - 370s 2ms/step - loss: 0.4027 - acc: 0.8455 - val_loss: 0.3453 - val_acc: 0.8667\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.34525, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_lle.hdf5\n",
      "Epoch 2/10\n",
      "210642/210642 [==============================] - 360s 2ms/step - loss: 0.3383 - acc: 0.8720 - val_loss: 0.3363 - val_acc: 0.8706\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.34525 to 0.33633, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_lle.hdf5\n",
      "Epoch 3/10\n",
      "210642/210642 [==============================] - 359s 2ms/step - loss: 0.3273 - acc: 0.8796 - val_loss: 0.3332 - val_acc: 0.8822\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.33633 to 0.33320, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_lle.hdf5\n",
      "Epoch 4/10\n",
      "210642/210642 [==============================] - 359s 2ms/step - loss: 0.3217 - acc: 0.8849 - val_loss: 0.3247 - val_acc: 0.8832\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.33320 to 0.32473, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_lle.hdf5\n",
      "Epoch 5/10\n",
      "210642/210642 [==============================] - 358s 2ms/step - loss: 0.3180 - acc: 0.8870 - val_loss: 0.3229 - val_acc: 0.8849\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.32473 to 0.32289, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_lle.hdf5\n",
      "Epoch 6/10\n",
      "210642/210642 [==============================] - 359s 2ms/step - loss: 0.3155 - acc: 0.8906 - val_loss: 0.3266 - val_acc: 0.8851\n",
      "\n",
      "Epoch 00006: val_loss did not improve\n",
      "Epoch 7/10\n",
      "210642/210642 [==============================] - 358s 2ms/step - loss: 0.3128 - acc: 0.8925 - val_loss: 0.3335 - val_acc: 0.8840\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      "Epoch 8/10\n",
      "210642/210642 [==============================] - 359s 2ms/step - loss: 0.3129 - acc: 0.8936 - val_loss: 0.3577 - val_acc: 0.8700\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      "Epoch 9/10\n",
      "210642/210642 [==============================] - 358s 2ms/step - loss: 0.3113 - acc: 0.8949 - val_loss: 0.3275 - val_acc: 0.8884\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      "Epoch 10/10\n",
      "210642/210642 [==============================] - 360s 2ms/step - loss: 0.3099 - acc: 0.8968 - val_loss: 0.3302 - val_acc: 0.8861\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n"
     ]
    }
   ],
   "source": [
    "history_LLE = model.fit(x_train, y_train, batch_size = batch_size, verbose=1, epochs=10 #epochs\n",
    "                        ,validation_data=(x_valid,y_valid), shuffle=True, callbacks=[save_model])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.load_weights(save_model_path)#('./params_milnet_adam_0718_2_fixed_weights.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 210642 samples, validate on 25206 samples\n",
      "Epoch 1/15\n",
      "210642/210642 [==============================] - 360s 2ms/step - loss: 0.5562 - acc: 0.8407 - val_loss: 0.4690 - val_acc: 0.8724\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.46901, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso.hdf5\n",
      "Epoch 2/15\n",
      "210642/210642 [==============================] - 356s 2ms/step - loss: 0.4566 - acc: 0.8718 - val_loss: 0.4689 - val_acc: 0.8673\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.46901 to 0.46895, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso.hdf5\n",
      "Epoch 3/15\n",
      "210642/210642 [==============================] - 355s 2ms/step - loss: 0.4408 - acc: 0.8777 - val_loss: 0.4454 - val_acc: 0.8759\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.46895 to 0.44541, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso.hdf5\n",
      "Epoch 4/15\n",
      "210642/210642 [==============================] - 355s 2ms/step - loss: 0.4339 - acc: 0.8817 - val_loss: 0.4534 - val_acc: 0.8722\n",
      "\n",
      "Epoch 00004: val_loss did not improve\n",
      "Epoch 5/15\n",
      "210642/210642 [==============================] - 356s 2ms/step - loss: 0.4307 - acc: 0.8849 - val_loss: 0.4392 - val_acc: 0.8771\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.44541 to 0.43917, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso.hdf5\n",
      "Epoch 6/15\n",
      "210642/210642 [==============================] - 356s 2ms/step - loss: 0.4295 - acc: 0.8858 - val_loss: 0.4378 - val_acc: 0.8803\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.43917 to 0.43781, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso.hdf5\n",
      "Epoch 7/15\n",
      "210642/210642 [==============================] - 356s 2ms/step - loss: 0.4263 - acc: 0.8874 - val_loss: 0.4530 - val_acc: 0.8744\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      "Epoch 8/15\n",
      "210642/210642 [==============================] - 355s 2ms/step - loss: 0.4250 - acc: 0.8892 - val_loss: 0.4391 - val_acc: 0.8807\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      "Epoch 9/15\n",
      "210642/210642 [==============================] - 356s 2ms/step - loss: 0.4227 - acc: 0.8908 - val_loss: 0.4404 - val_acc: 0.8823\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      "Epoch 10/15\n",
      "210642/210642 [==============================] - 355s 2ms/step - loss: 0.4217 - acc: 0.8917 - val_loss: 0.4409 - val_acc: 0.8815\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n",
      "Epoch 11/15\n",
      "210642/210642 [==============================] - 355s 2ms/step - loss: 0.4198 - acc: 0.8935 - val_loss: 0.4491 - val_acc: 0.8768\n",
      "\n",
      "Epoch 00011: val_loss did not improve\n",
      "Epoch 12/15\n",
      "210642/210642 [==============================] - 355s 2ms/step - loss: 0.4189 - acc: 0.8943 - val_loss: 0.4412 - val_acc: 0.8835\n",
      "\n",
      "Epoch 00012: val_loss did not improve\n",
      "Epoch 13/15\n",
      "210642/210642 [==============================] - 354s 2ms/step - loss: 0.4185 - acc: 0.8957 - val_loss: 0.4416 - val_acc: 0.8836\n",
      "\n",
      "Epoch 00013: val_loss did not improve\n",
      "Epoch 14/15\n",
      "210642/210642 [==============================] - 355s 2ms/step - loss: 0.4174 - acc: 0.8968 - val_loss: 0.4452 - val_acc: 0.8838\n",
      "\n",
      "Epoch 00014: val_loss did not improve\n",
      "Epoch 15/15\n",
      "210642/210642 [==============================] - 355s 2ms/step - loss: 0.4168 - acc: 0.8971 - val_loss: 0.4450 - val_acc: 0.8854\n",
      "\n",
      "Epoch 00015: val_loss did not improve\n"
     ]
    }
   ],
   "source": [
    "history_ISO = model.fit(x_train, y_train, batch_size = batch_size, verbose=1, epochs=15 #epochs\n",
    "                        ,validation_data=(x_valid,y_valid), shuffle=True, callbacks=[save_model])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 210642 samples, validate on 25206 samples\n",
      "Epoch 1/10\n",
      "210642/210642 [==============================] - 310s 1ms/step - loss: 0.4063 - acc: 0.8379 - val_loss: 0.3432 - val_acc: 0.8619\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.34322, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_lle_150.hdf5\n",
      "Epoch 2/10\n",
      "210642/210642 [==============================] - 302s 1ms/step - loss: 0.3367 - acc: 0.8682 - val_loss: 0.3911 - val_acc: 0.8379\n",
      "\n",
      "Epoch 00002: val_loss did not improve\n",
      "Epoch 3/10\n",
      "210642/210642 [==============================] - 303s 1ms/step - loss: 0.3251 - acc: 0.8749 - val_loss: 0.3226 - val_acc: 0.8754\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.34322 to 0.32265, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_lle_150.hdf5\n",
      "Epoch 4/10\n",
      "210642/210642 [==============================] - 302s 1ms/step - loss: 0.3187 - acc: 0.8790 - val_loss: 0.3203 - val_acc: 0.8776\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.32265 to 0.32026, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_lle_150.hdf5\n",
      "Epoch 5/10\n",
      "210642/210642 [==============================] - 303s 1ms/step - loss: 0.3130 - acc: 0.8830 - val_loss: 0.3342 - val_acc: 0.8713\n",
      "\n",
      "Epoch 00005: val_loss did not improve\n",
      "Epoch 6/10\n",
      "210642/210642 [==============================] - 303s 1ms/step - loss: 0.3101 - acc: 0.8854 - val_loss: 0.3453 - val_acc: 0.8644\n",
      "\n",
      "Epoch 00006: val_loss did not improve\n",
      "Epoch 7/10\n",
      "210642/210642 [==============================] - 303s 1ms/step - loss: 0.3063 - acc: 0.8877 - val_loss: 0.3200 - val_acc: 0.8814\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.32026 to 0.31996, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_lle_150.hdf5\n",
      "Epoch 8/10\n",
      "210642/210642 [==============================] - 304s 1ms/step - loss: 0.3037 - acc: 0.8899 - val_loss: 0.3233 - val_acc: 0.8793\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      "Epoch 9/10\n",
      "210642/210642 [==============================] - 303s 1ms/step - loss: 0.3022 - acc: 0.8913 - val_loss: 0.3215 - val_acc: 0.8804\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      "Epoch 10/10\n",
      "210642/210642 [==============================] - 302s 1ms/step - loss: 0.3015 - acc: 0.8917 - val_loss: 0.3268 - val_acc: 0.8767\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n"
     ]
    }
   ],
   "source": [
    "history_LLE_150 = model.fit(x_train, y_train, batch_size = batch_size, verbose=1, epochs=10 #epochs\n",
    "                        ,validation_data=(x_valid,y_valid), shuffle=True, callbacks=[save_model])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 210642 samples, validate on 25206 samples\n",
      "Epoch 1/10\n",
      "210642/210642 [==============================] - 319s 2ms/step - loss: 0.6279 - acc: 0.8103 - val_loss: 0.5169 - val_acc: 0.8533\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.51690, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso_150.hdf5\n",
      "Epoch 2/10\n",
      "210642/210642 [==============================] - 308s 1ms/step - loss: 0.4655 - acc: 0.8637 - val_loss: 0.4550 - val_acc: 0.8617\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.51690 to 0.45500, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso_150.hdf5\n",
      "Epoch 3/10\n",
      "210642/210642 [==============================] - 308s 1ms/step - loss: 0.4355 - acc: 0.8704 - val_loss: 0.4414 - val_acc: 0.8655\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.45500 to 0.44143, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso_150.hdf5\n",
      "Epoch 4/10\n",
      "210642/210642 [==============================] - 308s 1ms/step - loss: 0.4221 - acc: 0.8757 - val_loss: 0.4344 - val_acc: 0.8674\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.44143 to 0.43437, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso_150.hdf5\n",
      "Epoch 5/10\n",
      "210642/210642 [==============================] - 309s 1ms/step - loss: 0.4156 - acc: 0.8785 - val_loss: 0.4360 - val_acc: 0.8639\n",
      "\n",
      "Epoch 00005: val_loss did not improve\n",
      "Epoch 6/10\n",
      "210642/210642 [==============================] - 307s 1ms/step - loss: 0.4124 - acc: 0.8806 - val_loss: 0.4293 - val_acc: 0.8721\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.43437 to 0.42925, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso_150.hdf5\n",
      "Epoch 7/10\n",
      "210642/210642 [==============================] - 309s 1ms/step - loss: 0.4082 - acc: 0.8832 - val_loss: 0.4272 - val_acc: 0.8750\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.42925 to 0.42715, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso_150.hdf5\n",
      "Epoch 8/10\n",
      "210642/210642 [==============================] - 308s 1ms/step - loss: 0.4050 - acc: 0.8844 - val_loss: 0.4227 - val_acc: 0.8771\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.42715 to 0.42273, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_iso_150.hdf5\n",
      "Epoch 9/10\n",
      "210642/210642 [==============================] - 306s 1ms/step - loss: 0.4025 - acc: 0.8868 - val_loss: 0.4347 - val_acc: 0.8755\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      "Epoch 10/10\n",
      "210642/210642 [==============================] - 307s 1ms/step - loss: 0.4012 - acc: 0.8874 - val_loss: 0.4270 - val_acc: 0.8782\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n"
     ]
    }
   ],
   "source": [
    "history_iso_150 = model.fit(x_train, y_train, batch_size = batch_size, verbose=1, epochs=10 #epochs\n",
    "                        ,validation_data=(x_valid,y_valid), shuffle=True, callbacks=[save_model])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#plot history\n",
    "import matplotlib.pyplot as plt\n",
    "def plot_history(history):\n",
    "    # 精度の履歴をプロット\n",
    "    plt.plot(history.history['acc'],label=\"accuracy\")\n",
    "    plt.plot(history.history['val_acc'],\"o-\",label=\"val_acc\")\n",
    "    plt.title('model accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "    # 損失の履歴をプロット\n",
    "    plt.plot(history.history['loss'],label=\"loss\",)\n",
    "    plt.plot(history.history['val_loss'],\"o-\",label=\"val_loss\")\n",
    "    plt.title('model loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('loss')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UFadxwak9bA6mE1Fa0Pj28l3wvwOh35mQOh5SJ0CfMRBhbT3tkSUL065U1Xj4aM1OXl6Y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\nNCV9GmgtrJrZotMX5+7hQFU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EgKQYBiXHBv5mXbrDKZfCyplQU3Vke56tX2GMX6yaBX84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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9187be0898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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feYwx7wLvHrHtYa/Hd7bjHI/4v2ahxRnmYFxOgPotYlMgfbjVb1F4r/3ldWfG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\nKaVUmzQslFJKtUnDQimlVJv+P+yylENDI4OyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f918554d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_LLE_150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_model_path = '/home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001/params_adadelta_normal_0.3775_0.8758 .hdf5'\n",
    "model.load_weights(save_model_path)#('./params_milnet_adam_0718_2_fixed_weights.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25510/25510 [==============================] - 41s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "score=model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4227306628101062 0.8759310074433866\n"
     ]
    }
   ],
   "source": [
    "print(score[0],score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0.40012848]], dtype=float32),\n",
       " array([[0.6205897, 2.111729 ]], dtype=float32),\n",
       " array([[[0.02356183],\n",
       "         [0.02389398],\n",
       "         [0.02303003],\n",
       "         [0.02404708],\n",
       "         [0.02569389],\n",
       "         [0.02561416],\n",
       "         [0.02533291],\n",
       "         [0.03182819],\n",
       "         [0.02493797],\n",
       "         [0.02865283],\n",
       "         [0.02856121],\n",
       "         [0.0262622 ],\n",
       "         [0.02646551],\n",
       "         [0.0251376 ],\n",
       "         [0.02556333],\n",
       "         [0.02705708],\n",
       "         [0.02521041],\n",
       "         [0.02292651],\n",
       "         [0.0250116 ],\n",
       "         [0.02391998],\n",
       "         [0.0237686 ],\n",
       "         [0.02430354],\n",
       "         [0.0236428 ],\n",
       "         [0.0223533 ],\n",
       "         [0.02212404],\n",
       "         [0.02369267],\n",
       "         [0.0234232 ],\n",
       "         [0.02344516],\n",
       "         [0.02242457],\n",
       "         [0.02308172],\n",
       "         [0.02456088],\n",
       "         [0.02442872],\n",
       "         [0.02332065],\n",
       "         [0.02344203],\n",
       "         [0.02532757],\n",
       "         [0.02888473],\n",
       "         [0.02595239],\n",
       "         [0.02586877],\n",
       "         [0.02668209],\n",
       "         [0.02656429]]], dtype=float32)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_on_batch(x_test[0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/owner/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:24: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=[<tf.Tenso..., outputs=[<tf.Tenso...)`\n"
     ]
    },
    {
     "data": {
      "image/png": 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fpPOAyymOrDIz60nWQ6IAktYDtwDvB04BJiLitZY2J4YUxsbGPnb22WeXHqf1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\nUzR/VTkkeh7404jYn7Z7mZC0NyLaJ+P+jizBXQ5cAnxvps4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5faM9kOaDmJnVxQZgRzrf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ef51890f7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "def plot_weights(w):\n",
    "    l = len(w)\n",
    "    fig = plt.figure(figsize=(10,16))\n",
    "    gs = gridspec.GridSpec(int(l/2+1),2)\n",
    "    \n",
    "    for i in range(l):\n",
    "        j = int( i/2 )\n",
    "        k = i%2\n",
    "        if k==0:\n",
    "            ax = plt.subplot(gs[j,k])\n",
    "            ax.hist( w[i].flatten() ,bins=100 ,range=(-0.5,0.5) )\n",
    "            #ax.set_title(title[i])\n",
    "        else:\n",
    "            ax = plt.subplot(gs[j,k])\n",
    "            ax.hist( w[i].flatten() ,bins=100 ,range=(-0.5,0.5) )\n",
    "            #ax.set_title(title[i])\n",
    "    plt.show()\n",
    "\n",
    "save_model_path = '/home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/params_adadelta_normal 0.489.hdf5'\n",
    "model_test = Model(input=[x_in], output=[out,eij_normalized])\n",
    "model_test.load_weights(save_model_path)\n",
    "plot_weights(model_test.get_weights())\n",
    "##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 0.00613822, -0.01595823, -0.01638355, ..., -0.09339832,\n",
       "          0.23449233, -0.0163934 ],\n",
       "        [ 0.00570766, -0.01440853, -0.01028535, ...,  0.06878978,\n",
       "          0.02321846,  0.01221276],\n",
       "        [-0.00955735,  0.07102745,  0.05873981, ..., -0.04907799,\n",
       "         -0.11747277,  0.03790778],\n",
       "        ...,\n",
       "        [-0.01039937, -0.03331398, -0.03901637, ...,  0.07031082,\n",
       "          0.09033212, -0.02839166],\n",
       "        [-0.01678702,  0.02661383, -0.0132541 , ...,  0.0219114 ,\n",
       "         -0.02555005, -0.00124587],\n",
       "        [-0.00696072, -0.00591729, -0.00442338, ...,  0.14777248,\n",
       "         -0.08882821,  0.00289663]], dtype=float32),\n",
       " array([-0.08097516,  0.00817164, -0.05212835,  0.00857434,  0.01582111,\n",
       "         0.00218245, -0.12096858,  0.06406359,  0.17368901, -0.06494537],\n",
       "       dtype=float32)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_layer('dense_43').get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Input (InputLayer)              (None, 40, 15)       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence1 (Lambda (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence2 (Lambda (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence3 (Lambda (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence4 (Lambda (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence5 (Lambda (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence6 (Lambda (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence7 (Lambda (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence8 (Lambda (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence9 (Lambda (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence10 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence11 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence12 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence13 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence14 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence15 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence16 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence17 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence18 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence19 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence20 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence21 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence22 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence23 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence24 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence25 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence26 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence27 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence28 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence29 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence30 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence31 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence32 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence33 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence34 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence35 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence36 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence37 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence38 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence39 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convert_shape_sentence40 (Lambd (None, 15)           0           Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Embedding (Embedding)           (None, 15, 300)      18468000    convert_shape_sentence1[0][0]    \n",
      "                                                                 convert_shape_sentence1[0][0]    \n",
      "                                                                 convert_shape_sentence1[0][0]    \n",
      "                                                                 convert_shape_sentence2[0][0]    \n",
      "                                                                 convert_shape_sentence2[0][0]    \n",
      "                                                                 convert_shape_sentence2[0][0]    \n",
      "                                                                 convert_shape_sentence3[0][0]    \n",
      "                                                                 convert_shape_sentence3[0][0]    \n",
      "                                                                 convert_shape_sentence3[0][0]    \n",
      "                                                                 convert_shape_sentence4[0][0]    \n",
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      "                                                                 convert_shape_sentence5[0][0]    \n",
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      "                                                                 convert_shape_sentence6[0][0]    \n",
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      "                                                                 convert_shape_sentence7[0][0]    \n",
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      "                                                                 convert_shape_sentence8[0][0]    \n",
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      "                                                                 convert_shape_sentence9[0][0]    \n",
      "                                                                 convert_shape_sentence10[0][0]   \n",
      "                                                                 convert_shape_sentence10[0][0]   \n",
      "                                                                 convert_shape_sentence10[0][0]   \n",
      "                                                                 convert_shape_sentence11[0][0]   \n",
      "                                                                 convert_shape_sentence11[0][0]   \n",
      "                                                                 convert_shape_sentence11[0][0]   \n",
      "                                                                 convert_shape_sentence12[0][0]   \n",
      "                                                                 convert_shape_sentence12[0][0]   \n",
      "                                                                 convert_shape_sentence12[0][0]   \n",
      "                                                                 convert_shape_sentence13[0][0]   \n",
      "                                                                 convert_shape_sentence13[0][0]   \n",
      "                                                                 convert_shape_sentence13[0][0]   \n",
      "                                                                 convert_shape_sentence14[0][0]   \n",
      "                                                                 convert_shape_sentence14[0][0]   \n",
      "                                                                 convert_shape_sentence14[0][0]   \n",
      "                                                                 convert_shape_sentence15[0][0]   \n",
      "                                                                 convert_shape_sentence15[0][0]   \n",
      "                                                                 convert_shape_sentence15[0][0]   \n",
      "                                                                 convert_shape_sentence16[0][0]   \n",
      "                                                                 convert_shape_sentence16[0][0]   \n",
      "                                                                 convert_shape_sentence16[0][0]   \n",
      "                                                                 convert_shape_sentence17[0][0]   \n",
      "                                                                 convert_shape_sentence17[0][0]   \n",
      "                                                                 convert_shape_sentence17[0][0]   \n",
      "                                                                 convert_shape_sentence18[0][0]   \n",
      "                                                                 convert_shape_sentence18[0][0]   \n",
      "                                                                 convert_shape_sentence18[0][0]   \n",
      "                                                                 convert_shape_sentence19[0][0]   \n",
      "                                                                 convert_shape_sentence19[0][0]   \n",
      "                                                                 convert_shape_sentence19[0][0]   \n",
      "                                                                 convert_shape_sentence20[0][0]   \n",
      "                                                                 convert_shape_sentence20[0][0]   \n",
      "                                                                 convert_shape_sentence20[0][0]   \n",
      "                                                                 convert_shape_sentence21[0][0]   \n",
      "                                                                 convert_shape_sentence21[0][0]   \n",
      "                                                                 convert_shape_sentence21[0][0]   \n",
      "                                                                 convert_shape_sentence22[0][0]   \n",
      "                                                                 convert_shape_sentence22[0][0]   \n",
      "                                                                 convert_shape_sentence22[0][0]   \n",
      "                                                                 convert_shape_sentence23[0][0]   \n",
      "                                                                 convert_shape_sentence23[0][0]   \n",
      "                                                                 convert_shape_sentence23[0][0]   \n",
      "                                                                 convert_shape_sentence24[0][0]   \n",
      "                                                                 convert_shape_sentence24[0][0]   \n",
      "                                                                 convert_shape_sentence24[0][0]   \n",
      "                                                                 convert_shape_sentence25[0][0]   \n",
      "                                                                 convert_shape_sentence25[0][0]   \n",
      "                                                                 convert_shape_sentence25[0][0]   \n",
      "                                                                 convert_shape_sentence26[0][0]   \n",
      "                                                                 convert_shape_sentence26[0][0]   \n",
      "                                                                 convert_shape_sentence26[0][0]   \n",
      "                                                                 convert_shape_sentence27[0][0]   \n",
      "                                                                 convert_shape_sentence27[0][0]   \n",
      "                                                                 convert_shape_sentence27[0][0]   \n",
      "                                                                 convert_shape_sentence28[0][0]   \n",
      "                                                                 convert_shape_sentence28[0][0]   \n",
      "                                                                 convert_shape_sentence28[0][0]   \n",
      "                                                                 convert_shape_sentence29[0][0]   \n",
      "                                                                 convert_shape_sentence29[0][0]   \n",
      "                                                                 convert_shape_sentence29[0][0]   \n",
      "                                                                 convert_shape_sentence30[0][0]   \n",
      "                                                                 convert_shape_sentence30[0][0]   \n",
      "                                                                 convert_shape_sentence30[0][0]   \n",
      "                                                                 convert_shape_sentence31[0][0]   \n",
      "                                                                 convert_shape_sentence31[0][0]   \n",
      "                                                                 convert_shape_sentence31[0][0]   \n",
      "                                                                 convert_shape_sentence32[0][0]   \n",
      "                                                                 convert_shape_sentence32[0][0]   \n",
      "                                                                 convert_shape_sentence32[0][0]   \n",
      "                                                                 convert_shape_sentence33[0][0]   \n",
      "                                                                 convert_shape_sentence33[0][0]   \n",
      "                                                                 convert_shape_sentence33[0][0]   \n",
      "                                                                 convert_shape_sentence34[0][0]   \n",
      "                                                                 convert_shape_sentence34[0][0]   \n",
      "                                                                 convert_shape_sentence34[0][0]   \n",
      "                                                                 convert_shape_sentence35[0][0]   \n",
      "                                                                 convert_shape_sentence35[0][0]   \n",
      "                                                                 convert_shape_sentence35[0][0]   \n",
      "                                                                 convert_shape_sentence36[0][0]   \n",
      "                                                                 convert_shape_sentence36[0][0]   \n",
      "                                                                 convert_shape_sentence36[0][0]   \n",
      "                                                                 convert_shape_sentence37[0][0]   \n",
      "                                                                 convert_shape_sentence37[0][0]   \n",
      "                                                                 convert_shape_sentence37[0][0]   \n",
      "                                                                 convert_shape_sentence38[0][0]   \n",
      "                                                                 convert_shape_sentence38[0][0]   \n",
      "                                                                 convert_shape_sentence38[0][0]   \n",
      "                                                                 convert_shape_sentence39[0][0]   \n",
      "                                                                 convert_shape_sentence39[0][0]   \n",
      "                                                                 convert_shape_sentence39[0][0]   \n",
      "                                                                 convert_shape_sentence40[0][0]   \n",
      "                                                                 convert_shape_sentence40[0][0]   \n",
      "                                                                 convert_shape_sentence40[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 13, 100)      90100       Embedding[0][0]                  \n",
      "                                                                 Embedding[3][0]                  \n",
      "                                                                 Embedding[6][0]                  \n",
      "                                                                 Embedding[9][0]                  \n",
      "                                                                 Embedding[12][0]                 \n",
      "                                                                 Embedding[15][0]                 \n",
      "                                                                 Embedding[18][0]                 \n",
      "                                                                 Embedding[21][0]                 \n",
      "                                                                 Embedding[24][0]                 \n",
      "                                                                 Embedding[27][0]                 \n",
      "                                                                 Embedding[30][0]                 \n",
      "                                                                 Embedding[33][0]                 \n",
      "                                                                 Embedding[36][0]                 \n",
      "                                                                 Embedding[39][0]                 \n",
      "                                                                 Embedding[42][0]                 \n",
      "                                                                 Embedding[45][0]                 \n",
      "                                                                 Embedding[48][0]                 \n",
      "                                                                 Embedding[51][0]                 \n",
      "                                                                 Embedding[54][0]                 \n",
      "                                                                 Embedding[57][0]                 \n",
      "                                                                 Embedding[60][0]                 \n",
      "                                                                 Embedding[63][0]                 \n",
      "                                                                 Embedding[66][0]                 \n",
      "                                                                 Embedding[69][0]                 \n",
      "                                                                 Embedding[72][0]                 \n",
      "                                                                 Embedding[75][0]                 \n",
      "                                                                 Embedding[78][0]                 \n",
      "                                                                 Embedding[81][0]                 \n",
      "                                                                 Embedding[84][0]                 \n",
      "                                                                 Embedding[87][0]                 \n",
      "                                                                 Embedding[90][0]                 \n",
      "                                                                 Embedding[93][0]                 \n",
      "                                                                 Embedding[96][0]                 \n",
      "                                                                 Embedding[99][0]                 \n",
      "                                                                 Embedding[102][0]                \n",
      "                                                                 Embedding[105][0]                \n",
      "                                                                 Embedding[108][0]                \n",
      "                                                                 Embedding[111][0]                \n",
      "                                                                 Embedding[114][0]                \n",
      "                                                                 Embedding[117][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 12, 100)      120100      Embedding[1][0]                  \n",
      "                                                                 Embedding[4][0]                  \n",
      "                                                                 Embedding[7][0]                  \n",
      "                                                                 Embedding[10][0]                 \n",
      "                                                                 Embedding[13][0]                 \n",
      "                                                                 Embedding[16][0]                 \n",
      "                                                                 Embedding[19][0]                 \n",
      "                                                                 Embedding[22][0]                 \n",
      "                                                                 Embedding[25][0]                 \n",
      "                                                                 Embedding[28][0]                 \n",
      "                                                                 Embedding[31][0]                 \n",
      "                                                                 Embedding[34][0]                 \n",
      "                                                                 Embedding[37][0]                 \n",
      "                                                                 Embedding[40][0]                 \n",
      "                                                                 Embedding[43][0]                 \n",
      "                                                                 Embedding[46][0]                 \n",
      "                                                                 Embedding[49][0]                 \n",
      "                                                                 Embedding[52][0]                 \n",
      "                                                                 Embedding[55][0]                 \n",
      "                                                                 Embedding[58][0]                 \n",
      "                                                                 Embedding[61][0]                 \n",
      "                                                                 Embedding[64][0]                 \n",
      "                                                                 Embedding[67][0]                 \n",
      "                                                                 Embedding[70][0]                 \n",
      "                                                                 Embedding[73][0]                 \n",
      "                                                                 Embedding[76][0]                 \n",
      "                                                                 Embedding[79][0]                 \n",
      "                                                                 Embedding[82][0]                 \n",
      "                                                                 Embedding[85][0]                 \n",
      "                                                                 Embedding[88][0]                 \n",
      "                                                                 Embedding[91][0]                 \n",
      "                                                                 Embedding[94][0]                 \n",
      "                                                                 Embedding[97][0]                 \n",
      "                                                                 Embedding[100][0]                \n",
      "                                                                 Embedding[103][0]                \n",
      "                                                                 Embedding[106][0]                \n",
      "                                                                 Embedding[109][0]                \n",
      "                                                                 Embedding[112][0]                \n",
      "                                                                 Embedding[115][0]                \n",
      "                                                                 Embedding[118][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 11, 100)      150100      Embedding[2][0]                  \n",
      "                                                                 Embedding[5][0]                  \n",
      "                                                                 Embedding[8][0]                  \n",
      "                                                                 Embedding[11][0]                 \n",
      "                                                                 Embedding[14][0]                 \n",
      "                                                                 Embedding[17][0]                 \n",
      "                                                                 Embedding[20][0]                 \n",
      "                                                                 Embedding[23][0]                 \n",
      "                                                                 Embedding[26][0]                 \n",
      "                                                                 Embedding[29][0]                 \n",
      "                                                                 Embedding[32][0]                 \n",
      "                                                                 Embedding[35][0]                 \n",
      "                                                                 Embedding[38][0]                 \n",
      "                                                                 Embedding[41][0]                 \n",
      "                                                                 Embedding[44][0]                 \n",
      "                                                                 Embedding[47][0]                 \n",
      "                                                                 Embedding[50][0]                 \n",
      "                                                                 Embedding[53][0]                 \n",
      "                                                                 Embedding[56][0]                 \n",
      "                                                                 Embedding[59][0]                 \n",
      "                                                                 Embedding[62][0]                 \n",
      "                                                                 Embedding[65][0]                 \n",
      "                                                                 Embedding[68][0]                 \n",
      "                                                                 Embedding[71][0]                 \n",
      "                                                                 Embedding[74][0]                 \n",
      "                                                                 Embedding[77][0]                 \n",
      "                                                                 Embedding[80][0]                 \n",
      "                                                                 Embedding[83][0]                 \n",
      "                                                                 Embedding[86][0]                 \n",
      "                                                                 Embedding[89][0]                 \n",
      "                                                                 Embedding[92][0]                 \n",
      "                                                                 Embedding[95][0]                 \n",
      "                                                                 Embedding[98][0]                 \n",
      "                                                                 Embedding[101][0]                \n",
      "                                                                 Embedding[104][0]                \n",
      "                                                                 Embedding[107][0]                \n",
      "                                                                 Embedding[110][0]                \n",
      "                                                                 Embedding[113][0]                \n",
      "                                                                 Embedding[116][0]                \n",
      "                                                                 Embedding[119][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_127 (MaxPooling1D (None, 1, 100)       0           conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_128 (MaxPooling1D (None, 1, 100)       0           conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_129 (MaxPooling1D (None, 1, 100)       0           conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_130 (MaxPooling1D (None, 1, 100)       0           conv1d_4[1][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_131 (MaxPooling1D (None, 1, 100)       0           conv1d_5[1][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_132 (MaxPooling1D (None, 1, 100)       0           conv1d_6[1][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_133 (MaxPooling1D (None, 1, 100)       0           conv1d_4[2][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_134 (MaxPooling1D (None, 1, 100)       0           conv1d_5[2][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_135 (MaxPooling1D (None, 1, 100)       0           conv1d_6[2][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_136 (MaxPooling1D (None, 1, 100)       0           conv1d_4[3][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_137 (MaxPooling1D (None, 1, 100)       0           conv1d_5[3][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_138 (MaxPooling1D (None, 1, 100)       0           conv1d_6[3][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_139 (MaxPooling1D (None, 1, 100)       0           conv1d_4[4][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_140 (MaxPooling1D (None, 1, 100)       0           conv1d_5[4][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_141 (MaxPooling1D (None, 1, 100)       0           conv1d_6[4][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_142 (MaxPooling1D (None, 1, 100)       0           conv1d_4[5][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_143 (MaxPooling1D (None, 1, 100)       0           conv1d_5[5][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_144 (MaxPooling1D (None, 1, 100)       0           conv1d_6[5][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_145 (MaxPooling1D (None, 1, 100)       0           conv1d_4[6][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_146 (MaxPooling1D (None, 1, 100)       0           conv1d_5[6][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_147 (MaxPooling1D (None, 1, 100)       0           conv1d_6[6][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_148 (MaxPooling1D (None, 1, 100)       0           conv1d_4[7][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_149 (MaxPooling1D (None, 1, 100)       0           conv1d_5[7][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_150 (MaxPooling1D (None, 1, 100)       0           conv1d_6[7][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_151 (MaxPooling1D (None, 1, 100)       0           conv1d_4[8][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_152 (MaxPooling1D (None, 1, 100)       0           conv1d_5[8][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_153 (MaxPooling1D (None, 1, 100)       0           conv1d_6[8][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_154 (MaxPooling1D (None, 1, 100)       0           conv1d_4[9][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_155 (MaxPooling1D (None, 1, 100)       0           conv1d_5[9][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_156 (MaxPooling1D (None, 1, 100)       0           conv1d_6[9][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_157 (MaxPooling1D (None, 1, 100)       0           conv1d_4[10][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_158 (MaxPooling1D (None, 1, 100)       0           conv1d_5[10][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_159 (MaxPooling1D (None, 1, 100)       0           conv1d_6[10][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_160 (MaxPooling1D (None, 1, 100)       0           conv1d_4[11][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_161 (MaxPooling1D (None, 1, 100)       0           conv1d_5[11][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_162 (MaxPooling1D (None, 1, 100)       0           conv1d_6[11][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_163 (MaxPooling1D (None, 1, 100)       0           conv1d_4[12][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_164 (MaxPooling1D (None, 1, 100)       0           conv1d_5[12][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_165 (MaxPooling1D (None, 1, 100)       0           conv1d_6[12][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_166 (MaxPooling1D (None, 1, 100)       0           conv1d_4[13][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_167 (MaxPooling1D (None, 1, 100)       0           conv1d_5[13][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_168 (MaxPooling1D (None, 1, 100)       0           conv1d_6[13][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_169 (MaxPooling1D (None, 1, 100)       0           conv1d_4[14][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_170 (MaxPooling1D (None, 1, 100)       0           conv1d_5[14][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_171 (MaxPooling1D (None, 1, 100)       0           conv1d_6[14][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_172 (MaxPooling1D (None, 1, 100)       0           conv1d_4[15][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_173 (MaxPooling1D (None, 1, 100)       0           conv1d_5[15][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_174 (MaxPooling1D (None, 1, 100)       0           conv1d_6[15][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_175 (MaxPooling1D (None, 1, 100)       0           conv1d_4[16][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_176 (MaxPooling1D (None, 1, 100)       0           conv1d_5[16][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_177 (MaxPooling1D (None, 1, 100)       0           conv1d_6[16][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_178 (MaxPooling1D (None, 1, 100)       0           conv1d_4[17][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_179 (MaxPooling1D (None, 1, 100)       0           conv1d_5[17][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_180 (MaxPooling1D (None, 1, 100)       0           conv1d_6[17][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_181 (MaxPooling1D (None, 1, 100)       0           conv1d_4[18][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_182 (MaxPooling1D (None, 1, 100)       0           conv1d_5[18][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_183 (MaxPooling1D (None, 1, 100)       0           conv1d_6[18][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_184 (MaxPooling1D (None, 1, 100)       0           conv1d_4[19][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_185 (MaxPooling1D (None, 1, 100)       0           conv1d_5[19][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_186 (MaxPooling1D (None, 1, 100)       0           conv1d_6[19][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_187 (MaxPooling1D (None, 1, 100)       0           conv1d_4[20][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_188 (MaxPooling1D (None, 1, 100)       0           conv1d_5[20][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_189 (MaxPooling1D (None, 1, 100)       0           conv1d_6[20][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_190 (MaxPooling1D (None, 1, 100)       0           conv1d_4[21][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_191 (MaxPooling1D (None, 1, 100)       0           conv1d_5[21][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_192 (MaxPooling1D (None, 1, 100)       0           conv1d_6[21][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_193 (MaxPooling1D (None, 1, 100)       0           conv1d_4[22][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_194 (MaxPooling1D (None, 1, 100)       0           conv1d_5[22][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_195 (MaxPooling1D (None, 1, 100)       0           conv1d_6[22][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_196 (MaxPooling1D (None, 1, 100)       0           conv1d_4[23][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_197 (MaxPooling1D (None, 1, 100)       0           conv1d_5[23][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_198 (MaxPooling1D (None, 1, 100)       0           conv1d_6[23][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_199 (MaxPooling1D (None, 1, 100)       0           conv1d_4[24][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_200 (MaxPooling1D (None, 1, 100)       0           conv1d_5[24][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_201 (MaxPooling1D (None, 1, 100)       0           conv1d_6[24][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_202 (MaxPooling1D (None, 1, 100)       0           conv1d_4[25][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_203 (MaxPooling1D (None, 1, 100)       0           conv1d_5[25][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_204 (MaxPooling1D (None, 1, 100)       0           conv1d_6[25][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_205 (MaxPooling1D (None, 1, 100)       0           conv1d_4[26][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_206 (MaxPooling1D (None, 1, 100)       0           conv1d_5[26][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_207 (MaxPooling1D (None, 1, 100)       0           conv1d_6[26][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_208 (MaxPooling1D (None, 1, 100)       0           conv1d_4[27][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_209 (MaxPooling1D (None, 1, 100)       0           conv1d_5[27][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_210 (MaxPooling1D (None, 1, 100)       0           conv1d_6[27][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_211 (MaxPooling1D (None, 1, 100)       0           conv1d_4[28][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_212 (MaxPooling1D (None, 1, 100)       0           conv1d_5[28][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_213 (MaxPooling1D (None, 1, 100)       0           conv1d_6[28][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_214 (MaxPooling1D (None, 1, 100)       0           conv1d_4[29][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_215 (MaxPooling1D (None, 1, 100)       0           conv1d_5[29][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_216 (MaxPooling1D (None, 1, 100)       0           conv1d_6[29][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_217 (MaxPooling1D (None, 1, 100)       0           conv1d_4[30][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_218 (MaxPooling1D (None, 1, 100)       0           conv1d_5[30][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_219 (MaxPooling1D (None, 1, 100)       0           conv1d_6[30][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_220 (MaxPooling1D (None, 1, 100)       0           conv1d_4[31][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_221 (MaxPooling1D (None, 1, 100)       0           conv1d_5[31][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_222 (MaxPooling1D (None, 1, 100)       0           conv1d_6[31][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_223 (MaxPooling1D (None, 1, 100)       0           conv1d_4[32][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_224 (MaxPooling1D (None, 1, 100)       0           conv1d_5[32][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_225 (MaxPooling1D (None, 1, 100)       0           conv1d_6[32][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_226 (MaxPooling1D (None, 1, 100)       0           conv1d_4[33][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_227 (MaxPooling1D (None, 1, 100)       0           conv1d_5[33][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_228 (MaxPooling1D (None, 1, 100)       0           conv1d_6[33][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_229 (MaxPooling1D (None, 1, 100)       0           conv1d_4[34][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_230 (MaxPooling1D (None, 1, 100)       0           conv1d_5[34][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_231 (MaxPooling1D (None, 1, 100)       0           conv1d_6[34][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_232 (MaxPooling1D (None, 1, 100)       0           conv1d_4[35][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_233 (MaxPooling1D (None, 1, 100)       0           conv1d_5[35][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_234 (MaxPooling1D (None, 1, 100)       0           conv1d_6[35][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_235 (MaxPooling1D (None, 1, 100)       0           conv1d_4[36][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_236 (MaxPooling1D (None, 1, 100)       0           conv1d_5[36][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_237 (MaxPooling1D (None, 1, 100)       0           conv1d_6[36][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_238 (MaxPooling1D (None, 1, 100)       0           conv1d_4[37][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_239 (MaxPooling1D (None, 1, 100)       0           conv1d_5[37][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_240 (MaxPooling1D (None, 1, 100)       0           conv1d_6[37][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_241 (MaxPooling1D (None, 1, 100)       0           conv1d_4[38][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_242 (MaxPooling1D (None, 1, 100)       0           conv1d_5[38][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_243 (MaxPooling1D (None, 1, 100)       0           conv1d_6[38][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_244 (MaxPooling1D (None, 1, 100)       0           conv1d_4[39][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_245 (MaxPooling1D (None, 1, 100)       0           conv1d_5[39][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_246 (MaxPooling1D (None, 1, 100)       0           conv1d_6[39][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_121 (Lambda)             (None, 100)          0           max_pooling1d_127[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_122 (Lambda)             (None, 100)          0           max_pooling1d_128[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_123 (Lambda)             (None, 100)          0           max_pooling1d_129[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_124 (Lambda)             (None, 100)          0           max_pooling1d_130[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_125 (Lambda)             (None, 100)          0           max_pooling1d_131[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_126 (Lambda)             (None, 100)          0           max_pooling1d_132[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_127 (Lambda)             (None, 100)          0           max_pooling1d_133[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_128 (Lambda)             (None, 100)          0           max_pooling1d_134[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_129 (Lambda)             (None, 100)          0           max_pooling1d_135[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_130 (Lambda)             (None, 100)          0           max_pooling1d_136[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_131 (Lambda)             (None, 100)          0           max_pooling1d_137[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_132 (Lambda)             (None, 100)          0           max_pooling1d_138[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_133 (Lambda)             (None, 100)          0           max_pooling1d_139[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_134 (Lambda)             (None, 100)          0           max_pooling1d_140[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_135 (Lambda)             (None, 100)          0           max_pooling1d_141[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_136 (Lambda)             (None, 100)          0           max_pooling1d_142[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_137 (Lambda)             (None, 100)          0           max_pooling1d_143[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_138 (Lambda)             (None, 100)          0           max_pooling1d_144[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_139 (Lambda)             (None, 100)          0           max_pooling1d_145[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_140 (Lambda)             (None, 100)          0           max_pooling1d_146[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_141 (Lambda)             (None, 100)          0           max_pooling1d_147[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_142 (Lambda)             (None, 100)          0           max_pooling1d_148[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_143 (Lambda)             (None, 100)          0           max_pooling1d_149[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_144 (Lambda)             (None, 100)          0           max_pooling1d_150[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_145 (Lambda)             (None, 100)          0           max_pooling1d_151[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_146 (Lambda)             (None, 100)          0           max_pooling1d_152[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_147 (Lambda)             (None, 100)          0           max_pooling1d_153[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_148 (Lambda)             (None, 100)          0           max_pooling1d_154[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_149 (Lambda)             (None, 100)          0           max_pooling1d_155[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_150 (Lambda)             (None, 100)          0           max_pooling1d_156[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_151 (Lambda)             (None, 100)          0           max_pooling1d_157[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_152 (Lambda)             (None, 100)          0           max_pooling1d_158[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_153 (Lambda)             (None, 100)          0           max_pooling1d_159[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_154 (Lambda)             (None, 100)          0           max_pooling1d_160[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_155 (Lambda)             (None, 100)          0           max_pooling1d_161[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_156 (Lambda)             (None, 100)          0           max_pooling1d_162[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_157 (Lambda)             (None, 100)          0           max_pooling1d_163[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_158 (Lambda)             (None, 100)          0           max_pooling1d_164[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_159 (Lambda)             (None, 100)          0           max_pooling1d_165[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_160 (Lambda)             (None, 100)          0           max_pooling1d_166[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_161 (Lambda)             (None, 100)          0           max_pooling1d_167[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_162 (Lambda)             (None, 100)          0           max_pooling1d_168[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_163 (Lambda)             (None, 100)          0           max_pooling1d_169[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_164 (Lambda)             (None, 100)          0           max_pooling1d_170[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_165 (Lambda)             (None, 100)          0           max_pooling1d_171[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_166 (Lambda)             (None, 100)          0           max_pooling1d_172[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_167 (Lambda)             (None, 100)          0           max_pooling1d_173[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_168 (Lambda)             (None, 100)          0           max_pooling1d_174[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_169 (Lambda)             (None, 100)          0           max_pooling1d_175[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_170 (Lambda)             (None, 100)          0           max_pooling1d_176[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_171 (Lambda)             (None, 100)          0           max_pooling1d_177[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_172 (Lambda)             (None, 100)          0           max_pooling1d_178[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_173 (Lambda)             (None, 100)          0           max_pooling1d_179[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_174 (Lambda)             (None, 100)          0           max_pooling1d_180[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_175 (Lambda)             (None, 100)          0           max_pooling1d_181[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_176 (Lambda)             (None, 100)          0           max_pooling1d_182[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_177 (Lambda)             (None, 100)          0           max_pooling1d_183[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_178 (Lambda)             (None, 100)          0           max_pooling1d_184[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_179 (Lambda)             (None, 100)          0           max_pooling1d_185[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_180 (Lambda)             (None, 100)          0           max_pooling1d_186[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_181 (Lambda)             (None, 100)          0           max_pooling1d_187[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_182 (Lambda)             (None, 100)          0           max_pooling1d_188[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_183 (Lambda)             (None, 100)          0           max_pooling1d_189[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_184 (Lambda)             (None, 100)          0           max_pooling1d_190[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_185 (Lambda)             (None, 100)          0           max_pooling1d_191[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_186 (Lambda)             (None, 100)          0           max_pooling1d_192[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_187 (Lambda)             (None, 100)          0           max_pooling1d_193[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_188 (Lambda)             (None, 100)          0           max_pooling1d_194[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_189 (Lambda)             (None, 100)          0           max_pooling1d_195[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_190 (Lambda)             (None, 100)          0           max_pooling1d_196[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_191 (Lambda)             (None, 100)          0           max_pooling1d_197[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_192 (Lambda)             (None, 100)          0           max_pooling1d_198[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_193 (Lambda)             (None, 100)          0           max_pooling1d_199[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_194 (Lambda)             (None, 100)          0           max_pooling1d_200[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_195 (Lambda)             (None, 100)          0           max_pooling1d_201[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_196 (Lambda)             (None, 100)          0           max_pooling1d_202[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_197 (Lambda)             (None, 100)          0           max_pooling1d_203[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_198 (Lambda)             (None, 100)          0           max_pooling1d_204[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_199 (Lambda)             (None, 100)          0           max_pooling1d_205[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_200 (Lambda)             (None, 100)          0           max_pooling1d_206[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_201 (Lambda)             (None, 100)          0           max_pooling1d_207[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_202 (Lambda)             (None, 100)          0           max_pooling1d_208[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_203 (Lambda)             (None, 100)          0           max_pooling1d_209[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_204 (Lambda)             (None, 100)          0           max_pooling1d_210[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_205 (Lambda)             (None, 100)          0           max_pooling1d_211[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_206 (Lambda)             (None, 100)          0           max_pooling1d_212[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_207 (Lambda)             (None, 100)          0           max_pooling1d_213[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_208 (Lambda)             (None, 100)          0           max_pooling1d_214[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_209 (Lambda)             (None, 100)          0           max_pooling1d_215[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_210 (Lambda)             (None, 100)          0           max_pooling1d_216[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_211 (Lambda)             (None, 100)          0           max_pooling1d_217[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_212 (Lambda)             (None, 100)          0           max_pooling1d_218[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_213 (Lambda)             (None, 100)          0           max_pooling1d_219[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_214 (Lambda)             (None, 100)          0           max_pooling1d_220[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_215 (Lambda)             (None, 100)          0           max_pooling1d_221[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_216 (Lambda)             (None, 100)          0           max_pooling1d_222[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_217 (Lambda)             (None, 100)          0           max_pooling1d_223[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_218 (Lambda)             (None, 100)          0           max_pooling1d_224[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_219 (Lambda)             (None, 100)          0           max_pooling1d_225[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_220 (Lambda)             (None, 100)          0           max_pooling1d_226[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_221 (Lambda)             (None, 100)          0           max_pooling1d_227[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_222 (Lambda)             (None, 100)          0           max_pooling1d_228[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_223 (Lambda)             (None, 100)          0           max_pooling1d_229[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_224 (Lambda)             (None, 100)          0           max_pooling1d_230[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_225 (Lambda)             (None, 100)          0           max_pooling1d_231[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_226 (Lambda)             (None, 100)          0           max_pooling1d_232[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_227 (Lambda)             (None, 100)          0           max_pooling1d_233[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_228 (Lambda)             (None, 100)          0           max_pooling1d_234[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_229 (Lambda)             (None, 100)          0           max_pooling1d_235[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_230 (Lambda)             (None, 100)          0           max_pooling1d_236[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_231 (Lambda)             (None, 100)          0           max_pooling1d_237[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_232 (Lambda)             (None, 100)          0           max_pooling1d_238[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_233 (Lambda)             (None, 100)          0           max_pooling1d_239[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_234 (Lambda)             (None, 100)          0           max_pooling1d_240[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_235 (Lambda)             (None, 100)          0           max_pooling1d_241[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_236 (Lambda)             (None, 100)          0           max_pooling1d_242[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_237 (Lambda)             (None, 100)          0           max_pooling1d_243[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_238 (Lambda)             (None, 100)          0           max_pooling1d_244[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_239 (Lambda)             (None, 100)          0           max_pooling1d_245[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "lambda_240 (Lambda)             (None, 100)          0           max_pooling1d_246[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_42 (Concatenate)    (None, 300)          0           lambda_121[0][0]                 \n",
      "                                                                 lambda_122[0][0]                 \n",
      "                                                                 lambda_123[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_43 (Concatenate)    (None, 300)          0           lambda_124[0][0]                 \n",
      "                                                                 lambda_125[0][0]                 \n",
      "                                                                 lambda_126[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_44 (Concatenate)    (None, 300)          0           lambda_127[0][0]                 \n",
      "                                                                 lambda_128[0][0]                 \n",
      "                                                                 lambda_129[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_45 (Concatenate)    (None, 300)          0           lambda_130[0][0]                 \n",
      "                                                                 lambda_131[0][0]                 \n",
      "                                                                 lambda_132[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_46 (Concatenate)    (None, 300)          0           lambda_133[0][0]                 \n",
      "                                                                 lambda_134[0][0]                 \n",
      "                                                                 lambda_135[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_47 (Concatenate)    (None, 300)          0           lambda_136[0][0]                 \n",
      "                                                                 lambda_137[0][0]                 \n",
      "                                                                 lambda_138[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_48 (Concatenate)    (None, 300)          0           lambda_139[0][0]                 \n",
      "                                                                 lambda_140[0][0]                 \n",
      "                                                                 lambda_141[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_49 (Concatenate)    (None, 300)          0           lambda_142[0][0]                 \n",
      "                                                                 lambda_143[0][0]                 \n",
      "                                                                 lambda_144[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_50 (Concatenate)    (None, 300)          0           lambda_145[0][0]                 \n",
      "                                                                 lambda_146[0][0]                 \n",
      "                                                                 lambda_147[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_51 (Concatenate)    (None, 300)          0           lambda_148[0][0]                 \n",
      "                                                                 lambda_149[0][0]                 \n",
      "                                                                 lambda_150[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_52 (Concatenate)    (None, 300)          0           lambda_151[0][0]                 \n",
      "                                                                 lambda_152[0][0]                 \n",
      "                                                                 lambda_153[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_53 (Concatenate)    (None, 300)          0           lambda_154[0][0]                 \n",
      "                                                                 lambda_155[0][0]                 \n",
      "                                                                 lambda_156[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_54 (Concatenate)    (None, 300)          0           lambda_157[0][0]                 \n",
      "                                                                 lambda_158[0][0]                 \n",
      "                                                                 lambda_159[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_55 (Concatenate)    (None, 300)          0           lambda_160[0][0]                 \n",
      "                                                                 lambda_161[0][0]                 \n",
      "                                                                 lambda_162[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_56 (Concatenate)    (None, 300)          0           lambda_163[0][0]                 \n",
      "                                                                 lambda_164[0][0]                 \n",
      "                                                                 lambda_165[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_57 (Concatenate)    (None, 300)          0           lambda_166[0][0]                 \n",
      "                                                                 lambda_167[0][0]                 \n",
      "                                                                 lambda_168[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_58 (Concatenate)    (None, 300)          0           lambda_169[0][0]                 \n",
      "                                                                 lambda_170[0][0]                 \n",
      "                                                                 lambda_171[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_59 (Concatenate)    (None, 300)          0           lambda_172[0][0]                 \n",
      "                                                                 lambda_173[0][0]                 \n",
      "                                                                 lambda_174[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_60 (Concatenate)    (None, 300)          0           lambda_175[0][0]                 \n",
      "                                                                 lambda_176[0][0]                 \n",
      "                                                                 lambda_177[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_61 (Concatenate)    (None, 300)          0           lambda_178[0][0]                 \n",
      "                                                                 lambda_179[0][0]                 \n",
      "                                                                 lambda_180[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_62 (Concatenate)    (None, 300)          0           lambda_181[0][0]                 \n",
      "                                                                 lambda_182[0][0]                 \n",
      "                                                                 lambda_183[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_63 (Concatenate)    (None, 300)          0           lambda_184[0][0]                 \n",
      "                                                                 lambda_185[0][0]                 \n",
      "                                                                 lambda_186[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_64 (Concatenate)    (None, 300)          0           lambda_187[0][0]                 \n",
      "                                                                 lambda_188[0][0]                 \n",
      "                                                                 lambda_189[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_65 (Concatenate)    (None, 300)          0           lambda_190[0][0]                 \n",
      "                                                                 lambda_191[0][0]                 \n",
      "                                                                 lambda_192[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_66 (Concatenate)    (None, 300)          0           lambda_193[0][0]                 \n",
      "                                                                 lambda_194[0][0]                 \n",
      "                                                                 lambda_195[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_67 (Concatenate)    (None, 300)          0           lambda_196[0][0]                 \n",
      "                                                                 lambda_197[0][0]                 \n",
      "                                                                 lambda_198[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_68 (Concatenate)    (None, 300)          0           lambda_199[0][0]                 \n",
      "                                                                 lambda_200[0][0]                 \n",
      "                                                                 lambda_201[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_69 (Concatenate)    (None, 300)          0           lambda_202[0][0]                 \n",
      "                                                                 lambda_203[0][0]                 \n",
      "                                                                 lambda_204[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_70 (Concatenate)    (None, 300)          0           lambda_205[0][0]                 \n",
      "                                                                 lambda_206[0][0]                 \n",
      "                                                                 lambda_207[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_71 (Concatenate)    (None, 300)          0           lambda_208[0][0]                 \n",
      "                                                                 lambda_209[0][0]                 \n",
      "                                                                 lambda_210[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_72 (Concatenate)    (None, 300)          0           lambda_211[0][0]                 \n",
      "                                                                 lambda_212[0][0]                 \n",
      "                                                                 lambda_213[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_73 (Concatenate)    (None, 300)          0           lambda_214[0][0]                 \n",
      "                                                                 lambda_215[0][0]                 \n",
      "                                                                 lambda_216[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_74 (Concatenate)    (None, 300)          0           lambda_217[0][0]                 \n",
      "                                                                 lambda_218[0][0]                 \n",
      "                                                                 lambda_219[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_75 (Concatenate)    (None, 300)          0           lambda_220[0][0]                 \n",
      "                                                                 lambda_221[0][0]                 \n",
      "                                                                 lambda_222[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_76 (Concatenate)    (None, 300)          0           lambda_223[0][0]                 \n",
      "                                                                 lambda_224[0][0]                 \n",
      "                                                                 lambda_225[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_77 (Concatenate)    (None, 300)          0           lambda_226[0][0]                 \n",
      "                                                                 lambda_227[0][0]                 \n",
      "                                                                 lambda_228[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_78 (Concatenate)    (None, 300)          0           lambda_229[0][0]                 \n",
      "                                                                 lambda_230[0][0]                 \n",
      "                                                                 lambda_231[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_79 (Concatenate)    (None, 300)          0           lambda_232[0][0]                 \n",
      "                                                                 lambda_233[0][0]                 \n",
      "                                                                 lambda_234[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_80 (Concatenate)    (None, 300)          0           lambda_235[0][0]                 \n",
      "                                                                 lambda_236[0][0]                 \n",
      "                                                                 lambda_237[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_81 (Concatenate)    (None, 300)          0           lambda_238[0][0]                 \n",
      "                                                                 lambda_239[0][0]                 \n",
      "                                                                 lambda_240[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence1winSize5 ( (None, 1, 300)       0           concatenate_42[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence2winSize5 ( (None, 1, 300)       0           concatenate_43[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence3winSize5 ( (None, 1, 300)       0           concatenate_44[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence4winSize5 ( (None, 1, 300)       0           concatenate_45[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence5winSize5 ( (None, 1, 300)       0           concatenate_46[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence6winSize5 ( (None, 1, 300)       0           concatenate_47[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence7winSize5 ( (None, 1, 300)       0           concatenate_48[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence8winSize5 ( (None, 1, 300)       0           concatenate_49[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence9winSize5 ( (None, 1, 300)       0           concatenate_50[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence10winSize5  (None, 1, 300)       0           concatenate_51[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence11winSize5  (None, 1, 300)       0           concatenate_52[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence12winSize5  (None, 1, 300)       0           concatenate_53[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence13winSize5  (None, 1, 300)       0           concatenate_54[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence14winSize5  (None, 1, 300)       0           concatenate_55[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence15winSize5  (None, 1, 300)       0           concatenate_56[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence16winSize5  (None, 1, 300)       0           concatenate_57[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence17winSize5  (None, 1, 300)       0           concatenate_58[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence18winSize5  (None, 1, 300)       0           concatenate_59[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence19winSize5  (None, 1, 300)       0           concatenate_60[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence20winSize5  (None, 1, 300)       0           concatenate_61[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence21winSize5  (None, 1, 300)       0           concatenate_62[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence22winSize5  (None, 1, 300)       0           concatenate_63[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence23winSize5  (None, 1, 300)       0           concatenate_64[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence24winSize5  (None, 1, 300)       0           concatenate_65[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence25winSize5  (None, 1, 300)       0           concatenate_66[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence26winSize5  (None, 1, 300)       0           concatenate_67[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence27winSize5  (None, 1, 300)       0           concatenate_68[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence28winSize5  (None, 1, 300)       0           concatenate_69[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence29winSize5  (None, 1, 300)       0           concatenate_70[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence30winSize5  (None, 1, 300)       0           concatenate_71[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence31winSize5  (None, 1, 300)       0           concatenate_72[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence32winSize5  (None, 1, 300)       0           concatenate_73[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence33winSize5  (None, 1, 300)       0           concatenate_74[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence34winSize5  (None, 1, 300)       0           concatenate_75[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence35winSize5  (None, 1, 300)       0           concatenate_76[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence36winSize5  (None, 1, 300)       0           concatenate_77[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence37winSize5  (None, 1, 300)       0           concatenate_78[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence38winSize5  (None, 1, 300)       0           concatenate_79[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence39winSize5  (None, 1, 300)       0           concatenate_80[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "switch_axis_sentence40winSize5  (None, 1, 300)       0           concatenate_81[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_43 (Dense)                (None, 1, 10)        3010        switch_axis_sentence1winSize5[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense_44 (Dense)                (None, 1, 10)        3010        switch_axis_sentence2winSize5[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense_45 (Dense)                (None, 1, 10)        3010        switch_axis_sentence3winSize5[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense_46 (Dense)                (None, 1, 10)        3010        switch_axis_sentence4winSize5[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense_47 (Dense)                (None, 1, 10)        3010        switch_axis_sentence5winSize5[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense_48 (Dense)                (None, 1, 10)        3010        switch_axis_sentence6winSize5[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense_49 (Dense)                (None, 1, 10)        3010        switch_axis_sentence7winSize5[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense_50 (Dense)                (None, 1, 10)        3010        switch_axis_sentence8winSize5[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense_51 (Dense)                (None, 1, 10)        3010        switch_axis_sentence9winSize5[0][\n",
      "__________________________________________________________________________________________________\n",
      "dense_52 (Dense)                (None, 1, 10)        3010        switch_axis_sentence10winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_53 (Dense)                (None, 1, 10)        3010        switch_axis_sentence11winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_54 (Dense)                (None, 1, 10)        3010        switch_axis_sentence12winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_55 (Dense)                (None, 1, 10)        3010        switch_axis_sentence13winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_56 (Dense)                (None, 1, 10)        3010        switch_axis_sentence14winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_57 (Dense)                (None, 1, 10)        3010        switch_axis_sentence15winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_58 (Dense)                (None, 1, 10)        3010        switch_axis_sentence16winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_59 (Dense)                (None, 1, 10)        3010        switch_axis_sentence17winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_60 (Dense)                (None, 1, 10)        3010        switch_axis_sentence18winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_61 (Dense)                (None, 1, 10)        3010        switch_axis_sentence19winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_62 (Dense)                (None, 1, 10)        3010        switch_axis_sentence20winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_63 (Dense)                (None, 1, 10)        3010        switch_axis_sentence21winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_64 (Dense)                (None, 1, 10)        3010        switch_axis_sentence22winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_65 (Dense)                (None, 1, 10)        3010        switch_axis_sentence23winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_66 (Dense)                (None, 1, 10)        3010        switch_axis_sentence24winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_67 (Dense)                (None, 1, 10)        3010        switch_axis_sentence25winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_68 (Dense)                (None, 1, 10)        3010        switch_axis_sentence26winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_69 (Dense)                (None, 1, 10)        3010        switch_axis_sentence27winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_70 (Dense)                (None, 1, 10)        3010        switch_axis_sentence28winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_71 (Dense)                (None, 1, 10)        3010        switch_axis_sentence29winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_72 (Dense)                (None, 1, 10)        3010        switch_axis_sentence30winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_73 (Dense)                (None, 1, 10)        3010        switch_axis_sentence31winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_74 (Dense)                (None, 1, 10)        3010        switch_axis_sentence32winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_75 (Dense)                (None, 1, 10)        3010        switch_axis_sentence33winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_76 (Dense)                (None, 1, 10)        3010        switch_axis_sentence34winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_77 (Dense)                (None, 1, 10)        3010        switch_axis_sentence35winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_78 (Dense)                (None, 1, 10)        3010        switch_axis_sentence36winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_79 (Dense)                (None, 1, 10)        3010        switch_axis_sentence37winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_80 (Dense)                (None, 1, 10)        3010        switch_axis_sentence38winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_81 (Dense)                (None, 1, 10)        3010        switch_axis_sentence39winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_82 (Dense)                (None, 1, 10)        3010        switch_axis_sentence40winSize5[0]\n",
      "__________________________________________________________________________________________________\n",
      "DropDense0 (Dropout)            (None, 1, 10)        0           dense_43[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense1 (Dropout)            (None, 1, 10)        0           dense_44[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense2 (Dropout)            (None, 1, 10)        0           dense_45[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense3 (Dropout)            (None, 1, 10)        0           dense_46[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense4 (Dropout)            (None, 1, 10)        0           dense_47[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense5 (Dropout)            (None, 1, 10)        0           dense_48[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense6 (Dropout)            (None, 1, 10)        0           dense_49[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense7 (Dropout)            (None, 1, 10)        0           dense_50[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense8 (Dropout)            (None, 1, 10)        0           dense_51[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense9 (Dropout)            (None, 1, 10)        0           dense_52[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense10 (Dropout)           (None, 1, 10)        0           dense_53[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense11 (Dropout)           (None, 1, 10)        0           dense_54[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense12 (Dropout)           (None, 1, 10)        0           dense_55[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense13 (Dropout)           (None, 1, 10)        0           dense_56[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense14 (Dropout)           (None, 1, 10)        0           dense_57[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense15 (Dropout)           (None, 1, 10)        0           dense_58[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense16 (Dropout)           (None, 1, 10)        0           dense_59[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense17 (Dropout)           (None, 1, 10)        0           dense_60[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense18 (Dropout)           (None, 1, 10)        0           dense_61[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense19 (Dropout)           (None, 1, 10)        0           dense_62[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense20 (Dropout)           (None, 1, 10)        0           dense_63[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense21 (Dropout)           (None, 1, 10)        0           dense_64[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense22 (Dropout)           (None, 1, 10)        0           dense_65[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense23 (Dropout)           (None, 1, 10)        0           dense_66[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense24 (Dropout)           (None, 1, 10)        0           dense_67[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense25 (Dropout)           (None, 1, 10)        0           dense_68[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense26 (Dropout)           (None, 1, 10)        0           dense_69[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense27 (Dropout)           (None, 1, 10)        0           dense_70[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense28 (Dropout)           (None, 1, 10)        0           dense_71[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense29 (Dropout)           (None, 1, 10)        0           dense_72[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense30 (Dropout)           (None, 1, 10)        0           dense_73[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense31 (Dropout)           (None, 1, 10)        0           dense_74[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense32 (Dropout)           (None, 1, 10)        0           dense_75[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense33 (Dropout)           (None, 1, 10)        0           dense_76[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense34 (Dropout)           (None, 1, 10)        0           dense_77[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense35 (Dropout)           (None, 1, 10)        0           dense_78[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense36 (Dropout)           (None, 1, 10)        0           dense_79[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense37 (Dropout)           (None, 1, 10)        0           dense_80[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense38 (Dropout)           (None, 1, 10)        0           dense_81[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "DropDense39 (Dropout)           (None, 1, 10)        0           dense_82[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_82 (Concatenate)    (None, 40, 10)       0           DropDense0[0][0]                 \n",
      "                                                                 DropDense1[0][0]                 \n",
      "                                                                 DropDense2[0][0]                 \n",
      "                                                                 DropDense3[0][0]                 \n",
      "                                                                 DropDense4[0][0]                 \n",
      "                                                                 DropDense5[0][0]                 \n",
      "                                                                 DropDense6[0][0]                 \n",
      "                                                                 DropDense7[0][0]                 \n",
      "                                                                 DropDense8[0][0]                 \n",
      "                                                                 DropDense9[0][0]                 \n",
      "                                                                 DropDense10[0][0]                \n",
      "                                                                 DropDense11[0][0]                \n",
      "                                                                 DropDense12[0][0]                \n",
      "                                                                 DropDense13[0][0]                \n",
      "                                                                 DropDense14[0][0]                \n",
      "                                                                 DropDense15[0][0]                \n",
      "                                                                 DropDense16[0][0]                \n",
      "                                                                 DropDense17[0][0]                \n",
      "                                                                 DropDense18[0][0]                \n",
      "                                                                 DropDense19[0][0]                \n",
      "                                                                 DropDense20[0][0]                \n",
      "                                                                 DropDense21[0][0]                \n",
      "                                                                 DropDense22[0][0]                \n",
      "                                                                 DropDense23[0][0]                \n",
      "                                                                 DropDense24[0][0]                \n",
      "                                                                 DropDense25[0][0]                \n",
      "                                                                 DropDense26[0][0]                \n",
      "                                                                 DropDense27[0][0]                \n",
      "                                                                 DropDense28[0][0]                \n",
      "                                                                 DropDense29[0][0]                \n",
      "                                                                 DropDense30[0][0]                \n",
      "                                                                 DropDense31[0][0]                \n",
      "                                                                 DropDense32[0][0]                \n",
      "                                                                 DropDense33[0][0]                \n",
      "                                                                 DropDense34[0][0]                \n",
      "                                                                 DropDense35[0][0]                \n",
      "                                                                 DropDense36[0][0]                \n",
      "                                                                 DropDense37[0][0]                \n",
      "                                                                 DropDense38[0][0]                \n",
      "                                                                 DropDense39[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_2 (Bidirectional) (None, 40, 100)      18300       concatenate_82[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "biRnn_TF_Reminder1 (Lambda)     (None, 40, 100)      0           bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_84 (Dense)                (None, 40, 100)      10100       biRnn_TF_Reminder1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "attention_weights (Dense)       (None, 40, 1)        100         dense_84[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "permute_3 (Permute)             (None, 1, 40)        0           attention_weights[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "attention_softmax (Activation)  (None, 1, 40)        0           permute_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "permute_4 (Permute)             (None, 40, 1)        0           attention_softmax[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "attention_mult (Dot)            (None, 10, 1)        0           concatenate_82[0][0]             \n",
      "                                                                 permute_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "squeezeOutput (Lambda)          (None, 10)           0           attention_mult[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_85 (Dense)                (None, 1)            11          squeezeOutput[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 18,977,211\n",
      "Trainable params: 509,211\n",
      "Non-trainable params: 18,468,000\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 210642 samples, validate on 25206 samples\n",
      "Epoch 11/15\n",
      "210642/210642 [==============================] - 358s 2ms/step - loss: 0.3641 - acc: 0.8926 - val_loss: 0.3600 - val_acc: 0.8914\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.36792 to 0.35997, saving model to /home/owner/デスクトップ/milnet+edu/results/0724 (15,40)/lr = 0.001 with 10 units/params_adam_normal.hdf5\n",
      "Epoch 12/15\n",
      "210642/210642 [==============================] - 357s 2ms/step - loss: 0.3576 - acc: 0.8961 - val_loss: 0.3766 - val_acc: 0.8849\n",
      "\n",
      "Epoch 00012: val_loss did not improve\n",
      "Epoch 13/15\n",
      "210642/210642 [==============================] - 358s 2ms/step - loss: 0.3558 - acc: 0.8977 - val_loss: 0.3654 - val_acc: 0.8908\n",
      "\n",
      "Epoch 00013: val_loss did not improve\n",
      "Epoch 14/15\n",
      "210642/210642 [==============================] - 357s 2ms/step - loss: 0.3534 - acc: 0.9001 - val_loss: 0.3656 - val_acc: 0.8932\n",
      "\n",
      "Epoch 00014: val_loss did not improve\n",
      "Epoch 15/15\n",
      "210642/210642 [==============================] - 358s 2ms/step - loss: 0.3519 - acc: 0.9016 - val_loss: 0.4007 - val_acc: 0.8752\n",
      "\n",
      "Epoch 00015: val_loss did not improve\n"
     ]
    }
   ],
   "source": [
    "history_normal2 = model.fit(x_train, y_train, batch_size = batch_size, verbose=1, initial_epoch=10,epochs=15 #epochs\n",
    "                        ,validation_data=(x_valid,y_valid), shuffle=True, callbacks=[save_model])"
   ]
  }
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