notebooks/05_cnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 2,
  "metadata": {
    "colab": {
      "name": "05_cnn.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3.8.10 64-bit"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.10",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "accelerator": "GPU",
    "interpreter": {
      "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# CNN definition and training\n",
        "\n",
        "The following notebook is runned on google colab, therefore it can not be runned as is, as files are missing. \n",
        "\n",
        "Nonetheless, results obtained are reproducible by first extracting the required features and by loading them instead of the Google Drive content.\n",
        "\n",
        "## Setting up the environment\n",
        "\n",
        "The first step is to import the required libraries and fix random seeds."
      ],
      "metadata": {
        "id": "19Neq-rtkJRE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pezwSgwqLY5n",
        "outputId": "af0ac836-b46c-4fa6-bdd0-eacb26d47009"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "source": [
        "from sklearn.model_selection import StratifiedKFold, GridSearchCV, cross_val_score\n",
        "from sklearn.utils import class_weight\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from keras import layers, models\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import librosa\n",
        "import librosa.display"
      ],
      "outputs": [],
      "metadata": {
        "id": "fq6QoQNnDDay"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fixing random seeds"
      ],
      "metadata": {
        "id": "MfR9HkrVg1uF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "source": [
        "seed_value= 0\n",
        "import os\n",
        "os.environ['PYTHONHASHSEED']=str(seed_value)\n",
        "import random\n",
        "random.seed(seed_value)\n",
        "np.random.seed(seed_value)\n",
        "tensorflow.random.set_seed(seed_value)\n",
        "from keras import backend as K\n",
        "session_conf = tensorflow.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
        "sess = tensorflow.compat.v1.Session(graph=tensorflow.compat.v1.get_default_graph(), config=session_conf)\n",
        "tensorflow.compat.v1.keras.backend.set_session(sess)"
      ],
      "outputs": [],
      "metadata": {
        "id": "V0906Kz5g1Mi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load and display features\n",
        "The features are extracted locally and loaded into Google Colab. \n",
        "They are savved in a numpy array on disk."
      ],
      "metadata": {
        "id": "fzO0vD6bPFWd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "source": [
        "features = np.load(\"/content/gdrive/MyDrive/cnn/stft_features.npy\")\n",
        "labels = np.load(\"/content/gdrive/MyDrive/cnn/stft_labels.npy\")"
      ],
      "outputs": [],
      "metadata": {
        "id": "rdf9QqOxDX_W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting the classes\n",
        "Here we can see an example of image obtained for each class. "
      ],
      "metadata": {
        "id": "m6kTbuNBqstC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "source": [
        "dd = {\"air conditioner\": 369,\n",
        "      \"car horn\": 4420,\n",
        "      \"children playing\": 102,\n",
        "      \"dog bark\": 0,\n",
        "      \"drilling\": 59,\n",
        "      \"engine idling\": 19,\n",
        "      \"gun shot\": 14,\n",
        "      \"jackhammer\": 37,\n",
        "      \"siren\": 21,\n",
        "      \"street music\": 169}\n",
        "\n",
        "fig, ax = plt.subplots(2, 5, figsize=(15, 5))\n",
        "\n",
        "\n",
        "for i,(k,v) in enumerate(dd.items()):\n",
        "    ax[i//5][i%5].axis(\"off\")\n",
        "    img1 = librosa.display.specshow(librosa.amplitude_to_db(features[v], ref=np.max),\n",
        "                                    y_axis='log', \n",
        "                                    x_axis='time', \n",
        "                                    ax=ax[i//5][i%5])\n",
        "    ax[i//5][i%5].set_title(k)\n",
        "fig.tight_layout()\n",
        "#plt.savefig('class_images.png', dpi=300)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 10 Axes>"
            ],
            "image/png": 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"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "F4v9Z2WiqqtC",
        "outputId": "886794d3-e7fe-45fe-daeb-06d0d35343f3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scaling pixel values\n",
        "The scaler is a standard scaler, and it will be used on the test set later on."
      ],
      "metadata": {
        "id": "vJABIUIClEPZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "x, y, z = features.shape\n",
        "scaled_features = features.reshape(-1, z)\n",
        "scaled_features = scaler.fit_transform(scaled_features)\n",
        "scaled_features = scaled_features.reshape(x, y, z)\n",
        "X_train = scaled_features.reshape(x, y, z, 1)"
      ],
      "outputs": [],
      "metadata": {
        "id": "ohGtj4KXNIb5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model definition\n",
        "\n",
        "The first step is to define a cnn model and test it with cross validation on the training set. As the same stratified cross valiation is used, the results are comparable to the Multilayer perceptron."
      ],
      "metadata": {
        "id": "2vLx9uZdP_ea"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "source": [
        "def get_cnn(conv=16, neurons=[120, 50], learning_rate=0.001, conv_strides=1, pooling_strides=3):\n",
        "    input_shape=(128,256)\n",
        "\n",
        "    layer_list = [layers.Conv2D(conv, kernel_size=(5, 5), strides = (conv_strides, conv_strides), activation='relu',padding='same'),\n",
        "                  layers.Conv2D(conv, kernel_size=(3, 3), activation='relu'),\n",
        "                  layers.MaxPooling2D(pool_size=(2, 2),strides=pooling_strides),\n",
        "                  layers.Conv2D(conv*2, kernel_size=(3, 3), strides = (conv_strides, conv_strides), activation='relu'),\n",
        "                  layers.Conv2D(conv*2, kernel_size=(3, 3), activation='relu'),\n",
        "                  layers.MaxPooling2D(pool_size=(2, 2),strides=3),\n",
        "                  layers.Flatten()]\n",
        "\n",
        "    for n in neurons:\n",
        "      layer_list.append(layers.Dense(n, activation='relu'))\n",
        "    layer_list.append(layers.Dense(10, activation='softmax'))\n",
        "    \n",
        "    model = models.Sequential(layer_list)\n",
        "    opt = keras.optimizers.Adam(learning_rate=learning_rate)\n",
        "    model.compile(optimizer=opt,\n",
        "                  loss=keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
        "                  metrics=['accuracy'])\n",
        "    return model"
      ],
      "outputs": [],
      "metadata": {
        "id": "Yh39OyYuQBbJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing different models\n",
        "\n",
        "We now test four different architectures to see their performances with cross validation, we basically test if it is better to put strides on convolutional or pooling layers, then, we test an higher number of convolutional filters with the same logic.\n",
        "\n",
        "### 8 filters"
      ],
      "metadata": {
        "id": "xiuo8M5pgZFR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "source": [
        "net = get_cnn(conv=8, neurons=[2000, 800, 200, 60], conv_strides=1, pooling_strides=3)\n",
        "net.fit(X_train[[1,2]], labels[[1,2]], verbose=0)\n",
        "net.summary()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 128, 256, 8)       208       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 126, 254, 8)       584       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 42, 85, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 40, 83, 16)        1168      \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 38, 81, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 27, 16)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 5616)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 2000)              11234000  \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 800)               1600800   \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 200)               160200    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 60)                12060     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                610       \n",
            "=================================================================\n",
            "Total params: 13,011,950\n",
            "Trainable params: 13,011,950\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "FXtbLNf3TtgD",
        "outputId": "80eaacc9-db53-40e3-e2f6-c512737df3d4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "source": [
        "net = get_cnn(conv=8, neurons=[300, 150, 80, 25], conv_strides=2, pooling_strides=2)\n",
        "net.fit(X_train[[1,2]], labels[[1,2]], verbose=0)\n",
        "net.summary()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 64, 128, 8)        208       \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 62, 126, 8)        584       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 31, 63, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 15, 31, 16)        1168      \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 13, 29, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 10, 16)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 640)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 300)               192300    \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 150)               45150     \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 80)                12080     \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 25)                2025      \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 10)                260       \n",
            "=================================================================\n",
            "Total params: 256,095\n",
            "Trainable params: 256,095\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "ko3jVAiOTvh9",
        "outputId": "91e70051-6ab0-4077-ac34-ff8fdee2a11c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 16 filters"
      ],
      "metadata": {
        "id": "Cl2DdxwBZbe7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "source": [
        "net = get_cnn(conv=16, neurons=[5000, 2000, 800, 200, 60], conv_strides=1, pooling_strides=3)\n",
        "net.fit(X_train[[1,2]], labels[[1,2]], verbose=0)\n",
        "net.summary()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_8 (Conv2D)            (None, 128, 256, 16)      416       \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 126, 254, 16)      2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 42, 85, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 40, 83, 32)        4640      \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 38, 81, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 13, 27, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 11232)             0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 5000)              56165000  \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 2000)              10002000  \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 800)               1600800   \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 200)               160200    \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 60)                12060     \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 10)                610       \n",
            "=================================================================\n",
            "Total params: 67,957,294\n",
            "Trainable params: 67,957,294\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "7646DWwGTwPa",
        "outputId": "11c1c20d-1058-4667-bd08-f6df0b03a783"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "source": [
        "net = get_cnn(conv=16, neurons=[600, 200, 80, 25], conv_strides=2, pooling_strides=2)\n",
        "net.fit(X_train[[1,2]], labels[[1,2]], verbose=0)\n",
        "net.summary()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_12 (Conv2D)           (None, 64, 128, 16)       416       \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 62, 126, 16)       2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 31, 63, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 15, 31, 32)        4640      \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 13, 29, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 4, 10, 32)         0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 600)               768600    \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 200)               120200    \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 80)                16080     \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 25)                2025      \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 10)                260       \n",
            "=================================================================\n",
            "Total params: 923,789\n",
            "Trainable params: 923,789\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "c-C8xcVvUXdR",
        "outputId": "1a6c1d24-05aa-4fe1-e56e-be36628b78b4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "source": [
        "def get_cross_scores(conv=16, \n",
        "                     neurons=[100, 40], \n",
        "                     conv_strides=2, \n",
        "                     pooling_strides=3, \n",
        "                     epochs=10, \n",
        "                     learning_rate=0.001, \n",
        "                     batch_size=32):\n",
        "\n",
        "  kf = StratifiedKFold(n_splits=5)\n",
        "  class_weights = class_weight.compute_class_weight('balanced',\n",
        "                                            np.unique(labels),\n",
        "                                            labels)\n",
        "  weights_dict = dict(zip(np.unique(labels), class_weights))\n",
        "  stopper = EarlyStopping(monitor='accuracy', patience=2, verbose=1)\n",
        "\n",
        "  res = []\n",
        "  for train_index, test_index in kf.split(X_train, labels):\n",
        "    m = get_cnn(conv=conv, \n",
        "                neurons=neurons, \n",
        "                conv_strides=conv_strides, \n",
        "                pooling_strides=pooling_strides, \n",
        "                learning_rate=learning_rate)\n",
        "    m.fit(X_train[train_index],\n",
        "          labels[train_index],\n",
        "          epochs=epochs,\n",
        "          verbose=1,\n",
        "          class_weight=weights_dict, \n",
        "          callbacks=[stopper], \n",
        "          batch_size=batch_size)\n",
        "    \n",
        "    scores = m.evaluate(X_train[test_index], \n",
        "              labels[test_index])\n",
        "    \n",
        "    res.append(scores)\n",
        "  return np.mean([e[1] for e in res]), np.std([e[1] for e in res])"
      ],
      "outputs": [],
      "metadata": {
        "id": "rv33PPoQDMJo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cross validation run"
      ],
      "metadata": {
        "id": "5xHOd7J9Hhh9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "source": [
        "res1 = get_cross_scores(conv=8, neurons=[2000, 800, 200, 60], \n",
        "                        conv_strides=1, pooling_strides=3, epochs=15)\n",
        "print(f\"Mean accuracy: {res1[0]} Std: {res1[1]}\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "113/113 [==============================] - 8s 51ms/step - loss: 1.7973 - accuracy: 0.3579\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 1.1533 - accuracy: 0.5863\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.7193 - accuracy: 0.7452\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.4436 - accuracy: 0.8413\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.3610 - accuracy: 0.8741\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 0.2488 - accuracy: 0.9116\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 0.1570 - accuracy: 0.9455\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 5s 49ms/step - loss: 0.1075 - accuracy: 0.9647\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1739 - accuracy: 0.9464\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0886 - accuracy: 0.9714\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0763 - accuracy: 0.9744\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0446 - accuracy: 0.9839\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 5s 47ms/step - loss: 0.0787 - accuracy: 0.9769\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0440 - accuracy: 0.9844\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0322 - accuracy: 0.9889\n",
            "29/29 [==============================] - 1s 29ms/step - loss: 4.5486 - accuracy: 0.5289\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 6s 48ms/step - loss: 1.6464 - accuracy: 0.4096\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 5s 49ms/step - loss: 1.0394 - accuracy: 0.6179\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.6847 - accuracy: 0.7519\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.4494 - accuracy: 0.8411\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.2916 - accuracy: 0.8994\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.2046 - accuracy: 0.9291\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1466 - accuracy: 0.9469\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0976 - accuracy: 0.9675\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 5s 49ms/step - loss: 0.1591 - accuracy: 0.9522\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0727 - accuracy: 0.9783\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1354 - accuracy: 0.9705\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0736 - accuracy: 0.9783\n",
            "Epoch 00012: early stopping\n",
            "29/29 [==============================] - 1s 21ms/step - loss: 3.2964 - accuracy: 0.4956\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 6s 48ms/step - loss: 1.6454 - accuracy: 0.4126\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 1.1393 - accuracy: 0.5902\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 5s 49ms/step - loss: 0.8102 - accuracy: 0.6982\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.6114 - accuracy: 0.7716\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.4292 - accuracy: 0.8433\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.2859 - accuracy: 0.8952\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1996 - accuracy: 0.9283\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1452 - accuracy: 0.9500\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1902 - accuracy: 0.9469\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1397 - accuracy: 0.9619\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1032 - accuracy: 0.9639\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1127 - accuracy: 0.9717\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1019 - accuracy: 0.9714\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0709 - accuracy: 0.9819\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0682 - accuracy: 0.9842\n",
            "29/29 [==============================] - 1s 20ms/step - loss: 2.5480 - accuracy: 0.6133\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 7s 48ms/step - loss: 1.7912 - accuracy: 0.3607\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 1.1775 - accuracy: 0.5966\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.7402 - accuracy: 0.7252\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 5s 49ms/step - loss: 0.4903 - accuracy: 0.8199\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 0.4178 - accuracy: 0.8650\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 5s 49ms/step - loss: 0.3177 - accuracy: 0.8930\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1660 - accuracy: 0.9439\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 0.0976 - accuracy: 0.9669\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0995 - accuracy: 0.9664\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1183 - accuracy: 0.9639\n",
            "Epoch 00010: early stopping\n",
            "29/29 [==============================] - 1s 20ms/step - loss: 3.2534 - accuracy: 0.5267\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 7s 51ms/step - loss: 1.6880 - accuracy: 0.3894\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 1.0653 - accuracy: 0.6014\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 0.7190 - accuracy: 0.7325\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.4936 - accuracy: 0.8300\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.3579 - accuracy: 0.8744\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.2479 - accuracy: 0.9056\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 5s 49ms/step - loss: 0.2446 - accuracy: 0.9167\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 5s 49ms/step - loss: 0.2568 - accuracy: 0.9206\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.1579 - accuracy: 0.9469\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0988 - accuracy: 0.9658\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 5s 48ms/step - loss: 0.0590 - accuracy: 0.9800\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 0.0640 - accuracy: 0.9797\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 0.0436 - accuracy: 0.9844\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 0.0194 - accuracy: 0.9922\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 6s 49ms/step - loss: 0.0495 - accuracy: 0.9842\n",
            "29/29 [==============================] - 1s 28ms/step - loss: 3.7123 - accuracy: 0.5273\n",
            "Mean accuracy: 0.5383393824100494 Std: 0.039504517709036485\n"
          ]
        }
      ],
      "metadata": {
        "id": "gxp_br5riw8H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "outputId": "036abd7b-703f-4a73-b1f7-095b583226f8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "source": [
        "res2 = get_cross_scores(conv=8, neurons=[300, 150, 80, 25], \n",
        "                        conv_strides=2, pooling_strides=2, epochs=15)\n",
        "print(f\"Mean accuracy: {res2[0]} Std: {res2[1]}\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "113/113 [==============================] - 3s 16ms/step - loss: 1.9661 - accuracy: 0.2809\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 1.3586 - accuracy: 0.5274\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 1.0190 - accuracy: 0.6288\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.8017 - accuracy: 0.7038\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.7007 - accuracy: 0.7422\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.5776 - accuracy: 0.7936\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.4639 - accuracy: 0.8311\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 14ms/step - loss: 0.4179 - accuracy: 0.8516\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3795 - accuracy: 0.8547\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3043 - accuracy: 0.8830\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2555 - accuracy: 0.9061\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 14ms/step - loss: 0.2290 - accuracy: 0.9100\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2413 - accuracy: 0.9094\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.1761 - accuracy: 0.9341\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2251 - accuracy: 0.9233\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 3.9418 - accuracy: 0.4500\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 3s 15ms/step - loss: 1.9054 - accuracy: 0.3343\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 1.3112 - accuracy: 0.5296\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 1.0795 - accuracy: 0.6007\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.8796 - accuracy: 0.6674\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.7526 - accuracy: 0.7205\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.6495 - accuracy: 0.7591\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.5256 - accuracy: 0.8080\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.4706 - accuracy: 0.8233\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.4344 - accuracy: 0.8391\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3530 - accuracy: 0.8650\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3180 - accuracy: 0.8880\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3244 - accuracy: 0.8750\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 14ms/step - loss: 0.2490 - accuracy: 0.9058\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3103 - accuracy: 0.8980\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2007 - accuracy: 0.9253\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 2.9580 - accuracy: 0.5500\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 3s 16ms/step - loss: 1.8733 - accuracy: 0.3040\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 1.3793 - accuracy: 0.4871\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 1.1534 - accuracy: 0.5629\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.9769 - accuracy: 0.6232\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.8472 - accuracy: 0.6799\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.6843 - accuracy: 0.7455\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.6337 - accuracy: 0.7577\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.5128 - accuracy: 0.8072\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.4879 - accuracy: 0.8255\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3766 - accuracy: 0.8608\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3870 - accuracy: 0.8550\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3316 - accuracy: 0.8750\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2927 - accuracy: 0.8947\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2065 - accuracy: 0.9214\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.1965 - accuracy: 0.9236\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 3.2302 - accuracy: 0.5811\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 3s 16ms/step - loss: 1.9573 - accuracy: 0.2723\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 14ms/step - loss: 1.3743 - accuracy: 0.4990\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 1.0962 - accuracy: 0.5999\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 14ms/step - loss: 0.9353 - accuracy: 0.6527\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.7551 - accuracy: 0.7238\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.6474 - accuracy: 0.7730\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.5871 - accuracy: 0.7966\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.4482 - accuracy: 0.8375\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.4130 - accuracy: 0.8522\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3472 - accuracy: 0.8666\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3006 - accuracy: 0.8852\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2943 - accuracy: 0.8927\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2220 - accuracy: 0.9194\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2301 - accuracy: 0.9178\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.1898 - accuracy: 0.9286\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 3.8160 - accuracy: 0.5756\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 3s 16ms/step - loss: 1.8874 - accuracy: 0.3211\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 1.3760 - accuracy: 0.4883\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 1.1514 - accuracy: 0.5664\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.9659 - accuracy: 0.6286\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.8010 - accuracy: 0.6939\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.7005 - accuracy: 0.7369\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.5801 - accuracy: 0.7844\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.4972 - accuracy: 0.8133\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.4324 - accuracy: 0.8417\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3693 - accuracy: 0.8558\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.3203 - accuracy: 0.8811\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2939 - accuracy: 0.8861\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2449 - accuracy: 0.9067\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2340 - accuracy: 0.9103\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 2s 15ms/step - loss: 0.2770 - accuracy: 0.9053\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 3.4069 - accuracy: 0.5172\n",
            "Mean accuracy: 0.5347816109657287 Std: 0.048033578958531366\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "kRT4SijfO9G1",
        "outputId": "58b8fe41-2009-4b07-e9b4-9e70c915c1b1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "source": [
        "res3 = get_cross_scores(conv=16, neurons=[5000, 2000, 800, 200, 60], \n",
        "                        conv_strides=1, pooling_strides=3, epochs=15)\n",
        "print(f\"Mean accuracy: {res3[0]} Std: {res3[1]}\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "113/113 [==============================] - 13s 97ms/step - loss: 1.8772 - accuracy: 0.3001\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 1.2500 - accuracy: 0.5488\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.7962 - accuracy: 0.7158\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.5842 - accuracy: 0.7924\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.4988 - accuracy: 0.8302\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.3150 - accuracy: 0.8877\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.2016 - accuracy: 0.9311\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.2144 - accuracy: 0.9336\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.2552 - accuracy: 0.9264\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1233 - accuracy: 0.9611\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1160 - accuracy: 0.9669\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.0695 - accuracy: 0.9778\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.0343 - accuracy: 0.9894\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.0458 - accuracy: 0.9867\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.1663 - accuracy: 0.9628\n",
            "Epoch 00015: early stopping\n",
            "29/29 [==============================] - 1s 35ms/step - loss: 2.6207 - accuracy: 0.4856\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 12s 93ms/step - loss: 1.9786 - accuracy: 0.2756\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 1.3520 - accuracy: 0.5065\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.9832 - accuracy: 0.6330\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.7001 - accuracy: 0.7385\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.5138 - accuracy: 0.8266\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.3920 - accuracy: 0.8716\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.2808 - accuracy: 0.9116\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.2007 - accuracy: 0.9353\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1402 - accuracy: 0.9569\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1387 - accuracy: 0.9655\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.0954 - accuracy: 0.9703\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.1373 - accuracy: 0.9644\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.0615 - accuracy: 0.9786\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.0825 - accuracy: 0.9761\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.0375 - accuracy: 0.9894\n",
            "29/29 [==============================] - 1s 33ms/step - loss: 3.6922 - accuracy: 0.5044\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 11s 92ms/step - loss: 1.8117 - accuracy: 0.3256\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 1.2442 - accuracy: 0.5313\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 10s 92ms/step - loss: 0.9414 - accuracy: 0.6410\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.6374 - accuracy: 0.7688\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.4312 - accuracy: 0.8538\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.3238 - accuracy: 0.8961\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.2896 - accuracy: 0.9097\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1775 - accuracy: 0.9447\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1386 - accuracy: 0.9594\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1534 - accuracy: 0.9692\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.1501 - accuracy: 0.9580\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.0804 - accuracy: 0.9758\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.0400 - accuracy: 0.9867\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.0510 - accuracy: 0.9822\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.0559 - accuracy: 0.9853\n",
            "Epoch 00015: early stopping\n",
            "29/29 [==============================] - 1s 31ms/step - loss: 4.0356 - accuracy: 0.5689\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 1.9081 - accuracy: 0.2834\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 1.2288 - accuracy: 0.5757\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.8490 - accuracy: 0.6916\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 10s 92ms/step - loss: 0.5927 - accuracy: 0.7988\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.4865 - accuracy: 0.8466\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.2900 - accuracy: 0.9114\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.2278 - accuracy: 0.9233\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1264 - accuracy: 0.9592\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1327 - accuracy: 0.9661\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.1186 - accuracy: 0.9669\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.0846 - accuracy: 0.9744\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 0.0562 - accuracy: 0.9806\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.2358 - accuracy: 0.9511\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 11s 93ms/step - loss: 0.0800 - accuracy: 0.9772\n",
            "Epoch 00014: early stopping\n",
            "29/29 [==============================] - 1s 30ms/step - loss: 3.6422 - accuracy: 0.5211\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 12s 97ms/step - loss: 1.9433 - accuracy: 0.2681\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 10s 93ms/step - loss: 1.3875 - accuracy: 0.4808\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 1.0108 - accuracy: 0.6419\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.6493 - accuracy: 0.7686\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 11s 95ms/step - loss: 0.4692 - accuracy: 0.8361\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 11s 95ms/step - loss: 0.2985 - accuracy: 0.8981\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 11s 95ms/step - loss: 0.2526 - accuracy: 0.9194\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 11s 95ms/step - loss: 0.1447 - accuracy: 0.9511\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 11s 94ms/step - loss: 0.1993 - accuracy: 0.9417\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 11s 95ms/step - loss: 0.2185 - accuracy: 0.9353\n",
            "Epoch 00010: early stopping\n",
            "29/29 [==============================] - 1s 37ms/step - loss: 2.2548 - accuracy: 0.5628\n",
            "Mean accuracy: 0.5285695254802704 Std: 0.032522266765068045\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "kS7799cgTGh2",
        "outputId": "c9a5383a-27f6-4bcd-dc1c-b7d812735851"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "source": [
        "res4 = get_cross_scores(conv=16, neurons=[600, 200, 80, 25], \n",
        "                        conv_strides=2, pooling_strides=2, epochs=15)\n",
        "print(f\"Mean accuracy: {res4[0]} Std: {res4[1]}\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "113/113 [==============================] - 3s 21ms/step - loss: 1.8773 - accuracy: 0.3345\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 1.2526 - accuracy: 0.5604\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.9153 - accuracy: 0.6705\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.7069 - accuracy: 0.7366\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.6249 - accuracy: 0.7772\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.4856 - accuracy: 0.8124\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.3604 - accuracy: 0.8764\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.3310 - accuracy: 0.8797\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.3060 - accuracy: 0.8886\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2414 - accuracy: 0.9147\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.2446 - accuracy: 0.9197\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.1840 - accuracy: 0.9341\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.1262 - accuracy: 0.9553\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.0920 - accuracy: 0.9669\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.1499 - accuracy: 0.9497\n",
            "29/29 [==============================] - 1s 14ms/step - loss: 3.7410 - accuracy: 0.5311\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 3s 21ms/step - loss: 1.8603 - accuracy: 0.3465\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 1.2924 - accuracy: 0.5313\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.9826 - accuracy: 0.6274\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.7778 - accuracy: 0.7105\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.6140 - accuracy: 0.7774\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.4905 - accuracy: 0.8202\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.4401 - accuracy: 0.8405\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.3587 - accuracy: 0.8719\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.2633 - accuracy: 0.9069\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.1894 - accuracy: 0.9289\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.2760 - accuracy: 0.9080\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.1551 - accuracy: 0.9411\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.1396 - accuracy: 0.9489\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.1194 - accuracy: 0.9580\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.1352 - accuracy: 0.9533\n",
            "29/29 [==============================] - 1s 12ms/step - loss: 4.0735 - accuracy: 0.5056\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 3s 21ms/step - loss: 1.8453 - accuracy: 0.3390\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 1.2923 - accuracy: 0.5215\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 1.0553 - accuracy: 0.6166\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.7981 - accuracy: 0.6960\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.6038 - accuracy: 0.7691\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.4971 - accuracy: 0.8102\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.4747 - accuracy: 0.8338\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.3515 - accuracy: 0.8747\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2741 - accuracy: 0.9005\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2021 - accuracy: 0.9294\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2263 - accuracy: 0.9200\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.1380 - accuracy: 0.9553\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2042 - accuracy: 0.9372\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2066 - accuracy: 0.9294\n",
            "Epoch 00014: early stopping\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 2.0436 - accuracy: 0.6178\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 4s 20ms/step - loss: 1.8352 - accuracy: 0.3248\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 1.3116 - accuracy: 0.5199\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 1.0328 - accuracy: 0.6113\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.8133 - accuracy: 0.6844\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.5897 - accuracy: 0.7827\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.4752 - accuracy: 0.8252\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.3561 - accuracy: 0.8711\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.3236 - accuracy: 0.8886\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2684 - accuracy: 0.9008\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2542 - accuracy: 0.9219\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2529 - accuracy: 0.9136\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.1839 - accuracy: 0.9372\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.1585 - accuracy: 0.9472\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.1041 - accuracy: 0.9644\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.0930 - accuracy: 0.9644\n",
            "29/29 [==============================] - 1s 11ms/step - loss: 3.8355 - accuracy: 0.5678\n",
            "Epoch 1/15\n",
            "113/113 [==============================] - 3s 22ms/step - loss: 2.0381 - accuracy: 0.2253\n",
            "Epoch 2/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 1.5523 - accuracy: 0.4375\n",
            "Epoch 3/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 1.1464 - accuracy: 0.5706\n",
            "Epoch 4/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.8860 - accuracy: 0.6561\n",
            "Epoch 5/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.6827 - accuracy: 0.7400\n",
            "Epoch 6/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.5619 - accuracy: 0.7925\n",
            "Epoch 7/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.4490 - accuracy: 0.8333\n",
            "Epoch 8/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.3713 - accuracy: 0.8625\n",
            "Epoch 9/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.3082 - accuracy: 0.8867\n",
            "Epoch 10/15\n",
            "113/113 [==============================] - 2s 21ms/step - loss: 0.2500 - accuracy: 0.9044\n",
            "Epoch 11/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.1872 - accuracy: 0.9289\n",
            "Epoch 12/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.1540 - accuracy: 0.9369\n",
            "Epoch 13/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.1364 - accuracy: 0.9489\n",
            "Epoch 14/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.1469 - accuracy: 0.9450\n",
            "Epoch 15/15\n",
            "113/113 [==============================] - 2s 20ms/step - loss: 0.2715 - accuracy: 0.9064\n",
            "Epoch 00015: early stopping\n",
            "29/29 [==============================] - 1s 11ms/step - loss: 3.2921 - accuracy: 0.4839\n",
            "Mean accuracy: 0.5412186324596405 Std: 0.04739084241387039\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Ecdy-fSnYfRk",
        "outputId": "cff4c907-d157-4695-9bc1-bfc3338e1f2e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Results"
      ],
      "metadata": {
        "id": "Nnf0zJEwZiyU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "source": [
        "for i, r in enumerate([res1, res2, res3, res4]):\n",
        "  print(f\"Model n.{i+1}:\\n\\tMean accuracy: {r[0]} \\n\\tStd: {r[1]}\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model n.1:\n",
            "\tMean accuracy: 0.5383393824100494 \n",
            "\tStd: 0.039504517709036485\n",
            "Model n.2:\n",
            "\tMean accuracy: 0.5347816109657287 \n",
            "\tStd: 0.048033578958531366\n",
            "Model n.3:\n",
            "\tMean accuracy: 0.5285695254802704 \n",
            "\tStd: 0.032522266765068045\n",
            "Model n.4:\n",
            "\tMean accuracy: 0.5412186324596405 \n",
            "\tStd: 0.04739084241387039\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "URjKR0KYlztU",
        "outputId": "151b0f75-36d6-46e8-d336-03e0c8fa0d60"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Increasing learning rate"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "source": [
        "res1 = get_cross_scores(conv=8, neurons=[2000, 800, 200, 60], \n",
        "                        conv_strides=1, pooling_strides=3, epochs=15, \n",
        "                        learning_rate=0.003)\n",
        "res2 = get_cross_scores(conv=8, neurons=[300, 150, 80, 25], \n",
        "                        conv_strides=2, pooling_strides=2, epochs=15, \n",
        "                        learning_rate=0.003)\n",
        "res3 = get_cross_scores(conv=16, neurons=[5000, 2000, 800, 200, 60], \n",
        "                        conv_strides=1, pooling_strides=3, epochs=15, \n",
        "                        learning_rate=0.003)\n",
        "res4 = get_cross_scores(conv=16, neurons=[600, 200, 80, 25], \n",
        "                        conv_strides=2, pooling_strides=2, epochs=15, \n",
        "                        learning_rate=0.003)\n",
        "\n",
        "for i, r in enumerate([res1, res2, res3, res4]):\n",
        "  print(f\"Model n.{i+1}:\\n\\tMean accuracy: {r[0]} \\n\\tStd: {r[1]}\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model n.1:\n",
            "\tMean accuracy: 0.5149969160556793 \n",
            "\tStd: 0.025335887325786413\n",
            "Model n.2:\n",
            "\tMean accuracy: 0.5223361670970916 \n",
            "\tStd: 0.04799428250756756\n",
            "Model n.3:\n",
            "\tMean accuracy: 0.390914349257946 \n",
            "\tStd: 0.14548666861110163\n",
            "Model n.4:\n",
            "\tMean accuracy: 0.5341127216815948 \n",
            "\tStd: 0.04512383437330031\n",
            "    \n"
          ]
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Increasing batch size with 0.001 learning rate"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "source": [
        "res1 = get_cross_scores(conv=8, neurons=[2000, 800, 200, 60], \n",
        "                        conv_strides=1, pooling_strides=3, epochs=15, \n",
        "                        learning_rate=0.001, batch_size=64)\n",
        "res2 = get_cross_scores(conv=8, neurons=[300, 150, 80, 25], \n",
        "                        conv_strides=2, pooling_strides=2, epochs=15, \n",
        "                        learning_rate=0.001, batch_size=64)\n",
        "res3 = get_cross_scores(conv=16, neurons=[5000, 2000, 800, 200, 60], \n",
        "                        conv_strides=1, pooling_strides=3, epochs=15, \n",
        "                        learning_rate=0.001, batch_size=64)\n",
        "res4 = get_cross_scores(conv=16, neurons=[600, 200, 80, 25], \n",
        "                        conv_strides=2, pooling_strides=2, epochs=15, \n",
        "                        learning_rate=0.001, batch_size=64)\n",
        "\n",
        "for i, r in enumerate([res1, res2, res3, res4]):\n",
        "  print(f\"Model n.{i+1}:\\n\\tMean accuracy: {r[0]} \\n\\tStd: {r[1]}\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model n.1:\n",
            "\tMean accuracy: 0.5272245705127716 \n",
            "\tStd: 0.04467076440790341\n",
            "Model n.2:\n",
            "\tMean accuracy: 0.5096675276756286 \n",
            "\tStd: 0.04031284471460212\n",
            "Model n.3:\n",
            "\tMean accuracy: 0.5325561583042144 \n",
            "\tStd: 0.05123571582836969\n",
            "Model n.4:\n",
            "\tMean accuracy: 0.5212201178073883 \n",
            "\tStd: 0.04278779243029149\n",
            "  \n"
          ]
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test sets results\n",
        "We select the best model from the previous runs and run it on the test sets, after training on the entire training set. The best models is the last one with 0.001 learning rate and 32 batch size."
      ],
      "metadata": {
        "id": "SfahstNNisS0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "source": [
        "net = get_cnn(conv=16, neurons=[600, 200, 80, 25], conv_strides=2, pooling_strides=2)\n",
        "class_weights = class_weight.compute_class_weight('balanced',\n",
        "                                            np.unique(labels),\n",
        "                                            labels)\n",
        "weights_dict = dict(zip(np.unique(labels), class_weights))\n",
        "stopper = EarlyStopping(monitor='accuracy', patience=1, verbose=1)\n",
        "\n",
        "\n",
        "net.fit(X_train, \n",
        "      labels,\n",
        "      epochs=15, \n",
        "      class_weight=weights_dict, \n",
        "      callbacks=[stopper])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "141/141 [==============================] - 4s 22ms/step - loss: 1.7722 - accuracy: 0.3690\n",
            "Epoch 2/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 1.2607 - accuracy: 0.5419\n",
            "Epoch 3/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.9436 - accuracy: 0.6393\n",
            "Epoch 4/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.7576 - accuracy: 0.7119\n",
            "Epoch 5/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.6053 - accuracy: 0.7782\n",
            "Epoch 6/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.4951 - accuracy: 0.8186\n",
            "Epoch 7/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.3653 - accuracy: 0.8624\n",
            "Epoch 8/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.3108 - accuracy: 0.8831\n",
            "Epoch 9/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.2748 - accuracy: 0.8991\n",
            "Epoch 10/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.2154 - accuracy: 0.9195\n",
            "Epoch 11/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.1928 - accuracy: 0.9335\n",
            "Epoch 12/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.1707 - accuracy: 0.9427\n",
            "Epoch 13/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.0997 - accuracy: 0.9647\n",
            "Epoch 14/15\n",
            "141/141 [==============================] - 3s 20ms/step - loss: 0.1945 - accuracy: 0.9373\n",
            "Epoch 00014: early stopping\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7c8c348c10>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "metadata": {
        "id": "El0ysSjWXMHb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f99ba68b-d5cf-4fd5-9e12-ed6453942527"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "source": [
        "res = []\n",
        "for f in [5, 7, 8, 9, 10]:\n",
        "  test = np.load(f\"/content/gdrive/MyDrive/cnn/stft_test_{f}_features.npy\")\n",
        "  test_labels = np.load(f\"/content/gdrive/MyDrive/cnn/stft_test_{f}_labels.npy\")\n",
        "  \n",
        "  x, y, z = test.shape\n",
        "  scaled_test = test.reshape(-1, z)\n",
        "  scaled_test = scaler.transform(scaled_test)\n",
        "  scaled_test = scaled_test.reshape(x, y, z)\n",
        "  scaled_test = scaled_test.reshape(x, y, z, 1)\n",
        "\n",
        "  res.append(net.evaluate(scaled_test, test_labels))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30/30 [==============================] - 1s 14ms/step - loss: 4.7126 - accuracy: 0.5171\n",
            "27/27 [==============================] - 0s 13ms/step - loss: 3.2699 - accuracy: 0.5668\n",
            "26/26 [==============================] - 0s 11ms/step - loss: 4.2881 - accuracy: 0.5744\n",
            "26/26 [==============================] - 0s 10ms/step - loss: 6.6854 - accuracy: 0.5515\n",
            "27/27 [==============================] - 0s 13ms/step - loss: 3.0434 - accuracy: 0.5639\n"
          ]
        }
      ],
      "metadata": {
        "id": "aiNqyue9g_lK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50672355-cb1d-439e-e784-2580be106075"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "source": [
        "print(f\"Mean accuracy: {np.mean([e[1] for e in res])} Std: {np.std([e[1] for e in res])}\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean accuracy: 0.5547501564025878 Std: 0.02023056927597562\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "neBxonmQF8rY",
        "outputId": "4f7b02ea-53b1-4626-9e1a-b7810eafffac"
      }
    }
  ]
}