3_2_MLP_training.ipynb

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        "X04X25BsIP4R",
        "bNo7uP4lIm-T",
        "I-JBQ5QHIya6",
        "Jp9DWhs8I4ds",
        "JC6FMk96JApE",
        "avZpU4i7JOWw"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LouisVanLangendonck/UPC-AML-ArchitectureClassif/blob/main/MLP_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training an Multilayer Perceptron on extracted features"
      ],
      "metadata": {
        "id": "MSSG5mfXIDHq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Specify here the path to the MAIN DIRECTORY of the project"
      ],
      "metadata": {
        "id": "Ld7UMUJEIcyV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "project_dir = '/content/drive/MyDrive/FIB-2022-2023/aml'"
      ],
      "metadata": {
        "id": "7TWINUQ2IYp9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports and preparation"
      ],
      "metadata": {
        "id": "X04X25BsIP4R"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DI35D1qz0Ltv",
        "outputId": "55dd7bd7-e1f0-46b5-ea51-aaae15b7a975"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow-addons\n",
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            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.19.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting focal-loss\n",
            "  Downloading focal_loss-0.0.7-py3-none-any.whl (19 kB)\n",
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            "Installing collected packages: focal-loss\n",
            "Successfully installed focal-loss-0.0.7\n",
            "TF version: 2.9.2\n",
            "keras version: 2.9.0\n",
            "GPU is available\n",
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import itertools\n",
        "import os\n",
        "import matplotlib.pylab as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "import random\n",
        "import pandas as pd\n",
        "import random\n",
        "! pip install tensorflow-addons\n",
        "! pip install focal-loss\n",
        "import tensorflow_addons as tfa\n",
        "from keras import optimizers, models, layers\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.utils import to_categorical\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "\n",
        "print(\"TF version:\", tf.__version__)\n",
        "print(\"keras version:\", keras.__version__)\n",
        "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount = True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title To be able to load in the big feature vectors in numpy\n",
        "\n",
        "# Never load this twice in one session! Otherwise it will give a pickle error\n",
        "\n",
        "np_load_old = np.load\n",
        "\n",
        "# modify the default parameters of np.load\n",
        "np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)"
      ],
      "metadata": {
        "id": "NshUz1fW1JbV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading In the Features and optionally concatenating them"
      ],
      "metadata": {
        "id": "bNo7uP4lIm-T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load in features without concatenation\n",
        "feature_vector_family = 'efficientnetb7'\n",
        "(train_features, train_labels), (test_features, test_labels) = np.load(os.path.join(project_dir,'models/extracted_features/{}.npy'.format(feature_vector_family)))\n",
        "model_naming = 'feat+mlp_{}'.format(feature_vector_family)"
      ],
      "metadata": {
        "id": "lcb5JFnX0fZh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load in features with concatenation\n",
        "\n",
        "concat_path_base = os.path.join(project_dir,'models/extracted_features/')\n",
        "#concat_path_list = ['inception_resnet_v2.npy', 'vgg19.npy', 'xception.npy']\n",
        "concat_path_list = ['inception_resnet_v2.npy', 'vgg19.npy', 'xception.npy', 'SIFT.npy', 'efficientnetb7.npy']\n",
        "train_features = []\n",
        "test_features = []\n",
        "model_naming = 'feat+mlp_'\n",
        "\n",
        "for idx, feature_file in enumerate(concat_path_list):\n",
        "  \n",
        "    feature_path = os.path.join(concat_path_base, feature_file)\n",
        "    (train_features_to_concat, train_labels), (test_features_to_concat, test_labels) = np.load(feature_path)\n",
        "    if idx == 0:\n",
        "      train_features = train_features_to_concat\n",
        "      test_features = test_features_to_concat\n",
        "    else:\n",
        "      train_features=np.append(train_features, train_features_to_concat, 1)\n",
        "      test_features=np.append(test_features, test_features_to_concat, 1)\n",
        "\n",
        "    if idx+1 != len(concat_path_list):\n",
        "        model_naming = model_naming+(feature_file.split('.')[0])+'+'\n",
        "    else:\n",
        "        model_naming = model_naming+(feature_file.split('.')[0])"
      ],
      "metadata": {
        "id": "T_wKZwMdmSmz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train-validation split and class weights calculation"
      ],
      "metadata": {
        "id": "I-JBQ5QHIya6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title stratified train-val split (with random state fixed) for eventual model validation\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "        \n",
        "inp_train_features, inp_val_features, train_labels, val_labels = train_test_split(train_features, train_labels,\n",
        "                                                    stratify=train_labels,\n",
        "                                                    shuffle=True,\n",
        "                                                    test_size=0.20, \n",
        "                                                    random_state=1)\n",
        "\n",
        "nr_train_images = train_labels.shape[0]\n",
        "nr_test_images = test_labels.shape[0]\n",
        "nr_val_images = val_labels.shape[0]"
      ],
      "metadata": {
        "id": "0wZ5_4FwczvR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Computing class weight to use in training to combat class imbalance\n",
        "\n",
        "integer_coding_labels = np.array([np.where(r==1)[0][0] for r in train_labels])\n",
        "\n",
        "class_weights = compute_class_weight(class_weight ='balanced',classes =np.unique(integer_coding_labels),\n",
        "                                                 y = integer_coding_labels)\n",
        "\n",
        "class_weights_dict = {k: v for k, v in enumerate(class_weights)}\n",
        "class_weights_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "el1bJQ7Wd8h9",
        "outputId": "c2ac146d-1b9e-4dbb-e3dd-a2ec7cc185a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 1.0073770491803278,\n",
              " 1: 1.7285513361462728,\n",
              " 2: 1.440797186400938,\n",
              " 3: 0.6154231347020531,\n",
              " 4: 1.6474530831099197,\n",
              " 5: 0.5816374822527213,\n",
              " 6: 4.535055350553505,\n",
              " 7: 0.6397709526288391}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Writing a Model Builder Function with costum loss function. "
      ],
      "metadata": {
        "id": "Jp9DWhs8I4ds"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def focal_loss(gamma=2., alpha=class_weights):\n",
        "\n",
        "    gamma = float(gamma)\n",
        "    alpha = float(alpha)\n",
        "\n",
        "    def focal_loss_fixed(y_true, y_pred):\n",
        "        \"\"\"\n",
        "        https://arxiv.org/abs/1708.02002\n",
        "        \"\"\"\n",
        "        epsilon = 1.e-9\n",
        "        y_true = tf.convert_to_tensor(y_true, tf.float32)\n",
        "        y_pred = tf.convert_to_tensor(y_pred, tf.float32)\n",
        "\n",
        "        model_out = tf.add(y_pred, epsilon)\n",
        "        ce = tf.multiply(y_true, -tf.math.log(model_out))\n",
        "        weight = tf.multiply(y_true, tf.pow(tf.subtract(1., model_out), gamma))\n",
        "        fl = tf.multiply(alpha, tf.multiply(weight, ce))\n",
        "        reduced_fl = tf.reduce_max(fl, axis=1)\n",
        "        return tf.reduce_mean(reduced_fl)\n",
        "    return focal_loss_fixed"
      ],
      "metadata": {
        "id": "AhznAcXyhVP4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Model Builder function that returns keras model type. To be used often later.\n",
        "\n",
        "f1 = tfa.metrics.F1Score(8,'weighted')\n",
        "\n",
        "def model_builder(first_layer, second_layer,opt, learning_rate,third_layer=0, dropout = True, load_weights_path=None, loss_function='crossentropy'):\n",
        "    model = models.Sequential()\n",
        "    if dropout:\n",
        "        model.add(layers.Dropout(0.5))\n",
        "    model.add(layers.Dense(first_layer, activation='relu', input_dim=inp_train_features.shape[1]))\n",
        "    if second_layer != 0:\n",
        "        model.add(layers.Dense(second_layer, activation='relu'))\n",
        "    if second_layer != 0 and third_layer != 0:\n",
        "        model.add(layers.Dense(third_layer, activation='relu'))\n",
        "    model.add(layers.Dense(8,activation = 'softmax'))\n",
        "    if load_weights_path != None:\n",
        "        model.load_weights(load_weights_path)\n",
        "    if opt == 'adam' and loss_function == 'crossentropy':\n",
        "        model.compile(optimizer=optimizers.Adam(learning_rate = learning_rate),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=[f1, 'acc'])\n",
        "    elif opt == 'adam' and loss_function == 'focal':\n",
        "        model.compile(optimizer=optimizers.Adam(learning_rate = learning_rate),\n",
        "              loss=focal_loss(alpha=1),\n",
        "              metrics=[f1, 'acc'])\n",
        "    elif opt == 'SGD' and loss_function == 'crossentropy':\n",
        "        model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=[f1, 'acc'])\n",
        "    elif opt == 'SGD' and loss_function == 'focal':\n",
        "        model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),\n",
        "              loss=focal_loss(alpha=1),\n",
        "              metrics=[f1, 'acc'])\n",
        "    return model"
      ],
      "metadata": {
        "id": "cIGwuQbVsJe_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model training including costum Parameter Grid"
      ],
      "metadata": {
        "id": "JC6FMk96JApE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Manual Grid for configuration parameters (random seed fixed)\n",
        "\n",
        "primary_layer_choice = [512, 256, 128]\n",
        "secondary_layer_choice = [256, 128, 0]\n",
        "optimizers_choices = ['SGD', 'adam']\n",
        "third_layer_choice = [128, 0, 0]\n",
        "sgd_lr = [0.01, 0.05]\n",
        "adam_lr = [0.001, 0.005]\n",
        "\n",
        "random.seed(3)\n",
        "combs = [[random.choice(primary_layer_choice), random.choice(secondary_layer_choice), random.choice(optimizers_choices)] for _ in range (5)]\n",
        "for choice in combs:\n",
        "  if choice[2] == 'adam':\n",
        "          choice.append(random.choice(adam_lr))\n",
        "  elif choice[2] == 'SGD':\n",
        "          choice.append(random.choice(sgd_lr))\n",
        "  if choice[1] != 0:\n",
        "          choice.append(random.choice(third_layer_choice))\n",
        "combs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XaxcrtZqSQ7v",
        "outputId": "a618b737-15ef-43a6-c08a-bfa61a2e4773"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[512, 0, 'SGD', 0.01],\n",
              " [256, 0, 'adam', 0.005],\n",
              " [128, 0, 'SGD', 0.05],\n",
              " [128, 256, 'adam', 0.005, 0],\n",
              " [256, 0, 'SGD', 0.01]]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "configurations = combs\n",
        "use_focal_loss = True\n",
        "class_weights_included = False\n",
        "\n",
        "X_train, X_val = inp_train_features, inp_val_features\n",
        "y_train, y_val = train_labels, val_labels\n",
        "\n",
        "for conf in configurations:\n",
        "      if len(conf) > 4:\n",
        "        if use_focal_loss:\n",
        "          model_ = model_builder(conf[0], conf[1], conf[2], conf[3], third_layer=conf[4], loss_function='focal')      \n",
        "          model_path = os.path.join(project_dir,'models/mlp_models_selection/{}/model_{}_{}_{}_{}_lr:{}_{}/'.format(model_naming, conf[0],conf[1],conf[4],conf[2],conf[3],'focal'))\n",
        "        else:\n",
        "          model_ = model_builder(conf[0], conf[1], conf[2], conf[3], third_layer=conf[4])      \n",
        "          model_path = os.path.join(project_dir,'models/mlp_models_selection/{}/model_{}_{}_{}_{}_lr:{}/'.format(model_naming, conf[0],conf[1],conf[4],conf[2],conf[3]))\n",
        "      \n",
        "      else:\n",
        "        if use_focal_loss:\n",
        "          model_ = model_builder(conf[0], conf[1], conf[2], conf[3], loss_function='focal')      \n",
        "          model_path = os.path.join(project_dir,'models/mlp_models_selection/{}/model_{}_{}_{}_lr:{}_{}/'.format(model_naming, conf[0],conf[1],conf[2],conf[3],'focal'))\n",
        "        else:\n",
        "          model_ = model_builder(conf[0], conf[1], conf[2], conf[3])      \n",
        "          model_path = os.path.join(project_dir,'models/mlp_models_selection/{}/model_{}_{}_{}_lr:{}/'.format(model_naming, conf[0],conf[1],conf[2],conf[3]))\n",
        "\n",
        "      if not class_weights_included and not use_focal_loss:\n",
        "        model_path = model_path[:-1] + '_no_sample_weights/'\n",
        "\n",
        "      if not os.path.exists(model_path):\n",
        "          os.makedirs(model_path)\n",
        "\n",
        "      my_callbacks = [\n",
        "  tf.keras.callbacks.ModelCheckpoint(filepath= model_path + 'model.epo:{epoch:02d}-val_loss:{val_loss:.3f}--.h5', save_weights_only=True,monitor='val_acc'),\n",
        "  tf.keras.callbacks.ModelCheckpoint(filepath= model_path + 'best_val_loss.h5', save_weights_only=True,save_best_only=True, monitor='val_loss'),\n",
        "  tf.keras.callbacks.CSVLogger(model_path + 'history.csv', separator=\",\", append=False)\n",
        "      ]\n",
        "\n",
        "      if not class_weights_included:\n",
        "        model_.fit(X_train,\n",
        "                y_train, \n",
        "                epochs=90, \n",
        "                batch_size=10, \n",
        "                validation_data = (X_val, y_val),\n",
        "                callbacks=my_callbacks)\n",
        "      else:\n",
        "        model_.fit(X_train,\n",
        "                y_train, \n",
        "                epochs=90, \n",
        "                batch_size=10, \n",
        "                validation_data = (X_val, y_val),\n",
        "                callbacks=my_callbacks,\n",
        "                class_weight=class_weights_dict)\n",
        "      \n",
        "      del model_\n",
        "      keras.backend.clear_session()\n",
        "      tf.compat.v1.reset_default_graph()"
      ],
      "metadata": {
        "id": "ctV7_9JQttze",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe139f79-5981-45ef-e5bb-175b4f518779"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/90\n",
            "984/984 [==============================] - 10s 6ms/step - loss: 1.4366 - f1_score: 0.1743 - acc: 0.2213 - val_loss: 1.4033 - val_f1_score: 0.1652 - val_acc: 0.2582\n",
            "Epoch 2/90\n",
            "984/984 [==============================] - 5s 6ms/step - loss: 1.4076 - f1_score: 0.1854 - acc: 0.2428 - val_loss: 1.4021 - val_f1_score: 0.1902 - val_acc: 0.2631\n",
            "Epoch 3/90\n",
            "984/984 [==============================] - 7s 7ms/step - loss: 1.3945 - f1_score: 0.1902 - acc: 0.2473 - val_loss: 1.3807 - val_f1_score: 0.1170 - val_acc: 0.2338\n",
            "Epoch 4/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3881 - f1_score: 0.1958 - acc: 0.2554 - val_loss: 1.3645 - val_f1_score: 0.1653 - val_acc: 0.2684\n",
            "Epoch 5/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3842 - f1_score: 0.1964 - acc: 0.2549 - val_loss: 1.3710 - val_f1_score: 0.1802 - val_acc: 0.2505\n",
            "Epoch 6/90\n",
            "984/984 [==============================] - 6s 6ms/step - loss: 1.3764 - f1_score: 0.2030 - acc: 0.2623 - val_loss: 1.3584 - val_f1_score: 0.1945 - val_acc: 0.2814\n",
            "Epoch 7/90\n",
            "984/984 [==============================] - 6s 6ms/step - loss: 1.3750 - f1_score: 0.1989 - acc: 0.2581 - val_loss: 1.3549 - val_f1_score: 0.2147 - val_acc: 0.2879\n",
            "Epoch 8/90\n",
            "984/984 [==============================] - 7s 7ms/step - loss: 1.3690 - f1_score: 0.2034 - acc: 0.2647 - val_loss: 1.3540 - val_f1_score: 0.1776 - val_acc: 0.2757\n",
            "Epoch 9/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3644 - f1_score: 0.2073 - acc: 0.2689 - val_loss: 1.3518 - val_f1_score: 0.2396 - val_acc: 0.2879\n",
            "Epoch 10/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3639 - f1_score: 0.2073 - acc: 0.2668 - val_loss: 1.3696 - val_f1_score: 0.2006 - val_acc: 0.2668\n",
            "Epoch 11/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3574 - f1_score: 0.2143 - acc: 0.2718 - val_loss: 1.3530 - val_f1_score: 0.2003 - val_acc: 0.2668\n",
            "Epoch 12/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3555 - f1_score: 0.2177 - acc: 0.2764 - val_loss: 1.3574 - val_f1_score: 0.2090 - val_acc: 0.2712\n",
            "Epoch 13/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3586 - f1_score: 0.2129 - acc: 0.2711 - val_loss: 1.3906 - val_f1_score: 0.1502 - val_acc: 0.2493\n",
            "Epoch 14/90\n",
            "984/984 [==============================] - 4s 5ms/step - loss: 1.3550 - f1_score: 0.2137 - acc: 0.2697 - val_loss: 1.3477 - val_f1_score: 0.2170 - val_acc: 0.2900\n",
            "Epoch 15/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3528 - f1_score: 0.2125 - acc: 0.2709 - val_loss: 1.3378 - val_f1_score: 0.1862 - val_acc: 0.2717\n",
            "Epoch 16/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3504 - f1_score: 0.2175 - acc: 0.2756 - val_loss: 1.3379 - val_f1_score: 0.2153 - val_acc: 0.2900\n",
            "Epoch 17/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3519 - f1_score: 0.2202 - acc: 0.2771 - val_loss: 1.3431 - val_f1_score: 0.2008 - val_acc: 0.2839\n",
            "Epoch 18/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3463 - f1_score: 0.2193 - acc: 0.2766 - val_loss: 1.3377 - val_f1_score: 0.1724 - val_acc: 0.2688\n",
            "Epoch 19/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3500 - f1_score: 0.2195 - acc: 0.2769 - val_loss: 1.3438 - val_f1_score: 0.2053 - val_acc: 0.2717\n",
            "Epoch 20/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3455 - f1_score: 0.2155 - acc: 0.2712 - val_loss: 1.3392 - val_f1_score: 0.1927 - val_acc: 0.2656\n",
            "Epoch 21/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3406 - f1_score: 0.2252 - acc: 0.2793 - val_loss: 1.3396 - val_f1_score: 0.1954 - val_acc: 0.2790\n",
            "Epoch 22/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3416 - f1_score: 0.2242 - acc: 0.2801 - val_loss: 1.3359 - val_f1_score: 0.2232 - val_acc: 0.2924\n",
            "Epoch 23/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3405 - f1_score: 0.2242 - acc: 0.2769 - val_loss: 1.3506 - val_f1_score: 0.2342 - val_acc: 0.2814\n",
            "Epoch 24/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3391 - f1_score: 0.2220 - acc: 0.2777 - val_loss: 1.3487 - val_f1_score: 0.2343 - val_acc: 0.2810\n",
            "Epoch 25/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3361 - f1_score: 0.2255 - acc: 0.2810 - val_loss: 1.3316 - val_f1_score: 0.2064 - val_acc: 0.2834\n",
            "Epoch 26/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3377 - f1_score: 0.2240 - acc: 0.2803 - val_loss: 1.3434 - val_f1_score: 0.2444 - val_acc: 0.2855\n",
            "Epoch 27/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3377 - f1_score: 0.2271 - acc: 0.2810 - val_loss: 1.3531 - val_f1_score: 0.2027 - val_acc: 0.2708\n",
            "Epoch 28/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3363 - f1_score: 0.2248 - acc: 0.2782 - val_loss: 1.3296 - val_f1_score: 0.1912 - val_acc: 0.2729\n",
            "Epoch 29/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3351 - f1_score: 0.2247 - acc: 0.2754 - val_loss: 1.3226 - val_f1_score: 0.2281 - val_acc: 0.2891\n",
            "Epoch 30/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3333 - f1_score: 0.2311 - acc: 0.2813 - val_loss: 1.3458 - val_f1_score: 0.2017 - val_acc: 0.2875\n",
            "Epoch 31/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3338 - f1_score: 0.2329 - acc: 0.2864 - val_loss: 1.3432 - val_f1_score: 0.2162 - val_acc: 0.2717\n",
            "Epoch 32/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3308 - f1_score: 0.2381 - acc: 0.2897 - val_loss: 1.3446 - val_f1_score: 0.2174 - val_acc: 0.2802\n",
            "Epoch 33/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3359 - f1_score: 0.2277 - acc: 0.2828 - val_loss: 1.3333 - val_f1_score: 0.1938 - val_acc: 0.2729\n",
            "Epoch 34/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3320 - f1_score: 0.2296 - acc: 0.2810 - val_loss: 1.3323 - val_f1_score: 0.2101 - val_acc: 0.2704\n",
            "Epoch 35/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3293 - f1_score: 0.2352 - acc: 0.2882 - val_loss: 1.3160 - val_f1_score: 0.2507 - val_acc: 0.3017\n",
            "Epoch 36/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3287 - f1_score: 0.2316 - acc: 0.2819 - val_loss: 1.3225 - val_f1_score: 0.2222 - val_acc: 0.2965\n",
            "Epoch 37/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3235 - f1_score: 0.2336 - acc: 0.2855 - val_loss: 1.3386 - val_f1_score: 0.2491 - val_acc: 0.2981\n",
            "Epoch 38/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3270 - f1_score: 0.2312 - acc: 0.2826 - val_loss: 1.3285 - val_f1_score: 0.2473 - val_acc: 0.2851\n",
            "Epoch 39/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3288 - f1_score: 0.2364 - acc: 0.2896 - val_loss: 1.3203 - val_f1_score: 0.2545 - val_acc: 0.3017\n",
            "Epoch 40/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3230 - f1_score: 0.2337 - acc: 0.2819 - val_loss: 1.3297 - val_f1_score: 0.2092 - val_acc: 0.2786\n",
            "Epoch 41/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3196 - f1_score: 0.2361 - acc: 0.2853 - val_loss: 1.3352 - val_f1_score: 0.1814 - val_acc: 0.2570\n",
            "Epoch 42/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3232 - f1_score: 0.2386 - acc: 0.2901 - val_loss: 1.3252 - val_f1_score: 0.2087 - val_acc: 0.2977\n",
            "Epoch 43/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3144 - f1_score: 0.2398 - acc: 0.2909 - val_loss: 1.3210 - val_f1_score: 0.1811 - val_acc: 0.2696\n",
            "Epoch 44/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3234 - f1_score: 0.2338 - acc: 0.2849 - val_loss: 1.3115 - val_f1_score: 0.2377 - val_acc: 0.3022\n",
            "Epoch 45/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3203 - f1_score: 0.2368 - acc: 0.2883 - val_loss: 1.3328 - val_f1_score: 0.2105 - val_acc: 0.2717\n",
            "Epoch 46/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3181 - f1_score: 0.2406 - acc: 0.2899 - val_loss: 1.3261 - val_f1_score: 0.2295 - val_acc: 0.2965\n",
            "Epoch 47/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3204 - f1_score: 0.2395 - acc: 0.2900 - val_loss: 1.3141 - val_f1_score: 0.2410 - val_acc: 0.2985\n",
            "Epoch 48/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3191 - f1_score: 0.2398 - acc: 0.2893 - val_loss: 1.3310 - val_f1_score: 0.1968 - val_acc: 0.2806\n",
            "Epoch 49/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3223 - f1_score: 0.2374 - acc: 0.2904 - val_loss: 1.3187 - val_f1_score: 0.2477 - val_acc: 0.2900\n",
            "Epoch 50/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3166 - f1_score: 0.2428 - acc: 0.2913 - val_loss: 1.3145 - val_f1_score: 0.2315 - val_acc: 0.3038\n",
            "Epoch 51/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3192 - f1_score: 0.2348 - acc: 0.2857 - val_loss: 1.3123 - val_f1_score: 0.2310 - val_acc: 0.2912\n",
            "Epoch 52/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3166 - f1_score: 0.2422 - acc: 0.2915 - val_loss: 1.3183 - val_f1_score: 0.2286 - val_acc: 0.2985\n",
            "Epoch 53/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3147 - f1_score: 0.2386 - acc: 0.2872 - val_loss: 1.3316 - val_f1_score: 0.2442 - val_acc: 0.2944\n",
            "Epoch 54/90\n",
            "984/984 [==============================] - 4s 5ms/step - loss: 1.3181 - f1_score: 0.2399 - acc: 0.2899 - val_loss: 1.3178 - val_f1_score: 0.2176 - val_acc: 0.2965\n",
            "Epoch 55/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3113 - f1_score: 0.2387 - acc: 0.2896 - val_loss: 1.3099 - val_f1_score: 0.2435 - val_acc: 0.3030\n",
            "Epoch 56/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3127 - f1_score: 0.2486 - acc: 0.2974 - val_loss: 1.3219 - val_f1_score: 0.2127 - val_acc: 0.2887\n",
            "Epoch 57/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3166 - f1_score: 0.2400 - acc: 0.2891 - val_loss: 1.3285 - val_f1_score: 0.2333 - val_acc: 0.2883\n",
            "Epoch 58/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3118 - f1_score: 0.2467 - acc: 0.2925 - val_loss: 1.3277 - val_f1_score: 0.2330 - val_acc: 0.3005\n",
            "Epoch 59/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3121 - f1_score: 0.2452 - acc: 0.2926 - val_loss: 1.3234 - val_f1_score: 0.2588 - val_acc: 0.2867\n",
            "Epoch 60/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3073 - f1_score: 0.2463 - acc: 0.2934 - val_loss: 1.3111 - val_f1_score: 0.2517 - val_acc: 0.3001\n",
            "Epoch 61/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3104 - f1_score: 0.2456 - acc: 0.2941 - val_loss: 1.3176 - val_f1_score: 0.2181 - val_acc: 0.2765\n",
            "Epoch 62/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3116 - f1_score: 0.2433 - acc: 0.2909 - val_loss: 1.3117 - val_f1_score: 0.2403 - val_acc: 0.2981\n",
            "Epoch 63/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3086 - f1_score: 0.2481 - acc: 0.2945 - val_loss: 1.3924 - val_f1_score: 0.2166 - val_acc: 0.2631\n",
            "Epoch 64/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3078 - f1_score: 0.2446 - acc: 0.2924 - val_loss: 1.3197 - val_f1_score: 0.2347 - val_acc: 0.2895\n",
            "Epoch 65/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3093 - f1_score: 0.2436 - acc: 0.2898 - val_loss: 1.3156 - val_f1_score: 0.2323 - val_acc: 0.2997\n",
            "Epoch 66/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3083 - f1_score: 0.2471 - acc: 0.2944 - val_loss: 1.3216 - val_f1_score: 0.2039 - val_acc: 0.2664\n",
            "Epoch 67/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3041 - f1_score: 0.2510 - acc: 0.2977 - val_loss: 1.3232 - val_f1_score: 0.2364 - val_acc: 0.2761\n",
            "Epoch 68/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3062 - f1_score: 0.2474 - acc: 0.2953 - val_loss: 1.3123 - val_f1_score: 0.2264 - val_acc: 0.2834\n",
            "Epoch 69/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3043 - f1_score: 0.2489 - acc: 0.2971 - val_loss: 1.3369 - val_f1_score: 0.1810 - val_acc: 0.2786\n",
            "Epoch 70/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3070 - f1_score: 0.2479 - acc: 0.2971 - val_loss: 1.3232 - val_f1_score: 0.2522 - val_acc: 0.3017\n",
            "Epoch 71/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3084 - f1_score: 0.2445 - acc: 0.2923 - val_loss: 1.3118 - val_f1_score: 0.2228 - val_acc: 0.2961\n",
            "Epoch 72/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2996 - f1_score: 0.2503 - acc: 0.2969 - val_loss: 1.3107 - val_f1_score: 0.2424 - val_acc: 0.2989\n",
            "Epoch 73/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3065 - f1_score: 0.2428 - acc: 0.2893 - val_loss: 1.3231 - val_f1_score: 0.2492 - val_acc: 0.2989\n",
            "Epoch 74/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3033 - f1_score: 0.2450 - acc: 0.2910 - val_loss: 1.3070 - val_f1_score: 0.2362 - val_acc: 0.3038\n",
            "Epoch 75/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3040 - f1_score: 0.2508 - acc: 0.2988 - val_loss: 1.3140 - val_f1_score: 0.2407 - val_acc: 0.3022\n",
            "Epoch 76/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2962 - f1_score: 0.2493 - acc: 0.2964 - val_loss: 1.3323 - val_f1_score: 0.2059 - val_acc: 0.2769\n",
            "Epoch 77/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2989 - f1_score: 0.2542 - acc: 0.2998 - val_loss: 1.3245 - val_f1_score: 0.2608 - val_acc: 0.3046\n",
            "Epoch 78/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2999 - f1_score: 0.2541 - acc: 0.3001 - val_loss: 1.3123 - val_f1_score: 0.2324 - val_acc: 0.2895\n",
            "Epoch 79/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2989 - f1_score: 0.2486 - acc: 0.2957 - val_loss: 1.3358 - val_f1_score: 0.2435 - val_acc: 0.2924\n",
            "Epoch 80/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2984 - f1_score: 0.2506 - acc: 0.2962 - val_loss: 1.3151 - val_f1_score: 0.2328 - val_acc: 0.2798\n",
            "Epoch 81/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2994 - f1_score: 0.2507 - acc: 0.2984 - val_loss: 1.3075 - val_f1_score: 0.2411 - val_acc: 0.2952\n",
            "Epoch 82/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2959 - f1_score: 0.2519 - acc: 0.3000 - val_loss: 1.3106 - val_f1_score: 0.2295 - val_acc: 0.2969\n",
            "Epoch 83/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2949 - f1_score: 0.2579 - acc: 0.3033 - val_loss: 1.3154 - val_f1_score: 0.2200 - val_acc: 0.2814\n",
            "Epoch 84/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2943 - f1_score: 0.2536 - acc: 0.3016 - val_loss: 1.3119 - val_f1_score: 0.2369 - val_acc: 0.2920\n",
            "Epoch 85/90\n",
            "984/984 [==============================] - 4s 5ms/step - loss: 1.2986 - f1_score: 0.2550 - acc: 0.2985 - val_loss: 1.3068 - val_f1_score: 0.2347 - val_acc: 0.3009\n",
            "Epoch 86/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2966 - f1_score: 0.2538 - acc: 0.3002 - val_loss: 1.3221 - val_f1_score: 0.2562 - val_acc: 0.2944\n",
            "Epoch 87/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2970 - f1_score: 0.2531 - acc: 0.2978 - val_loss: 1.3185 - val_f1_score: 0.2283 - val_acc: 0.2932\n",
            "Epoch 88/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2960 - f1_score: 0.2564 - acc: 0.3024 - val_loss: 1.3231 - val_f1_score: 0.2077 - val_acc: 0.2904\n",
            "Epoch 89/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2948 - f1_score: 0.2547 - acc: 0.3008 - val_loss: 1.3166 - val_f1_score: 0.2275 - val_acc: 0.2749\n",
            "Epoch 90/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2914 - f1_score: 0.2594 - acc: 0.3078 - val_loss: 1.3115 - val_f1_score: 0.2283 - val_acc: 0.3022\n",
            "Epoch 1/90\n",
            "984/984 [==============================] - 5s 4ms/step - loss: 1.4178 - f1_score: 0.1890 - acc: 0.2291 - val_loss: 1.3731 - val_f1_score: 0.1376 - val_acc: 0.2436\n",
            "Epoch 2/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3743 - f1_score: 0.1946 - acc: 0.2581 - val_loss: 1.3526 - val_f1_score: 0.2123 - val_acc: 0.2826\n",
            "Epoch 3/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3619 - f1_score: 0.1991 - acc: 0.2611 - val_loss: 1.3523 - val_f1_score: 0.1912 - val_acc: 0.2745\n",
            "Epoch 4/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3515 - f1_score: 0.2083 - acc: 0.2725 - val_loss: 1.3403 - val_f1_score: 0.1924 - val_acc: 0.2745\n",
            "Epoch 5/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3468 - f1_score: 0.2131 - acc: 0.2762 - val_loss: 1.3423 - val_f1_score: 0.2349 - val_acc: 0.2924\n",
            "Epoch 6/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3389 - f1_score: 0.2155 - acc: 0.2791 - val_loss: 1.4035 - val_f1_score: 0.1594 - val_acc: 0.2416\n",
            "Epoch 7/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.3394 - f1_score: 0.2175 - acc: 0.2777 - val_loss: 1.3259 - val_f1_score: 0.2196 - val_acc: 0.2859\n",
            "Epoch 8/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3312 - f1_score: 0.2233 - acc: 0.2837 - val_loss: 1.3247 - val_f1_score: 0.2167 - val_acc: 0.2883\n",
            "Epoch 9/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3259 - f1_score: 0.2209 - acc: 0.2811 - val_loss: 1.3393 - val_f1_score: 0.1973 - val_acc: 0.2708\n",
            "Epoch 10/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3249 - f1_score: 0.2301 - acc: 0.2919 - val_loss: 1.3314 - val_f1_score: 0.2016 - val_acc: 0.2790\n",
            "Epoch 11/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3204 - f1_score: 0.2289 - acc: 0.2849 - val_loss: 1.3366 - val_f1_score: 0.2286 - val_acc: 0.2977\n",
            "Epoch 12/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3134 - f1_score: 0.2332 - acc: 0.2858 - val_loss: 1.3265 - val_f1_score: 0.2365 - val_acc: 0.2944\n",
            "Epoch 13/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3071 - f1_score: 0.2382 - acc: 0.2927 - val_loss: 1.3151 - val_f1_score: 0.2229 - val_acc: 0.2887\n",
            "Epoch 14/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3078 - f1_score: 0.2365 - acc: 0.2904 - val_loss: 1.3276 - val_f1_score: 0.2162 - val_acc: 0.2839\n",
            "Epoch 15/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3050 - f1_score: 0.2363 - acc: 0.2908 - val_loss: 1.3191 - val_f1_score: 0.2292 - val_acc: 0.2940\n",
            "Epoch 16/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3012 - f1_score: 0.2448 - acc: 0.2972 - val_loss: 1.3220 - val_f1_score: 0.2344 - val_acc: 0.2969\n",
            "Epoch 17/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.3060 - f1_score: 0.2394 - acc: 0.2942 - val_loss: 1.3328 - val_f1_score: 0.2150 - val_acc: 0.2782\n",
            "Epoch 18/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2993 - f1_score: 0.2446 - acc: 0.2967 - val_loss: 1.3316 - val_f1_score: 0.2153 - val_acc: 0.2920\n",
            "Epoch 19/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.2948 - f1_score: 0.2477 - acc: 0.3009 - val_loss: 1.3143 - val_f1_score: 0.2346 - val_acc: 0.3042\n",
            "Epoch 20/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2967 - f1_score: 0.2444 - acc: 0.2953 - val_loss: 1.3183 - val_f1_score: 0.2415 - val_acc: 0.3009\n",
            "Epoch 21/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2864 - f1_score: 0.2505 - acc: 0.3035 - val_loss: 1.3333 - val_f1_score: 0.2278 - val_acc: 0.2875\n",
            "Epoch 22/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2938 - f1_score: 0.2510 - acc: 0.2993 - val_loss: 1.3209 - val_f1_score: 0.2383 - val_acc: 0.2989\n",
            "Epoch 23/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2907 - f1_score: 0.2549 - acc: 0.3027 - val_loss: 1.3294 - val_f1_score: 0.2169 - val_acc: 0.2733\n",
            "Epoch 24/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2863 - f1_score: 0.2491 - acc: 0.2968 - val_loss: 1.3365 - val_f1_score: 0.2336 - val_acc: 0.2883\n",
            "Epoch 25/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2856 - f1_score: 0.2496 - acc: 0.3000 - val_loss: 1.3182 - val_f1_score: 0.2430 - val_acc: 0.3026\n",
            "Epoch 26/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2877 - f1_score: 0.2550 - acc: 0.3052 - val_loss: 1.3293 - val_f1_score: 0.2229 - val_acc: 0.2814\n",
            "Epoch 27/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2803 - f1_score: 0.2590 - acc: 0.3071 - val_loss: 1.3539 - val_f1_score: 0.2381 - val_acc: 0.2904\n",
            "Epoch 28/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2780 - f1_score: 0.2534 - acc: 0.3038 - val_loss: 1.3373 - val_f1_score: 0.2438 - val_acc: 0.2887\n",
            "Epoch 29/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2770 - f1_score: 0.2559 - acc: 0.3025 - val_loss: 1.3243 - val_f1_score: 0.2523 - val_acc: 0.3005\n",
            "Epoch 30/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2771 - f1_score: 0.2582 - acc: 0.3042 - val_loss: 1.3274 - val_f1_score: 0.2367 - val_acc: 0.2924\n",
            "Epoch 31/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2747 - f1_score: 0.2512 - acc: 0.3015 - val_loss: 1.3300 - val_f1_score: 0.2419 - val_acc: 0.3054\n",
            "Epoch 32/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2686 - f1_score: 0.2623 - acc: 0.3116 - val_loss: 1.3374 - val_f1_score: 0.2479 - val_acc: 0.2993\n",
            "Epoch 33/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2682 - f1_score: 0.2619 - acc: 0.3089 - val_loss: 1.3301 - val_f1_score: 0.2596 - val_acc: 0.3066\n",
            "Epoch 34/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2708 - f1_score: 0.2636 - acc: 0.3099 - val_loss: 1.3412 - val_f1_score: 0.2501 - val_acc: 0.3009\n",
            "Epoch 35/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2641 - f1_score: 0.2649 - acc: 0.3082 - val_loss: 1.3366 - val_f1_score: 0.2408 - val_acc: 0.3005\n",
            "Epoch 36/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2679 - f1_score: 0.2647 - acc: 0.3112 - val_loss: 1.3293 - val_f1_score: 0.2466 - val_acc: 0.2965\n",
            "Epoch 37/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2643 - f1_score: 0.2651 - acc: 0.3085 - val_loss: 1.3376 - val_f1_score: 0.2206 - val_acc: 0.2952\n",
            "Epoch 38/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.2613 - f1_score: 0.2674 - acc: 0.3116 - val_loss: 1.3348 - val_f1_score: 0.2364 - val_acc: 0.2944\n",
            "Epoch 39/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2588 - f1_score: 0.2667 - acc: 0.3124 - val_loss: 1.3459 - val_f1_score: 0.2418 - val_acc: 0.2871\n",
            "Epoch 40/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2603 - f1_score: 0.2682 - acc: 0.3118 - val_loss: 1.3290 - val_f1_score: 0.2536 - val_acc: 0.3054\n",
            "Epoch 41/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2582 - f1_score: 0.2675 - acc: 0.3112 - val_loss: 1.3491 - val_f1_score: 0.2512 - val_acc: 0.2895\n",
            "Epoch 42/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2514 - f1_score: 0.2735 - acc: 0.3158 - val_loss: 1.3507 - val_f1_score: 0.2086 - val_acc: 0.2851\n",
            "Epoch 43/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2517 - f1_score: 0.2743 - acc: 0.3190 - val_loss: 1.3305 - val_f1_score: 0.2550 - val_acc: 0.3042\n",
            "Epoch 44/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2539 - f1_score: 0.2719 - acc: 0.3135 - val_loss: 1.3684 - val_f1_score: 0.2522 - val_acc: 0.3042\n",
            "Epoch 45/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2507 - f1_score: 0.2771 - acc: 0.3201 - val_loss: 1.3361 - val_f1_score: 0.2383 - val_acc: 0.3005\n",
            "Epoch 46/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2532 - f1_score: 0.2744 - acc: 0.3193 - val_loss: 1.3423 - val_f1_score: 0.2550 - val_acc: 0.3042\n",
            "Epoch 47/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2563 - f1_score: 0.2737 - acc: 0.3155 - val_loss: 1.3619 - val_f1_score: 0.2308 - val_acc: 0.2985\n",
            "Epoch 48/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2506 - f1_score: 0.2754 - acc: 0.3188 - val_loss: 1.3362 - val_f1_score: 0.2453 - val_acc: 0.3026\n",
            "Epoch 49/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2481 - f1_score: 0.2828 - acc: 0.3268 - val_loss: 1.3561 - val_f1_score: 0.2556 - val_acc: 0.2993\n",
            "Epoch 50/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2507 - f1_score: 0.2747 - acc: 0.3175 - val_loss: 1.3559 - val_f1_score: 0.2358 - val_acc: 0.3030\n",
            "Epoch 51/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2423 - f1_score: 0.2779 - acc: 0.3225 - val_loss: 1.3462 - val_f1_score: 0.2549 - val_acc: 0.3070\n",
            "Epoch 52/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2431 - f1_score: 0.2740 - acc: 0.3192 - val_loss: 1.3428 - val_f1_score: 0.2587 - val_acc: 0.3083\n",
            "Epoch 53/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2474 - f1_score: 0.2665 - acc: 0.3084 - val_loss: 1.3548 - val_f1_score: 0.2433 - val_acc: 0.2940\n",
            "Epoch 54/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2432 - f1_score: 0.2743 - acc: 0.3164 - val_loss: 1.3571 - val_f1_score: 0.2352 - val_acc: 0.3062\n",
            "Epoch 55/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2503 - f1_score: 0.2700 - acc: 0.3106 - val_loss: 1.3664 - val_f1_score: 0.2294 - val_acc: 0.2920\n",
            "Epoch 56/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2437 - f1_score: 0.2829 - acc: 0.3233 - val_loss: 1.3545 - val_f1_score: 0.2400 - val_acc: 0.2993\n",
            "Epoch 57/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2384 - f1_score: 0.2812 - acc: 0.3217 - val_loss: 1.3675 - val_f1_score: 0.2391 - val_acc: 0.2916\n",
            "Epoch 58/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2407 - f1_score: 0.2833 - acc: 0.3242 - val_loss: 1.3590 - val_f1_score: 0.2527 - val_acc: 0.3026\n",
            "Epoch 59/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2395 - f1_score: 0.2825 - acc: 0.3201 - val_loss: 1.3591 - val_f1_score: 0.2556 - val_acc: 0.3017\n",
            "Epoch 60/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2385 - f1_score: 0.2848 - acc: 0.3251 - val_loss: 1.3620 - val_f1_score: 0.2399 - val_acc: 0.2989\n",
            "Epoch 61/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2426 - f1_score: 0.2793 - acc: 0.3189 - val_loss: 1.3539 - val_f1_score: 0.2510 - val_acc: 0.3013\n",
            "Epoch 62/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2381 - f1_score: 0.2815 - acc: 0.3209 - val_loss: 1.3561 - val_f1_score: 0.2427 - val_acc: 0.2895\n",
            "Epoch 63/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2339 - f1_score: 0.2844 - acc: 0.3249 - val_loss: 1.3688 - val_f1_score: 0.2582 - val_acc: 0.3115\n",
            "Epoch 64/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2376 - f1_score: 0.2780 - acc: 0.3212 - val_loss: 1.3561 - val_f1_score: 0.2461 - val_acc: 0.2936\n",
            "Epoch 65/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2306 - f1_score: 0.2844 - acc: 0.3252 - val_loss: 1.3572 - val_f1_score: 0.2592 - val_acc: 0.3042\n",
            "Epoch 66/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2333 - f1_score: 0.2835 - acc: 0.3247 - val_loss: 1.3605 - val_f1_score: 0.2467 - val_acc: 0.3034\n",
            "Epoch 67/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2320 - f1_score: 0.2847 - acc: 0.3269 - val_loss: 1.3541 - val_f1_score: 0.2378 - val_acc: 0.2997\n",
            "Epoch 68/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2262 - f1_score: 0.2870 - acc: 0.3257 - val_loss: 1.3556 - val_f1_score: 0.2441 - val_acc: 0.2928\n",
            "Epoch 69/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2239 - f1_score: 0.2918 - acc: 0.3301 - val_loss: 1.3545 - val_f1_score: 0.2405 - val_acc: 0.2944\n",
            "Epoch 70/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2326 - f1_score: 0.2851 - acc: 0.3264 - val_loss: 1.3690 - val_f1_score: 0.2272 - val_acc: 0.2904\n",
            "Epoch 71/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2290 - f1_score: 0.2910 - acc: 0.3318 - val_loss: 1.3705 - val_f1_score: 0.2535 - val_acc: 0.2977\n",
            "Epoch 72/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2280 - f1_score: 0.2858 - acc: 0.3262 - val_loss: 1.3732 - val_f1_score: 0.2461 - val_acc: 0.2952\n",
            "Epoch 73/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2227 - f1_score: 0.2937 - acc: 0.3307 - val_loss: 1.3679 - val_f1_score: 0.2557 - val_acc: 0.2948\n",
            "Epoch 74/90\n",
            "984/984 [==============================] - 5s 5ms/step - loss: 1.2187 - f1_score: 0.2870 - acc: 0.3303 - val_loss: 1.3689 - val_f1_score: 0.2611 - val_acc: 0.2952\n",
            "Epoch 75/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2191 - f1_score: 0.2865 - acc: 0.3247 - val_loss: 1.3695 - val_f1_score: 0.2599 - val_acc: 0.3013\n",
            "Epoch 76/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2265 - f1_score: 0.2866 - acc: 0.3254 - val_loss: 1.3900 - val_f1_score: 0.2402 - val_acc: 0.2879\n",
            "Epoch 77/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2283 - f1_score: 0.2856 - acc: 0.3227 - val_loss: 1.3823 - val_f1_score: 0.2549 - val_acc: 0.3026\n",
            "Epoch 78/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2238 - f1_score: 0.2854 - acc: 0.3231 - val_loss: 1.3638 - val_f1_score: 0.2394 - val_acc: 0.3022\n",
            "Epoch 79/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2255 - f1_score: 0.2919 - acc: 0.3315 - val_loss: 1.3778 - val_f1_score: 0.2396 - val_acc: 0.2973\n",
            "Epoch 80/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2222 - f1_score: 0.2942 - acc: 0.3331 - val_loss: 1.3828 - val_f1_score: 0.2536 - val_acc: 0.3001\n",
            "Epoch 81/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2201 - f1_score: 0.2867 - acc: 0.3267 - val_loss: 1.3738 - val_f1_score: 0.2485 - val_acc: 0.3026\n",
            "Epoch 82/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2187 - f1_score: 0.2976 - acc: 0.3340 - val_loss: 1.3964 - val_f1_score: 0.2543 - val_acc: 0.2948\n",
            "Epoch 83/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2192 - f1_score: 0.2910 - acc: 0.3261 - val_loss: 1.3851 - val_f1_score: 0.2404 - val_acc: 0.2940\n",
            "Epoch 84/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2137 - f1_score: 0.2942 - acc: 0.3327 - val_loss: 1.3911 - val_f1_score: 0.2585 - val_acc: 0.3078\n",
            "Epoch 85/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2152 - f1_score: 0.2964 - acc: 0.3326 - val_loss: 1.3782 - val_f1_score: 0.2548 - val_acc: 0.2952\n",
            "Epoch 86/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2170 - f1_score: 0.2975 - acc: 0.3361 - val_loss: 1.3831 - val_f1_score: 0.2249 - val_acc: 0.2940\n",
            "Epoch 87/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2110 - f1_score: 0.2941 - acc: 0.3330 - val_loss: 1.3776 - val_f1_score: 0.2568 - val_acc: 0.3001\n",
            "Epoch 88/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2156 - f1_score: 0.2967 - acc: 0.3346 - val_loss: 1.3929 - val_f1_score: 0.2427 - val_acc: 0.2891\n",
            "Epoch 89/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2105 - f1_score: 0.3028 - acc: 0.3404 - val_loss: 1.3979 - val_f1_score: 0.2464 - val_acc: 0.3009\n",
            "Epoch 90/90\n",
            "984/984 [==============================] - 4s 4ms/step - loss: 1.2108 - f1_score: 0.2990 - acc: 0.3352 - val_loss: 1.4010 - val_f1_score: 0.2399 - val_acc: 0.2961\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Performance on the Validation Set"
      ],
      "metadata": {
        "id": "avZpU4i7JOWw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_model_params = [512, 0, 'SGD', 0.01]\n",
        "use_focal_loss = False\n",
        "class_weights_included = True\n",
        "\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report, confusion_matrix,f1_score\n",
        "\n",
        "if len(best_model_params) > 4:\n",
        "      if use_focal_loss:     \n",
        "          model_path = os.path.join(project_dir,'models/mlp_models_selection/{}/model_{}_{}_{}_{}_lr:{}_{}/'.format(model_naming, best_model_params[0],best_model_params[1],best_model_params[4],best_model_params[2],best_model_params[3],'focal'))\n",
        "          if not class_weights_included and not use_focal_loss:\n",
        "              model_path = model_path[:-1] + '_no_sample_weights/'\n",
        "          model1 = model_builder(best_model_params[0], best_model_params[1], best_model_params[2], best_model_params[3], third_layer=best_model_params[4], loss_function='focal', dropout=False, load_weights_path=os.path.join(model_path, 'best_val_loss.h5')) \n",
        "      else:\n",
        "          model_path = os.path.join(project_dir,'models/mlp_models_selection/{}/model_{}_{}_{}_{}_lr:{}/'.format(model_naming, best_model_params[0],best_model_params[1],best_model_params[4],best_model_params[2],best_model_params[3]))\n",
        "          if not class_weights_included and not use_focal_loss:\n",
        "              model_path = model_path[:-1] + '_no_sample_weights/'\n",
        "          model1 = model_builder(best_model_params[0], best_model_params[1], best_model_params[2], best_model_params[3], third_layer=best_model_params[4], dropout=False, load_weights_path=os.path.join(model_path, 'best_val_loss.h5'))\n",
        "      \n",
        "else:\n",
        "      if use_focal_loss:     \n",
        "          model_path = os.path.join(project_dir,'models/mlp_models_selection/{}/model_{}_{}_{}_lr:{}_{}/'.format(model_naming, best_model_params[0],best_model_params[1],best_model_params[2],best_model_params[3],'focal'))\n",
        "          if not class_weights_included and not use_focal_loss:\n",
        "              model_path = model_path[:-1] + '_no_sample_weights/'\n",
        "          model1 = model_builder(best_model_params[0], best_model_params[1], best_model_params[2], best_model_params[3], loss_function='focal', dropout=False, load_weights_path=os.path.join(model_path, 'best_val_loss.h5')) \n",
        "      else:\n",
        "          model_path = os.path.join(project_dir,'models/mlp_models_selection/{}/model_{}_{}_{}_lr:{}/'.format(model_naming, best_model_params[0],best_model_params[1],best_model_params[2],best_model_params[3]))\n",
        "          if not class_weights_included and not use_focal_loss:\n",
        "              model_path = model_path[:-1] + '_no_sample_weights/'\n",
        "          model1 = model_builder(best_model_params[0], best_model_params[1], best_model_params[2], best_model_params[3], dropout=False, load_weights_path=os.path.join(model_path, 'best_val_loss.h5'))\n",
        "\n",
        "def pretty_confusion(y_true, y_pred, labels, normalize=True):\n",
        "  if normalize:\n",
        "    cm = confusion_matrix(y_true=y_true, y_pred=y_pred, normalize='true')\n",
        "  else:\n",
        "    cm = confusion_matrix(y_true=y_true, y_pred=y_pred)\n",
        "  fig = plt.figure(figsize=(16, 14))\n",
        "  ax= plt.subplot()\n",
        "  if normalize:\n",
        "    sns.heatmap(cm, annot=True, ax = ax, fmt=\".2f\", cmap='Blues', annot_kws={\n",
        "                'fontsize': 16})\n",
        "  else:\n",
        "    sns.set(font_scale=1)\n",
        "    sns.heatmap(cm, annot=True, ax = ax, fmt=\"g\", cmap='Blues', annot_kws={\n",
        "                'fontsize': 16})\n",
        "  # labels, title and ticks\n",
        "  ax.set_xlabel('Predicted', fontsize=20)\n",
        "  ax.xaxis.set_label_position('bottom')\n",
        "  plt.xticks(rotation=90)\n",
        "  ax.xaxis.set_ticklabels(labels, fontsize = 15)\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  ax.set_ylabel('True', fontsize=20)\n",
        "  ax.yaxis.set_ticklabels(labels, fontsize = 15)\n",
        "  plt.yticks(rotation=0)\n",
        "\n",
        "  plt.title('Refined Confusion Matrix', fontsize=20)\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "def val_report(loaded_model, validation_features_set):\n",
        "    y_predic = loaded_model.predict(validation_features_set)\n",
        "    label_list = ['baroque', 'contemporary', 'gothic', 'modernism', 'neoclassicism', 'noucentisme', 'renaissance', 'romanesque']\n",
        "\n",
        "    y_true = []\n",
        "    for true in val_labels.argmax(axis=1):\n",
        "      y_true.append(label_list[true])\n",
        "\n",
        "    y_pred = []\n",
        "    for pred in y_predic.argmax(axis=1):\n",
        "      y_pred.append(label_list[pred])\n",
        "\n",
        "    print('\\nClassification Report\\n')\n",
        "    print(classification_report(y_true, y_pred))\n",
        "    print('More precise weighted avg F1-score (not to be confused with the macro-F1 score from above): {}'.format(f1_score(y_true, y_pred, average='weighted')))\n",
        "    pretty_confusion(y_true, y_pred, label_list, True)\n",
        "\n",
        "\n",
        "val_report(model1, inp_val_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QIhz8ym2cBdW",
        "outputId": "83f37bcf-5069-4aa4-9d93-786816046da6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "77/77 [==============================] - 0s 2ms/step\n",
            "\n",
            "Classification Report\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "      baroque       0.46      0.55      0.50       305\n",
            " contemporary       0.75      0.73      0.74       178\n",
            "       gothic       0.42      0.38      0.40       214\n",
            "    modernism       0.64      0.54      0.58       499\n",
            "neoclassicism       0.52      0.21      0.30       186\n",
            "  noucentisme       0.56      0.70      0.62       529\n",
            "  renaissance       0.17      0.16      0.17        68\n",
            "   romanesque       0.76      0.82      0.78       480\n",
            "\n",
            "     accuracy                           0.59      2459\n",
            "    macro avg       0.54      0.51      0.51      2459\n",
            " weighted avg       0.59      0.59      0.58      2459\n",
            "\n",
            "More precise weighted avg F1-score (not to be confused with the macro-F1 score from above): 0.5841514187023347\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x1008 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}