notebook367ba4bf72 (1).ipynb

{
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kaggle": {
      "accelerator": "nvidiaTeslaT4",
      "dataSources": [
        {
          "sourceId": 234911,
          "sourceType": "datasetVersion",
          "datasetId": 99505
        }
      ],
      "dockerImageVersionId": 30698,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": true
    },
    "colab": {
      "name": "notebook367ba4bf72",
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'face-expression-recognition-dataset:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F99505%2F234911%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240430%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240430T221659Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%3D3d54a109b371ac71e86167bf1eb7a5ecdf08ec5b199dc611875f2e4bf0961b3581a21919630fe4f35d6c6050a0f010b3f1effc4120623e6828e1b9daf10917e0a5ccd23a595043b30b49e2391d2caeb4bf791618c9069c6db4ac9b18f8e3ed1ea3bc078698c2bf0cc774baefdb05d874e1282ffe9d3ae3ebcc9e27ea604ce70613b82657632f9b85c355465987124b658dacb294d86ce3e7a8306caa946f554adca502e26772b3b16d235475a99d14ec5899234cacf7fa72827eab3f6fa38c605b6245b11e12b26b6b6dc5cc41abb7e4a2fc206d72dae9186eb130b196749a86d59d0bdd0075f084deaa6d04f84259b123107a9dddf1372b53a89c1aa24b9d42'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fl0yDC_AFSHW",
        "outputId": "d23dfdc0-c115-4cae-e126-d1870256badf"
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading face-expression-recognition-dataset, 126358582 bytes compressed\n",
            "[==================================================] 126358582 bytes downloaded\n",
            "Downloaded and uncompressed: face-expression-recognition-dataset\n",
            "Data source import complete.\n"
          ]
        }
      ],
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "source": [
        "# import statements\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array, ImageDataGenerator\n",
        "from tensorflow.keras.layers import Dense, Input, Dropout, GlobalAveragePooling2D, Flatten, Conv2D, BatchNormalization, Activation, MaxPooling2D\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.optimizers import Adam, SGD, RMSprop"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
          "iopub.status.busy": "2024-04-30T22:00:08.314686Z",
          "iopub.execute_input": "2024-04-30T22:00:08.315054Z",
          "iopub.status.idle": "2024-04-30T22:00:23.595925Z",
          "shell.execute_reply.started": "2024-04-30T22:00:08.31502Z",
          "shell.execute_reply": "2024-04-30T22:00:23.59493Z"
        },
        "trusted": true,
        "id": "j5Lan83XFSHb"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# setting the directory\n",
        "picture_size = 48\n",
        "folder_path = \"../input/face-expression-recognition-dataset/images/\"\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-30T22:00:23.597678Z",
          "iopub.execute_input": "2024-04-30T22:00:23.598301Z",
          "iopub.status.idle": "2024-04-30T22:00:23.602559Z",
          "shell.execute_reply.started": "2024-04-30T22:00:23.598272Z",
          "shell.execute_reply": "2024-04-30T22:00:23.601522Z"
        },
        "trusted": true,
        "id": "H4QOBPEqFSHc"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "expression = 'surprise'\n",
        "\n",
        "plt.figure(figsize= (12,12))\n",
        "for i in range(1, 10, 1):\n",
        "    plt.subplot(3,3,i)\n",
        "    img = load_img(folder_path+\"train/\"+expression+\"/\"+\n",
        "                  os.listdir(folder_path + \"train/\" + expression)[i], target_size=(picture_size, picture_size))\n",
        "    plt.imshow(img)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-30T22:00:23.604144Z",
          "iopub.execute_input": "2024-04-30T22:00:23.604801Z",
          "iopub.status.idle": "2024-04-30T22:00:25.331804Z",
          "shell.execute_reply.started": "2024-04-30T22:00:23.604769Z",
          "shell.execute_reply": "2024-04-30T22:00:25.330887Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 987
        },
        "id": "0aZNu4YKFSHd",
        "outputId": "ac0e3509-b3ef-4899-c50d-c08935299826"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size  = 64\n",
        "\n",
        "datagen_train  = ImageDataGenerator()\n",
        "datagen_val = ImageDataGenerator()\n",
        "\n",
        "train_set = datagen_train.flow_from_directory(folder_path+\"train\",\n",
        "                                              target_size = (picture_size,picture_size),\n",
        "                                              color_mode = \"grayscale\",\n",
        "                                              batch_size=batch_size,\n",
        "                                              class_mode='categorical',\n",
        "                                              shuffle=True)\n",
        "\n",
        "\n",
        "test_set = datagen_val.flow_from_directory(folder_path+\"validation\",\n",
        "                                              target_size = (picture_size,picture_size),\n",
        "                                              color_mode = \"grayscale\",\n",
        "                                              batch_size=batch_size,\n",
        "                                              class_mode='categorical',\n",
        "                                              shuffle=False)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-30T22:00:25.334286Z",
          "iopub.execute_input": "2024-04-30T22:00:25.33473Z",
          "iopub.status.idle": "2024-04-30T22:00:47.737173Z",
          "shell.execute_reply.started": "2024-04-30T22:00:25.334695Z",
          "shell.execute_reply": "2024-04-30T22:00:47.736232Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lm86nknXFSHd",
        "outputId": "c90cc265-1d71-4f59-b1fc-fa2580b38b37"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 28821 images belonging to 7 classes.\n",
            "Found 7066 images belonging to 7 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPooling2D, Dropout, Flatten, Dense, Input\n",
        "\n",
        "no_of_classes = 7\n",
        "\n",
        "model = Sequential([\n",
        "    #1st CNN layer\n",
        "    Input(shape=(48, 48, 1)),\n",
        "    Conv2D(64, (3, 3), padding='same'),\n",
        "    BatchNormalization(),\n",
        "    Activation('relu'),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "    Dropout(0.25),\n",
        "\n",
        "    #2nd CNN layer\n",
        "    Conv2D(128, (5, 5), padding='same'),\n",
        "    BatchNormalization(),\n",
        "    Activation('relu'),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "    Dropout(0.25),\n",
        "\n",
        "    #3rd CNN layer\n",
        "    Conv2D(512, (3, 3), padding='same'),\n",
        "    BatchNormalization(),\n",
        "    Activation('relu'),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "    Dropout(0.20),\n",
        "\n",
        "    #4th CNN layer\n",
        "    Conv2D(512, (3, 3), padding='same'),\n",
        "    BatchNormalization(),\n",
        "    Activation('relu'),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "    Dropout(0.20),\n",
        "\n",
        "    Flatten(),\n",
        "\n",
        "    #Fully connected 1st layer\n",
        "    Dense(256),\n",
        "    BatchNormalization(),\n",
        "    Activation('relu'),\n",
        "    Dropout(0.25),\n",
        "\n",
        "    # Fully connected layer 2nd layer\n",
        "    Dense(512),\n",
        "    BatchNormalization(),\n",
        "    Activation('relu'),\n",
        "    Dropout(0.25),\n",
        "\n",
        "    Dense(no_of_classes, activation='softmax')\n",
        "])\n",
        "\n",
        "# Set the learning rate in the Adam optimizer\n",
        "opt = Adam(learning_rate=0.0001)\n",
        "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-30T22:00:47.738599Z",
          "iopub.execute_input": "2024-04-30T22:00:47.739411Z",
          "iopub.status.idle": "2024-04-30T22:00:48.759436Z",
          "shell.execute_reply.started": "2024-04-30T22:00:47.739384Z",
          "shell.execute_reply": "2024-04-30T22:00:48.758572Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBfA3zc2FSHe",
        "outputId": "9702344d-56e4-4066-980f-65ac96ff3595"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_8 (Conv2D)           (None, 48, 48, 64)        640       \n",
            "                                                                 \n",
            " batch_normalization_12 (Ba  (None, 48, 48, 64)        256       \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_12 (Activation)  (None, 48, 48, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPoolin  (None, 24, 24, 64)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 24, 24, 64)        0         \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 24, 24, 128)       204928    \n",
            "                                                                 \n",
            " batch_normalization_13 (Ba  (None, 24, 24, 128)       512       \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_13 (Activation)  (None, 24, 24, 128)       0         \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPoolin  (None, 12, 12, 128)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_13 (Dropout)        (None, 12, 12, 128)       0         \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 12, 12, 512)       590336    \n",
            "                                                                 \n",
            " batch_normalization_14 (Ba  (None, 12, 12, 512)       2048      \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_14 (Activation)  (None, 12, 12, 512)       0         \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPooli  (None, 6, 6, 512)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_14 (Dropout)        (None, 6, 6, 512)         0         \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 6, 6, 512)         2359808   \n",
            "                                                                 \n",
            " batch_normalization_15 (Ba  (None, 6, 6, 512)         2048      \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_15 (Activation)  (None, 6, 6, 512)         0         \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPooli  (None, 3, 3, 512)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_15 (Dropout)        (None, 3, 3, 512)         0         \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 4608)              0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 256)               1179904   \n",
            "                                                                 \n",
            " batch_normalization_16 (Ba  (None, 256)               1024      \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_16 (Activation)  (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_16 (Dropout)        (None, 256)               0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 512)               131584    \n",
            "                                                                 \n",
            " batch_normalization_17 (Ba  (None, 512)               2048      \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_17 (Activation)  (None, 512)               0         \n",
            "                                                                 \n",
            " dropout_17 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 7)                 3591      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4478727 (17.08 MB)\n",
            "Trainable params: 4474759 (17.07 MB)\n",
            "Non-trainable params: 3968 (15.50 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import RMSprop, SGD, Adam\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
        "\n",
        "# Monitoring metric\n",
        "checkpoint = ModelCheckpoint(\n",
        "    \"./model.h5\",\n",
        "    monitor='accuracy',\n",
        "    verbose=1,\n",
        "    save_best_only=True,\n",
        "    mode='max'\n",
        ")\n",
        "\n",
        "early_stopping = EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    min_delta=0,\n",
        "    patience=3,\n",
        "    verbose=1,\n",
        "    restore_best_weights=True\n",
        ")\n",
        "\n",
        "reduce_learningrate = ReduceLROnPlateau(\n",
        "    monitor='val_loss',\n",
        "    factor=0.5,\n",
        "    patience=10,\n",
        "    verbose=1,\n",
        "    min_delta=0.0001\n",
        ")\n",
        "\n",
        "callbacks_list = [early_stopping, checkpoint, reduce_learningrate]\n",
        "\n",
        "epochs = 48\n",
        "\n",
        "# Update the learning rate parameter\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer=Adam(learning_rate=0.001),# here we are setting the learning rate\n",
        "    metrics=['accuracy']\n",
        ")\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-30T22:00:48.76058Z",
          "iopub.execute_input": "2024-04-30T22:00:48.760846Z",
          "iopub.status.idle": "2024-04-30T22:00:48.774231Z",
          "shell.execute_reply.started": "2024-04-30T22:00:48.760823Z",
          "shell.execute_reply": "2024-04-30T22:00:48.773318Z"
        },
        "trusted": true,
        "id": "ARwGC51vFSHe"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x=train_set,\n",
        "    steps_per_epoch=train_set.n // train_set.batch_size,\n",
        "    epochs=epochs,\n",
        "    validation_data=test_set,\n",
        "    validation_steps=test_set.n // test_set.batch_size,\n",
        "    callbacks=callbacks_list\n",
        ")\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-30T22:00:48.775601Z",
          "iopub.execute_input": "2024-04-30T22:00:48.775895Z",
          "iopub.status.idle": "2024-04-30T22:09:36.49181Z",
          "shell.execute_reply.started": "2024-04-30T22:00:48.775867Z",
          "shell.execute_reply": "2024-04-30T22:09:36.49103Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "juqcQTf3FSHf",
        "outputId": "94ffe968-a8aa-4d26-8f19-265cda1f10d8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/48\n",
            "449/450 [============================>.] - ETA: 0s - loss: 1.7111 - accuracy: 0.3430\n",
            "Epoch 1: accuracy improved from -inf to 0.34319, saving model to ./model.h5\n",
            "450/450 [==============================] - 25s 43ms/step - loss: 1.7110 - accuracy: 0.3432 - val_loss: 1.6568 - val_accuracy: 0.4091 - lr: 0.0010\n",
            "Epoch 2/48\n",
            "450/450 [==============================] - ETA: 0s - loss: 1.3561 - accuracy: 0.4841\n",
            "Epoch 2: accuracy improved from 0.34319 to 0.48413, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 42ms/step - loss: 1.3561 - accuracy: 0.4841 - val_loss: 1.3996 - val_accuracy: 0.4536 - lr: 0.0010\n",
            "Epoch 3/48\n",
            "450/450 [==============================] - ETA: 0s - loss: 1.2254 - accuracy: 0.5324\n",
            "Epoch 3: accuracy improved from 0.48413 to 0.53236, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 43ms/step - loss: 1.2254 - accuracy: 0.5324 - val_loss: 1.4441 - val_accuracy: 0.4443 - lr: 0.0010\n",
            "Epoch 4/48\n",
            "450/450 [==============================] - ETA: 0s - loss: 1.1464 - accuracy: 0.5626\n",
            "Epoch 4: accuracy improved from 0.53236 to 0.56261, saving model to ./model.h5\n",
            "450/450 [==============================] - 20s 44ms/step - loss: 1.1464 - accuracy: 0.5626 - val_loss: 1.2787 - val_accuracy: 0.4859 - lr: 0.0010\n",
            "Epoch 5/48\n",
            "450/450 [==============================] - ETA: 0s - loss: 1.0877 - accuracy: 0.5867\n",
            "Epoch 5: accuracy improved from 0.56261 to 0.58674, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 43ms/step - loss: 1.0877 - accuracy: 0.5867 - val_loss: 1.2774 - val_accuracy: 0.5209 - lr: 0.0010\n",
            "Epoch 6/48\n",
            "449/450 [============================>.] - ETA: 0s - loss: 1.0342 - accuracy: 0.6090\n",
            "Epoch 6: accuracy improved from 0.58674 to 0.60896, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 42ms/step - loss: 1.0343 - accuracy: 0.6090 - val_loss: 1.1970 - val_accuracy: 0.5460 - lr: 0.0010\n",
            "Epoch 7/48\n",
            "450/450 [==============================] - ETA: 0s - loss: 0.9835 - accuracy: 0.6321\n",
            "Epoch 7: accuracy improved from 0.60896 to 0.63212, saving model to ./model.h5\n",
            "450/450 [==============================] - 20s 43ms/step - loss: 0.9835 - accuracy: 0.6321 - val_loss: 1.2949 - val_accuracy: 0.5044 - lr: 0.0010\n",
            "Epoch 8/48\n",
            "449/450 [============================>.] - ETA: 0s - loss: 0.9375 - accuracy: 0.6489\n",
            "Epoch 8: accuracy improved from 0.63212 to 0.64892, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 42ms/step - loss: 0.9375 - accuracy: 0.6489 - val_loss: 1.1404 - val_accuracy: 0.5732 - lr: 0.0010\n",
            "Epoch 9/48\n",
            "449/450 [============================>.] - ETA: 0s - loss: 0.8846 - accuracy: 0.6697\n",
            "Epoch 9: accuracy improved from 0.64892 to 0.66954, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 41ms/step - loss: 0.8848 - accuracy: 0.6695 - val_loss: 1.2202 - val_accuracy: 0.5423 - lr: 0.0010\n",
            "Epoch 10/48\n",
            "449/450 [============================>.] - ETA: 0s - loss: 0.8278 - accuracy: 0.6893\n",
            "Epoch 10: accuracy improved from 0.66954 to 0.68940, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 42ms/step - loss: 0.8274 - accuracy: 0.6894 - val_loss: 1.2204 - val_accuracy: 0.5415 - lr: 0.0010\n",
            "Epoch 11/48\n",
            "449/450 [============================>.] - ETA: 0s - loss: 0.7766 - accuracy: 0.7100\n",
            "Epoch 11: accuracy improved from 0.68940 to 0.70998, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 41ms/step - loss: 0.7765 - accuracy: 0.7100 - val_loss: 1.0724 - val_accuracy: 0.6109 - lr: 0.0010\n",
            "Epoch 12/48\n",
            "449/450 [============================>.] - ETA: 0s - loss: 0.7136 - accuracy: 0.7323\n",
            "Epoch 12: accuracy improved from 0.70998 to 0.73238, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 42ms/step - loss: 0.7135 - accuracy: 0.7324 - val_loss: 1.1277 - val_accuracy: 0.6129 - lr: 0.0010\n",
            "Epoch 13/48\n",
            "449/450 [============================>.] - ETA: 0s - loss: 0.6495 - accuracy: 0.7584\n",
            "Epoch 13: accuracy improved from 0.73238 to 0.75846, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 43ms/step - loss: 0.6494 - accuracy: 0.7585 - val_loss: 1.0979 - val_accuracy: 0.6243 - lr: 0.0010\n",
            "Epoch 14/48\n",
            "450/450 [==============================] - ETA: 0s - loss: 0.6020 - accuracy: 0.7787Restoring model weights from the end of the best epoch: 11.\n",
            "\n",
            "Epoch 14: accuracy improved from 0.75846 to 0.77866, saving model to ./model.h5\n",
            "450/450 [==============================] - 19s 43ms/step - loss: 0.6020 - accuracy: 0.7787 - val_loss: 1.3014 - val_accuracy: 0.5422 - lr: 0.0010\n",
            "Epoch 14: early stopping\n"
          ]
        }
      ]
    }
  ]
}