face_emotion .ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vFqw6nnd6PjC"
      },
      "outputs": [],
      "source": [
        "from keras.utils import to_categorical\n",
        "from keras.preprocessing.image import load_img\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D, AveragePooling2D, BatchNormalization, Activation\n",
        "import os\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip '/content/images.zip'"
      ],
      "metadata": {
        "id": "lda2po-pCJJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "gxipNDMhpQ3I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "4bb4d3fa-c6bf-41cc-a79c-0448a6875aba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ],
      "source": [
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "NYhGvDblf8yU"
      },
      "outputs": [],
      "source": [
        "TRAIN_DIR = '/content/images/train'\n",
        "TEST_DIR = '/content/images/test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "AZIKB5s_gJYQ"
      },
      "outputs": [],
      "source": [
        "def createdataframe(dir):\n",
        "    image_paths = []\n",
        "    labels = {'angry':[],'disgust':[],'fear':[],'happy':[],'neutral':[],'sad':[],'surprise':[]}\n",
        "    for label in os.listdir(dir):\n",
        "        for imagename in os.listdir(os.path.join(dir,label)):\n",
        "            image_paths.append(os.path.join(dir,label,imagename))\n",
        "            for i in labels.keys():\n",
        "              if i == label:\n",
        "                labels[i].append(1)\n",
        "              else:\n",
        "                labels[i].append(0)\n",
        "        print(label, \"completed\")\n",
        "    return [image_paths,labels['angry'],labels['disgust'],labels['fear'],labels['happy'],labels['neutral'],labels['sad'],labels['surprise']]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tqYMaWWEgLvk",
        "outputId": "6e798d31-7e36-48ae-da76-0f2b6ae38b1b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "angry completed\n",
            "disgust completed\n",
            "neutral completed\n",
            "fear completed\n",
            "sad completed\n",
            "surprise completed\n",
            "happy completed\n"
          ]
        }
      ],
      "source": [
        "train = pd.DataFrame()\n",
        "train['image'], train['angry'], train['disgust'], train['fear'], train['happy'], train['neutral'], train['sad'], train['surprise'] = createdataframe(TRAIN_DIR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "je3-6dzMgQ6a",
        "outputId": "bf8e5562-15c5-441a-b165-d75851894644"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                   image  angry  disgust  fear  happy  \\\n",
            "0  /content/images/train/angry/15988.jpg      1        0     0      0   \n",
            "1  /content/images/train/angry/34360.jpg      1        0     0      0   \n",
            "2  /content/images/train/angry/13415.jpg      1        0     0      0   \n",
            "3   /content/images/train/angry/3716.jpg      1        0     0      0   \n",
            "4   /content/images/train/angry/5598.jpg      1        0     0      0   \n",
            "\n",
            "   neutral  sad  surprise  \n",
            "0        0    0         0  \n",
            "1        0    0         0  \n",
            "2        0    0         0  \n",
            "3        0    0         0  \n",
            "4        0    0         0  \n"
          ]
        }
      ],
      "source": [
        "print(train.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "RZTdjZw9gdWD",
        "outputId": "7496a4e7-3922-483b-d9ba-76a9eeb54b91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "angry completed\n",
            "disgust completed\n",
            "neutral completed\n",
            "fear completed\n",
            "sad completed\n",
            "surprise completed\n",
            "happy completed\n"
          ]
        }
      ],
      "source": [
        "test = pd.DataFrame()\n",
        "test['image'], test['angry'], test['disgust'], test['fear'], test['happy'], test['neutral'], test['sad'], test['surprise'] = createdataframe(TEST_DIR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "HatksjSxglFb",
        "outputId": "7386e8c2-6adf-4027-e604-aaf3eec58400"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                  image  angry  disgust  fear  happy  neutral  \\\n",
            "0  /content/images/test/angry/21068.jpg      1        0     0      0        0   \n",
            "1  /content/images/test/angry/28560.jpg      1        0     0      0        0   \n",
            "2   /content/images/test/angry/5400.jpg      1        0     0      0        0   \n",
            "3  /content/images/test/angry/15835.jpg      1        0     0      0        0   \n",
            "4  /content/images/test/angry/15654.jpg      1        0     0      0        0   \n",
            "\n",
            "   sad  surprise  \n",
            "0    0         0  \n",
            "1    0         0  \n",
            "2    0         0  \n",
            "3    0         0  \n",
            "4    0         0  \n"
          ]
        }
      ],
      "source": [
        "print(test.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "mDnj4XP4gm-4"
      },
      "outputs": [],
      "source": [
        "def extract_features(images):\n",
        "    features = []\n",
        "    for image in tqdm(images):\n",
        "        img = load_img(image,color_mode='grayscale',target_size=(48,48))\n",
        "        img = np.array(img)\n",
        "        features.append(img)\n",
        "    features = np.array(features)\n",
        "    features = features.reshape(len(features),48,48,1)\n",
        "    return features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "490d9fbdb3e54a65b281da3f6c4c7022",
            "6932bbcb4dac458795962259bbb3e003",
            "9218db517a5f43b89ed347777cd991e2",
            "7f5a454638784189aa0372e7356f527e",
            "65a40bff91ae4306ae1d770b4ff48f5a",
            "2a9d439d3b8a4ed4bcbcd4d19e6b9a72",
            "8de2ba0152d945faa4878edc769d077b",
            "29b5839632424c229c68d7752f61b453",
            "7af3010298044cb6a58ab78782b89943",
            "f02e7892b4e74a688384f1b76ffffc9c",
            "1c9d043708be4ce99809930606430e70"
          ]
        },
        "id": "8JhNw-9-gpvF",
        "outputId": "bebe6681-1290-45aa-e143-088ca3de50fa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28821 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "490d9fbdb3e54a65b281da3f6c4c7022"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_features = extract_features(train['image'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6dc04bdef77543b9bca1765dad1200b3",
            "69497d449a6f4cf387858782b5481611",
            "4752f512cfe34a63aef2be1ff8fc5af8",
            "3ade8f9790384e6c9162848a7069e591",
            "10a4a4ff41324734aaea34c2a150f30a",
            "2cf70266ec47478f8a37d621c9ed25a5",
            "4ab8e1b24f574975956d9fc3063358dd",
            "6d9ed5ca375a4a199760525a47dc6fff",
            "2c8664b63e2745c9b8c6db386bed0111",
            "33540c35040243eab704d11e1f19c19c",
            "36145c6a43664eeb8e7010b21533b4e5"
          ]
        },
        "id": "vMi-6K1zgtxY",
        "outputId": "a9d2d386-209d-42a8-c092-645f9d4fd7fe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/7066 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6dc04bdef77543b9bca1765dad1200b3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_features = extract_features(test['image'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "4VbWdyDxgv1k"
      },
      "outputs": [],
      "source": [
        "x_train = train_features/255.0\n",
        "x_test = test_features/255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "DVe9TfN3gxpZ"
      },
      "outputs": [],
      "source": [
        "le = LabelEncoder()\n",
        "y_train = train[['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']].apply(le.fit_transform)\n",
        "y_test = test[['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']].apply(le.fit_transform)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Shape of y_train: {y_train.shape}\")\n",
        "print(f\"Shape of y_test: {y_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "iMFCIXP3J2ga",
        "outputId": "58a0d53c-af87-4f02-f09f-721d29f37077"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of y_train: (28821, 7)\n",
            "Shape of y_test: (7066, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train.head(1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "zE7wOZzjKHxu",
        "outputId": "2223e1d3-a779-4d59-842c-0f8eff372b69"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   angry  disgust  fear  happy  neutral  sad  surprise\n",
            "0      1        0     0      0        0    0         0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size=(7, 7), padding='same', input_shape=(48,48,1)))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size=(7, 7), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(5, 5), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(5, 5), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Conv2D(filters=128, kernel_size=(3, 3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(filters=256, kernel_size=(3, 3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Conv2D(filters=256, kernel_size=(3, 3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Conv2D(filters=512, kernel_size=(3, 3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(7, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
        "\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vLnjlTfKphQM",
        "outputId": "8a125b26-c718-4b5b-9bb1-45a18ef13c77"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 48, 48, 32)        1600      \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 48, 48, 32)        128       \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 48, 48, 32)        50208     \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 48, 48, 32)        128       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation (Activation)     (None, 48, 48, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 24, 24, 32)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 24, 24, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 24, 24, 64)        51264     \n",
            "                                                                 \n",
            " batch_normalization_2 (Bat  (None, 24, 24, 64)        256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 24, 24, 64)        102464    \n",
            "                                                                 \n",
            " batch_normalization_3 (Bat  (None, 24, 24, 64)        256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 24, 24, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 12, 12, 64)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 12, 12, 64)        0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 12, 12, 128)       73856     \n",
            "                                                                 \n",
            " batch_normalization_4 (Bat  (None, 12, 12, 128)       512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 12, 12, 128)       147584    \n",
            "                                                                 \n",
            " batch_normalization_5 (Bat  (None, 12, 12, 128)       512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 12, 12, 128)       0         \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 6, 6, 128)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 6, 6, 128)         0         \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 6, 6, 256)         295168    \n",
            "                                                                 \n",
            " batch_normalization_6 (Bat  (None, 6, 6, 256)         1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 6, 6, 256)         590080    \n",
            "                                                                 \n",
            " batch_normalization_7 (Bat  (None, 6, 6, 256)         1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 6, 6, 256)         0         \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 3, 3, 256)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 3, 3, 256)         0         \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 3, 3, 512)         1180160   \n",
            "                                                                 \n",
            " batch_normalization_8 (Bat  (None, 3, 3, 512)         2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 3, 3, 512)         2359808   \n",
            "                                                                 \n",
            " batch_normalization_9 (Bat  (None, 3, 3, 512)         2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 3, 3, 512)         2359808   \n",
            "                                                                 \n",
            " batch_normalization_10 (Ba  (None, 3, 3, 512)         2048      \n",
            " tchNormalization)                                               \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 3, 3, 512)         0         \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 2, 2, 512)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 2, 2, 512)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 2048)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               1049088   \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 7)                 1799      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8404199 (32.06 MB)\n",
            "Trainable params: 8399207 (32.04 MB)\n",
            "Non-trainable params: 4992 (19.50 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "db7_DnGBhM3S",
        "outputId": "a7335821-ebc4-448a-8a41-47aa8c851df7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/128\n",
            "226/226 [==============================] - 36s 91ms/step - loss: 1.8960 - accuracy: 0.2251 - val_loss: 1.8226 - val_accuracy: 0.2583\n",
            "Epoch 2/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.8118 - accuracy: 0.2466 - val_loss: 1.8207 - val_accuracy: 0.2583\n",
            "Epoch 3/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.7827 - accuracy: 0.2614 - val_loss: 2.0815 - val_accuracy: 0.1342\n",
            "Epoch 4/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.6952 - accuracy: 0.3070 - val_loss: 1.6554 - val_accuracy: 0.3218\n",
            "Epoch 5/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.6267 - accuracy: 0.3470 - val_loss: 1.7381 - val_accuracy: 0.3098\n",
            "Epoch 6/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.5700 - accuracy: 0.3747 - val_loss: 1.5127 - val_accuracy: 0.3865\n",
            "Epoch 7/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.5332 - accuracy: 0.3884 - val_loss: 1.6259 - val_accuracy: 0.3290\n",
            "Epoch 8/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.4955 - accuracy: 0.3982 - val_loss: 1.4412 - val_accuracy: 0.4249\n",
            "Epoch 9/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.4691 - accuracy: 0.4105 - val_loss: 1.4132 - val_accuracy: 0.4326\n",
            "Epoch 10/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.4551 - accuracy: 0.4123 - val_loss: 1.4241 - val_accuracy: 0.4284\n",
            "Epoch 11/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.4430 - accuracy: 0.4210 - val_loss: 1.3947 - val_accuracy: 0.4331\n",
            "Epoch 12/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.4252 - accuracy: 0.4276 - val_loss: 1.3572 - val_accuracy: 0.4474\n",
            "Epoch 13/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.4225 - accuracy: 0.4278 - val_loss: 1.4168 - val_accuracy: 0.4346\n",
            "Epoch 14/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.4089 - accuracy: 0.4311 - val_loss: 1.3700 - val_accuracy: 0.4547\n",
            "Epoch 15/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.3975 - accuracy: 0.4416 - val_loss: 1.3470 - val_accuracy: 0.4590\n",
            "Epoch 16/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.4002 - accuracy: 0.4373 - val_loss: 1.3348 - val_accuracy: 0.4740\n",
            "Epoch 17/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.3782 - accuracy: 0.4541 - val_loss: 1.3228 - val_accuracy: 0.4723\n",
            "Epoch 18/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.3662 - accuracy: 0.4569 - val_loss: 1.3063 - val_accuracy: 0.4993\n",
            "Epoch 19/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.3567 - accuracy: 0.4648 - val_loss: 1.2710 - val_accuracy: 0.5160\n",
            "Epoch 20/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.3490 - accuracy: 0.4765 - val_loss: 1.2715 - val_accuracy: 0.5051\n",
            "Epoch 21/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.3320 - accuracy: 0.4808 - val_loss: 1.2671 - val_accuracy: 0.5085\n",
            "Epoch 22/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.3220 - accuracy: 0.4876 - val_loss: 1.2782 - val_accuracy: 0.5136\n",
            "Epoch 23/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.3108 - accuracy: 0.4931 - val_loss: 1.3059 - val_accuracy: 0.4953\n",
            "Epoch 24/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.3032 - accuracy: 0.4942 - val_loss: 1.3483 - val_accuracy: 0.4789\n",
            "Epoch 25/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2983 - accuracy: 0.4985 - val_loss: 1.2169 - val_accuracy: 0.5333\n",
            "Epoch 26/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2931 - accuracy: 0.5045 - val_loss: 1.2345 - val_accuracy: 0.5316\n",
            "Epoch 27/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.2766 - accuracy: 0.5072 - val_loss: 1.2356 - val_accuracy: 0.5379\n",
            "Epoch 28/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2814 - accuracy: 0.5082 - val_loss: 1.2511 - val_accuracy: 0.5122\n",
            "Epoch 29/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2637 - accuracy: 0.5169 - val_loss: 1.2106 - val_accuracy: 0.5340\n",
            "Epoch 30/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.2554 - accuracy: 0.5197 - val_loss: 1.2297 - val_accuracy: 0.5367\n",
            "Epoch 31/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2539 - accuracy: 0.5211 - val_loss: 1.2250 - val_accuracy: 0.5419\n",
            "Epoch 32/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2379 - accuracy: 0.5276 - val_loss: 1.1945 - val_accuracy: 0.5417\n",
            "Epoch 33/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2440 - accuracy: 0.5235 - val_loss: 1.1951 - val_accuracy: 0.5393\n",
            "Epoch 34/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2330 - accuracy: 0.5289 - val_loss: 1.1962 - val_accuracy: 0.5408\n",
            "Epoch 35/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.2259 - accuracy: 0.5350 - val_loss: 1.1933 - val_accuracy: 0.5500\n",
            "Epoch 36/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.2216 - accuracy: 0.5338 - val_loss: 1.1700 - val_accuracy: 0.5570\n",
            "Epoch 37/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.2198 - accuracy: 0.5339 - val_loss: 1.1601 - val_accuracy: 0.5596\n",
            "Epoch 38/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2143 - accuracy: 0.5361 - val_loss: 1.2217 - val_accuracy: 0.5405\n",
            "Epoch 39/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2068 - accuracy: 0.5421 - val_loss: 1.1572 - val_accuracy: 0.5706\n",
            "Epoch 40/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2011 - accuracy: 0.5474 - val_loss: 1.1406 - val_accuracy: 0.5631\n",
            "Epoch 41/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.2005 - accuracy: 0.5446 - val_loss: 1.1424 - val_accuracy: 0.5621\n",
            "Epoch 42/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.1959 - accuracy: 0.5504 - val_loss: 1.1254 - val_accuracy: 0.5723\n",
            "Epoch 43/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.1812 - accuracy: 0.5541 - val_loss: 1.1732 - val_accuracy: 0.5638\n",
            "Epoch 44/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.1795 - accuracy: 0.5555 - val_loss: 1.1401 - val_accuracy: 0.5732\n",
            "Epoch 45/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.1729 - accuracy: 0.5579 - val_loss: 1.1409 - val_accuracy: 0.5733\n",
            "Epoch 46/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.1701 - accuracy: 0.5608 - val_loss: 1.1095 - val_accuracy: 0.5785\n",
            "Epoch 47/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.1618 - accuracy: 0.5644 - val_loss: 1.1276 - val_accuracy: 0.5766\n",
            "Epoch 48/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.1545 - accuracy: 0.5686 - val_loss: 1.1309 - val_accuracy: 0.5668\n",
            "Epoch 49/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.1550 - accuracy: 0.5653 - val_loss: 1.1055 - val_accuracy: 0.5824\n",
            "Epoch 50/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.1485 - accuracy: 0.5694 - val_loss: 1.0915 - val_accuracy: 0.5903\n",
            "Epoch 51/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.1431 - accuracy: 0.5745 - val_loss: 1.1052 - val_accuracy: 0.5962\n",
            "Epoch 52/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.1415 - accuracy: 0.5744 - val_loss: 1.1299 - val_accuracy: 0.5793\n",
            "Epoch 53/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.1375 - accuracy: 0.5695 - val_loss: 1.1046 - val_accuracy: 0.5866\n",
            "Epoch 54/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.1276 - accuracy: 0.5748 - val_loss: 1.0951 - val_accuracy: 0.5896\n",
            "Epoch 55/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.1252 - accuracy: 0.5794 - val_loss: 1.0930 - val_accuracy: 0.5865\n",
            "Epoch 56/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.1245 - accuracy: 0.5782 - val_loss: 1.0829 - val_accuracy: 0.5947\n",
            "Epoch 57/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.1220 - accuracy: 0.5830 - val_loss: 1.0871 - val_accuracy: 0.6035\n",
            "Epoch 58/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.1122 - accuracy: 0.5830 - val_loss: 1.1024 - val_accuracy: 0.5812\n",
            "Epoch 59/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.1053 - accuracy: 0.5879 - val_loss: 1.0725 - val_accuracy: 0.5962\n",
            "Epoch 60/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.1050 - accuracy: 0.5862 - val_loss: 1.0850 - val_accuracy: 0.5930\n",
            "Epoch 61/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.1013 - accuracy: 0.5896 - val_loss: 1.0579 - val_accuracy: 0.6054\n",
            "Epoch 62/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0956 - accuracy: 0.5874 - val_loss: 1.0737 - val_accuracy: 0.6023\n",
            "Epoch 63/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.0988 - accuracy: 0.5916 - val_loss: 1.0605 - val_accuracy: 0.5951\n",
            "Epoch 64/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0865 - accuracy: 0.5944 - val_loss: 1.0714 - val_accuracy: 0.5991\n",
            "Epoch 65/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0844 - accuracy: 0.5940 - val_loss: 1.0513 - val_accuracy: 0.6059\n",
            "Epoch 66/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0838 - accuracy: 0.5978 - val_loss: 1.0482 - val_accuracy: 0.6019\n",
            "Epoch 67/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0803 - accuracy: 0.5960 - val_loss: 1.0538 - val_accuracy: 0.6053\n",
            "Epoch 68/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0766 - accuracy: 0.5981 - val_loss: 1.0412 - val_accuracy: 0.6087\n",
            "Epoch 69/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0741 - accuracy: 0.6012 - val_loss: 1.0657 - val_accuracy: 0.6076\n",
            "Epoch 70/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0738 - accuracy: 0.5996 - val_loss: 1.0646 - val_accuracy: 0.6118\n",
            "Epoch 71/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0704 - accuracy: 0.6001 - val_loss: 1.0351 - val_accuracy: 0.6155\n",
            "Epoch 72/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0679 - accuracy: 0.6029 - val_loss: 1.0536 - val_accuracy: 0.6107\n",
            "Epoch 73/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0542 - accuracy: 0.6057 - val_loss: 1.0617 - val_accuracy: 0.6039\n",
            "Epoch 74/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0570 - accuracy: 0.6045 - val_loss: 1.0503 - val_accuracy: 0.6187\n",
            "Epoch 75/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.0528 - accuracy: 0.6053 - val_loss: 1.0458 - val_accuracy: 0.6094\n",
            "Epoch 76/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.0536 - accuracy: 0.6066 - val_loss: 1.0540 - val_accuracy: 0.6080\n",
            "Epoch 77/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0537 - accuracy: 0.6072 - val_loss: 1.0478 - val_accuracy: 0.6153\n",
            "Epoch 78/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0469 - accuracy: 0.6094 - val_loss: 1.0375 - val_accuracy: 0.6108\n",
            "Epoch 79/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0473 - accuracy: 0.6113 - val_loss: 1.0332 - val_accuracy: 0.6162\n",
            "Epoch 80/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0417 - accuracy: 0.6097 - val_loss: 1.0198 - val_accuracy: 0.6176\n",
            "Epoch 81/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0402 - accuracy: 0.6100 - val_loss: 1.0213 - val_accuracy: 0.6213\n",
            "Epoch 82/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0325 - accuracy: 0.6123 - val_loss: 1.0157 - val_accuracy: 0.6226\n",
            "Epoch 83/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0248 - accuracy: 0.6174 - val_loss: 1.0395 - val_accuracy: 0.6074\n",
            "Epoch 84/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0340 - accuracy: 0.6118 - val_loss: 1.0258 - val_accuracy: 0.6170\n",
            "Epoch 85/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0263 - accuracy: 0.6149 - val_loss: 1.0275 - val_accuracy: 0.6127\n",
            "Epoch 86/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0283 - accuracy: 0.6173 - val_loss: 0.9982 - val_accuracy: 0.6204\n",
            "Epoch 87/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0223 - accuracy: 0.6162 - val_loss: 1.0275 - val_accuracy: 0.6117\n",
            "Epoch 88/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0223 - accuracy: 0.6160 - val_loss: 1.0067 - val_accuracy: 0.6262\n",
            "Epoch 89/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0197 - accuracy: 0.6215 - val_loss: 1.0265 - val_accuracy: 0.6182\n",
            "Epoch 90/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0211 - accuracy: 0.6212 - val_loss: 1.0054 - val_accuracy: 0.6237\n",
            "Epoch 91/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0168 - accuracy: 0.6235 - val_loss: 1.0094 - val_accuracy: 0.6182\n",
            "Epoch 92/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.0168 - accuracy: 0.6210 - val_loss: 1.0269 - val_accuracy: 0.6145\n",
            "Epoch 93/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0146 - accuracy: 0.6229 - val_loss: 1.0225 - val_accuracy: 0.6194\n",
            "Epoch 94/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 1.0177 - accuracy: 0.6219 - val_loss: 1.0058 - val_accuracy: 0.6265\n",
            "Epoch 95/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 1.0139 - accuracy: 0.6231 - val_loss: 1.0026 - val_accuracy: 0.6278\n",
            "Epoch 96/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0006 - accuracy: 0.6253 - val_loss: 1.0144 - val_accuracy: 0.6228\n",
            "Epoch 97/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9972 - accuracy: 0.6266 - val_loss: 1.0044 - val_accuracy: 0.6227\n",
            "Epoch 98/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0029 - accuracy: 0.6244 - val_loss: 1.0069 - val_accuracy: 0.6176\n",
            "Epoch 99/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 1.0021 - accuracy: 0.6259 - val_loss: 1.0123 - val_accuracy: 0.6258\n",
            "Epoch 100/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9957 - accuracy: 0.6275 - val_loss: 0.9965 - val_accuracy: 0.6231\n",
            "Epoch 101/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9933 - accuracy: 0.6314 - val_loss: 1.0196 - val_accuracy: 0.6148\n",
            "Epoch 102/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9989 - accuracy: 0.6280 - val_loss: 1.0004 - val_accuracy: 0.6252\n",
            "Epoch 103/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9944 - accuracy: 0.6309 - val_loss: 0.9988 - val_accuracy: 0.6228\n",
            "Epoch 104/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 0.9906 - accuracy: 0.6321 - val_loss: 0.9985 - val_accuracy: 0.6264\n",
            "Epoch 105/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9888 - accuracy: 0.6306 - val_loss: 1.0314 - val_accuracy: 0.6091\n",
            "Epoch 106/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9887 - accuracy: 0.6352 - val_loss: 0.9965 - val_accuracy: 0.6272\n",
            "Epoch 107/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9793 - accuracy: 0.6360 - val_loss: 0.9932 - val_accuracy: 0.6299\n",
            "Epoch 108/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9868 - accuracy: 0.6351 - val_loss: 0.9970 - val_accuracy: 0.6245\n",
            "Epoch 109/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9766 - accuracy: 0.6358 - val_loss: 0.9990 - val_accuracy: 0.6272\n",
            "Epoch 110/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9737 - accuracy: 0.6379 - val_loss: 0.9919 - val_accuracy: 0.6275\n",
            "Epoch 111/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9813 - accuracy: 0.6356 - val_loss: 1.0002 - val_accuracy: 0.6238\n",
            "Epoch 112/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9744 - accuracy: 0.6396 - val_loss: 1.0068 - val_accuracy: 0.6252\n",
            "Epoch 113/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9737 - accuracy: 0.6356 - val_loss: 1.0140 - val_accuracy: 0.6197\n",
            "Epoch 114/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 0.9776 - accuracy: 0.6372 - val_loss: 0.9885 - val_accuracy: 0.6390\n",
            "Epoch 115/128\n",
            "226/226 [==============================] - 18s 77ms/step - loss: 0.9758 - accuracy: 0.6365 - val_loss: 1.0023 - val_accuracy: 0.6267\n",
            "Epoch 116/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9652 - accuracy: 0.6399 - val_loss: 0.9896 - val_accuracy: 0.6357\n",
            "Epoch 117/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9688 - accuracy: 0.6373 - val_loss: 0.9836 - val_accuracy: 0.6330\n",
            "Epoch 118/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9659 - accuracy: 0.6409 - val_loss: 0.9907 - val_accuracy: 0.6305\n",
            "Epoch 119/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9568 - accuracy: 0.6460 - val_loss: 1.0026 - val_accuracy: 0.6278\n",
            "Epoch 120/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9716 - accuracy: 0.6384 - val_loss: 0.9848 - val_accuracy: 0.6357\n",
            "Epoch 121/128\n",
            "226/226 [==============================] - 17s 76ms/step - loss: 0.9569 - accuracy: 0.6413 - val_loss: 0.9754 - val_accuracy: 0.6394\n",
            "Epoch 122/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9560 - accuracy: 0.6440 - val_loss: 1.0278 - val_accuracy: 0.6179\n",
            "Epoch 123/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9540 - accuracy: 0.6455 - val_loss: 1.0075 - val_accuracy: 0.6200\n",
            "Epoch 124/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9596 - accuracy: 0.6441 - val_loss: 1.0497 - val_accuracy: 0.6107\n",
            "Epoch 125/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9616 - accuracy: 0.6452 - val_loss: 0.9846 - val_accuracy: 0.6344\n",
            "Epoch 126/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9516 - accuracy: 0.6460 - val_loss: 0.9751 - val_accuracy: 0.6353\n",
            "Epoch 127/128\n",
            "226/226 [==============================] - 17s 75ms/step - loss: 0.9542 - accuracy: 0.6443 - val_loss: 0.9803 - val_accuracy: 0.6371\n",
            "Epoch 128/128\n",
            "226/226 [==============================] - 17s 77ms/step - loss: 0.9521 - accuracy: 0.6464 - val_loss: 0.9889 - val_accuracy: 0.6268\n"
          ]
        }
      ],
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  model.fit(x= x_train,y = y_train, batch_size = 128, epochs = 128, validation_data = (x_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "VxXi4IKohNhh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "5b3eec98-0bd0-49bc-f77a-d1ddce1a871e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ],
      "source": [
        "model_json = model.to_json()\n",
        "with open(\"emotiondetector.json\",'w') as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save(\"emotiondetector.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "PlzPjEwLz0B5"
      },
      "outputs": [],
      "source": [
        "label = ['angry','disgust','fear','happy','neutral','sad','surprise']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "qSFZSA-pz1ET"
      },
      "outputs": [],
      "source": [
        "def ef(image):\n",
        "    img = load_img(image,grayscale =  True )\n",
        "    feature = np.array(img)\n",
        "    feature = feature.reshape(1,48,48,1)\n",
        "    return feature/255.0\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "-JpTDiHWzsuV"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "2Nsz7efbz7gw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "outputId": "b25e1262-f9c8-4e0e-bbed-0a750c9b5e90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original image is of sad\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/utils/image_utils.py:409: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 792ms/step\n",
            "model prediction is  neutral\n",
            "[[0.09794245 0.00098136 0.06377047 0.04842517 0.6610793  0.11983601\n",
            "  0.00796534]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ee7f9fa0d60>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "image = 'images/test/sad/10171.jpg'\n",
        "print(\"original image is of sad\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "print(pred)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "dIoG6JHi0BdK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "5baab683-cf44-4689-bdda-e0e015e824f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original image is of fear\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "[[0.00805158 0.00056159 0.5260823  0.00278683 0.01011924 0.02696295\n",
            "  0.42543548]]\n",
            "model prediction is  fear\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ee80d632a10>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "image = 'images/train/fear/16273.jpg'\n",
        "print(\"original image is of fear\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "print(pred)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "oHmSDyD80GLp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "df042681-d89f-43e6-ea46-aa83e74b8bfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original image is of disgust\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "model prediction is  fear\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ee80d60f730>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "image = 'images/test/disgust/10532.jpg'\n",
        "print(\"original image is of disgust\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "xQYGyn360KIW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "352ebf66-b289-4c33-8f5a-dbd0b505a58c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original image is of happy\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "model prediction is  surprise\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ee80e212e90>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "image = 'images/test/happy/10141.jpg'\n",
        "print(\"original image is of happy\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "eoDNi28O0LlX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "49cc62eb-2b56-482c-f282-d9710a67931b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original image is of surprise\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "model prediction is  surprise\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ee80d6a9b40>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "image = 'images/test/surprise/10185.jpg'\n",
        "print(\"original image is of surprise\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image = 'images/test/angry/10163.jpg'\n",
        "print(\"original image is of angry\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "3USCTuwJm6sA",
        "outputId": "e6740b41-4a02-49b9-ae2e-e6493d92a1e8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original image is of angry\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "model prediction is  angry\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ee80e0fe2c0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "490d9fbdb3e54a65b281da3f6c4c7022": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_6932bbcb4dac458795962259bbb3e003",
              "IPY_MODEL_9218db517a5f43b89ed347777cd991e2",
              "IPY_MODEL_7f5a454638784189aa0372e7356f527e"
            ],
            "layout": "IPY_MODEL_65a40bff91ae4306ae1d770b4ff48f5a"
          }
        },
        "6932bbcb4dac458795962259bbb3e003": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2a9d439d3b8a4ed4bcbcd4d19e6b9a72",
            "placeholder": "​",
            "style": "IPY_MODEL_8de2ba0152d945faa4878edc769d077b",
            "value": "100%"
          }
        },
        "9218db517a5f43b89ed347777cd991e2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_29b5839632424c229c68d7752f61b453",
            "max": 28821,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_7af3010298044cb6a58ab78782b89943",
            "value": 28821
          }
        },
        "7f5a454638784189aa0372e7356f527e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f02e7892b4e74a688384f1b76ffffc9c",
            "placeholder": "​",
            "style": "IPY_MODEL_1c9d043708be4ce99809930606430e70",
            "value": " 28821/28821 [00:05&lt;00:00, 6664.17it/s]"
          }
        },
        "65a40bff91ae4306ae1d770b4ff48f5a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2a9d439d3b8a4ed4bcbcd4d19e6b9a72": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8de2ba0152d945faa4878edc769d077b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "29b5839632424c229c68d7752f61b453": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7af3010298044cb6a58ab78782b89943": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "f02e7892b4e74a688384f1b76ffffc9c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1c9d043708be4ce99809930606430e70": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6dc04bdef77543b9bca1765dad1200b3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_69497d449a6f4cf387858782b5481611",
              "IPY_MODEL_4752f512cfe34a63aef2be1ff8fc5af8",
              "IPY_MODEL_3ade8f9790384e6c9162848a7069e591"
            ],
            "layout": "IPY_MODEL_10a4a4ff41324734aaea34c2a150f30a"
          }
        },
        "69497d449a6f4cf387858782b5481611": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2cf70266ec47478f8a37d621c9ed25a5",
            "placeholder": "​",
            "style": "IPY_MODEL_4ab8e1b24f574975956d9fc3063358dd",
            "value": "100%"
          }
        },
        "4752f512cfe34a63aef2be1ff8fc5af8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6d9ed5ca375a4a199760525a47dc6fff",
            "max": 7066,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_2c8664b63e2745c9b8c6db386bed0111",
            "value": 7066
          }
        },
        "3ade8f9790384e6c9162848a7069e591": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_33540c35040243eab704d11e1f19c19c",
            "placeholder": "​",
            "style": "IPY_MODEL_36145c6a43664eeb8e7010b21533b4e5",
            "value": " 7066/7066 [00:01&lt;00:00, 6716.80it/s]"
          }
        },
        "10a4a4ff41324734aaea34c2a150f30a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2cf70266ec47478f8a37d621c9ed25a5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4ab8e1b24f574975956d9fc3063358dd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6d9ed5ca375a4a199760525a47dc6fff": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2c8664b63e2745c9b8c6db386bed0111": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "33540c35040243eab704d11e1f19c19c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "36145c6a43664eeb8e7010b21533b4e5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}