Train.ipynb

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    "kernelspec": {
      "display_name": "Python 2",
      "language": "python",
      "name": "python2"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.15"
    },
    "colab": {
      "name": "Face expression recognition mini project (training).ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "2fc206564d516020e9c3001c8a1448766aace848",
        "id": "BObx73HfFh6y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a278af3f-af5c-4159-f047-4e235bc61fb9"
      },
      "source": [
        "\n",
        "import keras\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.optimizers import RMSprop, SGD, Adam\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54Z55dxVIq5C"
      },
      "source": [
        "img_rows, img_cols = 48, 48\n",
        "batch_size = 128\n",
        "\n",
        "train_data_dir = '/content/drive/My Drive/Mini project/train'\n",
        "validation_data_dir = '/content/drive/My Drive/Test mini folder/test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "0041128a27f4da936ad63e90959797391736fc8b",
        "id": "p6s54SUqFh62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "841b2fb4-8f46-4d69-adac-d631da5e9f15"
      },
      "source": [
        "datagen_train = ImageDataGenerator(rescale=1./255,\n",
        "      rotation_range=30,\n",
        "      shear_range=0.3,\n",
        "      zoom_range=0.3,\n",
        "      horizontal_flip=True)\n",
        "datagen_validation = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = datagen_train.flow_from_directory(train_data_dir,\n",
        "                                                    target_size=(img_rows,img_cols),\n",
        "                                                    color_mode=\"grayscale\",\n",
        "                                                    batch_size=batch_size,\n",
        "                                                    class_mode='categorical',\n",
        "                                                    shuffle=True)\n",
        "\n",
        "validation_generator = datagen_validation.flow_from_directory(validation_data_dir,\n",
        "                                                    target_size=(img_rows,img_cols),\n",
        "                                                    color_mode=\"grayscale\",\n",
        "                                                    batch_size=batch_size,\n",
        "                                                    class_mode='categorical',\n",
        "                                                    shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 25641 images belonging to 7 classes.\n",
            "Found 7178 images belonging to 7 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "4914afe556e0db3c03614ed1ac2138fd68d03815",
        "id": "A_Otq0alFh65"
      },
      "source": [
        "nb_classes = 7\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64,(3,3), padding='same', input_shape=(48, 48,1)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(128,(5,5), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(512,(3,3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(512,(3,3), padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(256))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Dense(512))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Dense(nb_classes, activation='softmax'))\n",
        "\n",
        "opt = Adam(lr=0.0001)\n",
        "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "8d3db222ae6b74ab55df0d840d372b4306af6db0",
        "scrolled": true,
        "id": "ZSh-V-Y5Fh6-",
        "outputId": "b4b2c59a-50b2-47f1-9b01-e203d2c898bd"
      },
      "source": [
        "epochs = 50\n",
        "\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "checkpoint = ModelCheckpoint(\"model_weights.h5\", monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
        "callbacks_list = [checkpoint]\n",
        "\n",
        "history = model.fit_generator(generator=train_generator,\n",
        "                                steps_per_epoch=train_generator.n//train_generator.batch_size,\n",
        "                                epochs=epochs,\n",
        "                                validation_data = validation_generator,\n",
        "                                validation_steps = validation_generator.n//validation_generator.batch_size,\n",
        "                                callbacks=callbacks_list\n",
        "                                )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "225/225 [==============================] - 36s 161ms/step - loss: 2.0174 - acc: 0.2333 - val_loss: 1.7391 - val_acc: 0.2966\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.29659, saving model to model_weights.h5\n",
            "Epoch 2/50\n",
            "225/225 [==============================] - 31s 138ms/step - loss: 1.8401 - acc: 0.2873 - val_loss: 1.7091 - val_acc: 0.3311\n",
            "\n",
            "Epoch 00002: val_acc improved from 0.29659 to 0.33108, saving model to model_weights.h5\n",
            "Epoch 3/50\n",
            "225/225 [==============================] - 31s 138ms/step - loss: 1.7354 - acc: 0.3318 - val_loss: 1.6665 - val_acc: 0.3711\n",
            "\n",
            "Epoch 00003: val_acc improved from 0.33108 to 0.37114, saving model to model_weights.h5\n",
            "Epoch 4/50\n",
            "225/225 [==============================] - 32s 142ms/step - loss: 1.6591 - acc: 0.3643 - val_loss: 1.6144 - val_acc: 0.3890\n",
            "\n",
            "Epoch 00004: val_acc improved from 0.37114 to 0.38902, saving model to model_weights.h5\n",
            "Epoch 5/50\n",
            "225/225 [==============================] - 31s 138ms/step - loss: 1.5971 - acc: 0.3886 - val_loss: 1.5129 - val_acc: 0.4226\n",
            "\n",
            "Epoch 00005: val_acc improved from 0.38902 to 0.42260, saving model to model_weights.h5\n",
            "Epoch 6/50\n",
            "225/225 [==============================] - 31s 136ms/step - loss: 1.5372 - acc: 0.4093 - val_loss: 1.4734 - val_acc: 0.4344\n",
            "\n",
            "Epoch 00006: val_acc improved from 0.42260 to 0.43442, saving model to model_weights.h5\n",
            "Epoch 7/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 1.4836 - acc: 0.4330 - val_loss: 1.4811 - val_acc: 0.4318\n",
            "\n",
            "Epoch 00007: val_acc did not improve from 0.43442\n",
            "Epoch 8/50\n",
            "225/225 [==============================] - 31s 136ms/step - loss: 1.4470 - acc: 0.4421 - val_loss: 1.4688 - val_acc: 0.4421\n",
            "\n",
            "Epoch 00008: val_acc improved from 0.43442 to 0.44206, saving model to model_weights.h5\n",
            "Epoch 9/50\n",
            "225/225 [==============================] - 31s 136ms/step - loss: 1.3993 - acc: 0.4620 - val_loss: 1.3453 - val_acc: 0.4847\n",
            "\n",
            "Epoch 00009: val_acc improved from 0.44206 to 0.48472, saving model to model_weights.h5\n",
            "Epoch 10/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 1.3667 - acc: 0.4760 - val_loss: 1.3301 - val_acc: 0.4963\n",
            "\n",
            "Epoch 00010: val_acc improved from 0.48472 to 0.49625, saving model to model_weights.h5\n",
            "Epoch 11/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 1.3313 - acc: 0.4853 - val_loss: 1.2498 - val_acc: 0.5215\n",
            "\n",
            "Epoch 00011: val_acc improved from 0.49625 to 0.52148, saving model to model_weights.h5\n",
            "Epoch 12/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 1.2999 - acc: 0.5017 - val_loss: 1.2690 - val_acc: 0.5228\n",
            "\n",
            "Epoch 00012: val_acc improved from 0.52148 to 0.52277, saving model to model_weights.h5\n",
            "Epoch 13/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 1.2721 - acc: 0.5160 - val_loss: 1.2040 - val_acc: 0.5381\n",
            "\n",
            "Epoch 00013: val_acc improved from 0.52277 to 0.53805, saving model to model_weights.h5\n",
            "Epoch 14/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 1.2481 - acc: 0.5216 - val_loss: 1.2038 - val_acc: 0.5438\n",
            "\n",
            "Epoch 00014: val_acc improved from 0.53805 to 0.54382, saving model to model_weights.h5\n",
            "Epoch 15/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 1.2171 - acc: 0.5372 - val_loss: 1.2361 - val_acc: 0.5284\n",
            "\n",
            "Epoch 00015: val_acc did not improve from 0.54382\n",
            "Epoch 16/50\n",
            "225/225 [==============================] - 31s 136ms/step - loss: 1.1997 - acc: 0.5451 - val_loss: 1.1829 - val_acc: 0.5483\n",
            "\n",
            "Epoch 00016: val_acc improved from 0.54382 to 0.54828, saving model to model_weights.h5\n",
            "Epoch 17/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 1.1770 - acc: 0.5482 - val_loss: 1.2091 - val_acc: 0.5362\n",
            "\n",
            "Epoch 00017: val_acc did not improve from 0.54828\n",
            "Epoch 18/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 1.1561 - acc: 0.5598 - val_loss: 1.1495 - val_acc: 0.5712\n",
            "\n",
            "Epoch 00018: val_acc improved from 0.54828 to 0.57120, saving model to model_weights.h5\n",
            "Epoch 19/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 1.1332 - acc: 0.5692 - val_loss: 1.1241 - val_acc: 0.5749\n",
            "\n",
            "Epoch 00019: val_acc improved from 0.57120 to 0.57495, saving model to model_weights.h5\n",
            "Epoch 20/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 1.1245 - acc: 0.5720 - val_loss: 1.1069 - val_acc: 0.5899\n",
            "\n",
            "Epoch 00020: val_acc improved from 0.57495 to 0.58994, saving model to model_weights.h5\n",
            "Epoch 21/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 1.1041 - acc: 0.5799 - val_loss: 1.1192 - val_acc: 0.5729\n",
            "\n",
            "Epoch 00021: val_acc did not improve from 0.58994\n",
            "Epoch 22/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 1.0798 - acc: 0.5880 - val_loss: 1.1452 - val_acc: 0.5761\n",
            "\n",
            "Epoch 00022: val_acc did not improve from 0.58994\n",
            "Epoch 23/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 1.0635 - acc: 0.6001 - val_loss: 1.0998 - val_acc: 0.5953\n",
            "\n",
            "Epoch 00023: val_acc improved from 0.58994 to 0.59527, saving model to model_weights.h5\n",
            "Epoch 24/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 1.0499 - acc: 0.6025 - val_loss: 1.0977 - val_acc: 0.5918\n",
            "\n",
            "Epoch 00024: val_acc did not improve from 0.59527\n",
            "Epoch 25/50\n",
            "225/225 [==============================] - 31s 137ms/step - loss: 1.0348 - acc: 0.6067 - val_loss: 1.1280 - val_acc: 0.5904\n",
            "\n",
            "Epoch 00025: val_acc did not improve from 0.59527\n",
            "Epoch 26/50\n",
            "225/225 [==============================] - 31s 136ms/step - loss: 1.0202 - acc: 0.6145 - val_loss: 1.0765 - val_acc: 0.6007\n",
            "\n",
            "Epoch 00026: val_acc improved from 0.59527 to 0.60075, saving model to model_weights.h5\n",
            "Epoch 27/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 1.0023 - acc: 0.6207 - val_loss: 1.0696 - val_acc: 0.6016\n",
            "\n",
            "Epoch 00027: val_acc improved from 0.60075 to 0.60161, saving model to model_weights.h5\n",
            "Epoch 28/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.9899 - acc: 0.6258 - val_loss: 1.0555 - val_acc: 0.6082\n",
            "\n",
            "Epoch 00028: val_acc improved from 0.60161 to 0.60824, saving model to model_weights.h5\n",
            "Epoch 29/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.9636 - acc: 0.6392 - val_loss: 1.0827 - val_acc: 0.5951\n",
            "\n",
            "Epoch 00029: val_acc did not improve from 0.60824\n",
            "Epoch 30/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 0.9683 - acc: 0.6348 - val_loss: 1.0603 - val_acc: 0.6116\n",
            "\n",
            "Epoch 00030: val_acc improved from 0.60824 to 0.61156, saving model to model_weights.h5\n",
            "Epoch 31/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.9433 - acc: 0.6426 - val_loss: 1.1015 - val_acc: 0.5925\n",
            "\n",
            "Epoch 00031: val_acc did not improve from 0.61156\n",
            "Epoch 32/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 0.9205 - acc: 0.6543 - val_loss: 1.0848 - val_acc: 0.6033\n",
            "\n",
            "Epoch 00032: val_acc did not improve from 0.61156\n",
            "Epoch 33/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.9164 - acc: 0.6540 - val_loss: 1.0945 - val_acc: 0.6054\n",
            "\n",
            "Epoch 00033: val_acc did not improve from 0.61156\n",
            "Epoch 34/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 0.8944 - acc: 0.6613 - val_loss: 1.0408 - val_acc: 0.6231\n",
            "\n",
            "Epoch 00034: val_acc improved from 0.61156 to 0.62309, saving model to model_weights.h5\n",
            "Epoch 35/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.8857 - acc: 0.6651 - val_loss: 1.0440 - val_acc: 0.6231\n",
            "\n",
            "Epoch 00035: val_acc improved from 0.62309 to 0.62309, saving model to model_weights.h5\n",
            "Epoch 36/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 0.8667 - acc: 0.6760 - val_loss: 1.0408 - val_acc: 0.6198\n",
            "\n",
            "Epoch 00036: val_acc did not improve from 0.62309\n",
            "Epoch 37/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.8513 - acc: 0.6819 - val_loss: 1.0553 - val_acc: 0.6280\n",
            "\n",
            "Epoch 00037: val_acc improved from 0.62309 to 0.62799, saving model to model_weights.h5\n",
            "Epoch 38/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 0.8282 - acc: 0.6882 - val_loss: 1.0357 - val_acc: 0.6268\n",
            "\n",
            "Epoch 00038: val_acc did not improve from 0.62799\n",
            "Epoch 39/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.8224 - acc: 0.6916 - val_loss: 1.0770 - val_acc: 0.6180\n",
            "\n",
            "Epoch 00039: val_acc did not improve from 0.62799\n",
            "Epoch 40/50\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "225/225 [==============================] - 30s 133ms/step - loss: 0.8138 - acc: 0.6960 - val_loss: 1.0631 - val_acc: 0.6248\n",
            "\n",
            "Epoch 00040: val_acc did not improve from 0.62799\n",
            "Epoch 41/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.7960 - acc: 0.7033 - val_loss: 1.0399 - val_acc: 0.6329\n",
            "\n",
            "Epoch 00041: val_acc improved from 0.62799 to 0.63289, saving model to model_weights.h5\n",
            "Epoch 42/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 0.7713 - acc: 0.7117 - val_loss: 1.0080 - val_acc: 0.6473\n",
            "\n",
            "Epoch 00042: val_acc improved from 0.63289 to 0.64730, saving model to model_weights.h5\n",
            "Epoch 43/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 0.7598 - acc: 0.7161 - val_loss: 1.0069 - val_acc: 0.6467\n",
            "\n",
            "Epoch 00043: val_acc did not improve from 0.64730\n",
            "Epoch 44/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 0.7463 - acc: 0.7196 - val_loss: 1.0760 - val_acc: 0.6300\n",
            "\n",
            "Epoch 00044: val_acc did not improve from 0.64730\n",
            "Epoch 45/50\n",
            "225/225 [==============================] - 31s 136ms/step - loss: 0.7406 - acc: 0.7236 - val_loss: 1.0751 - val_acc: 0.6276\n",
            "\n",
            "Epoch 00045: val_acc did not improve from 0.64730\n",
            "Epoch 46/50\n",
            "225/225 [==============================] - 30s 135ms/step - loss: 0.7208 - acc: 0.7317 - val_loss: 1.0152 - val_acc: 0.6506\n",
            "\n",
            "Epoch 00046: val_acc improved from 0.64730 to 0.65062, saving model to model_weights.h5\n",
            "Epoch 47/50\n",
            "225/225 [==============================] - 30s 134ms/step - loss: 0.6982 - acc: 0.7412 - val_loss: 1.0375 - val_acc: 0.6522\n",
            "\n",
            "Epoch 00047: val_acc improved from 0.65062 to 0.65221, saving model to model_weights.h5\n",
            "Epoch 48/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.6907 - acc: 0.7437 - val_loss: 1.0152 - val_acc: 0.6490\n",
            "\n",
            "Epoch 00048: val_acc did not improve from 0.65221\n",
            "Epoch 49/50\n",
            "225/225 [==============================] - 30s 133ms/step - loss: 0.6879 - acc: 0.7431 - val_loss: 1.0627 - val_acc: 0.6398\n",
            "\n",
            "Epoch 00049: val_acc did not improve from 0.65221\n",
            "Epoch 50/50\n",
            "225/225 [==============================] - 30s 132ms/step - loss: 0.6723 - acc: 0.7499 - val_loss: 1.1159 - val_acc: 0.6384\n",
            "\n",
            "Epoch 00050: val_acc did not improve from 0.65221\n",
            "CPU times: user 26min 13s, sys: 6min 22s, total: 32min 35s\n",
            "Wall time: 25min 35s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "6076d860425d9e1bf4e837d1d618823436739e37",
        "id": "bSRyT0ctFh7H"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}