fer2013_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "fer2013_model.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN8YABkViyneS4C2pKtbeVJ"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9TnONmBezs_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "172b3a94-fd1b-4cc9-fa2b-3062a782369a"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, Activation, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization\n",
        "from keras.metrics import categorical_accuracy\n",
        "from keras.models import model_from_json\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import *\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jZO46nMhCiz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "15c090b2-d6d0-4135-c00c-69fe44ed9dea"
      },
      "source": [
        "# using the FER2013 dataset from kaggle: https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data\n",
        "df = pd.read_csv('fer2013.csv')\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(35887, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>emotion</th>\n",
              "      <th>pixels</th>\n",
              "      <th>Usage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>151 150 147 155 148 133 111 140 170 174 182 15...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>231 212 156 164 174 138 161 173 182 200 106 38...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...</td>\n",
              "      <td>Training</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   emotion                                             pixels     Usage\n",
              "0        0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...  Training\n",
              "1        0  151 150 147 155 148 133 111 140 170 174 182 15...  Training\n",
              "2        2  231 212 156 164 174 138 161 173 182 200 106 38...  Training\n",
              "3        4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...  Training\n",
              "4        6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...  Training"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzLn81voqJfN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_usage = df.groupby(['Usage']).count()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpazIl_so3rO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "7bfa690a-3def-4f48-bd01-d79bb14662b4"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0, 0, 1, 1])\n",
        "datasets = df_usage.index.tolist()\n",
        "counts = df_usage['emotion'].tolist()\n",
        "ax.bar(datasets, counts)\n",
        "plt.xlabel(\"Datasets\")\n",
        "plt.ylabel(\"Images\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lKUhMGGsBW1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_set = df[df['Usage'] == 'Training']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szjefD3PiFKf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "ad329678-1659-4057-9546-2d86f8fe869a"
      },
      "source": [
        "# (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral)\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0, 0, 1, 1])\n",
        "emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']\n",
        "image_counts = train_set.groupby(['emotion']).count()['pixels'].tolist()\n",
        "ax.bar(emotions, image_counts)\n",
        "plt.xlabel(\"Emotions\")\n",
        "plt.ylabel(\"Images\")\n",
        "plt.title(\"Emotion label distribution in Training set\")\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJ95AyvUaxqE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "d85b11ce-87cb-4771-ad54-4ccf26838b38"
      },
      "source": [
        "# randomly select 9 images\n",
        "image_sample = df.sample(9, random_state=5)\n",
        " \n",
        "# Create subplots for images\n",
        "_, axs = plt.subplots(3, 3, figsize=(10,10))\n",
        "axs = axs.ravel()\n",
        "i = 0\n",
        "for _, row in image_sample.iterrows():\n",
        "    one_image = np.array([int(pixel) for pixel in row['pixels'].split()]).reshape(48, 48)\n",
        "    axs[i].imshow(one_image, cmap='gray')\n",
        "    axs[i].set_title(emotions[row['emotion']])\n",
        "    axs[i].axis('off')\n",
        "    i += 1"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lkq64EGJtTIm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getData(file_name):\n",
        "    # data consists of 48x48 pixel grayscale images of faces\n",
        "    # Y will contain the emotion labels\n",
        "    # X will contain the image pixels\n",
        "    Y = []\n",
        "    X = []\n",
        "    # skip the first line in file\n",
        "    first = True\n",
        "    for line in open(file_name):\n",
        "        if first:\n",
        "            first = False\n",
        "        else:\n",
        "            row = line.split(',')\n",
        "            Y.append(int(row[0]))\n",
        "            X.append([int(pixel) for pixel in row[1].split()])\n",
        "\n",
        "# scale pixel values to be b/w 0 and 1\n",
        "    X, Y = np.array(X) / 255.0, np.array(Y)\n",
        "    return X, Y\n",
        "\n",
        "X, Y = getData(\"fer2013.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TKU1v528AKZm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# keras with tensorflow backend\n",
        "images, pixels = X.shape\n",
        "X = X.reshape(images, 48, 48, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEosi7FS_NiL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, random_state=0)\n",
        "# there are 7 classes of emotions\n",
        "y_train = (np.arange(7) == y_train[:, None]).astype(np.float32)\n",
        "y_test = (np.arange(7) == y_test[:, None]).astype(np.float32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRWqSa3E3_MR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "outputId": "ce53a30a-0e63-499d-bd94-d8c5713e3c47"
      },
      "source": [
        "def model():\n",
        "    model = Sequential()\n",
        "    input_shape = (48, 48, 1)\n",
        "    model.add(Conv2D(64, (5, 5), input_shape=input_shape, activation='relu', padding='same'))\n",
        "    model.add(Conv2D(64, (5, 5), activation='relu', padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Conv2D(128, (5, 5),activation='relu',padding='same'))\n",
        "    model.add(Conv2D(128, (5, 5),activation='relu',padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Conv2D(256, (3, 3),activation='relu',padding='same'))\n",
        "    model.add(Conv2D(256, (3, 3),activation='relu',padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(128))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Activation('relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(7))\n",
        "    model.add(Activation('softmax'))\n",
        "    \n",
        "    model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')\n",
        "    return model\n",
        "\n",
        "model = model()\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 48, 48, 64)        1664      \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 48, 48, 64)        102464    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 48, 48, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 24, 24, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 24, 24, 128)       204928    \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 24, 24, 128)       409728    \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 24, 24, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 12, 12, 256)       295168    \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 12, 12, 256)       590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 12, 12, 256)       1024      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               1179776   \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 7)                 903       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 7)                 0         \n",
            "=================================================================\n",
            "Total params: 2,787,015\n",
            "Trainable params: 2,785,863\n",
            "Non-trainable params: 1,152\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1T1lIoZ_v8ly",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "outputId": "a303b98d-6652-4c2f-e249-3923a6584b1d"
      },
      "source": [
        "from keras import backend as K\n",
        "# save model at this location after each epoch\n",
        "path_model = 'model.h5'\n",
        "K.tensorflow_backend.clear_session() \n",
        "model = model() \n",
        "# set the learning rate\n",
        "K.set_value(model.optimizer.lr, 0.001)\n",
        "# fit the model\n",
        "# history\n",
        "h = model.fit(x=X_train,     \n",
        "            y=y_train, \n",
        "            batch_size=64, \n",
        "            epochs=20, \n",
        "            # progress bar\n",
        "            verbose=1, \n",
        "            validation_data=(X_test, y_test),\n",
        "            shuffle=True,\n",
        "            callbacks=[\n",
        "                ModelCheckpoint(filepath=path_model),])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 32298 samples, validate on 3589 samples\n",
            "Epoch 1/20\n",
            "32298/32298 [==============================] - 52s 2ms/step - loss: 1.6605 - accuracy: 0.3527 - val_loss: 1.5806 - val_accuracy: 0.4021\n",
            "Epoch 2/20\n",
            "32298/32298 [==============================] - 45s 1ms/step - loss: 1.3065 - accuracy: 0.5020 - val_loss: 1.4322 - val_accuracy: 0.4246\n",
            "Epoch 3/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 1.1255 - accuracy: 0.5772 - val_loss: 1.1731 - val_accuracy: 0.5595\n",
            "Epoch 4/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.9988 - accuracy: 0.6300 - val_loss: 1.1837 - val_accuracy: 0.5653\n",
            "Epoch 5/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.8790 - accuracy: 0.6740 - val_loss: 1.2406 - val_accuracy: 0.5467\n",
            "Epoch 6/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.7356 - accuracy: 0.7308 - val_loss: 1.1198 - val_accuracy: 0.5921\n",
            "Epoch 7/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.5717 - accuracy: 0.7943 - val_loss: 1.1765 - val_accuracy: 0.6018\n",
            "Epoch 8/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.4098 - accuracy: 0.8563 - val_loss: 1.3488 - val_accuracy: 0.6049\n",
            "Epoch 9/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.2896 - accuracy: 0.9000 - val_loss: 1.6041 - val_accuracy: 0.5773\n",
            "Epoch 10/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.2081 - accuracy: 0.9314 - val_loss: 1.7227 - val_accuracy: 0.5968\n",
            "Epoch 11/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.1544 - accuracy: 0.9487 - val_loss: 1.8535 - val_accuracy: 0.6071\n",
            "Epoch 12/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.1360 - accuracy: 0.9565 - val_loss: 1.8452 - val_accuracy: 0.6032\n",
            "Epoch 13/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.1327 - accuracy: 0.9558 - val_loss: 1.9600 - val_accuracy: 0.5954\n",
            "Epoch 14/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.1117 - accuracy: 0.9626 - val_loss: 2.0033 - val_accuracy: 0.6130\n",
            "Epoch 15/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.0980 - accuracy: 0.9679 - val_loss: 1.9866 - val_accuracy: 0.6108\n",
            "Epoch 16/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.0965 - accuracy: 0.9671 - val_loss: 1.9578 - val_accuracy: 0.6133\n",
            "Epoch 17/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.0956 - accuracy: 0.9685 - val_loss: 1.8831 - val_accuracy: 0.6085\n",
            "Epoch 18/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.0783 - accuracy: 0.9743 - val_loss: 2.2383 - val_accuracy: 0.6250\n",
            "Epoch 19/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.0727 - accuracy: 0.9769 - val_loss: 2.1174 - val_accuracy: 0.5985\n",
            "Epoch 20/20\n",
            "32298/32298 [==============================] - 44s 1ms/step - loss: 0.0734 - accuracy: 0.9770 - val_loss: 2.0407 - val_accuracy: 0.6244\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-D2MW7mMGUHv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a12ed20c-b36a-4b25-c40b-26ba45e1c9aa"
      },
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_test.shape"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3589, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPWnx4DDQ2XJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "outputId": "8c57a3f9-a99a-4f70-cef4-30f472f42c1f"
      },
      "source": [
        "# Loss Curves\n",
        "plt.figure(figsize = [8,6])\n",
        "plt.plot(h.history['loss'],'r', linewidth=2.0)\n",
        "plt.plot(h.history['val_loss'],'b', linewidth=2.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'], fontsize=18)\n",
        "plt.xlabel('Epochs ', fontsize=16)\n",
        "plt.ylabel('Loss', fontsize=16)\n",
        "plt.title('Loss Curves', fontsize=16)\n",
        " \n",
        "# Accuracy Curves\n",
        "plt.figure(figsize = [8,6])\n",
        "plt.plot(h.history['accuracy'],'r', linewidth=2.0)\n",
        "plt.plot(h.history['val_accuracy'],'b', linewidth=2.0)\n",
        "plt.legend(['Training Accuracy', 'Validation Accuracy'], fontsize=18)\n",
        "plt.xlabel('Epochs ', fontsize=16)\n",
        "plt.ylabel('Accuracy', fontsize=16)\n",
        "plt.title('Accuracy Curves', fontsize=16)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFDigoGEjs5H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "0e0a3ad3-fc79-4167-99e8-815fd7144713"
      },
      "source": [
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "y_true = [np.argmax(i) for i in y_test]\n",
        "y_prediction = [np.argmax(i) for i in y_pred]\n",
        "cm = confusion_matrix(y_true, y_prediction)\n",
        "title='Confusion matrix'\n",
        "print(cm)\n",
        "\n",
        "plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "plt.title(title)\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(7)\n",
        "plt.xticks(tick_marks, emotions, rotation=45)\n",
        "plt.yticks(tick_marks, emotions)\n",
        "fmt = 'd'\n",
        "thresh = cm.max() / 2.\n",
        "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, format(cm[i, j], fmt),\n",
        "            horizontalalignment=\"center\",\n",
        "            color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[267   1  42  23  69   8  74]\n",
            " [ 11  30   5   1   4   1   4]\n",
            " [ 86   3 227  26  64  37  59]\n",
            " [ 55   0  27 728  33  23  54]\n",
            " [ 79   1  91  32 292   9  95]\n",
            " [ 17   0  27  24   9 347  18]\n",
            " [ 42   1  49  49  80  15 350]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}