DataAug_16layers CNNs.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "04d5265d-551d-44cb-b575-da2f31a72037",
      "metadata": {
        "id": "04d5265d-551d-44cb-b575-da2f31a72037"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "import cv2\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "1d9c1e01-dcd0-4e68-8e54-3439611b9897",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "1d9c1e01-dcd0-4e68-8e54-3439611b9897",
        "outputId": "b9b0a417-ebeb-4dc1-e636-fcafb0fb0e23"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.15.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "import keras\n",
        "keras.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "925b0753-f831-40bc-bdca-c20eb1e4591c",
      "metadata": {
        "id": "925b0753-f831-40bc-bdca-c20eb1e4591c"
      },
      "outputs": [],
      "source": [
        "# Reading Images from data set test and train\n",
        "\n",
        "df = pd.read_csv(r\"/content/train_dataset.csv\")\n",
        "def pixels_to_image(pixel_str):\n",
        "    # Assuming each image is 48x48 pixels\n",
        "    pixels = np.array([int(p) for p in pixel_str.split()], dtype='uint8')\n",
        "    # print(len(pixels))\n",
        "    img = pixels.reshape(48, 48)  # Adjust if image dimensions are different\n",
        "    img = cv2.resize(img, (48, 48))\n",
        "    img = cv2.GaussianBlur(img,(3,3),0)\n",
        "\n",
        "    return img\n",
        "\n",
        "# Add a column with images\n",
        "df['image'] = df['pixels'].apply(pixels_to_image)\n",
        "\n",
        "# Display the first few rows with images and emotion\n",
        "# for idx, row in df.iterrows():\n",
        "    # plt.figure()\n",
        "    # plt.imshow(row['image'], cmap='gray')\n",
        "    # plt.title(f\"Emotion: {row['emotion']}\")\n",
        "    # plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "8cbef042-bb62-4efd-a580-4dfaa02ce79e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cbef042-bb62-4efd-a580-4dfaa02ce79e",
        "outputId": "832e1274-86a6-4458-860d-530440dca3b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 69  70  69 ...  53  48  45]\n",
            " [ 62  62  62 ...  52  50  47]\n",
            " [ 49  51  56 ...  48  50  49]\n",
            " ...\n",
            " [ 73  65  52 ...  78  62  55]\n",
            " [ 78  76  71 ...  92  76  65]\n",
            " [ 77  78  80 ... 100  87  77]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Implies the data is 224*224 and 5000 images\n",
        "\n",
        "print(df['image'][0])\n",
        "len(df['image'][0])"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BFxqihFKfHwK"
      },
      "id": "BFxqihFKfHwK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "a9dcaca6-2aaf-47b3-9bfc-196f54041bee",
      "metadata": {
        "id": "a9dcaca6-2aaf-47b3-9bfc-196f54041bee"
      },
      "outputs": [],
      "source": [
        "X_train = df['image']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "627067a2-b42c-4bd2-9bf4-0eb753853b31",
      "metadata": {
        "id": "627067a2-b42c-4bd2-9bf4-0eb753853b31"
      },
      "outputs": [],
      "source": [
        "y_train = df['emotion']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f25c5fcd-8e75-4578-a7c2-91bdafb8853a",
      "metadata": {
        "id": "f25c5fcd-8e75-4578-a7c2-91bdafb8853a"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "4ec5763b-5849-458b-896d-edb6e2e8ef7e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ec5763b-5849-458b-896d-edb6e2e8ef7e",
        "outputId": "2744bf46-3c45-406f-c115-4682a155b0a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[714, 72, 726, 1278, 817, 525, 868]\n"
          ]
        }
      ],
      "source": [
        "emo_meta = [0,0,0,0,0,0,0]\n",
        "\n",
        "for i in y_train:\n",
        "    emo_meta[i] += 1\n",
        "print(emo_meta)\n",
        "\n",
        "\n",
        "# Original data set [714, 72, 726, 1278, 817, 525, 868]\n",
        "# y_train [643, 65, 653, 1150, 735, 473, 781]\n",
        "# y_test [71, 7, 73, 128, 82, 52, 87]"
      ]
    },
    {
      "cell_type": "raw",
      "id": "ca6092d8-65ed-4073-99cf-ccc64e9ff27a",
      "metadata": {
        "id": "ca6092d8-65ed-4073-99cf-ccc64e9ff27a"
      },
      "source": [
        "Need Data Augmentation"
      ]
    },
    {
      "cell_type": "raw",
      "id": "ebfcb38c-3fe0-462c-bc65-f3b8e6b6e09b",
      "metadata": {
        "id": "ebfcb38c-3fe0-462c-bc65-f3b8e6b6e09b"
      },
      "source": [
        "So Class 1 has Least images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "89aaf1c1-2ff9-4e86-a317-f96295dc22c9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89aaf1c1-2ff9-4e86-a317-f96295dc22c9",
        "outputId": "c93165e4-6876-4dbf-86d2-271475be9038"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 48, 48, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "\n",
        "\n",
        "X_train_flattened = np.array(X_train.tolist())\n",
        "num_images = X_train_flattened.shape[0]\n",
        "X_train = X_train_flattened.reshape((num_images, 48, 48, 1))\n",
        "\n",
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "56e75429-c6f6-42df-ade4-f24b978c93ed",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56e75429-c6f6-42df-ade4-f24b978c93ed",
        "outputId": "155623ea-2736-4737-85d5-e5c4266f77e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Augmented X_train shape: (1082, 48, 48, 1)\n",
            "Augmented y_train shape: (1082,)\n",
            "Original X_train shape: (5000, 48, 48, 1)\n",
            "Original y_train shape: (5000,)\n",
            "Total dataset size after augmentation: 4865\n",
            "X_train1 shape: (4865, 48, 48, 1)\n",
            "X_val1 shape: (1217, 48, 48, 1)\n",
            "y_train1 shape: (4865,)\n",
            "y_val1 shape: (1217,)\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Optional: Normalize images if required\n",
        "# X_train = X_train.astype(np.float32) / 255.0\n",
        "\n",
        "# Define augmentation generator\n",
        "datagen = ImageDataGenerator(\n",
        "    horizontal_flip=True,               # Horizontal Flip\n",
        "    rotation_range=20,                  # Random Rotation\n",
        "    width_shift_range=0.2,              # Random Shift\n",
        "    height_shift_range=0.2,             # Random Shift\n",
        "    brightness_range=[0.7, 1.3],        # Random Brightness Contrast\n",
        "    zoom_range=0.2,                     # Zoom\n",
        "    fill_mode='nearest'                 # Fill mode for newly created pixels\n",
        ")\n",
        "\n",
        "# Define target number of images based on the class with the most images (class 3)\n",
        "target_images_per_class = 1150\n",
        "\n",
        "# Prepare lists for augmented data\n",
        "augmented_X_train = []\n",
        "augmented_y_train = []\n",
        "\n",
        "# Augment images for each class\n",
        "for class_label in np.unique(y_train):\n",
        "    # Get all images of the current class\n",
        "    X_class = X_train[y_train == class_label]\n",
        "    current_num_images = X_class.shape[0]\n",
        "\n",
        "    # Calculate how many images to augment\n",
        "    num_to_augment = target_images_per_class - current_num_images\n",
        "\n",
        "    if num_to_augment > 0:\n",
        "        num_per_image = int(num_to_augment // current_num_images) + 1\n",
        "\n",
        "        # Apply augmentation to the class\n",
        "        for image in X_class:\n",
        "            image = image[np.newaxis, ...]  # Add batch dimension\n",
        "            image_gen = datagen.flow(image, batch_size=1)  # Use batch_size=1\n",
        "\n",
        "            # Generate images for this particular image\n",
        "            for _ in range(num_per_image):\n",
        "                augmented_image = next(image_gen)[0]  # Get the augmented image\n",
        "                augmented_X_train.append(augmented_image)\n",
        "                augmented_y_train.append(class_label)\n",
        "\n",
        "                if len(augmented_X_train) >= num_to_augment:\n",
        "                    break\n",
        "            if len(augmented_X_train) >= num_to_augment:\n",
        "                break\n",
        "\n",
        "# Convert lists to NumPy arrays\n",
        "if augmented_X_train:\n",
        "    augmented_X_train = np.array(augmented_X_train, dtype=np.uint8)\n",
        "    augmented_y_train = np.array(augmented_y_train, dtype=np.int32)\n",
        "    print(f\"Augmented X_train shape: {augmented_X_train.shape}\")\n",
        "    print(f\"Augmented y_train shape: {augmented_y_train.shape}\")\n",
        "else:\n",
        "    # Handle case where no images were augmented\n",
        "    augmented_X_train = np.empty((0, 48, 48, 1), dtype=np.uint8)\n",
        "    augmented_y_train = np.empty((0,), dtype=np.int32)\n",
        "    print(\"No images were augmented.\")\n",
        "\n",
        "# Combine augmented images with original training data\n",
        "print(f\"Original X_train shape: {X_train.shape}\")\n",
        "print(f\"Original y_train shape: {y_train.shape}\")\n",
        "\n",
        "X_train = np.concatenate([X_train, augmented_X_train], axis=0)\n",
        "y_train = np.concatenate([y_train, augmented_y_train], axis=0)\n",
        "\n",
        "# Shuffle the dataset\n",
        "indices = np.arange(X_train.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "X_train = X_train[indices]\n",
        "y_train = y_train[indices]\n",
        "\n",
        "# Optionally, split into train and validation sets\n",
        "X_train1, X_val1, y_train1, y_val1 = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
        "\n",
        "print(f\"Total dataset size after augmentation: {X_train1.shape[0]}\")\n",
        "print(f\"X_train1 shape: {X_train1.shape}\")\n",
        "print(f\"X_val1 shape: {X_val1.shape}\")\n",
        "print(f\"y_train1 shape: {y_train1.shape}\")\n",
        "print(f\"y_val1 shape: {y_val1.shape}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "b9c4e31a-20b8-402e-974c-4cbda3dc04a3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9c4e31a-20b8-402e-974c-4cbda3dc04a3",
        "outputId": "3e9624b3-4268-4ecd-faae-574cf149f62a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[896, 577, 587, 1044, 642, 431, 688]\n"
          ]
        }
      ],
      "source": [
        "emo_meta = [0,0,0,0,0,0,0]\n",
        "\n",
        "for i in y_train1:\n",
        "    emo_meta[i] += 1\n",
        "print(emo_meta)\n",
        "# [923, 923, 910, 1037, 918, 908, 923] y_train\n",
        "# [227, 227, 240, 241, 232, 242, 227] y_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "fad2b535-675e-400a-9aea-6e68e8a89085",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fad2b535-675e-400a-9aea-6e68e8a89085",
        "outputId": "4e50dd91-624b-4d6f-8069-dc222435180b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-6f59a8042d4b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_val1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Emotion: {y_val1[row]}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m   2693\u001b[0m         \u001b[0minterpolation_stage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2694\u001b[0m         resample=None, url=None, data=None, **kwargs):\n\u001b[0;32m-> 2695\u001b[0;31m     __ret = gca().imshow(\n\u001b[0m\u001b[1;32m   2696\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2697\u001b[0m         \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mgca\u001b[0;34m()\u001b[0m\n\u001b[1;32m   2307\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0m_copy_docstring_and_deprecators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2308\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2309\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mgca\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1626\u001b[0m         \"\"\"\n\u001b[1;32m   1627\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1628\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1629\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1630\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_gci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, facecolor, frameon, sharex, sharey, label, xscale, yscale, box_aspect, *args, **kwargs)\u001b[0m\n\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterization_zorder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 683\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    685\u001b[0m         \u001b[0;31m# funcs used to format x and y - fall back on major formatters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mclear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1393\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1394\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1395\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__clear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1397\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__clear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1279\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Also resets the scale to linear.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1280\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mspine\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1281\u001b[0;31m             \u001b[0mspine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1283\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_existing_data_limits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/spines.py\u001b[0m in \u001b[0;36mclear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_position\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# clear position\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_adjust_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mclear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    875\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabelpad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'axes.labelpad'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 877\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    878\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_scale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'linear'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_init\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2250\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel_position\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'bottom'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2252\u001b[0;31m         self.offsetText.set(\n\u001b[0m\u001b[1;32m   2253\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2254\u001b[0m             \u001b[0mverticalalignment\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'top'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhorizontalalignment\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'right'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m         \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mArtist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m         \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"set\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__qualname__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{cls.__qualname__}.set\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mset\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   1229\u001b[0m         \u001b[0;31m# Artist._update_set_signature_and_docstring() at the end of the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m         \u001b[0;31m# module.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1231\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_internal_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1233\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcontextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontextmanager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_internal_update\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m   1221\u001b[0m         \u001b[0mThe\u001b[0m \u001b[0mlack\u001b[0m \u001b[0mof\u001b[0m \u001b[0mprenormalization\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mto\u001b[0m \u001b[0mmaintain\u001b[0m \u001b[0mbackcompatibility\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1222\u001b[0m         \"\"\"\n\u001b[0;32m-> 1223\u001b[0;31m         return self._update_props(\n\u001b[0m\u001b[1;32m   1224\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"{cls.__name__}.set() got an unexpected keyword argument \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1225\u001b[0m             \"{prop_name!r}\")\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_update_props\u001b[0;34m(self, props, errfmt)\u001b[0m\n\u001b[1;32m   1186\u001b[0m         \"\"\"\n\u001b[1;32m   1187\u001b[0m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1188\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1189\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1190\u001b[0m                 \u001b[0;31m# Allow attributes we want to be able to update through\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_setattr_cm\u001b[0;34m(obj, **kwargs)\u001b[0m\n\u001b[1;32m   2026\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2027\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2028\u001b[0;31m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2029\u001b[0m         \u001b[0;32myield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2030\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "for row in range(len(X_val1)):\n",
        "    if y_val1[row] == 1:\n",
        "        plt.figure()\n",
        "        plt.imshow(X_val1[row], cmap='gray')\n",
        "        plt.title(f\"Emotion: {y_val1[row]}\")\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "f876bed6-54c6-437e-9398-287dc27c8761",
      "metadata": {
        "id": "f876bed6-54c6-437e-9398-287dc27c8761"
      },
      "outputs": [],
      "source": [
        "X_train = X_train1/255\n",
        "y_train = y_train1\n",
        "X_val = X_val1\n",
        "y_val = y_val1"
      ]
    },
    {
      "cell_type": "raw",
      "id": "46c07cf0-9cd2-4bbd-81be-6d18e69bfdc3",
      "metadata": {
        "id": "46c07cf0-9cd2-4bbd-81be-6d18e69bfdc3"
      },
      "source": [
        "MODEL TRAINING CNNS (METHOD 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "136dc9d6-3721-43f1-9c86-26c8a35c9daa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "136dc9d6-3721-43f1-9c86-26c8a35c9daa",
        "outputId": "f72b5b4f-16ea-4ce8-8918-d760f8e5b895"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 48, 48, 64)        640       \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 48, 48, 64)        256       \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 48, 48, 64)        36928     \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 48, 48, 64)        256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 24, 24, 64)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 24, 24, 64)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 24, 24, 128)       73856     \n",
            "                                                                 \n",
            " batch_normalization_2 (Bat  (None, 24, 24, 128)       512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 24, 24, 128)       147584    \n",
            "                                                                 \n",
            " batch_normalization_3 (Bat  (None, 24, 24, 128)       512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 12, 12, 128)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 12, 12, 128)       0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 12, 12, 256)       295168    \n",
            "                                                                 \n",
            " batch_normalization_4 (Bat  (None, 12, 12, 256)       1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 12, 12, 256)       590080    \n",
            "                                                                 \n",
            " batch_normalization_5 (Bat  (None, 12, 12, 256)       1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 6, 6, 256)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 6, 6, 256)         0         \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 6, 6, 512)         1180160   \n",
            "                                                                 \n",
            " batch_normalization_6 (Bat  (None, 6, 6, 512)         2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 6, 6, 512)         2359808   \n",
            "                                                                 \n",
            " batch_normalization_7 (Bat  (None, 6, 6, 512)         2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 3, 3, 512)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 3, 3, 512)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4608)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               2359808   \n",
            "                                                                 \n",
            " batch_normalization_8 (Bat  (None, 512)               2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               65664     \n",
            "                                                                 \n",
            " batch_normalization_9 (Bat  (None, 128)               512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 7)                 903       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7120839 (27.16 MB)\n",
            "Trainable params: 7115719 (27.14 MB)\n",
            "Non-trainable params: 5120 (20.00 KB)\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "153/153 [==============================] - 35s 208ms/step - loss: 3.1015 - accuracy: 0.1609 - val_loss: 27.3550 - val_accuracy: 0.1126\n",
            "Epoch 2/50\n",
            "153/153 [==============================] - 31s 205ms/step - loss: 2.8748 - accuracy: 0.1710 - val_loss: 238.7752 - val_accuracy: 0.1126\n",
            "Epoch 3/50\n",
            "153/153 [==============================] - 31s 201ms/step - loss: 2.7160 - accuracy: 0.1755 - val_loss: 775.0842 - val_accuracy: 0.1142\n",
            "Epoch 4/50\n",
            "153/153 [==============================] - 31s 203ms/step - loss: 2.6478 - accuracy: 0.1891 - val_loss: 1138.9884 - val_accuracy: 0.2087\n",
            "Epoch 5/50\n",
            "153/153 [==============================] - 31s 202ms/step - loss: 2.5625 - accuracy: 0.1877 - val_loss: 2661.8901 - val_accuracy: 0.1126\n",
            "Epoch 6/50\n",
            "153/153 [==============================] - 31s 202ms/step - loss: 2.4557 - accuracy: 0.2045 - val_loss: 2159.7061 - val_accuracy: 0.1126\n",
            "Epoch 7/50\n",
            "153/153 [==============================] - 31s 202ms/step - loss: 2.3771 - accuracy: 0.2220 - val_loss: 1962.4783 - val_accuracy: 0.1126\n",
            "Epoch 8/50\n",
            "153/153 [==============================] - 31s 203ms/step - loss: 2.3067 - accuracy: 0.2329 - val_loss: 1445.3987 - val_accuracy: 0.1200\n",
            "Epoch 9/50\n",
            "153/153 [==============================] - 31s 203ms/step - loss: 2.2751 - accuracy: 0.2245 - val_loss: 2264.0952 - val_accuracy: 0.1159\n",
            "Epoch 10/50\n",
            "153/153 [==============================] - 31s 201ms/step - loss: 2.1975 - accuracy: 0.2411 - val_loss: 1422.2361 - val_accuracy: 0.1183\n",
            "Epoch 11/50\n",
            "153/153 [==============================] - 30s 197ms/step - loss: 2.1730 - accuracy: 0.2532 - val_loss: 2103.4915 - val_accuracy: 0.1126\n",
            "Epoch 12/50\n",
            "153/153 [==============================] - 30s 196ms/step - loss: 2.1209 - accuracy: 0.2666 - val_loss: 2688.0417 - val_accuracy: 0.1159\n",
            "Epoch 13/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 2.0540 - accuracy: 0.2826 - val_loss: 3292.5969 - val_accuracy: 0.1126\n",
            "Epoch 14/50\n",
            "153/153 [==============================] - 31s 200ms/step - loss: 2.0439 - accuracy: 0.2861 - val_loss: 2914.4377 - val_accuracy: 0.1126\n",
            "Epoch 15/50\n",
            "153/153 [==============================] - 31s 200ms/step - loss: 2.0079 - accuracy: 0.3030 - val_loss: 3104.2026 - val_accuracy: 0.1126\n",
            "Epoch 16/50\n",
            "153/153 [==============================] - 30s 199ms/step - loss: 1.9522 - accuracy: 0.3052 - val_loss: 2129.3555 - val_accuracy: 0.1159\n",
            "Epoch 17/50\n",
            "153/153 [==============================] - 30s 196ms/step - loss: 1.9154 - accuracy: 0.3291 - val_loss: 3734.2021 - val_accuracy: 0.1126\n",
            "Epoch 18/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.8793 - accuracy: 0.3324 - val_loss: 1273.9720 - val_accuracy: 0.1767\n",
            "Epoch 19/50\n",
            "153/153 [==============================] - 30s 199ms/step - loss: 1.8547 - accuracy: 0.3468 - val_loss: 2343.0776 - val_accuracy: 0.1126\n",
            "Epoch 20/50\n",
            "153/153 [==============================] - 30s 199ms/step - loss: 1.7952 - accuracy: 0.3472 - val_loss: 2738.3232 - val_accuracy: 0.1126\n",
            "Epoch 21/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.7636 - accuracy: 0.3655 - val_loss: 2425.0925 - val_accuracy: 0.1142\n",
            "Epoch 22/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.7332 - accuracy: 0.3743 - val_loss: 2853.1765 - val_accuracy: 0.1126\n",
            "Epoch 23/50\n",
            "153/153 [==============================] - 30s 199ms/step - loss: 1.6998 - accuracy: 0.3825 - val_loss: 2698.6494 - val_accuracy: 0.1126\n",
            "Epoch 24/50\n",
            "153/153 [==============================] - 31s 200ms/step - loss: 1.6590 - accuracy: 0.3936 - val_loss: 2301.0583 - val_accuracy: 0.1126\n",
            "Epoch 25/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.6218 - accuracy: 0.4092 - val_loss: 2302.6111 - val_accuracy: 0.1126\n",
            "Epoch 26/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.6213 - accuracy: 0.4095 - val_loss: 3218.0151 - val_accuracy: 0.1126\n",
            "Epoch 27/50\n",
            "153/153 [==============================] - 30s 199ms/step - loss: 1.5859 - accuracy: 0.4162 - val_loss: 3028.9404 - val_accuracy: 0.1126\n",
            "Epoch 28/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.5463 - accuracy: 0.4251 - val_loss: 2259.9529 - val_accuracy: 0.1191\n",
            "Epoch 29/50\n",
            "153/153 [==============================] - 31s 200ms/step - loss: 1.5329 - accuracy: 0.4310 - val_loss: 1594.7174 - val_accuracy: 0.1265\n",
            "Epoch 30/50\n",
            "153/153 [==============================] - 31s 200ms/step - loss: 1.4963 - accuracy: 0.4563 - val_loss: 2716.9463 - val_accuracy: 0.1126\n",
            "Epoch 31/50\n",
            "153/153 [==============================] - 30s 199ms/step - loss: 1.4734 - accuracy: 0.4530 - val_loss: 3016.1406 - val_accuracy: 0.1126\n",
            "Epoch 32/50\n",
            "153/153 [==============================] - 31s 200ms/step - loss: 1.4181 - accuracy: 0.4705 - val_loss: 2502.2480 - val_accuracy: 0.1126\n",
            "Epoch 33/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.4022 - accuracy: 0.4798 - val_loss: 2595.5603 - val_accuracy: 0.1126\n",
            "Epoch 34/50\n",
            "153/153 [==============================] - 31s 199ms/step - loss: 1.3589 - accuracy: 0.4921 - val_loss: 2603.3333 - val_accuracy: 0.1126\n",
            "Epoch 35/50\n",
            "153/153 [==============================] - 31s 203ms/step - loss: 1.3370 - accuracy: 0.4941 - val_loss: 2193.2671 - val_accuracy: 0.1126\n",
            "Epoch 36/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.2939 - accuracy: 0.5122 - val_loss: 2767.2734 - val_accuracy: 0.1126\n",
            "Epoch 37/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.2811 - accuracy: 0.5336 - val_loss: 3503.0679 - val_accuracy: 0.1126\n",
            "Epoch 38/50\n",
            "153/153 [==============================] - 30s 197ms/step - loss: 1.2540 - accuracy: 0.5404 - val_loss: 2562.6301 - val_accuracy: 0.1126\n",
            "Epoch 39/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.2054 - accuracy: 0.5517 - val_loss: 2058.1812 - val_accuracy: 0.1126\n",
            "Epoch 40/50\n",
            "153/153 [==============================] - 31s 202ms/step - loss: 1.1961 - accuracy: 0.5587 - val_loss: 2123.1350 - val_accuracy: 0.1126\n",
            "Epoch 41/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.1573 - accuracy: 0.5683 - val_loss: 2336.4009 - val_accuracy: 0.1126\n",
            "Epoch 42/50\n",
            "153/153 [==============================] - 30s 199ms/step - loss: 1.1094 - accuracy: 0.5866 - val_loss: 2602.7002 - val_accuracy: 0.1126\n",
            "Epoch 43/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 1.0940 - accuracy: 0.5967 - val_loss: 2518.7427 - val_accuracy: 0.1126\n",
            "Epoch 44/50\n",
            "153/153 [==============================] - 30s 199ms/step - loss: 1.0586 - accuracy: 0.6092 - val_loss: 2906.6294 - val_accuracy: 0.1126\n",
            "Epoch 45/50\n",
            "153/153 [==============================] - 31s 200ms/step - loss: 1.0292 - accuracy: 0.6173 - val_loss: 2677.8354 - val_accuracy: 0.1126\n",
            "Epoch 46/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 0.9964 - accuracy: 0.6319 - val_loss: 2551.3264 - val_accuracy: 0.1126\n",
            "Epoch 47/50\n",
            "153/153 [==============================] - 30s 197ms/step - loss: 0.9585 - accuracy: 0.6551 - val_loss: 2204.7791 - val_accuracy: 0.1126\n",
            "Epoch 48/50\n",
            "153/153 [==============================] - 30s 198ms/step - loss: 0.9026 - accuracy: 0.6701 - val_loss: 2722.4473 - val_accuracy: 0.1126\n",
            "Epoch 49/50\n",
            "153/153 [==============================] - 30s 197ms/step - loss: 0.8823 - accuracy: 0.6738 - val_loss: 2214.0625 - val_accuracy: 0.1126\n",
            "Epoch 50/50\n",
            "153/153 [==============================] - 30s 199ms/step - loss: 0.8468 - accuracy: 0.6857 - val_loss: 2450.2666 - val_accuracy: 0.1126\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Input shape (48, 48, 1) for 48x48 grayscale images\n",
        "input_shape = (48, 48, 1)\n",
        "\n",
        "# One-hot encode the training and validation labels\n",
        "y_train_one_hot = to_categorical(y_train, num_classes=7)\n",
        "y_val_one_hot = to_categorical(y_val, num_classes=7)\n",
        "\n",
        "# Initialize the model\n",
        "model = Sequential()\n",
        "\n",
        "# First Block: 2 Conv layers + MaxPooling\n",
        "model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=input_shape))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "# Second Block: 2 Conv layers + MaxPooling\n",
        "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "# Third Block: 2 Conv layers + MaxPooling\n",
        "model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "# Fourth Block: 2 Conv layers + MaxPooling\n",
        "model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "# Fully Connected Layers\n",
        "model.add(Flatten())  # Flatten the 3D output to 1D\n",
        "model.add(Dense(512, activation='relu'))  # First Dense Layer\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))  # Regularization\n",
        "\n",
        "model.add(Dense(128, activation='relu'))  # Second Dense Layer\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "# Output layer: 7 classes (softmax activation for multiclass classification)\n",
        "model.add(Dense(7, activation='softmax'))\n",
        "\n",
        "# Model Summary\n",
        "model.summary()\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=Adam(learning_rate=0.0001),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Train the model without data augmentation\n",
        "history = model.fit(X_train, y_train_one_hot,\n",
        "                    epochs=50,  # Adjust the number of epochs as needed\n",
        "                    validation_data=(X_val, y_val_one_hot),\n",
        "                    batch_size=32)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "8e31d549-ff59-44f6-b60b-862c81933bbe",
      "metadata": {
        "id": "8e31d549-ff59-44f6-b60b-862c81933bbe"
      },
      "outputs": [],
      "source": [
        "model.save_weights('/content/my_facial_expression_model_weights.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "13aa5dad-48ca-46cb-bb2d-7eabc03285cb",
      "metadata": {
        "id": "13aa5dad-48ca-46cb-bb2d-7eabc03285cb",
        "outputId": "123be3a1-7e4e-47a8-ec5d-44c667d544da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num GPUs Available:  0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Check if GPU is detected\n",
        "print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f80689e3-ce5f-4ff7-b176-77e43b5c47e9",
      "metadata": {
        "id": "f80689e3-ce5f-4ff7-b176-77e43b5c47e9"
      },
      "outputs": [],
      "source": [
        "# Reading Images from data set validate\n",
        "\n",
        "df = pd.read_csv(r\"C:\\Users\\gsanj\\OneDrive\\Desktop\\Jupyter\\FACIAL EMOTIONS\\test_dataset\\test_dataset.csv\")\n",
        "def pixels_to_image(pixel_str):\n",
        "    # Assuming each image is 48x48 pixels\n",
        "    pixels = np.array([int(p) for p in pixel_str.split()], dtype='uint8')\n",
        "    # print(len(pixels))\n",
        "    img = pixels.reshape(48, 48)  # Adjust if image dimensions are different\n",
        "    return img\n",
        "\n",
        "# Add a column with images\n",
        "df['test_image'] = df['pixels'].apply(pixels_to_image)\n",
        "\n",
        "# Display the first few rows with images and emotion\n",
        "# for idx, row in df.iterrows():\n",
        "#     plt.figure()\n",
        "#     plt.imshow(row['image'], cmap='gray')\n",
        "#     plt.title(f\"Emotion: {row['emotion']}\")\n",
        "#     plt.show()\n",
        "len(df['test_image'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "761a693f-768f-43ba-9345-c55824464995",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "761a693f-768f-43ba-9345-c55824464995",
        "outputId": "2b255fab-ddd7-4a44-af0c-38f811c8b04f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2500"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Reading Images from data set validate\n",
        "\n",
        "df = pd.read_csv(r\"/content/test_dataset.csv\")\n",
        "def pixels_to_image(pixel_str):\n",
        "    # Assuming each image is 48x48 pixels\n",
        "    pixels = np.array([int(p) for p in pixel_str.split()], dtype='uint8')\n",
        "    # print(len(pixels))\n",
        "    img = pixels.reshape(48, 48)  # Adjust if image dimensions are different\n",
        "    return img\n",
        "\n",
        "# Add a column with images\n",
        "df['test_image'] = df['pixels'].apply(pixels_to_image)\n",
        "\n",
        "# Display the first few rows with images and emotion\n",
        "# for idx, row in df.iterrows():\n",
        "#     plt.figure()\n",
        "#     plt.imshow(row['image'], cmap='gray')\n",
        "#     plt.title(f\"Emotion: {row['emotion']}\")\n",
        "#     plt.show()\n",
        "len(df['test_image'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = df['test_image']\n",
        "# Reshape the flattened images into 48x48x1 format\n",
        "\n",
        "X_test_flattened = np.array(X_test.tolist())\n",
        "num_images = X_test_flattened.shape[0]\n",
        "X_test = X_test_flattened.reshape((num_images, 48, 48, 1))\n",
        "\n",
        "X_test.shape\n",
        "\n",
        "# Normalize pixel values (if necessary)\n",
        "X_test = X_test.astype('float32') / 255.0"
      ],
      "metadata": {
        "id": "pyFwSwKsfJ_V"
      },
      "id": "pyFwSwKsfJ_V",
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the classes for X_test\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Convert predicted probabilities to class labels\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "\n",
        "# Save the predictions to a CSV file in the requested format (comma-separated)\n",
        "np.savetxt('predicted_labels.csv', y_pred_classes, delimiter=',', fmt='%d')\n",
        "\n",
        "print(\"Predictions saved to 'predicted_labels.csv' in comma-separated format\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gj_J-P5xgft-",
        "outputId": "b8471cd9-ca14-471a-8f66-d778f8dae5a7"
      },
      "id": "gj_J-P5xgft-",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 3s 40ms/step\n",
            "Predictions saved to 'predicted_labels.csv' in comma-separated format\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_ids = df['id']\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "\n",
        "# Create a DataFrame to store the predictions with the corresponding IDs\n",
        "df_predictions = pd.DataFrame({\n",
        "    'ImageID': image_ids,\n",
        "    'PredictedLabel': y_pred_classes\n",
        "})\n",
        "\n",
        "# Save the DataFrame to a CSV file\n",
        "df_predictions.to_csv('predicted_labels_with_ids.csv', index=False)\n",
        "\n",
        "print(\"Predictions saved to 'predicted_labels_with_ids.csv' with ImageID and PredictedLabel\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJxfHEQPhxVc",
        "outputId": "3c7ce596-3985-4bd2-8436-da845bb22da6"
      },
      "id": "mJxfHEQPhxVc",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions saved to 'predicted_labels_with_ids.csv' with ImageID and PredictedLabel\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.3"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}