Beauty_Classification.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Mounting the google drive \n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bVtSR4I1XPJx",
        "outputId": "c13811ce-eaba-4f64-c3b8-cd572fe65abf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, Dense\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "GMfQRLc85km0"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_datagen = ImageDataGenerator(\n",
        "                                    rescale=1./255,\n",
        "                                    shear_range=0.2,\n",
        "                                    zoom_range=0.2,\n",
        "                                    horizontal_flip=True,\n",
        "                                    width_shift_range=0.4,\n",
        "                                    height_shift_range=0.4\n",
        "                             )\n",
        "training_set = training_datagen.flow_from_directory(\n",
        "                                    directory='/content/drive/MyDrive/beauty classification/train',\n",
        "                                    batch_size=32,\n",
        "                                    target_size=(64,64),\n",
        "                                    class_mode='binary'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GpTbTNX85kpv",
        "outputId": "7679dad7-ba65-4524-85f3-b585e4e4f374"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4000 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_set = test_datagen.flow_from_directory(\n",
        "                                    directory='/content/drive/MyDrive/beauty classification/test',\n",
        "                                    batch_size=32,\n",
        "                                    target_size=(64,64),\n",
        "                                    class_mode='binary'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewRSi_Vy9swp",
        "outputId": "a05c0c69-ceba-40c0-ca5e-1920292d4845"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 300 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
        "validation_set = validation_datagen.flow_from_directory(\n",
        "                                    directory='/content/drive/MyDrive/beauty classification/valid',\n",
        "                                    batch_size=32,\n",
        "                                    target_size=(64,64),\n",
        "                                    class_mode='binary'\n",
        ")"
      ],
      "metadata": {
        "id": "rcrZ9AYl9-do",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c44bee26-4fcc-4781-da6f-5414886f465b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 300 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = Sequential()"
      ],
      "metadata": {
        "id": "0_VMFVFo-5Rv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(Conv2D(filters=32,kernel_size=(3,3),input_shape=(64,64,3)))\n",
        "cnn.add(MaxPooling2D(pool_size=2))"
      ],
      "metadata": {
        "id": "nVZq57o6-5UH"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(Conv2D(filters=64,kernel_size=(3,3)))\n",
        "cnn.add(MaxPooling2D(pool_size=2))\n",
        "cnn.add(Dropout(0.2))"
      ],
      "metadata": {
        "id": "5nv-3EVa-5X8"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(Conv2D(filters=128,kernel_size=3))\n",
        "cnn.add(MaxPooling2D(pool_size=2))\n",
        "cnn.add(Dropout(0.2))"
      ],
      "metadata": {
        "id": "c08Wu8U7_FkB"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(Conv2D(filters=256,kernel_size=3))\n",
        "cnn.add(MaxPooling2D(pool_size=2))\n",
        "cnn.add(Dropout(0.3))"
      ],
      "metadata": {
        "id": "sAKbz5Ro_FmF"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(Flatten())"
      ],
      "metadata": {
        "id": "0IYm9E_G_FqS"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(Dense(units=128,activation='relu'))\n",
        "cnn.add(Dropout(0.3))"
      ],
      "metadata": {
        "id": "NB746-YH_FsK"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(Dense(units=1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "ZzqHsHkN_Fwj"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.summary()"
      ],
      "metadata": {
        "id": "W9DIOM3P_UhC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e251f20a-76b2-444b-a8cd-5ab5b5354512"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 62, 62, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 31, 31, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 29, 29, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 14, 14, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 14, 14, 64)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 12, 12, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 6, 6, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 6, 6, 128)         0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 4, 4, 256)         295168    \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 2, 2, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 2, 2, 256)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               131200    \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 519,745\n",
            "Trainable params: 519,745\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "EUeAW9wT_Uiy"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r = cnn.fit(x=training_set,validation_data=validation_set,epochs=50,batch_size=32)"
      ],
      "metadata": {
        "id": "4oOTxLts_UnB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87da07b8-d415-4436-f500-bf43f1fe3e00"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "125/125 [==============================] - 884s 7s/step - loss: 0.6705 - accuracy: 0.5870 - val_loss: 0.5927 - val_accuracy: 0.7233\n",
            "Epoch 2/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.6499 - accuracy: 0.6143 - val_loss: 0.6287 - val_accuracy: 0.5800\n",
            "Epoch 3/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.6320 - accuracy: 0.6410 - val_loss: 0.5970 - val_accuracy: 0.7033\n",
            "Epoch 4/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.6082 - accuracy: 0.6653 - val_loss: 0.4799 - val_accuracy: 0.7933\n",
            "Epoch 5/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.5641 - accuracy: 0.7153 - val_loss: 0.4835 - val_accuracy: 0.7833\n",
            "Epoch 6/50\n",
            "125/125 [==============================] - 15s 118ms/step - loss: 0.5175 - accuracy: 0.7570 - val_loss: 0.4473 - val_accuracy: 0.8333\n",
            "Epoch 7/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.4663 - accuracy: 0.7815 - val_loss: 0.4082 - val_accuracy: 0.8200\n",
            "Epoch 8/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.4373 - accuracy: 0.8037 - val_loss: 0.5744 - val_accuracy: 0.7867\n",
            "Epoch 9/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.4150 - accuracy: 0.8195 - val_loss: 0.4473 - val_accuracy: 0.7967\n",
            "Epoch 10/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.3629 - accuracy: 0.8465 - val_loss: 0.5210 - val_accuracy: 0.7733\n",
            "Epoch 11/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.3901 - accuracy: 0.8332 - val_loss: 0.4375 - val_accuracy: 0.8300\n",
            "Epoch 12/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.3164 - accuracy: 0.8695 - val_loss: 0.4226 - val_accuracy: 0.8300\n",
            "Epoch 13/50\n",
            "125/125 [==============================] - 14s 113ms/step - loss: 0.3209 - accuracy: 0.8737 - val_loss: 0.4707 - val_accuracy: 0.8200\n",
            "Epoch 14/50\n",
            "125/125 [==============================] - 14s 113ms/step - loss: 0.2876 - accuracy: 0.8915 - val_loss: 0.3026 - val_accuracy: 0.8800\n",
            "Epoch 15/50\n",
            "125/125 [==============================] - 15s 120ms/step - loss: 0.2483 - accuracy: 0.8967 - val_loss: 0.2436 - val_accuracy: 0.9033\n",
            "Epoch 16/50\n",
            "125/125 [==============================] - 14s 113ms/step - loss: 0.2701 - accuracy: 0.8920 - val_loss: 0.1592 - val_accuracy: 0.9367\n",
            "Epoch 17/50\n",
            "125/125 [==============================] - 14s 112ms/step - loss: 0.2498 - accuracy: 0.9032 - val_loss: 0.4755 - val_accuracy: 0.8367\n",
            "Epoch 18/50\n",
            "125/125 [==============================] - 14s 113ms/step - loss: 0.2294 - accuracy: 0.9090 - val_loss: 0.6356 - val_accuracy: 0.7633\n",
            "Epoch 19/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.2401 - accuracy: 0.9115 - val_loss: 0.1370 - val_accuracy: 0.9567\n",
            "Epoch 20/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.2087 - accuracy: 0.9185 - val_loss: 0.2411 - val_accuracy: 0.9100\n",
            "Epoch 21/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.2305 - accuracy: 0.9155 - val_loss: 0.1093 - val_accuracy: 0.9533\n",
            "Epoch 22/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.2154 - accuracy: 0.9215 - val_loss: 0.1408 - val_accuracy: 0.9467\n",
            "Epoch 23/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.1914 - accuracy: 0.9283 - val_loss: 0.1022 - val_accuracy: 0.9633\n",
            "Epoch 24/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.2071 - accuracy: 0.9225 - val_loss: 0.1541 - val_accuracy: 0.9433\n",
            "Epoch 25/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1780 - accuracy: 0.9340 - val_loss: 0.1029 - val_accuracy: 0.9667\n",
            "Epoch 26/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.1816 - accuracy: 0.9337 - val_loss: 0.1197 - val_accuracy: 0.9700\n",
            "Epoch 27/50\n",
            "125/125 [==============================] - 15s 118ms/step - loss: 0.1653 - accuracy: 0.9410 - val_loss: 0.0955 - val_accuracy: 0.9700\n",
            "Epoch 28/50\n",
            "125/125 [==============================] - 14s 116ms/step - loss: 0.1781 - accuracy: 0.9333 - val_loss: 0.1379 - val_accuracy: 0.9533\n",
            "Epoch 29/50\n",
            "125/125 [==============================] - 15s 120ms/step - loss: 0.1896 - accuracy: 0.9323 - val_loss: 0.1309 - val_accuracy: 0.9600\n",
            "Epoch 30/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1637 - accuracy: 0.9398 - val_loss: 0.1797 - val_accuracy: 0.9267\n",
            "Epoch 31/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1893 - accuracy: 0.9300 - val_loss: 0.0796 - val_accuracy: 0.9733\n",
            "Epoch 32/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1561 - accuracy: 0.9465 - val_loss: 0.1402 - val_accuracy: 0.9533\n",
            "Epoch 33/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1867 - accuracy: 0.9367 - val_loss: 0.0617 - val_accuracy: 0.9700\n",
            "Epoch 34/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1613 - accuracy: 0.9457 - val_loss: 0.0921 - val_accuracy: 0.9633\n",
            "Epoch 35/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1619 - accuracy: 0.9405 - val_loss: 0.0979 - val_accuracy: 0.9733\n",
            "Epoch 36/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1653 - accuracy: 0.9452 - val_loss: 0.1015 - val_accuracy: 0.9633\n",
            "Epoch 37/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.1742 - accuracy: 0.9370 - val_loss: 0.0563 - val_accuracy: 0.9833\n",
            "Epoch 38/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1513 - accuracy: 0.9467 - val_loss: 0.1097 - val_accuracy: 0.9667\n",
            "Epoch 39/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1447 - accuracy: 0.9498 - val_loss: 0.0854 - val_accuracy: 0.9700\n",
            "Epoch 40/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.1436 - accuracy: 0.9507 - val_loss: 0.1794 - val_accuracy: 0.9433\n",
            "Epoch 41/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1588 - accuracy: 0.9442 - val_loss: 0.2325 - val_accuracy: 0.9100\n",
            "Epoch 42/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.1486 - accuracy: 0.9467 - val_loss: 0.0560 - val_accuracy: 0.9800\n",
            "Epoch 43/50\n",
            "125/125 [==============================] - 15s 121ms/step - loss: 0.1441 - accuracy: 0.9495 - val_loss: 0.0855 - val_accuracy: 0.9667\n",
            "Epoch 44/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1444 - accuracy: 0.9492 - val_loss: 0.2436 - val_accuracy: 0.9233\n",
            "Epoch 45/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.1509 - accuracy: 0.9520 - val_loss: 0.0769 - val_accuracy: 0.9733\n",
            "Epoch 46/50\n",
            "125/125 [==============================] - 14s 115ms/step - loss: 0.1438 - accuracy: 0.9520 - val_loss: 0.3045 - val_accuracy: 0.9100\n",
            "Epoch 47/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1439 - accuracy: 0.9520 - val_loss: 0.0447 - val_accuracy: 0.9833\n",
            "Epoch 48/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1453 - accuracy: 0.9532 - val_loss: 0.0729 - val_accuracy: 0.9667\n",
            "Epoch 49/50\n",
            "125/125 [==============================] - 15s 118ms/step - loss: 0.1441 - accuracy: 0.9465 - val_loss: 0.1416 - val_accuracy: 0.9467\n",
            "Epoch 50/50\n",
            "125/125 [==============================] - 14s 114ms/step - loss: 0.1453 - accuracy: 0.9515 - val_loss: 0.0765 - val_accuracy: 0.9700\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(r.history['loss'],label='train loss')\n",
        "plt.plot(r.history['val_loss'],label='test loss')\n",
        "plt.legend();"
      ],
      "metadata": {
        "id": "7DTI6bTx-5bD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "60f903e3-c40a-407f-ca2b-9960d1d5135e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(r.history['accuracy'],label='train accuracy')\n",
        "plt.plot(r.history['val_accuracy'],label='test accuracy')\n",
        "plt.legend();"
      ],
      "metadata": {
        "id": "Wfqgrtyp_lYr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "outputId": "160df236-bb08-48c5-8fee-2aba49009966"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zcC0-bub_lay"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y5ptTwwF_lfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c-NfS7j4_liA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}