CNN(3Conv_4Conv).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN(3Conv/4Conv).ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "mg-CGBkpcUYK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import gdown"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9r7MIsuHciJy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "url = \"https://drive.google.com/uc?id=1cZJu7L55oeE3qRpESnGSE_v6CV2NW9pw\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OOoytTnyckwN",
        "colab_type": "code",
        "outputId": "940a4320-f785-441e-8e1b-f2d2011da85f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "cell_type": "code",
      "source": [
        "gdown.download(url,'data1',quiet=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1cZJu7L55oeE3qRpESnGSE_v6CV2NW9pw\n",
            "To: /content/data1\n",
            "74.6MB [00:00, 120MB/s] \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'data1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "xB3bi1YAcnjn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import zipfile \n",
        "with zipfile.ZipFile(\"data1\",'r') as zip_ref:\n",
        "   zip_ref.extractall('data2/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oVBmLTR6crZy",
        "colab_type": "code",
        "outputId": "19cb77b0-68a5-4214-9381-04e4e21d5634",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# Part 1 - Building the CNN\n",
        "#importing the Keras libraries and packages\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Convolution2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras import optimizers\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "JMAoh-Rzlf1-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# **3 Convolution & Dropout 0.25**"
      ]
    },
    {
      "metadata": {
        "id": "H20trXILcum2",
        "colab_type": "code",
        "outputId": "a0ed713d-f61e-4969-d7e2-5819d6631178",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1446
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialing the CNN\n",
        "classifier = Sequential()\n",
        "\n",
        "# Step 1 - Convolution Layer \n",
        "classifier.add(Convolution2D(32, 3,  3, input_shape = (640, 480, 3), activation = 'relu'))\n",
        "\n",
        "#step 2 - Pooling\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\n",
        "\n",
        "# Adding second convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\n",
        "\n",
        "# Adding third convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(3,2)))\n",
        "\n",
        "\n",
        "#Step 3 - Flattening\n",
        "classifier.add(Flatten())\n",
        "\n",
        "#Step 4 - Full Connection\n",
        "classifier.add(Dense(512, activation = 'relu'))\n",
        "classifier.add(Dropout(0.25))\n",
        "classifier.add(Dense(24, activation = 'softmax'))\n",
        "\n",
        "#Compiling The CNN\n",
        "classifier.compile(\n",
        "              optimizer = optimizers.SGD(lr = 0.01),\n",
        "              loss = 'categorical_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "#Part 2 Fittting the CNN to the image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator(\n",
        "        rescale=1./255,\n",
        "        shear_range=0.2,\n",
        "        zoom_range=0.2,\n",
        "        horizontal_flip=True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "training_set = train_datagen.flow_from_directory(\n",
        "        'data2/data1/train1',\n",
        "        target_size=(640, 480),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical')\n",
        "\n",
        "test_set = test_datagen.flow_from_directory(\n",
        "        'data2/data1/test1',\n",
        "        target_size=(640, 480),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical')\n",
        "\n",
        "model = classifier.fit_generator(\n",
        "        training_set,\n",
        "        steps_per_epoch=125,\n",
        "        epochs=15,\n",
        "        validation_data = test_set,\n",
        "        validation_steps = 650\n",
        "      )\n",
        "\n",
        "#Saving the model\n",
        "#import h5py\n",
        "#classifier.save('E:\\\\M.Tech_Project\\\\Trained_model.h5')\n",
        "#print(model.history.keys())\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# summarize history for accuracy\n",
        "plt.plot(model.history['acc'])\n",
        "plt.plot(model.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(model.history['loss'])\n",
        "plt.plot(model.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), input_shape=(640, 480,..., activation=\"relu\")`\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\")`\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\")`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Found 3947 images belonging to 24 classes.\n",
            "Found 953 images belonging to 24 classes.\n",
            "Epoch 1/15\n",
            "125/125 [==============================] - 493s 4s/step - loss: 3.0159 - acc: 0.1170 - val_loss: 2.4322 - val_acc: 0.2932\n",
            "Epoch 2/15\n",
            "125/125 [==============================] - 463s 4s/step - loss: 2.2562 - acc: 0.3266 - val_loss: 1.7659 - val_acc: 0.5182\n",
            "Epoch 3/15\n",
            "125/125 [==============================] - 460s 4s/step - loss: 1.7385 - acc: 0.4563 - val_loss: 1.2047 - val_acc: 0.6506\n",
            "Epoch 4/15\n",
            "125/125 [==============================] - 460s 4s/step - loss: 1.3604 - acc: 0.5733 - val_loss: 0.9624 - val_acc: 0.7167\n",
            "Epoch 5/15\n",
            "125/125 [==============================] - 459s 4s/step - loss: 1.0517 - acc: 0.6598 - val_loss: 0.5820 - val_acc: 0.8290\n",
            "Epoch 6/15\n",
            "125/125 [==============================] - 459s 4s/step - loss: 0.8267 - acc: 0.7217 - val_loss: 0.4861 - val_acc: 0.8469\n",
            "Epoch 7/15\n",
            "125/125 [==============================] - 464s 4s/step - loss: 0.6283 - acc: 0.7910 - val_loss: 0.3202 - val_acc: 0.9150\n",
            "Epoch 8/15\n",
            "125/125 [==============================] - 463s 4s/step - loss: 0.5428 - acc: 0.8148 - val_loss: 0.2583 - val_acc: 0.9398\n",
            "Epoch 9/15\n",
            "125/125 [==============================] - 464s 4s/step - loss: 0.4395 - acc: 0.8493 - val_loss: 0.1832 - val_acc: 0.9465\n",
            "Epoch 10/15\n",
            "125/125 [==============================] - 464s 4s/step - loss: 0.3357 - acc: 0.8877 - val_loss: 0.1631 - val_acc: 0.9525\n",
            "Epoch 11/15\n",
            "125/125 [==============================] - 465s 4s/step - loss: 0.2741 - acc: 0.9092 - val_loss: 0.1464 - val_acc: 0.9475\n",
            "Epoch 12/15\n",
            "125/125 [==============================] - 465s 4s/step - loss: 0.2410 - acc: 0.9215 - val_loss: 0.1005 - val_acc: 0.9686\n",
            "Epoch 13/15\n",
            "125/125 [==============================] - 464s 4s/step - loss: 0.2015 - acc: 0.9337 - val_loss: 0.0923 - val_acc: 0.9726\n",
            "Epoch 14/15\n",
            "125/125 [==============================] - 464s 4s/step - loss: 0.1987 - acc: 0.9370 - val_loss: 0.0437 - val_acc: 0.9907\n",
            "Epoch 15/15\n",
            "125/125 [==============================] - 458s 4s/step - loss: 0.2102 - acc: 0.9328 - val_loss: 0.0653 - val_acc: 0.9874\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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qqqpYsWIFBw8e5NFHH2XFihUAdHZ28s9//pNPP/0UrVbLnXfeye7duxkzZoyn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\nGJW3iIiIxai8RURELEblLSIiYjH/BT7SP0gqUUS7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f92ac34cd30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kphjeTjYtLAUBKj/sKNuN6rbBuZGIRBDw0I3xnIFOROSmGN5OJpfIsDDmRtjs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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f92ad466b70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "aA6FjijQl6PN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 3 Convolution & Dropout 0.5"
      ]
    },
    {
      "metadata": {
        "id": "uzW15iq9dFYS",
        "colab_type": "code",
        "outputId": "84ad9619-a5ee-4acf-d09b-838b0380186c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1446
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialing the CNN\n",
        "classifier = Sequential()\n",
        "\n",
        "# Step 1 - Convolution Layer \n",
        "classifier.add(Convolution2D(32, 3,  3, input_shape = (640, 480, 3), activation = 'relu'))\n",
        "\n",
        "#step 2 - Pooling\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\n",
        "\n",
        "# Adding second convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\n",
        "\n",
        "# Adding third convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(3,2)))\n",
        "\n",
        "\n",
        "#Step 3 - Flattening\n",
        "classifier.add(Flatten())\n",
        "\n",
        "#Step 4 - Full Connection\n",
        "classifier.add(Dense(512, activation = 'relu'))\n",
        "classifier.add(Dropout(0.5))\n",
        "classifier.add(Dense(24, activation = 'softmax'))\n",
        "\n",
        "#Compiling The CNN\n",
        "classifier.compile(\n",
        "              optimizer = optimizers.SGD(lr = 0.01),\n",
        "              loss = 'categorical_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "#Part 2 Fittting the CNN to the image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator(\n",
        "        rescale=1./255,\n",
        "        shear_range=0.2,\n",
        "        zoom_range=0.2,\n",
        "        horizontal_flip=True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "training_set = train_datagen.flow_from_directory(\n",
        "        'data2/data1/train1',\n",
        "        target_size=(640, 480),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical')\n",
        "\n",
        "test_set = test_datagen.flow_from_directory(\n",
        "        'data2/data1/test1',\n",
        "        target_size=(640, 480),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical')\n",
        "\n",
        "model = classifier.fit_generator(\n",
        "        training_set,\n",
        "        steps_per_epoch=125,\n",
        "        epochs=15,\n",
        "        validation_data = test_set,\n",
        "        validation_steps = 650\n",
        "      )\n",
        "\n",
        "#Saving the model\n",
        "#import h5py\n",
        "#classifier.save('E:\\\\M.Tech_Project\\\\Trained_model.h5')\n",
        "#print(model.history.keys())\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# summarize history for accuracy\n",
        "plt.plot(model.history['acc'])\n",
        "plt.plot(model.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(model.history['loss'])\n",
        "plt.plot(model.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), input_shape=(640, 480,..., activation=\"relu\")`\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\")`\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\")`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Found 3947 images belonging to 24 classes.\n",
            "Found 953 images belonging to 24 classes.\n",
            "Epoch 1/15\n",
            "125/125 [==============================] - 483s 4s/step - loss: 3.1240 - acc: 0.0752 - val_loss: 2.9341 - val_acc: 0.1541\n",
            "Epoch 2/15\n",
            "125/125 [==============================] - 463s 4s/step - loss: 2.6074 - acc: 0.2309 - val_loss: 2.0140 - val_acc: 0.4717\n",
            "Epoch 3/15\n",
            "125/125 [==============================] - 459s 4s/step - loss: 2.0027 - acc: 0.3779 - val_loss: 1.5299 - val_acc: 0.5194\n",
            "Epoch 4/15\n",
            "125/125 [==============================] - 459s 4s/step - loss: 1.6185 - acc: 0.4884 - val_loss: 1.0007 - val_acc: 0.7391\n",
            "Epoch 5/15\n",
            "125/125 [==============================] - 456s 4s/step - loss: 1.2526 - acc: 0.5966 - val_loss: 0.7435 - val_acc: 0.7786\n",
            "Epoch 6/15\n",
            "125/125 [==============================] - 454s 4s/step - loss: 1.0176 - acc: 0.6701 - val_loss: 0.6270 - val_acc: 0.8332\n",
            "Epoch 7/15\n",
            "125/125 [==============================] - 450s 4s/step - loss: 0.8505 - acc: 0.7166 - val_loss: 0.4676 - val_acc: 0.8560\n",
            "Epoch 8/15\n",
            "125/125 [==============================] - 444s 4s/step - loss: 0.7274 - acc: 0.7626 - val_loss: 0.2938 - val_acc: 0.9444\n",
            "Epoch 9/15\n",
            "125/125 [==============================] - 445s 4s/step - loss: 0.5853 - acc: 0.8032 - val_loss: 0.3018 - val_acc: 0.9178\n",
            "Epoch 10/15\n",
            "125/125 [==============================] - 445s 4s/step - loss: 0.5227 - acc: 0.8270 - val_loss: 0.1884 - val_acc: 0.9475\n",
            "Epoch 11/15\n",
            "125/125 [==============================] - 444s 4s/step - loss: 0.4556 - acc: 0.8507 - val_loss: 0.1662 - val_acc: 0.9603\n",
            "Epoch 12/15\n",
            "125/125 [==============================] - 452s 4s/step - loss: 0.3966 - acc: 0.8677 - val_loss: 0.1215 - val_acc: 0.9718\n",
            "Epoch 13/15\n",
            "125/125 [==============================] - 446s 4s/step - loss: 0.3990 - acc: 0.8733 - val_loss: 0.1109 - val_acc: 0.9748\n",
            "Epoch 14/15\n",
            "125/125 [==============================] - 443s 4s/step - loss: 0.3160 - acc: 0.8970 - val_loss: 0.0602 - val_acc: 0.9906\n",
            "Epoch 15/15\n",
            "125/125 [==============================] - 442s 4s/step - loss: 0.2969 - acc: 0.9048 - val_loss: 0.0568 - val_acc: 0.9874\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GjmMrHIZpQ0kOSyQ2JIa4kFhig11f40J0xARF+/zYhf/wm/Bec2Qt3zYU9Pn+AWoVDueZ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\nqLxFRERMRuUtIiJiMipvERERk/k/x+rs9ZUvkZsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f92ac01e9e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IO1PGxt25Q9KvEEKIgZPwtiGJgZOYFDCB87W5pBbsGbJ+nXQafnnrBIzernyx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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f92acf4cef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8pjmdsmtmpog",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 4 Convolution & Dropout 0.5"
      ]
    },
    {
      "metadata": {
        "id": "Ddfc4pD_6S4e",
        "colab_type": "code",
        "outputId": "c44db81e-81c2-48bd-c471-813b69b79ffa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1465
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialing the CNN\n",
        "classifier = Sequential()\n",
        "\n",
        "# Step 1 - Convolution Layer \n",
        "classifier.add(Convolution2D(32, 3,  3, input_shape = (640, 480, 3), activation = 'relu'))\n",
        "\n",
        "#step 2 - Pooling\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\n",
        "\n",
        "# Adding second convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\n",
        "\n",
        "# Adding third convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(3,2)))\n",
        "\n",
        "# Adding forth convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(3,2)))\n",
        "\n",
        "\n",
        "#Step 3 - Flattening\n",
        "classifier.add(Flatten())\n",
        "\n",
        "#Step 4 - Full Connection\n",
        "classifier.add(Dense(512, activation = 'relu'))\n",
        "classifier.add(Dropout(0.5))\n",
        "classifier.add(Dense(24, activation = 'softmax'))\n",
        "\n",
        "#Compiling The CNN\n",
        "classifier.compile(\n",
        "              optimizer = optimizers.SGD(lr = 0.01),\n",
        "              loss = 'categorical_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "#Part 2 Fittting the CNN to the image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator(\n",
        "        rescale=1./255,\n",
        "        shear_range=0.2,\n",
        "        zoom_range=0.2,\n",
        "        horizontal_flip=True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "training_set = train_datagen.flow_from_directory(\n",
        "        'data2/data1/train1',\n",
        "        target_size=(640, 480),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical')\n",
        "\n",
        "test_set = test_datagen.flow_from_directory(\n",
        "        'data2/data1/test1',\n",
        "        target_size=(640, 480),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical')\n",
        "\n",
        "model = classifier.fit_generator(\n",
        "        training_set,\n",
        "        steps_per_epoch=125,\n",
        "        epochs=15,\n",
        "        validation_data = test_set,\n",
        "        validation_steps = 650\n",
        "      )\n",
        "\n",
        "#Saving the model\n",
        "#import h5py\n",
        "#classifier.save('E:\\\\M.Tech_Project\\\\Trained_model.h5')\n",
        "#print(model.history.keys())\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# summarize history for accuracy\n",
        "plt.plot(model.history['acc'])\n",
        "plt.plot(model.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(model.history['loss'])\n",
        "plt.plot(model.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), input_shape=(640, 480,..., activation=\"relu\")`\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\")`\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\")`\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\")`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Found 3947 images belonging to 24 classes.\n",
            "Found 953 images belonging to 24 classes.\n",
            "Epoch 1/15\n",
            "125/125 [==============================] - 514s 4s/step - loss: 3.1617 - acc: 0.0525 - val_loss: 3.1325 - val_acc: 0.0501\n",
            "Epoch 2/15\n",
            "125/125 [==============================] - 484s 4s/step - loss: 3.0761 - acc: 0.0935 - val_loss: 2.8211 - val_acc: 0.2246\n",
            "Epoch 3/15\n",
            "125/125 [==============================] - 487s 4s/step - loss: 2.5516 - acc: 0.2530 - val_loss: 2.0318 - val_acc: 0.4230\n",
            "Epoch 4/15\n",
            "125/125 [==============================] - 494s 4s/step - loss: 2.0593 - acc: 0.3814 - val_loss: 1.5874 - val_acc: 0.5959\n",
            "Epoch 5/15\n",
            "125/125 [==============================] - 489s 4s/step - loss: 1.6905 - acc: 0.4721 - val_loss: 1.1948 - val_acc: 0.6880\n",
            "Epoch 6/15\n",
            "125/125 [==============================] - 493s 4s/step - loss: 1.3937 - acc: 0.5587 - val_loss: 0.9972 - val_acc: 0.7511\n",
            "Epoch 7/15\n",
            "125/125 [==============================] - 491s 4s/step - loss: 1.0945 - acc: 0.6464 - val_loss: 0.9081 - val_acc: 0.7102\n",
            "Epoch 8/15\n",
            "125/125 [==============================] - 491s 4s/step - loss: 0.9388 - acc: 0.6933 - val_loss: 0.4666 - val_acc: 0.8902\n",
            "Epoch 9/15\n",
            "125/125 [==============================] - 492s 4s/step - loss: 0.7694 - acc: 0.7508 - val_loss: 0.3533 - val_acc: 0.9055\n",
            "Epoch 10/15\n",
            "125/125 [==============================] - 489s 4s/step - loss: 0.6856 - acc: 0.7748 - val_loss: 0.3189 - val_acc: 0.9076\n",
            "Epoch 11/15\n",
            "125/125 [==============================] - 490s 4s/step - loss: 0.6396 - acc: 0.7978 - val_loss: 0.2669 - val_acc: 0.9317\n",
            "Epoch 12/15\n",
            "125/125 [==============================] - 492s 4s/step - loss: 0.5058 - acc: 0.8350 - val_loss: 0.2647 - val_acc: 0.9213\n",
            "Epoch 13/15\n",
            "125/125 [==============================] - 488s 4s/step - loss: 0.4322 - acc: 0.8591 - val_loss: 0.2497 - val_acc: 0.9335\n",
            "Epoch 14/15\n",
            "125/125 [==============================] - 489s 4s/step - loss: 0.4798 - acc: 0.8583 - val_loss: 0.1287 - val_acc: 0.9624\n",
            "Epoch 15/15\n",
            "125/125 [==============================] - 493s 4s/step - loss: 0.3655 - acc: 0.8783 - val_loss: 0.1136 - val_acc: 0.9677\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Eta+XXLsuejcJbyG6qNnazJrs9XxZuh8dOtJDU8muzWVjwTY+L9zO4NBBTIoZ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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff6feed8278>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MFt5qtRoXFxcA1q1bR2JiImq1GoDS0lK8vLw6Puvl5UVpaam5SumxFEXh/qHz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\nQggRZSS8hRBCiCjz/48CfoGKlnfdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff700896c50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "aSckbm495eBy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qe3EKvm1ShZ3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}