AlexNet_Adam_SGD(momentum).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AlexNet_Adam/SGD(momentum).ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "wjYExVeUUGFy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import gdown"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eSEOsBJnUZAs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "url = \"https://drive.google.com/uc?id=1cZJu7L55oeE3qRpESnGSE_v6CV2NW9pw\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1SmQlVlyUcV5",
        "colab_type": "code",
        "outputId": "1fe3ce00-131c-4de9-ccf9-1f828016d4ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "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 [00:01, 48.4MB/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'data1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "Z53f1PEmUhxq",
        "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": "6nbr-nlyUl9A",
        "colab_type": "code",
        "outputId": "9d09c9fa-67c2-46d6-cb13-debb38414122",
        "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": "qKZCUk8TWter",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 5 Convolution / dropout=.25 / Adam / lr =.001, -- "
      ]
    },
    {
      "metadata": {
        "id": "e_6bevltVAS0",
        "colab_type": "code",
        "outputId": "d11be68a-6b20-43d7-9ef3-76b3e1f03aca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1490
        }
      },
      "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 fourth convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(3,2)))\n",
        "\n",
        "#Adding 5th Concolution Layer\n",
        "classifier.add(Convolution2D(64, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\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.Adam(lr=0.001, beta_1=0.9, \n",
        "                          beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),\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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:22: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (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 [==============================] - 493s 4s/step - loss: 2.3550 - acc: 0.2859 - val_loss: 1.0369 - val_acc: 0.6545\n",
            "Epoch 2/15\n",
            "125/125 [==============================] - 463s 4s/step - loss: 0.6579 - acc: 0.7848 - val_loss: 0.4418 - val_acc: 0.8614\n",
            "Epoch 3/15\n",
            "125/125 [==============================] - 467s 4s/step - loss: 0.2975 - acc: 0.9120 - val_loss: 0.1156 - val_acc: 0.9641\n",
            "Epoch 4/15\n",
            "125/125 [==============================] - 470s 4s/step - loss: 0.1754 - acc: 0.9465 - val_loss: 0.0766 - val_acc: 0.9793\n",
            "Epoch 5/15\n",
            "125/125 [==============================] - 471s 4s/step - loss: 0.1366 - acc: 0.9587 - val_loss: 0.1027 - val_acc: 0.9748\n",
            "Epoch 6/15\n",
            "125/125 [==============================] - 470s 4s/step - loss: 0.0975 - acc: 0.9692 - val_loss: 0.0329 - val_acc: 0.9894\n",
            "Epoch 7/15\n",
            "125/125 [==============================] - 467s 4s/step - loss: 0.0757 - acc: 0.9800 - val_loss: 0.0147 - val_acc: 0.9958\n",
            "Epoch 8/15\n",
            "125/125 [==============================] - 469s 4s/step - loss: 0.0650 - acc: 0.9773 - val_loss: 0.0116 - val_acc: 0.9957\n",
            "Epoch 9/15\n",
            "125/125 [==============================] - 489s 4s/step - loss: 0.0622 - acc: 0.9822 - val_loss: 0.0184 - val_acc: 0.9958\n",
            "Epoch 10/15\n",
            "125/125 [==============================] - 500s 4s/step - loss: 0.0468 - acc: 0.9852 - val_loss: 0.0115 - val_acc: 0.9969\n",
            "Epoch 11/15\n",
            "125/125 [==============================] - 491s 4s/step - loss: 0.0492 - acc: 0.9837 - val_loss: 0.0209 - val_acc: 0.9925\n",
            "Epoch 12/15\n",
            "125/125 [==============================] - 497s 4s/step - loss: 0.0670 - acc: 0.9817 - val_loss: 0.0209 - val_acc: 0.9926\n",
            "Epoch 13/15\n",
            "125/125 [==============================] - 496s 4s/step - loss: 0.0455 - acc: 0.9847 - val_loss: 0.0327 - val_acc: 0.9905\n",
            "Epoch 14/15\n",
            "125/125 [==============================] - 462s 4s/step - loss: 0.0394 - acc: 0.9882 - val_loss: 0.0079 - val_acc: 0.9979\n",
            "Epoch 15/15\n",
            "125/125 [==============================] - 461s 4s/step - loss: 0.0348 - acc: 0.9891 - val_loss: 0.0071 - val_acc: 0.9969\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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ePhnnpE/p1bZ1jW68++UR6BKV+MX5oyWqkIiIomnABqyJohh6LQgCHn/8cRQX\nF0Ov12PEiBEn3d5g0EChiMytQNuYTIPv2uYdlbvxSfm/MVxvxl3nXg+1sneXcr2yYR88vgBu/8Xp\nyMk2AhicxykFHmd84XHGFx5nZEkW3mazuVNruqamBiaTKTQ9ffp0rFmzBgDw1FNPITMzs8f92e3O\niNZnMulhtTZFdJ/9ZXfX49lt/4BCpsAN469DU70PTfCFvf3eI3X4cmclxmQkYVKOAVZr06A8Tinw\nOOMLjzO+8Dj7t8/uSNZtnpeXh9LSUgDA3r17YTabodO1P0rylltuQW1tLZxOJz777DPMmDFDqlJi\nQiAYwKt716LZ58SVYy9Dlj6jV9v7A0Gs/mg/BAFYMGccB6kREcUxyVrekydPRm5uLoqKiiAIAhYv\nXoySkhLo9XoUFBTgmmuuwc033wxBEHDbbbfBaDRKVUpM2HBkIw41/IQzTadjZmbvv8h8tK0cljon\nZk/OxMj0odE9RUQ0VEl6znvRokWdpsePHx96PWfOHMyZM0fKt48Z++oO4MMjnyJVbcAvx1/V6weD\n1DW68e5/foJew0FqRERDAW+3FWWN3ib84/u1EAQBN+X+EhplYq/38cYnB+D1BXH1rLHQ8ranRERx\nj+EdRUExiNf2voEmrwM/H3MxcpKze72PPT/V4psfrRibmYxzT0+XoEoiIhpsGN5R9HHZJuyzH0Bu\n6njMzprZ6+19/iBWf3ygdZDaqRykRkQ0RDC8o+RQ/RG899NHSFYlYeFp8yETev+n+GjbUVTXOTF7\n8gg+j5uIaAhheEdBs8+JV/eugSiKuCn3WuhU2l7vo7bBjf/7zxEkaZT4xcwcCaokIqLBiuE9wERR\nxKof1sPuqcclOfk4xTCmT/t545MD8PqDuPrCsXw2NxHREMPwHmCbjv0Hu23f49SUMZg76qI+7WP3\n4Vps32/FKSOSce5EDlIjIhpqGN4DqKyxHP88+D50Si1uzL22T+e5Wwap7YdMELBgzrheXxNORESx\nj+E9QFx+N17ZuwYBMYAbJhQhOSGpT/v5cOtR1NhdmD0lE1lm3ck3ICKiuMPwHiCfH/sKNlctCrJn\nYULquD7tw1bvwvtfHUGSVoXLz+Od1IiIhiqG9wAQRRFbLduhkClQOOrCPu9nbesgtfkXjoVGPWBP\ncyUiokGG4T0AyprKUe204oxhuUhU9P72pwCw65AN3x6w4dQRyTgnNy3CFRIRUSxheA+ALVU7AADT\n0yf3aXufP4A1Hx/gIDUiIgLA8JacP+jH9urvoFfqcJrx1D7tY8OWo6ipdyF/6giM4CA1IqIhj+Et\nsb21+9Dsd2Ja+lmQy+S93t6zIiiVAAAgAElEQVRa78L7m8uQrFXh5+fxTmpERMTwltwWS1uX+ZQ+\nbb924wH4/EHMnz0WiQkcpEZERAxvSTl8zdhj+wEZ2nSM0A3v9fbfHbThu4M2jMtKwdkTOEiNiIha\nMLwltKN6JwJiAGcPn9LrQWZeXwBrQndSO5WD1IiIKIThLaEtlh0QIGBa2lm93nbDlqOwNbhRMG0E\nMk0cpEZERO0Y3hKpbq7BkcajOM14aq9vhVrTOkgtRafCz/I4SI2IiDpjeEukbaDa2X24tnvtx/vh\nDwQxf/YpHKRGRERdMLwlEBSD2GrZAbU8AZNMub3a9rsDNuw8VIvx2SmYfppZogqJiCiWMbwlcLD+\nMOyeepxlngSVXBX2dl5fAGs27odcJuCXvJMaERGdAMNbAm23Q+1tl/kHX5e1DlLLQuYwrRSlERFR\nHGB4R5gn4MW31l0wqg0YkxL+YLNquxMffH0UKToV5p07SroCiYgo5jG8I2yndQ88AS+mp0+GTAjv\n1yuKItZ8fAD+QBBFF3GQGhER9YzhHWFbLb1/gth3B2zYfbgWp400YNp4DlIjIqKeMbwjqN7TgH11\nB5CTlI00jSmsbTy+ANZsPNAySK2Ad1IjIqKTY3hH0DbLtxAh9uohJB9sLkNtoxtzpmUhg4PUiIgo\nDJKeXF22bBl27twJQRBQXFyMSZMmhZatXr0a7777LmQyGSZOnIiHH35YylIkJ4oitli2Qy7IMSXt\njLC2qbY7sWFLGQz6BMzLGyVtgUREFDcka3lv3boVZWVlWLduHZYuXYqlS5eGljkcDrz88stYvXo1\n1q5di0OHDuG7776TqpQBccxRiarmapw+7DRolZqTrt8+SE1E0UWnQK3iIDUiIgqPZOG9efNm5Ofn\nAwDGjBmDhoYGOBwOAIBSqYRSqYTT6YTf74fL5UJycrJUpQyILZbtAMJ/bve3rYPUJowyYOq48M6P\nExERARKGt81mg8FgCE0bjUZYrVYAQEJCAu68807k5+fjwgsvxBlnnIGcnNh9AEcgGMA3lu+gVWqQ\nmzrupOt7fAGsbbuTGgepERFRLw1YX60oiqHXDocDL7zwAj788EPodDrccMMN2LdvH8aPH3/C7Q0G\nDRQKeURrMpn0EdnPjsrdaPI5MHfsLAxPM5x0/VUbfkBtowdXzT4Fk8anR6SGnkTqOAc7Hmd84XHG\nFx5nZEkW3mazGTabLTRdU1MDk6mle/jQoUPIysqC0WgEAEydOhV79uzpMbztdmdE6zOZ9LBamyKy\nr49+/BIAMCnl9JPu01LnRMlnB2BMSsBFZ2ZErIYTieRxDmY8zvjC44wvPM7+7bM7knWb5+XlobS0\nFACwd+9emM1m6HQ6AEBmZiYOHToEt9sNANizZw9GjRolVSmScvpc2GX7HukaM7L1I3pcVxRFrP54\nf8sgtdmnIEEV2Z4EIiIaGiRreU+ePBm5ubkoKiqCIAhYvHgxSkpKoNfrUVBQgF/96ldYuHAh5HI5\nzjrrLEydOlWqUiS1o2Yn/EE/zk6fctJz1zv2W7H3pzrk5hgxhYPUiIiojyQ9571o0aJO0x27xYuK\nilBUVCTl2w+ILZYdECBgWvpZPa7n8Qaw9hPeSY2IiPqPd1jrB6uzFocbjuBUwxgY1Ck9rrvlh2rU\nNXpQOD0b6caTXwdORER0Igzvftjaem332WFc211hbQYAnHXqMElrIiKi+Mfw7qOW26HugEqmxBmm\niSddv7p1tPxwtrqJiKifGN59dKjhCGrddTjTfDrUioSTrm+pdSJJo4RGrRyA6oiIKJ4xvPuoN13m\nPn8Q1gYX0tjqJiKiCGB494E34MOOml1ISUjGqYYxJ12/pt4FUQQHqhERUUQwvPtgt+17uPxuTEs7\nCzLh5L9CS23L+e70VIY3ERH1H8O7D7aGniA2Oaz1LXUtI83Z8iYiokhgePdSo7cJ39ftR7Y+Exm6\n8B4qYqlrbXkzvImIKAIY3r30TfV3CIrBsJ/bDbSEt1wmwJSSKGFlREQ0VDC8e2lr1XbIBBmmpp0Z\n9jaWWieGpSRCIeevm4iI+o9p0gsVjiqUOyqRmzoOepUurG2anF40u/28OQsREUUMw7sXtlp2AECv\nu8wBnu8mIqLIYXiHKSgGsc2yA4mKRJw+bELY2/EyMSIiijSGd5h+rDuIBm8TpqSdAaUs/CepWuxs\neRMRUWQxvMO0pRe3Q+0o1PJmeBMRUYQwvMPg8rvxnXUPTImpyEnK7tW2ljonNAkK6DV8IAkREUUG\nwzsM39Xshi/ow9npUyAIQtjbBYJB1NhbHkjSm+2IiIh6wvAOQ1uX+bQwb4faxtbgRiAossuciIgi\niuF9ErUuOw7UH8bYlBwMSzT2aluONCciIikwvE9iW3XLtd29HagGtF/jzRu0EBFRJDG8eyCKIrZY\ntkMpU+As8+m93p43aCEiIikwvHtwpLEcNU4bJg3LRaKi9w8VsdQ6IQAwG/hAEiIiihyGdw/antt9\n9vDed5kDLS3v1GQ1VEp5JMsiIqIhjuF9Ar6gH9urd0Kv0mG84ZReb+/y+NHQ7GWXORERRRzD+wT2\n1u5Ds9+JaWlnQS7rfcuZ57uJiEgqDO8T2FrVt9uhtuFlYkREJBWGdzcc3mbsqd2HTN1wjNBn9Gkf\nbHkTEZFUGN7d2F6zEwExgOm9vKNaRwxvIiKSSvjPtuyDZcuWYefOnRAEAcXFxZg0aRIAoLq6GosW\nLQqtV15ejt/+9reYN2+elOWEbYtlOwQImJbWv/BWKWVI0SdEsDIiIiIJw3vr1q0oKyvDunXrcOjQ\nIRQXF2PdunUAgLS0NKxatQoA4Pf7cf3112P27NlSldIrluYalDWWY0LqOCQn6Pu0j6AoorrOiXSj\nBjI+kISIiCJMsm7zzZs3Iz8/HwAwZswYNDQ0wOFwdFnvn//8JwoLC6HVaqUqpVe2Wvp+O9Q29kYP\nvP4gB6sREZEkJAtvm80Gg8EQmjYajbBarV3We/PNN3HVVVdJVUavBMUgtlp2QC1XY9Kw3D7vh+e7\niYhISpKe8+5IFMUu87799luMHj0aOp3upNsbDBooFJG9U5nJ1LlbfE/1Ptg99Zidcy4y03v3BLGO\nHD+2fEk5ZVRql/eIhsFQw0DgccYXHmd84XFGlmThbTabYbPZQtM1NTUwmUyd1tm0aRNmzJgR1v7s\ndmdE6zOZ9LBamzrNK933JQBgUsqkLst642CZHQCgVQr92k8kdHec8YjHGV94nPGFx9m/fXYnrG7z\n7lrNJ5OXl4fS0lIAwN69e2E2m7u0sHfv3o3x48f3et9S8AS8+Na6G6lqA8akjOrXvix1zQCANAO7\nzYmIKPLCanlfeOGF+PnPf46rrroKWVlZYe148uTJyM3NRVFREQRBwOLFi1FSUgK9Xo+CggIAgNVq\nRWpqat+rj6Cd1j3wBryYnjUTMqF/QwEsdU6k6FRITBiwsxJERDSEhJUub775JkpLS1FcXAyFQoEr\nrrgChYWFUKlUPW7X8VpuAF1a2f/3f//Xy3Kls6X1dqj9uTELAHh8AdQ2ejA+OyUSZREREXURVhPT\nZDJhwYIFWLVqFR555BGsXbsWM2fOxNNPPw2PxyN1jZKr9zTgR/tB5CSNhFljOvkGPahuG2meOjgu\nfSMiovgTdv/wtm3b8NBDD+HWW2/F5MmTsWbNGiQlJeGee+6Rsr4Bsc3yLUSIOHt4/1rdAFBtdwHg\nZWJERCSdsLrNCwoKkJmZiWuuuQZ/+tOfoFQqAbTcfGXjxo2SFig1URTxtWU7FIIck81n9Ht/ltqW\nwWoMbyIikkpY4f3SSy9BFEWMGjUKAPD9999jwoQJAIA1a9ZIVtxAKHdUwNJcjTNNp0Or7H/gtt+g\nJbHf+yIiIupOWN3mJSUleOGFF0LTL774Ip588kkAgBDj9+7eWtV2O9T+d5kDLeGtkAsYlszwJiIi\naYQV3lu2bMHy5ctD0ytWrMD27dslK2qgBIIBbKv+FjqlFhNSx/V7f6IowlLnhNmggUwW219qiIho\n8AorvH0+H7xeb2i6ubkZfr9fsqIGyvd1P8Lha8aUtDOhkPX/muzGZi9cngDPdxMRkaTCSqyioiJc\ncsklmDhxIoLBIHbv3o277rpL6tokt6X1CWLn9OMJYh3xgSRERDQQwgrvq6++Gnl5edi9ezcEQcBD\nDz0U1sNEBjOHtxm7bd8jXZuGLH1mRPZZxfAmIqIBEPZ13k6nE0ajEQaDAYcPH8Y111wjZV2S+7p8\nB/xBP85OnxyxQXeW2rYbtDC8iYhIOmG1vB977DH85z//gc1mQ3Z2NsrLy3HzzTdLXZukPj+yBQIE\nTEs7K2L7ZLc5ERENhLBa3rt378aGDRswfvx4vP3223jllVfgcrmkrk0yVmctfrQdwjjDWBjUkbsH\nuaXOCV2iErpEZcT2SUREdLywwrvtASQ+nw+iKGLixInYsWOHpIVJyeauBQDkZZ4dsX36A0HY6t3s\nMiciIsmF1W2ek5OD1atXY+rUqbjpppuQk5ODpqbYfbD6acZT8dxlj0Fw9vxUtN6osbsQFEV2mRMR\nkeTCCu9HH30UDQ0NSEpKwvvvv4/a2lrcfvvtUtcmKbM2FVZn5L6AtD1NbDjDm4iIJBZWeC9btgwP\nP/wwAGDevHmSFhSrOFiNiIgGSljnvOVyOTZv3gyPx4NgMBj6R+3arvFOY3gTEZHEwmp5v/nmm3jt\ntdcgimJoniAI+OGHHyQrLNZY6pyQCQLMBj6QhIiIpBVWeMfDQ0ikZql1YliKGgp52Pe9ISIi6pOw\nwvuZZ57pdv4999wT0WJilcPlg8Plw+iMpGiXQkREQ0DY57zb/gWDQWzZsiWmLxWLNA5WIyKigRRW\ny/v4J4gFAgH85je/kaSgWMR7mhMR0UDq0wlav9+Po0ePRrqWmGXhNd5ERDSAwmp5X3DBBZ2evNXQ\n0IBf/OIXkhUVa9htTkREAyms8F6zZk3otSAI0Ol0SEri4Kw2ljonEhPkSNJG7narREREJxJWt7nL\n5cIbb7yBzMxMZGRkYPny5Thw4IDUtcWEYFBEjd2JdKMmYs8FJyIi6klY4f3oo4/iggsuCE1feeWV\n+NOf/iRZUbHE1uCCP8AHkhAR0cAJK7wDgQCmTp0amp46dWqnu60NZZa6lueaM7yJiGighHXOW6/X\nY82aNTj77LMRDAbxxRdfQKvVSl1bTLDwnuZERDTAwgrv5cuX46mnnsLatWsBAJMnT8by5ctPut2y\nZcuwc+dOCIKA4uJiTJo0KbSsqqoK999/P3w+HyZMmBCz3fAcaU5ERAMtrPA2Go249dZbMWrUKADA\n999/D6PR2OM2W7duRVlZGdatW4dDhw6huLgY69atCy1//PHHcfPNN6OgoACPPvooKisrkZGR0fcj\niRJLbTMAtryJiGjghHXO++mnn8YLL7wQmn7xxRfx5JNP9rjN5s2bkZ+fDwAYM2YMGhoa4HA4AADB\nYBDbt2/H7NmzAQCLFy+OyeAGWlreqUkJSFDKo10KERENEWGF95YtWzp1k69YseKkTxqz2WwwGAyh\naaPRCKvVCgCoq6uDVqvF8uXLce211+Kpp57qS+1R5/L4Ue/wssuciIgGVFjd5j6fD16vFypVy01I\nmpub4ff7e/VGHUeni6KI6upqLFy4EJmZmbjtttuwadMmzJo164TbGwwaKBSRbd2aTPp+bX+wvB4A\nkJOZ0u99SWkw1xZJPM74wuOMLzzOyAorvIuKinDJJZdg4sSJCAaD2L17N2644YYetzGbzbDZbKHp\nmpoamEwmAIDBYEBGRgays7MBADNmzMCBAwd6DG+73RlOqWEzmfSwWvv3ZLQfDrX0JCRrlP3el1Qi\ncZyxgMcZX3ic8YXH2b99diesbvOrr74ay5cvxyWXXIJ58+bhnnvu6TT4rDt5eXkoLS0FAOzduxdm\nsxk6nQ4AoFAokJWVhSNHjoSW5+TkhHssgwZHmhMRUTSE1fJeunQpvvzyS9hsNmRnZ6O8vBw333xz\nj9tMnjwZubm5KCoqgiAIWLx4MUpKSqDX61FQUIDi4mI8+OCDEEURp556amjwWixheBMRUTSEFd67\ndu3Chg0bcP3112PVqlXYs2cPPv7445Nut2jRok7T48ePD70eOXJk6LrxWGWpdUKlkMGQlBDtUoiI\naAgJq9u8baCaz+eDKIqYOHEiduzYIWlhg11QFGGxO5Fm1EDGB5IQEdEACqvlnZOTg9WrV2Pq1Km4\n6aabkJOTg6am+B980JP6Jg+8viC7zImIaMCFFd6PPvooGhoakJSUhPfffx+1tbW4/fbbpa5tUOM9\nzYmIKFrCCm9BEJCSkgIAmDdvnqQFxYq28B7O8CYiogEW1jlv6spS2zrSPJXhTUREA4vh3Ue8TIyI\niKKF4d1HljonkrUqJCaEdeaBiIgoYhjefeD1BVDb4Garm4iIooLh3Qc1dhdE8Hw3ERFFB8O7D3i+\nm4iIoonh3QdVDG8iIooihncf8DIxIiKKJoZ3H1jqnJDLBAxLVke7FCIiGoIY3r0kiiIsdU6YDYmQ\ny/jrIyKigcf06aUmpw8uj5/nu4mIKGoY3r3EkeZERBRtDO9eYngTEVG0Mbx7iSPNiYgo2hjevcSW\nNxERRRvDu5eq6pzQqhXQa1TRLoWIiIYohncv+ANB2Opd7DInIqKoYnj3grXehUBQZJc5ERFFFcO7\nF3i+m4iIBgOGdy+0h7c2ypUQEdFQxvDuBV4mRkREgwHDuxcsdU4IAmBOSYx2KURENIQxvHuhus4J\nU3IilAr+2oiIKHqYQmFyun1odPqQxsFqREQUZQzvMFVxpDkREQ0SDO8wcbAaERENFgopd75s2TLs\n3LkTgiCguLgYkyZNCi2bPXs20tPTIZfLAQBPPvkk0tLSpCynX3iNNxERDRaShffWrVtRVlaGdevW\n4dChQyguLsa6des6rbNy5UpotbFxzTTDm4iIBgvJus03b96M/Px8AMCYMWPQ0NAAh8Mh1dtJzlLn\nRIJKjhQdH0hCRETRJVnL22azITc3NzRtNBphtVqh0+lC8xYvXoyKigpMmTIFv/3tbyEIwgn3ZzBo\noFDII1qjyaQPa71AUESN3YWR6XqYzUkRrWEghHucsY7HGV94nPGFxxlZkp7z7kgUxU7Td999N2bO\nnInk5GTceeedKC0txdy5c0+4vd3ujGg9JpMeVmtTWOta613w+YMYlqQOe5vBojfHGct4nPGFxxlf\neJz922d3JOs2N5vNsNlsoemamhqYTKbQ9OWXX47U1FQoFAqcf/752L9/v1Sl9BvPdxMR0WAiWXjn\n5eWhtLQUALB3716YzeZQl3lTUxN+9atfwev1AgC2bduGU045RapS+o2XiRER0WAiWbf55MmTkZub\ni6KiIgiCgMWLF6OkpAR6vR4FBQU4//zzMX/+fCQkJGDChAk9dplHG1veREQ0mEh6znvRokWdpseP\nHx96fcMNN+CGG26Q8u0jpi280wwMbyIiij7eYS0MljonDPoEJKgiO9qdiIioLxjeJ+HxBmBv8rDL\nnIiIBg2G90mEzndzsBoREQ0SDO+T4GA1IiIabBjeJ9EW3sMZ3kRENEgwvE+CLW8iIhpsGN4nYal1\nQqmQwZisjnYpREREABjePRJFERa7E2mGRMh6eGgKERHRQGJ496De4YXHG2CXORERDSoM7x5YapsB\n8DIxIiIaXBjePeBgNSIiGowY3j2oCoW3NsqVEBERtWN496C6zgUASDcmRrkSIiKidgzvHljqmpGk\nUUKjVka7FCIiohCG9wn4/EHYGtw8301ERIMOw/sEauxOiCJHmhMR0eDD8D4BCwerERHRIMXwPgFe\nJkZERIMVw/sELLV8jjcREQ1ODO8TsNQ5IZcJGMYHkhAR0SDD8O6GKIqw1DlhSkmEQs5fERERDS5M\npm40uXxodvt5vpuIiAYlhnc3eL6biIgGM4Z3NzjSnIiIBjOGdzcY3kRENJgxvLtRzfAmIqJBjOHd\nDUudE5oEBfQaPpCEiIgGH4b3cQLBIGrsLqSnaiAIQrTLISIi6oLhfRxbvRuBoMgucyIiGrQkDe9l\ny5Zh/vz5KCoqwq5du7pd56mnnsL1118vZRm9UsXz3URENMhJFt5bt25FWVkZ1q1bh6VLl2Lp0qVd\n1jl48CC2bdsmVQl9ErrGm+FNRESDlGThvXnzZuTn5wMAxowZg4aGBjgcjk7rPP7447jvvvukKqFP\nQpeJ8QYtREQ0SCmk2rHNZkNubm5o2mg0wmq1QqfTAQBKSkowffp0ZGZmhrU/g0EDhUIe0RpNJn2X\nebVNHggCkHuKGSplZN8vWro7znjE44wvPM74wuOMLMnC+3iiKIZe19fXo6SkBK+++iqqq6vD2t5u\nd0a0HpNJD6u1qcv88uompCap0VAf2feLlhMdZ7zhccYXHmd84XH2b5/dkazb3Gw2w2azhaZrampg\nMpkAAF9//TXq6urwy1/+EnfddRf27t2LZcuWSVVK2JxuPxqbvewyJyKiQU2y8M7Ly0NpaSkAYO/e\nvTCbzaEu87lz5+KDDz7A+vXr8dxzzyE3NxfFxcVSlRI23haViIhigWTd5pMnT0Zubi6KioogCAIW\nL16MkpIS6PV6FBQUSPW2/WKpawYADGd4ExHRICbpOe9FixZ1mh4/fnyXdUaMGIFVq1ZJWUbYLHUu\nAEAaw5uIiAYx3mGtA3abExFRLGB4d2CpdSJBKYdBnxDtUoiIiE6I4d0qKIqosTuRZkzkA0mIiGhQ\nY3i3qmt0w+sPssuciIgGPYZ3K57vJiKiWMHwbhV6IAlv0EJERIMcw7tVW8t7uFEb5UqIiIh6xvBu\n1RbeacbEKFdCRETUM4Z3K0udEwZ9AtSqAXtWCxERUZ8wvAF4vAHUNXo4WI2IiGICwxtAtZ0jzYmI\nKHYwvNHxfDfDm4iIBj+GN3iNNxERxRaGNzqEN6/xJiKiGMDwRssNWhRyGYYlqaNdChER0UkN+fAW\nRRGWOifSDImQyfhAEiIiGvyGfHg3NHvh9gZ4vpuIiGLGkA9v3tOciIhiDcObI82JiCjGMLwZ3kRE\nFGMY3rxMjIiIYgzDu9YJvUYJrVoZ7VKIiIjCMqTD2+cPwtrgYpc5ERHFlCEd3jX1Logi72lORESx\nZUiHd3Xr+e7hDG8iIoohQzq8OdKciIhi0dAOb96ghYiIYtDQDu86J2SCAFNKYrRLISIiCtuQD29T\nihoK+ZD+NRARUYxRSLnzZcuWYefOnRAEAcXFxZg0aVJo2fr16/HWW29BJpNh/PjxWLx4MQRh4J7q\n1djshcPlw5iMpAF7TyIiokiQrMm5detWlJWVYd26dVi6dCmWLl0aWuZyufD+++9j9erVeOONN3D4\n8GF8++23UpXSrYoaBwCe7yYiotgjWXhv3rwZ+fn5AIAxY8agoaEBDkdLYCYmJuK1116DUqmEy+WC\nw+GAyWSSqpRuVVibAHCkORERxR7Jus1tNhtyc3ND00ajEVarFTqdLjTvxRdfxP/+7/9i4cKFyMrK\n6nF/BoMGCoU8YvUd23IUADB+9DCYTPqI7Xcwivfja8PjjC88zvjC44wsSc95dySKYpd5t912GxYu\nXIhbb70VU6ZMwZQpU064vd3ujGg9FdaWXgC1XIC1tRUej0wmfVwfXxseZ3zhccYXHmf/9tkdybrN\nzWYzbDZbaLqmpibUNV5fX49t27YBANRqNc4//3zs2LFDqlK6VWF1IDFBgSQNH0hCRESxRbLwzsvL\nQ2lpKQBg7969MJvNoS5zv9+PBx98EM3NzQCA3bt3IycnR6pSuggEg6iyNSPdqBnQEe5ERESRIFm3\n+eTJk5Gbm4uioiIIgoDFixejpKQEer0eBQUFuPPOO7Fw4UIoFAqMGzcOF110kVSldGFrcMMfEJFu\n5M1ZiIgo9kh6znvRokWdpsePHx96fcUVV+CKK66Q8u1PqJr3NCciohg2JG8tplYpoFTIMC7bEO1S\niIiIem3ARpsPJqdmpWD9skthr2uOdilERES9NiRb3gB4P3MiIopZTDAiIqIYw/AmIiKKMQxvIiKi\nGMPwJiIiijEMbyIiohjD8CYiIooxDG8iIqIYw/AmIiKKMQxvIiKiGMPwJiIiijEMbyIiohgjiKIo\nRrsIIiIiCh9b3kRERDGG4U1ERBRjGN5EREQxhuFNREQUYxjeREREMYbhTUREFGOGZHgvW7YM8+fP\nR1FREXbt2hXtciTz5z//GfPnz8eVV16Jjz76KNrlSMrtdiM/Px8lJSXRLkUy7777Ln72s5/hiiuu\nwKZNm6JdjiSam5tx11134frrr0dRURG++OKLaJcUUfv370d+fj5ef/11AEBVVRWuv/56XHfddbjn\nnnvg9XqjXGFkdHecN954IxYsWIAbb7wRVqs1yhVGxvHH2eaLL77AuHHjJH3vIRfeW7duRVlZGdat\nW4elS5di6dKl0S5JEl9//TUOHDiAdevW4aWXXsKyZcuiXZKk/va3vyE5OTnaZUjGbrfj+eefx5o1\na/D3v/8dn3zySbRLksQ///lP5OTkYNWqVXjmmWfi6v9Pp9OJJUuWYMaMGaF5//M//4PrrrsOa9as\nwciRI/HWW29FscLI6O44V6xYgWuuuQavv/46CgoK8Oqrr0axwsjo7jgBwOPx4MUXX4TJZJL0/Ydc\neG/evBn5+fkAgDFjxqChoQEOhyPKVUXetGnT8MwzzwAAkpKS4HK5EAgEolyVNA4dOoSDBw9i1qxZ\n0S5FMps3b8aMGTOg0+lgNpuxZMmSaJckCYPBgPr6egBAY2MjDAZDlCuKHJVKhZUrV8JsNofmbdmy\nBRdddBEA4MILL8TmzZujVV7EdHecixcvRmFhIYDOf+NY1t1xAsDf//53XHfddVCpVJK+/5ALb5vN\n1ukDwWg0xk0XTkdyuRwajQYA8NZbb+H888+HXC6PclXSeOKJJ/Dggw9GuwxJHTt2DG63G7/+9a9x\n3XXXxcWHfHcuvfRSVJELBHsAAAV5SURBVFZWoqCgAAsWLMDvf//7aJcUMQqFAmq1utM8l8sV+pBP\nTU2Ni8+i7o5To9FALpcjEAhgzZo1mDdvXpSqi5zujvOnn37Cvn37cPHFF0v//pK/wyAX73eH3bhx\nI9566y288sor0S5FEu+88w7OPPNMZGVlRbsUydXX1+O5555DZWUlFi5ciM8++wyCIES7rIj617/+\nhYyMDLz88svYt28fiouL43ocQ0fx/lkUCATwwAMP4JxzzunS1Rwvli9fjj/84Q8D8l5DLrzNZjNs\nNltouqamRvJzE9HyxRdf4O9//zteeukl6PX6aJcjiU2bNqG8vBybNm2CxWKBSqVCeno6zj333GiX\nFlGpqak466yzoFAokJ2dDa1Wi7q6OqSmpka7tIjasWMHzjvvPADA+PHjUVNTg0AgELe9RhqNBm63\nG2q1GtXV1V26YOPJQw89hJEjR+Kuu+6KdimSqK6uxuHDh7Fo0SIALdmyYMGCLoPZImXIdZvn5eWh\ntLQUALB3716YzWbodLooVxV5TU1N+POf/4wXXngBKSkp0S5HMitWrMDbb7+N9evX4+qrr8Ydd9wR\nd8ENAOeddx6+/vprBINB2O12OJ3OuDof3GbkyJHYuXMnAKCiogJarTZugxsAzj333NDn0UcffYSZ\nM2dGuSJpvPvuu1Aqlbj77rujXYpk0tLSsHHjRqxfvx7r16+H2WyWLLiBIdjynjx5MnJzc1FUVARB\nELB48eJolySJDz74AHa7Hffee29o3hNPPIGMjIwoVkV9lZaWhsLCQlxzzTUAgD/84Q+QyeLvu/f8\n+fNRXFyMBQsWwO/345FHHol2SRGzZ88ePPHEE6ioqIBCoUBpaSmefPJJPPjgg1i3bh0yMjJw+eWX\nR7vMfuvuOGtra5GQkIDrr78eQMtg4Vj/23Z3nM8+++yANZb4SFAiIqIYE39f3YmIiOIcw5uIiCjG\nMLyJiIhiDMObiIgoxjC8iYiIYgzDm4j6raSkJHRzCiKSHsObiIgoxgy5m7QQDWWrVq3Chg0bEAgE\nMHr0aNxyyy24/fbbcf7552Pfvn0AgKeffhppaWnYtGkTnn/+eajVaiQmJmLJkiVIS0vDzp07sWzZ\nMiiVSiQnJ+OJJ54AADgcDixatAiHDh1CRkYGnnvuubi79zrRYMGWN9EQsWvXLnz88cdYvXo11q1b\nB71ej6+++grl5eW44oor8P/bu2PVRKIojON/IRgjBEYEtUxlmgyBgJMuRR7BMmLIK6SwkzTDBEEI\ngr0QzIiVD5Au04itoE1SiEhAxIARtZwUQlh22WKLXZmd79fNreZUH+dcOLfVamFZFo1Gg81mQ7lc\npl6v02w2ubi4oFarAVAqlbBtm6enJ3K5HC8vLwC8vb1h2zadTofX11cGg8EuyxX5r6nzFgmJXq/H\neDzm+voagPV6zXQ6xTAMTk5OgO364MfHR0ajEclkkkwmA4BlWbTbbT4+Pvj8/CSbzQJwc3MDbO+8\nTdPk4OAA2K5zXS6X/7hCkfBQeIuERDQa5fLykru7u++zyWRCPp///vZ9n0gk8su4+8fz321U/vkB\nEW1eFvl7NDYXCYmzszM8z2O1WgHgui6z2YzFYsFwOAS2T3IeHx9zdHTEfD7n/f0dgG63y+npKYlE\nAsMw6Pf7ADQaDVzX3U1BIiGmzlskJEzTpFAoUCwW2d/fJ5VKcX5+TjqdptPpUKlU8H2fh4cHYrEY\njuNwe3tLNBolHo/jOA4A1WqV+/t79vb2ODw8pFqt8vz8vOPqRMJFr4qJhNhkMuHq6grP83b9KyLy\nBzQ2FxERCRh13iIiIgGjzltERCRgFN4iIiIBo/AWEREJGIW3iIhIwCi8RUREAkbhLSIiEjBfxd1j\n42X73/EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f47fe8b61d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dcHs7H/508dyRSDdq8XbBSRQW1/ROgURElHAY3j2kllSYnjUFDW4Hdpd/1em2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\ndEb6MaShbAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f47ffc0c6a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "DJljSUpLVaoB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 5 Convolution / dropout=.5 / Adam / lr =.001, --"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "22a1791c-0df7-4c75-c709-0819231115b7",
        "id": "rAXqiQ6VVA-g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1490
        }
      },
      "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 fourth convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(3,2)))\n",
        "\n",
        "#Adding 5th Concolution Layer\n",
        "classifier.add(Convolution2D(64, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\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.Adam(lr=0.001, beta_1=0.9, \n",
        "                          beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),\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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:22: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (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 [==============================] - 498s 4s/step - loss: 2.4842 - acc: 0.2417 - val_loss: 1.0951 - val_acc: 0.6720\n",
            "Epoch 2/15\n",
            "125/125 [==============================] - 474s 4s/step - loss: 0.9348 - acc: 0.7013 - val_loss: 0.2448 - val_acc: 0.9318\n",
            "Epoch 3/15\n",
            "125/125 [==============================] - 475s 4s/step - loss: 0.4173 - acc: 0.8690 - val_loss: 0.1639 - val_acc: 0.9517\n",
            "Epoch 4/15\n",
            "125/125 [==============================] - 475s 4s/step - loss: 0.2698 - acc: 0.9150 - val_loss: 0.0818 - val_acc: 0.9801\n",
            "Epoch 5/15\n",
            "125/125 [==============================] - 475s 4s/step - loss: 0.1714 - acc: 0.9445 - val_loss: 0.0780 - val_acc: 0.9750\n",
            "Epoch 6/15\n",
            "125/125 [==============================] - 477s 4s/step - loss: 0.1378 - acc: 0.9593 - val_loss: 0.0228 - val_acc: 0.9958\n",
            "Epoch 7/15\n",
            "125/125 [==============================] - 475s 4s/step - loss: 0.1047 - acc: 0.9680 - val_loss: 0.0287 - val_acc: 0.9926\n",
            "Epoch 8/15\n",
            "125/125 [==============================] - 477s 4s/step - loss: 0.1051 - acc: 0.9657 - val_loss: 0.0159 - val_acc: 0.9948\n",
            "Epoch 9/15\n",
            "125/125 [==============================] - 476s 4s/step - loss: 0.0939 - acc: 0.9740 - val_loss: 0.0194 - val_acc: 0.9958\n",
            "Epoch 10/15\n",
            "125/125 [==============================] - 477s 4s/step - loss: 0.0725 - acc: 0.9787 - val_loss: 0.0202 - val_acc: 0.9937\n",
            "Epoch 11/15\n",
            "125/125 [==============================] - 479s 4s/step - loss: 0.0833 - acc: 0.9765 - val_loss: 0.0169 - val_acc: 0.9958\n",
            "Epoch 12/15\n",
            "125/125 [==============================] - 480s 4s/step - loss: 0.0452 - acc: 0.9865 - val_loss: 0.0181 - val_acc: 0.9947\n",
            "Epoch 13/15\n",
            "125/125 [==============================] - 479s 4s/step - loss: 0.0646 - acc: 0.9812 - val_loss: 0.0048 - val_acc: 0.9990\n",
            "Epoch 14/15\n",
            "125/125 [==============================] - 479s 4s/step - loss: 0.0580 - acc: 0.9828 - val_loss: 0.0145 - val_acc: 0.9967\n",
            "Epoch 15/15\n",
            "125/125 [==============================] - 480s 4s/step - loss: 0.0526 - acc: 0.9822 - val_loss: 0.0129 - val_acc: 0.9958\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qRRGSJGGbrR46lRbTzVOVLmfU9h/uwssf7sHeZmd4v/ZZE3Hp/Ek8zzgRkQLi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\nRETEYj4BHCr3l5qFaIkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb3849e1da0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fo1ELhUIeoqr8LBZDj+sm+Pyv1Q2uXreLBtFef1+xnbGF7YwtbGdoSRre27Ztw7PPPosX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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb3848db6d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "JH1E3TNHsHnS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 5 conv / dropout=.25 / SGD(momentum, nesterov) / lr=.01"
      ]
    },
    {
      "metadata": {
        "id": "ULzT8ll8Vh0o",
        "colab_type": "code",
        "outputId": "9ed29adc-801c-42ec-db4f-f26e2dd16ef1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1490
        }
      },
      "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 fourth convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(3,2)))\n",
        "\n",
        "#Adding 5th Concolution Layer\n",
        "classifier.add(Convolution2D(64, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\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, decay=1e-6, momentum=0.9, nesterov=True),\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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:22: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (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 [==============================] - 505s 4s/step - loss: 3.0156 - acc: 0.1173 - val_loss: 2.3015 - val_acc: 0.3591\n",
            "Epoch 2/15\n",
            "125/125 [==============================] - 476s 4s/step - loss: 1.5770 - acc: 0.5184 - val_loss: 0.7416 - val_acc: 0.7580\n",
            "Epoch 3/15\n",
            "125/125 [==============================] - 481s 4s/step - loss: 0.6783 - acc: 0.7815 - val_loss: 0.3462 - val_acc: 0.9013\n",
            "Epoch 4/15\n",
            "125/125 [==============================] - 480s 4s/step - loss: 0.3941 - acc: 0.8743 - val_loss: 0.1734 - val_acc: 0.9454\n",
            "Epoch 5/15\n",
            "125/125 [==============================] - 479s 4s/step - loss: 0.2512 - acc: 0.9216 - val_loss: 0.0761 - val_acc: 0.9808\n",
            "Epoch 6/15\n",
            "125/125 [==============================] - 481s 4s/step - loss: 0.1910 - acc: 0.9395 - val_loss: 0.1055 - val_acc: 0.9728\n",
            "Epoch 7/15\n",
            "125/125 [==============================] - 480s 4s/step - loss: 0.1464 - acc: 0.9518 - val_loss: 0.0404 - val_acc: 0.9883\n",
            "Epoch 8/15\n",
            "125/125 [==============================] - 479s 4s/step - loss: 0.0981 - acc: 0.9705 - val_loss: 0.0433 - val_acc: 0.9845\n",
            "Epoch 9/15\n",
            "125/125 [==============================] - 480s 4s/step - loss: 0.0868 - acc: 0.9740 - val_loss: 0.0319 - val_acc: 0.9906\n",
            "Epoch 10/15\n",
            "125/125 [==============================] - 480s 4s/step - loss: 0.0784 - acc: 0.9742 - val_loss: 0.0323 - val_acc: 0.9926\n",
            "Epoch 11/15\n",
            "125/125 [==============================] - 479s 4s/step - loss: 0.0636 - acc: 0.9805 - val_loss: 0.0200 - val_acc: 0.9938\n",
            "Epoch 12/15\n",
            "125/125 [==============================] - 480s 4s/step - loss: 0.0419 - acc: 0.9862 - val_loss: 0.0248 - val_acc: 0.9926\n",
            "Epoch 13/15\n",
            "125/125 [==============================] - 478s 4s/step - loss: 0.0640 - acc: 0.9812 - val_loss: 0.0123 - val_acc: 0.9947\n",
            "Epoch 14/15\n",
            "125/125 [==============================] - 478s 4s/step - loss: 0.0433 - acc: 0.9875 - val_loss: 0.0303 - val_acc: 0.9892\n",
            "Epoch 15/15\n",
            "125/125 [==============================] - 479s 4s/step - loss: 0.0322 - acc: 0.9905 - val_loss: 0.0079 - val_acc: 0.9979\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HftsBTMoaj9NzpsakBrvbj//rDu4lpxfg0rPK2NomIpIRwzuB2f0ObKj4J3RK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\npIMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb385ac7cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A2/ZxwBYiIuq7M97zXrp0aWwCknA4jKKiIgCAy+XCfffdd8Z73smuwJiLnfY9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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb385ae2b00>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "G7Kmx914lDN4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# # 5 conv / dropout=.5 / SGD(momentum, nesterov) / lr=.01"
      ]
    },
    {
      "metadata": {
        "id": "QPNaA062k8rl",
        "colab_type": "code",
        "outputId": "ce655f30-099d-47d0-ac90-dcbe459d036c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1490
        }
      },
      "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 fourth convolution layer\n",
        "classifier.add(Convolution2D(32, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(3,2)))\n",
        "\n",
        "#Adding 5th Concolution Layer\n",
        "classifier.add(Convolution2D(64, 3,  3, activation = 'relu'))\n",
        "classifier.add(MaxPooling2D(pool_size =(2,2)))\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, decay=1e-6, momentum=0.9, nesterov=True),\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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:22: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (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 [==============================] - 466s 4s/step - loss: 3.1590 - acc: 0.0498 - val_loss: 3.1235 - val_acc: 0.0695\n",
            "Epoch 2/15\n",
            "125/125 [==============================] - 431s 3s/step - loss: 2.4370 - acc: 0.2628 - val_loss: 1.5406 - val_acc: 0.5823\n",
            "Epoch 3/15\n",
            "125/125 [==============================] - 422s 3s/step - loss: 1.2402 - acc: 0.6120 - val_loss: 0.5658 - val_acc: 0.8625\n",
            "Epoch 4/15\n",
            "125/125 [==============================] - 419s 3s/step - loss: 0.6803 - acc: 0.7890 - val_loss: 0.2194 - val_acc: 0.9362\n",
            "Epoch 5/15\n",
            "125/125 [==============================] - 416s 3s/step - loss: 0.4326 - acc: 0.8633 - val_loss: 0.1149 - val_acc: 0.9652\n",
            "Epoch 6/15\n",
            "125/125 [==============================] - 414s 3s/step - loss: 0.3117 - acc: 0.9030 - val_loss: 0.0779 - val_acc: 0.9821\n",
            "Epoch 7/15\n",
            "125/125 [==============================] - 414s 3s/step - loss: 0.2530 - acc: 0.9203 - val_loss: 0.0700 - val_acc: 0.9824\n",
            "Epoch 8/15\n",
            "125/125 [==============================] - 409s 3s/step - loss: 0.1960 - acc: 0.9407 - val_loss: 0.0523 - val_acc: 0.9832\n",
            "Epoch 9/15\n",
            "125/125 [==============================] - 414s 3s/step - loss: 0.1710 - acc: 0.9468 - val_loss: 0.0304 - val_acc: 0.9916\n",
            "Epoch 10/15\n",
            "125/125 [==============================] - 412s 3s/step - loss: 0.1600 - acc: 0.9572 - val_loss: 0.0190 - val_acc: 0.9957\n",
            "Epoch 11/15\n",
            "125/125 [==============================] - 413s 3s/step - loss: 0.1263 - acc: 0.9590 - val_loss: 0.0210 - val_acc: 0.9937\n",
            "Epoch 12/15\n",
            "125/125 [==============================] - 410s 3s/step - loss: 0.1052 - acc: 0.9680 - val_loss: 0.0175 - val_acc: 0.9949\n",
            "Epoch 13/15\n",
            "125/125 [==============================] - 408s 3s/step - loss: 0.0948 - acc: 0.9705 - val_loss: 0.0095 - val_acc: 0.9990\n",
            "Epoch 14/15\n",
            "125/125 [==============================] - 404s 3s/step - loss: 0.0793 - acc: 0.9758 - val_loss: 0.0131 - val_acc: 0.9979\n",
            "Epoch 15/15\n",
            "125/125 [==============================] - 397s 3s/step - loss: 0.0785 - acc: 0.9740 - val_loss: 0.0100 - val_acc: 0.9979\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xYlfr1wCAWTEwtvtArQXPvVsFq8ODqWOzceuyyXzWNhHREMga3qtWrcKePXsg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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb3c748fc18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u2mKCRlIjRZeE8qYKeH1e2drNy7bA6/PjSJlDtjaJiCg69Cu8//u//xu33nor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\ngg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb3c8e1dda0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pzWXgEBSmYbc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}