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        "## CNN\n",
        "img_size = 128\n",
        "    \n",
        "def cnn_model():\n",
        "    model = Sequential()\n",
        "    model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(1, img_size, img_size), activation='relu'))\n",
        "    model.add(Convolution2D(32, 3, 3, activation='relu',border_mode='same'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "    \n",
        "    model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))\n",
        "    model.add(Convolution2D(64, 3, 3, activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.2))\n",
        "\n",
        "    model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))\n",
        "    model.add(Convolution2D(128, 3, 3, activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.2))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(512, activation='relu'))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(1))\n",
        "    model.add(Activation('sigmoid'))\n",
        "\n",
        "    return model\n",
        "\n",
        "\n",
        "model1 = cnn_model()\n",
        "\n",
        "\n",
        "model1.compile(loss='binary_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['accuracy'])"
      ]
    }
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