gistfile1.txt

def get_model():
    keras.backend.clear_session()
    model = keras.models.Sequential()
    model.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(268, 650, 1),padding='same'))
    model.add(keras.layers.MaxPooling2D((2, 2),padding='same'))
    model.add(keras.layers.Conv2D(32, (3, 3), activation='relu',padding='same'))
    model.add(keras.layers.MaxPooling2D((2, 2),padding='same'))
    model.add(keras.layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
    model.add(keras.layers.MaxPooling2D((2, 2),padding='same'))
    model.add(keras.layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
    model.add(keras.layers.MaxPooling2D((2, 2),padding='same'))
    model.add(keras.layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
    model.add(keras.layers.MaxPooling2D((2, 2),padding='same'))
    model.add(keras.layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
    model.add(keras.layers.MaxPooling2D((2, 2),padding='same'))
    model.add(keras.layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
    model.add(keras.layers.MaxPooling2D((2, 2),padding='same'))
    model.add(keras.layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
    model.add(keras.layers.MaxPooling2D((2, 2),padding='same'))
    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dense(32, activation='relu'))
    model.add(keras.layers.Dense(1, activation='sigmoid'))
    model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])
    model.summary()
    return model