def create_cnn():
    model = Sequential()

    model.add(Convolution2D(nb_filter=10, nb_row=2, nb_col=2,
                            input_shape=(1, 64, 64)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(3, 3)))

    model.add(Convolution2D(nb_filter=10, nb_row=2, nb_col=2,
                            input_shape=(10, 21, 21)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Convolution2D(nb_filter=10, nb_row=2, nb_col=2,
                            input_shape=(10, 10, 10)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(3, 3)))

    model.add(Convolution2D(nb_filter=3, nb_row=2, nb_col=2,
                            input_shape=(10, 3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    # The model needs to be compiled before it can be used for prediction
    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy', optimizer=sgd)

    return model
Create a convolutional neural network for compressing the input images.
dataset/ETHPy150Open cosmoharrigan/neuroevolution/neuroevolution/cnn.py/create_cnn