cnn_model.py

def cnn_model(n_ch):
    '''Assuming cifar-10 '''
    input_shape = (3, 32, 32)
    model = Sequential()
    for i in range(N_LAYER):
        if i == 0:
            model.add(Convolution2D(n_ch, 3, 3, border_mode='same',
                                    input_shape=input_shape,
                                    name='conv%d' % i))
        else:
            model.add(Convolution2D(n_ch, 3, 3, border_mode='same', 
                                    name='conv%d' % i))
        model.add(Activation('elu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(N_CLASS))
    model.add(Activation('softmax'))
    return model