def get_dfn_relu(output_size, img_height, img_width, show=True):
    model_input = Input(shape=((img_height * img_width),), name='Main_input')
    x = BatchNormalization(name='BN_1')(model_input)
    x = Dense(256, activation='relu', name='Dense_relu_1')(x)
    x = Dense(256, activation='relu', name='Dense_relu_2')(x)
    x = Dense(256, activation='relu', name='Dense_relu_3')(x)
    dfn_output = Dense(output_size, activation='linear', name='Output_Dense_linear')(x)
    dfn = Model(inputs=model_input, outputs=dfn_output, name='DFN_relu')
    if show:
        print('DFN_relu summary:')
        dfn.summary()
        print()
    return dfn
def get_dfn_relu(output_size, img_height, img_width, show=True):
    model_input = Input(shape=((img_height * img_width),), name='Main_input')
    x = BatchNormalization(name='BN_1')(model_input)
    x = Dense(256, activation='relu', name='Dense_relu_1')(x)
    x = Dense(256, activation='relu', name='Dense_relu_2')(x)
    x = Dense(256, activation='relu', name='Dense_relu_3')(x)
    dfn_output = Dense(output_size, activation='linear', name='Output_Dense_linear')(x)
    dfn = Model(inputs=model_input, outputs=dfn_output, name='DFN_relu')
    if show:
        print('DFN_relu summary:')
        dfn.summary()
        print()
    return dfn