Question

我的数据集是音频数据，我尝试训练一个cycleGAN模型来练习样式转换。数据格式为.npy。但是当我启动代码时。我收到了ValueError：

模型的输出张量必须是TensorFlow Layer的输出（因此保存过去的图层元数据）。找到：Tensor（“ Reshape：0”， shape =（?, 2），dtype = float32）。

代码：

def build_discriminator_classifier(options, name='Discriminator_Classifier'):

    initializer = tf.random_normal_initializer(0., 0.02)

    inputs = Input(shape=(options.time_step,
                          options.pitch_range,
                          options.output_nc))

    x = inputs
    # (batch * 64, 84, 1)

    x = layers.Conv2D(filters=options.df_dim,
                      kernel_size=[1, 12],
                      strides=[1, 12],
                      padding='same',
                      kernel_initializer=initializer,
                      use_bias=False,
                      name='CONV2D_1')(x)
    x = layers.LeakyReLU(alpha=0.2)(x)
    # (batch * 64 * 7 * 64)

    x = layers.Conv2D(filters=options.df_dim * 2,
                      kernel_size=[4, 1],
                      strides=[4, 1],
                      padding='same',
                      kernel_initializer=initializer,
                      use_bias=False,
                      name='CONV2D_2')(x)
    x = layers.Lambda(instance_norm,
                      name='IN_1')(x)
    x = layers.LeakyReLU(alpha=0.2)(x)
    # (batch * 16 * 7 * 128)

    x = layers.Conv2D(filters=options.df_dim * 4,
                      kernel_size=[2, 1],
                      strides=[2, 1],
                      padding='same',
                      kernel_initializer=initializer,
                      use_bias=False,
                      name='CONV2D_3')(x)
    x = layers.Lambda(instance_norm,
                      name='IN_2')(x)
    x = layers.LeakyReLU(alpha=0.2)(x)
    # (batch * 8 * 7 * 256)

    x = layers.Conv2D(filters=options.df_dim * 8,
                      kernel_size=[8, 1],
                      strides=[8, 1],
                      padding='same',
                      kernel_initializer=initializer,
                      use_bias=False,
                      name='CONV2D_4')(x)
    x = layers.Lambda(instance_norm,
                      name='IN_3')(x)
    x = layers.LeakyReLU(alpha=0.2)(x)
    # (batch * 1 * 7 * 512)

    x = layers.Conv2D(filters=2,
                      kernel_size=[1, 7],
                      strides=[1, 7],
                      padding='same',
                      kernel_initializer=initializer,
                      use_bias=False,
                      name='CONV2D_5')(x)
    # (batch * 1 * 1 * 2)

    x = tf.reshape(x, [-1, 2])
    # (batch * 2)

    outputs = x

    return Model(inputs=inputs,
                 outputs=outputs,
                 name=name)

cyclegan ValueError：模型的输出张量必须是TensorFlow`Layer`的输出

0 个答案: