我正在尝试修改官方pix2Pix Tensorflow 2.0 tutorial ，以保存生成器的模型，然后能够重新加载它并执行图像生成（推断）
为此，我遵循了'Using the SavedModel format' tutorial中的示例，此笔记本代码执行得很好，没有任何问题。
因此，基本上，我在训练循环的末尾添加了以下代码，用于将生成器的模型保存到磁盘：
tf.saved_model.save(generator, checkpoint_dir + "/pix2pix/1/")
print("\nGenerator model saved in {}/pix2pix/1/".format(checkpoint_dir))
然后，为了重新加载模型并进行推断，我使用以下代码：
def generate_images2(model, test_input, tar, debug=True):
# Key name of the output layer in the model:
key_name = list(model.structured_outputs.keys())[0]
prediction = model(test_input)[key_name]
if debug:
print("Key name of the output layer in the model:", key_name)
print("\nprediction:\n", prediction)
plt.figure(figsize=(15,15))
display_list = [test_input[0], tar[0], prediction[0]]
title = ['Input Image', 'Ground Truth', 'Predicted Image']
for i in range(3):
plt.subplot(1, 3, i+1)
plt.title(title[i])
# getting the pixel values between [0, 1] to plot it.
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
plt.show()
checkpoint_dir = './training_checkpoints'
loaded = tf.saved_model.load(checkpoint_dir + "/pix2pix/1/")
print("Load Model - signature keys:", list(loaded.signatures.keys()))
infer = loaded.signatures["serving_default"]
print("\nInference structure output:", infer.structured_outputs)
# Run the trained model on one exampel of the test dataset
for inp, tar in test_dataset.take(1):
generate_images2(infer, inp, tar, debug=True)
代码运行无错误，但推理步骤（基于重新加载的模型）的输出张量仅包含NaN值。这是推断的输出：
Load Model - signature keys: ['serving_default']
Inference structure output: {'conv2d_transpose_8': TensorSpec(shape=(None, None, None, 3), dtype=tf.float32, name='conv2d_transpose_8')}
Key name of the output layer in the model: conv2d_transpose_8
prediction:
tf.Tensor(
[[[[nan nan nan]
[nan nan nan]
[nan nan nan]
...
[nan nan nan]
[nan nan nan]
[nan nan nan]]
[[nan nan nan]
[nan nan nan]
[nan nan nan]
...
[nan nan nan]
[nan nan nan]
[nan nan nan]]
[[nan nan nan]
[nan nan nan]
[nan nan nan]
...
[nan nan nan]
[nan nan nan]
[nan nan nan]]
...
[[nan nan nan]
[nan nan nan]
[nan nan nan]
...
[nan nan nan]
[nan nan nan]
[nan nan nan]]
[[nan nan nan]
[nan nan nan]
[nan nan nan]
...
[nan nan nan]
[nan nan nan]
[nan nan nan]]
[[nan nan nan]
[nan nan nan]
[nan nan nan]
...
[nan nan nan]
[nan nan nan]
[nan nan nan]]]], shape=(1, 256, 256, 3), dtype=float32)
我是否错过了某些事情，或者Tensorflow 2.0是否存在问题。有任何想法或建议吗？
当前，我会怀疑通过将几个Keras顺序模型（作为构建块）组合在一起来构建模型的方式可能会导致模型保存/恢复功能出现问题。 （类似地，Tensorboard也无法正确显示模型的图形：每个构建块看上去都与其他构建块断开连接）
注意：