如何在带有Keras的Tensorflow 2.0中保存/恢复大型模型？

Question

我有一个使用新的tensorflow 2.0并混合keras和tensorflow制作的大型自定义模型。我要保存它（体系结构和权重）。精确的命令重现：

import tensorflow as tf


OUTPUT_CHANNELS = 3

def downsample(filters, size, apply_batchnorm=True):
  initializer = tf.random_normal_initializer(0., 0.02)

  result = tf.keras.Sequential()
  result.add(
      tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',
                             kernel_initializer=initializer, use_bias=False))

  if apply_batchnorm:
    result.add(tf.keras.layers.BatchNormalization())

  result.add(tf.keras.layers.LeakyReLU())

  return result

def upsample(filters, size, apply_dropout=False):
  initializer = tf.random_normal_initializer(0., 0.02)

  result = tf.keras.Sequential()
  result.add(
    tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
                                    padding='same',
                                    kernel_initializer=initializer,
                                    use_bias=False))

  result.add(tf.keras.layers.BatchNormalization())

  if apply_dropout:
      result.add(tf.keras.layers.Dropout(0.5))

  result.add(tf.keras.layers.ReLU())

  return result


def Generator():
  down_stack = [
    downsample(64, 4, apply_batchnorm=False), # (bs, 128, 128, 64)
    downsample(128, 4), # (bs, 64, 64, 128)
    downsample(256, 4), # (bs, 32, 32, 256)
    downsample(512, 4), # (bs, 16, 16, 512)
    downsample(512, 4), # (bs, 8, 8, 512)
    downsample(512, 4), # (bs, 4, 4, 512)
    downsample(512, 4), # (bs, 2, 2, 512)
    downsample(512, 4), # (bs, 1, 1, 512)
  ]

  up_stack = [
    upsample(512, 4, apply_dropout=True), # (bs, 2, 2, 1024)
    upsample(512, 4, apply_dropout=True), # (bs, 4, 4, 1024)
    upsample(512, 4, apply_dropout=True), # (bs, 8, 8, 1024)
    upsample(512, 4), # (bs, 16, 16, 1024)
    upsample(256, 4), # (bs, 32, 32, 512)
    upsample(128, 4), # (bs, 64, 64, 256)
    upsample(64, 4), # (bs, 128, 128, 128)
  ]

  initializer = tf.random_normal_initializer(0., 0.02)
  last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,
                                         strides=2,
                                         padding='same',
                                         kernel_initializer=initializer,
                                         activation='tanh') # (bs, 256, 256, 3)

  concat = tf.keras.layers.Concatenate()

  inputs = tf.keras.layers.Input(shape=[None,None,3])
  x = inputs

  # Downsampling through the model
  skips = []
  for down in down_stack:
    x = down(x)
    skips.append(x)

  skips = reversed(skips[:-1])

  # Upsampling and establishing the skip connections
  for up, skip in zip(up_stack, skips):
    x = up(x)
    x = concat([x, skip])

  x = last(x)

  return tf.keras.Model(inputs=inputs, outputs=x)

generator = Generator()
generator.summary()

generator.save('generator.h5')
generator_loaded = tf.keras.models.load_model('generator.h5')

我设法用以下方式保存模型：

generator.save('generator.h5')

但是当我尝试加载它时：

generator_loaded = tf.keras.models.load_model('generator.h5')

它永远不会结束（无错误消息）。型号是否太大？我尝试将JSON和model.to_json()完整的API 另存为JSON tf.keras.models.save_model()，但存在相同的问题，无法加载（或至少太长）。

在Windows / Linux和带有/不带有GPU的同一个问题。

使用完整的Keras和简单的模型，保存和恢复效果很好。

编辑

• 节省权重然后加载它们效果很好，但是无法加载模型结构。
• 我放了用来重现该错误的模型，它来自Pix2Pix示例（https://www.tensorflow.org/alpha/tutorials/generative/pix2pix）
• 我还在tensorflow github上写了一个问题：https : //github.com/tensorflow/tensorflow/issues/28281

Answer 1

尝试将模型另存为：

model.save('model_name.model')

然后加载它：

model = tf.keras.models.load_model('model_name.model')