tensorflow2.0 - TensorFlow 2.0 : Save and load a model that contains a LSTM layer,，而负载通用失败并出现 ValueError

Question

当我尝试保存和加载包含 LSTM 层的模型时，加载通用失败 ValueError: 找不到匹配的函数来调用从 SavedModel 加载 .

class RegNet(Model):
    def __init__(self,
             intermediate_dim=50,
             state_dim=9,
             name='RegNet',
             **kwargs):
        super(RegNet, self).__init__()
        self.d1 = Dense(intermediate_dim, activation='relu')
        self.d2 = Dense(state_dim, activation='relu')
        self.h = LSTM(state_dim, activation='sigmoid', return_sequences=True)
        self.o = Dense(state_dim, activation='softmax')

    def call(self, x):
        x = self.d1(x)
        x = self.d2(x)
        x = self.h(x)
        y = self.o(x)
        return y

regNet = RegNet()
...
# Export the model to a SavedModel
regNet.save(regNet_ckpt_dir, save_format='tf')
# Recreate the exact same model
tf.keras.models.load_model(regNet_ckpt_dir)

错误报告:

> ValueError: Could not find matching function to call loaded from the SavedModel. Got:
  Positional arguments (2 total):
    * Tensor("x:0", shape=(None, 1, 20), dtype=float32)
    * Tensor("training:0", shape=(), dtype=bool)
  Keyword arguments: {}

Expected these arguments to match one of the following 4 option(s):

Option 1:
  Positional arguments (2 total):
    * TensorSpec(shape=(None, 1, 20), dtype=tf.float32, name='input_1')
    * False
  Keyword arguments: {}

Option 2:
  Positional arguments (2 total):
    * TensorSpec(shape=(None, 1, 20), dtype=tf.float32, name='x')
    * False
  Keyword arguments: {}

Option 3:
  Positional arguments (2 total):
    * TensorSpec(shape=(None, 1, 20), dtype=tf.float32, name='x')
    * True
  Keyword arguments: {}

Option 4:
  Positional arguments (2 total):
    * TensorSpec(shape=(None, 1, 20), dtype=tf.float32, name='input_1')
    * True
  Keyword arguments: {}

当我评论 LSTM 层时，加载命令将成功。问题出在哪儿？我们无法在 TensorFlow 2.0 中保存和加载带有 LSTM 层的模型？

Answer 1

万一其他人偶然发现这个，这个解决方案对我有用:

# Save model
tf.keras.models.save_model(model, "saved_model.hp5", save_format="h5")

# Load model
loaded_model = tf.keras.models.load_model("saved_model.hp5")

不确定为什么“model.save(filename)”语法不适用于 LSTM，但我遇到了同样的问题。