我正在尝试使用TensorFlow 2.9.2训练一个模型。我的模型定义如下：
import tensorflow as tf
encoder_layers = 1
encoder_bidirectional = False
def get_model():    
  model = tf.keras.Sequential(name='model')
  model.add(tf.keras.layers.Dropout(0.5))
  for _ in range(encoder_layers):
    rnn = tf.keras.layers.LSTM(2**6, return_sequences=True)
    if encoder_bidirectional:
      rnn = tf.keras.layers.Bidirectional(rnn)
    model.add(rnn)
  model.add(tf.keras.layers.Dense(2, activation='softmax'))
  return model
def build_model():
  model = get_model()
  model.build(input_shape=(None, None, 25))
  model.compile(
      loss='sparse_categorical_crossentropy',
      optimizer=tf.keras.optimizers.Adam(0.001),
      metrics=['accuracy']
  )
  model.summary()
  return model
然后，我使用以下方式训练模型：
# 训练模型
train, dev, test = get_datasets()
model = build_model()
es = EarlyStopping(
      monitor='val_accuracy',
      mode='max',
      verbose=1,
      patience=10)
mc = ModelCheckpoint(
      'model.h5',
      monitor='val_accuracy',
      mode='max',
      verbose=1,
      save_best_only=True)
with tf.device("/GPU:0"):
  model.fit(
      train,
      epochs=500,
      steps_per_epoch=32,
      validation_data=dev,
      callbacks=[es, mc])
best_model = load_model('model.h5')
best_model.evaluate(test)
在`best_model = load_model('model.h5')`处，我收到以下错误：
Traceback (most recent call last):
  File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/experiments/train.py", line 76, in <module>
    app.run(main)
  File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 308, in run
    _run_main(main, args)
  File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 254, in _run_main
    sys.exit(main(argv))
  File "/experiments/train.py", line 70, in main
    best_model = load_model(FLAGS.model_path)
  File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/usr/local/lib/python3.8/dist-packages/keras/initializers/initializers_v2.py", line 1056, in _compute_fans
    return int(fan_in), int(fan_out)
TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
在找到此帖子后，我检查了我的`model.h5`文件，实际上它具有`batch_input_shape=[null,null,null]`。但是如何防止我的检查点模型保存具有输入形状的null值？是否有任何方法可以解决这个问题？
EDIT:
我刚刚在这个Colab中使用我的一些数据示例复制了这个错误：https://colab.research.google.com/drive/1z63TN-P_WKtTWTZs2IhGBU0NjD7TE6m_#scrollTo=f1oD4G6QEq4k。

在您的模型代码中，在开头添加以下层次。
```python
def get_model():    
  model = tf.keras.Sequential(name='model')
  model.add(tf.keras.layers.InputLayer(input_shape=(None, 25)))
  ...
best_model = keras.models.load_model('/content/model.h5') # OK
best_model.evaluate(test)

请注意，代码部分没有进行翻译。
<details>
<summary>英文:</summary>
In your model code, add the following layers at the beginning. 
```python
def get_model():    
model = tf.keras.Sequential(name=&#39;model&#39;)
model.add(tf.keras.layers.InputLayer(input_shape=(None, 25)))
...
best_model = keras.models.load_model(&#39;/content/model.h5&#39;) # OK
best_model.evaluate(test)