在Tensorflow 2中使用自定义估算器时，如果模型包含BatchNorm或Dropout层，则在构建图形时tf会失败，并出现以下错误。当我注释掉Dropout和BatchNorm层时，它工作得很好。

我使用的模型是一个简单的cnn模型，其中有两个conv块，最后是密集层：

def build_conv_block(x: Model,filter_map_count: int,name: str):
    x = Conv2D(filter_map_count,(3,3),name=f'{name}_conv_2d')(x)
    x = BatchNormalization(name=f'{name}_bn')(x)               <------- Error when not commented out
    x = ReLU(name=f'{name}_relu')(x)
    x = MaxPool2D((2,2),name=f'{name}_max_pool_2d')(x)
    x = Dropout(0.25,name=f'{name}_dropout')(x)               <------- Error when not commented out
    return x


def get_model(params):
    input_image = Input(shape=params.input_shape)
    x = build_conv_block(input_image,filter_map_count=64,name='layer_1')
    x = build_conv_block(x,filter_map_count=128,name='layer_2')
    x = flatten(name='flatten_conv')(x)
    output_pred = Dense(10,activation='softmax',name='output')(x)

    model = Model(inputs=input_image,outputs=output_pred)
    model.optimizer = Adam(learning_rate=params.learning_rate)
    return model

我在train_op中有一个标准的model_fn，它以简单的图像和标签作为输入，并以类作为输出：

# Calculate gradients
with tf.GradientTape() as tape:
    y_pred = model(features,training=training)
    loss = tf.losses.categorical_crossentropy(labels,y_pred)

if mode == tf.estimator.ModeKeys.TRAIN:
    gradients = tape.gradient(loss,model.trainable_variables)
    train_op = model.optimizer.apply_gradients(zip(gradients,model.trainable_variables))
    return tf.estimator.EstimatorSpec(mode,loss=loss,train_op=train_op)

这是我得到的错误的回溯：

Traceback (most recent call last):
  File "F:/Projects/python/my_project/train.py",line 38,in <module>
    tf.estimator.train_and_evaluate(estimator,train_spec=train_spec,eval_spec=eval_spec)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\training.py",line 473,in train_and_evaluate
    return executor.run()
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\training.py",line 613,in run
    return self.run_local()
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\training.py",line 714,in run_local
    saving_listeners=saving_listeners)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py",line 370,in train
    loss = self._train_model(input_fn,hooks,saving_listeners)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py",line 1160,in _train_model
    return self._train_model_default(input_fn,line 1190,in _train_model_default
    features,labels,ModeKeys.TRAIN,self.config)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py",line 1148,in _call_model_fn
    model_fn_results = self._model_fn(features=features,**kwargs)
  File "F:\Projects\python\my_project\model.py",line 62,in model_fn
    gradients = tape.gradient(loss,model.trainable_variables)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\eager\backprop.py",line 1014,in gradient
    unconnected_gradients=unconnected_gradients)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\eager\imperative_grad.py",line 76,in imperative_grad
    compat.as_str(unconnected_gradients.value))
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\eager\backprop.py",line 138,in _gradient_function
    return grad_fn(mock_op,*out_grads)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\cond_v2.py",line 120,in _IfGrad
    true_graph,grads,util.unique_grad_fn_name(true_graph.name))
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\cond_v2.py",line 395,in _create_grad_func
    func_graph=_CondGradFuncGraph(name,func_graph))
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\framework\func_graph.py",line 915,in func_graph_from_py_func
    func_outputs = python_func(*func_args,**func_kwargs)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\cond_v2.py",line 394,in <lambda>
    lambda: _grad_fn(func_graph,grads),[],{},File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\cond_v2.py",line 373,in _grad_fn
    src_graph=func_graph)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\gradients_util.py",line 550,in _GradientsHelper
    gradient_uid)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\gradients_util.py",line 175,in _DefaultGradYs
    constant_op.constant(1,dtype=y.dtype,name="grad_ys_%d" % i)))
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\framework\constant_op.py",line 227,in constant
    allow_broadcast=True)
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\framework\constant_op.py",line 265,in _constant_impl
    allow_broadcast=allow_broadcast))
  File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\framework\tensor_util.py",line 484,in make_tensor_proto
    (dtype,nparray.dtype,values))
TypeError: Incompatible types: <dtype: 'variant'> vs. int32. Value is 1

它看起来类似于TF Issue #31894中提到的错误，但似乎无法解决此问题。 TypeError并没有告诉您错误发生的位置和原因，直接进行谷歌搜索也无济于事。