每当我在Tensorflow中训练模型时，都会遇到“ ValueError：形状（无，无）和（无，8、8、7）不兼容”的问题。至今：
history = model.fit(train_batches,
steps_per_epoch=train_steps,
class_weight=class_weights,
validation_data=validation_batches,
validation_steps=val_steps,
epochs=30,
verbose=1,
callbacks=callbacks_list
)
给出以下堆栈跟踪：
Traceback (most recent call last):
File "/home/brian/Desktop/381-deep-learning/main.py", line 410, in <module>
epochs=30
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 324, in new_func
return func(*args, **kwargs)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1479, in fit_generator
initial_epoch=initial_epoch)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 66, in _method_wrapper
return method(self, *args, **kwargs)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 848, in fit
tmp_logs = train_function(iterator)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 580, in __call__
result = self._call(*args, **kwds)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 506, in _initialize
*args, **kwds))
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 2446, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 2667, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py", line 981, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:571 train_function *
outputs = self.distribute_strategy.run(
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:533 train_step **
y, y_pred, sample_weight, regularization_losses=self.losses)
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/losses.py:143 __call__
losses = self.call(y_true, y_pred)
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/losses.py:246 call
return self.fn(y_true, y_pred, **self._fn_kwargs)
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/home/brian/Desktop/381-deep-learning/venv/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, None) and (None, 8, 8, 7) are incompatible
Process finished with exit code 1
到达纪元1/30之后。
如果有人想知道，这是我的模型定义：
# Create Inception Res Net model as used in paper
resnet = tf.keras.applications.inception_resnet_v2.InceptionResNetV2()
print("Layers of ResNet: "+str(len(resnet.layers))) //782 layers
x = resnet.layers[-28].output
x = tf.keras.layers.Dropout(0.25)(x)
# Make a prediction layer with 7 nodes for the 7 dir in our train_dir.
predictions_layer = tf.keras.layers.Dense(7, activation='softmax')(x)
# print(resnet.input)
# inputs=resnet.input selects the input layer, outputs=predictions refers to the
# dense layer we created above.
model = tf.keras.Model(inputs=resnet.input, outputs=predictions_layer)
我认为可能是我的问题的原因是我的模型声明，因为当我观察我的model.summary（）时，我看到的是这个（当然，所有层之间都排除在外）：
model.summary（）的输出
input_1 (InputLayer) [(None, 299, 299, 3) 0
__________________________________________________________________________________________________
dropout (Dropout) (None, 8, 8, 192) 0 batch_normalization_195[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 8, 8, 7) 1351 dropout[0][0]
==================================================================================================
Total params: 47,465,959
Trainable params: 47,411,559
Non-trainable params: 54,400
I included a pastebin of my entire file in case I missed anything: https://pastebin.com/raw/E0VQ83JQ
我知道这是期望的类型（无，无），并且我的输出层被发送到形状的密集层（无，8、8、7），但是我将如何进行重塑？
感谢您提供任何帮助，包括您认为我认为对该主题有用的文档。
