编辑：我通过如下重塑我的数据来解决该错误消息：
train_x = np.array(train_x)
train_y = np.array(train_y)
x_size = train_x.shape[0] * train_x.shape[1]
train_x = train_x.reshape(x_size, train_x.shape[2])
train_x = np.expand_dims(train_x, 1)
train_x = train_x.transpose(0,2,1)
train_y = train_y.flatten()
shape = train_x.shape # 3D: number of texts * number of padded paragraphs, number of features, 1
time_steps = shape[0] # number of padded pars * number of texts
features = shape[1] # number of features
model = Sequential()
model.add(layers.Masking(mask_value=0, input_shape=(time_steps, features)))
model.add(layers.LSTM(128, return_sequences=True, return_state=False, input_shape=(time_steps, features))) # 128 internal units
model.add(layers.TimeDistributed(layers.Dense(1, activation='sigmoid')))
#model.add(layers.Dense(len(train_y))) # Dense layer
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(train_x, train_y, batch_size=train_y.shape[0])
predictions = model.predict(test_x)
我收到一条新的错误消息：
ValueError: Input 0 is incompatible with layer lstm: expected shape=(None, None, 3), found shape=[288, 3, 1]
如果有人遇到类似问题，我会不断更新这个问题。仍然对任何输入感到高兴。
原始问题：我想建立一个顺序 LSTM 模型，在每个时间步预测二元分类。更准确地说，我想预测文本中每个段落的输出（48 是段落数）。这是我的代码：
shape = np.shape(train_x) # 3D: number of texts, number of padded paragraphs, number of features
n = shape[0] # number of texts
time_steps = shape[1] # number of padded pars
features = shape[2] # number of features
model = Sequential()
model.add(layers.Masking(mask_value=0.0, input_shape=(time_steps, features)))
model.add(layers.LSTM(128, return_sequences=True, return_state=False))
model.add(layers.TimeDistributed(layers.Dense(1)))
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.summary()
#train_x = np.array(train_x).reshape(2, input_shape, 3)
train_x = tf.convert_to_tensor(train_x) # data needs to be tensor object
train_y = tf.convert_to_tensor(train_y)
model.fit(train_x, train_y, batch_size=2)
predictions = model.predict(test_x)
这是我收到的错误消息：
ValueError: Can not squeeze dim[1], expected a dimension of 1,
got 48 for '{{node categorical_crossentropy/weighted_loss/Squeeze}} = Squeeze[T=DT_FLOAT,
squeeze_dims=[-1]](Cast)' with input shapes: [2,48].
我真的不知道该怎么办，我需要重塑我的数据吗？如何？还是我需要更改模型中的某些内容？谢谢！（将损失函数更改为'binary_crossentropy'会引发相同的错误）
这是整个回溯：
Traceback (most recent call last):
File "program.py", line 247, in <module>
eval_scores = train_classifier(x_train, y_train_sc, x_test, y_test_sc)
File "program.py", line 201, in train_classifier
model.fit(train_x, train_y, batch_size=2)
File "C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py", line 108, in _method_wrapper
return method(self, *args, **kwargs)
File "C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1098, in fit
tmp_logs = train_function(iterator)
File "C:\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 780, in __call__
result = self._call(*args, **kwds)
File "C:\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 823, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "C:\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 696, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "C:\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 2855, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "C:\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 3213, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 3065, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "C:\Python38\lib\site-packages\tensorflow\python\framework\func_graph.py", line 986, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 600, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\Python38\lib\site-packages\tensorflow\python\framework\func_graph.py", line 973, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self, iterator)
C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:748 train_step
loss = self.compiled_loss(
C:\Python38\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:204 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
C:\Python38\lib\site-packages\tensorflow\python\keras\losses.py:150 __call__
return losses_utils.compute_weighted_loss(
C:\Python38\lib\site-packages\tensorflow\python\keras\utils\losses_utils.py:111 compute_weighted_loss
weighted_losses = tf_losses_utils.scale_losses_by_sample_weight(
C:\Python38\lib\site-packages\tensorflow\python\ops\losses\util.py:142 scale_losses_by_sample_weight
losses, _, sample_weight = squeeze_or_expand_dimensions(
C:\Python38\lib\site-packages\tensorflow\python\ops\losses\util.py:95 squeeze_or_expand_dimensions
sample_weight = array_ops.squeeze(sample_weight, [-1])
C:\Python38\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\Python38\lib\site-packages\tensorflow\python\util\deprecation.py:507 new_func
return func(*args, **kwargs)
C:\Python38\lib\site-packages\tensorflow\python\ops\array_ops.py:4259 squeeze
return gen_array_ops.squeeze(input, axis, name)
C:\Python38\lib\site-packages\tensorflow\python\ops\gen_array_ops.py:10043 squeeze
_, _, _op, _outputs = _op_def_library._apply_op_helper(
C:\Python38\lib\site-packages\tensorflow\python\framework\op_def_library.py:742 _apply_op_helper
op = g._create_op_internal(op_type_name, inputs, dtypes=None,
C:\Python38\lib\site-packages\tensorflow\python\framework\func_graph.py:591 _create_op_internal
return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
C:\Python38\lib\site-packages\tensorflow\python\framework\ops.py:3477 _create_op_internal
ret = Operation(
C:\Python38\lib\site-packages\tensorflow\python\framework\ops.py:1974 __init__
self._c_op = _create_c_op(self._graph, node_def, inputs,
C:\Python38\lib\site-packages\tensorflow\python\framework\ops.py:1815 _create_c_op
raise ValueError(str(e))
ValueError: Can not squeeze dim[1], expected a dimension of 1, got 48 for '{{node categorical_crossentropy/weighted_loss/Squeeze}} = Squeeze[T=DT_FLOAT, squeeze_dims=[-1]](Cast)' with input shapes: [2,48].