【发布时间】：2020-09-16 15:21:50
【问题描述】：
我尝试构建一个具有单个输入 X（大小为 Xa*Xb 的二维矩阵）和 2 个输出 Y1 和 Y2（均为一维）的网络。尽管我在下面发布的代码中不是这种情况，但 Y1 应该是输出单热向量的分类器，而 Y2 应该用于回归（原始代码引发了相同的错误）。
训练网络时出现以下错误：
ValueError: Shapes (None, None) and (None, 17, 29) are incompatible
显然，(None, 17, 29) 转换为 (None, size_Xa, size_Y1)，我不明白为什么 Xa 和 Y1 首先应该相关（独立于 Xb）。
这是我的代码。为了更容易理解，我尝试将其减少到最低限度。
import numpy as np
from keras.layers import Dense, LSTM, Input
from keras.models import Model
def dataGenerator():
while True:
yield makeBatch()
def makeBatch():
"""generates a batch of artificial training data"""
x_batch, y_batch = [], {}
x_batch = np.random.rand(batch_size, size_Xa, size_Xb)
#x_batch = np.random.rand(batch_size, size_Xa)
y_batch['output1'] = np.random.rand(batch_size, size_Y1)
y_batch['output2'] = np.random.rand(batch_size, size_Y2)
return x_batch, y_batch
def generate_model():
input_layer = Input(shape=(size_Xa, size_Xb))
#input_layer = Input(shape=(size_Xa))
common_branch = Dense(128, activation='relu')(input_layer)
branch_1 = Dense(size_Y1, activation='softmax', name='output1')(common_branch)
branch_2 = Dense(size_Y2, activation='relu', name='output2')(common_branch)
model = Model(inputs=input_layer,outputs=[branch_1,branch_2])
losses = {"output1":"categorical_crossentropy", "output2":"mean_absolute_error"}
model.compile(optimizer="adam",
loss=losses,
metrics=['accuracy'])
return model
batch_size=5
size_Xa = 17
size_Xb = 13
size_Y2 = 100
size_Y1 = 29
model = generate_model()
model.fit( x=dataGenerator(),
steps_per_epoch=50,
epochs=15,
validation_data=dataGenerator(), validation_steps=50, verbose=1)
如果我取消注释 makeBatch 和 generate_model 中的 2 条注释行，错误就会消失。因此，如果输入 X 是 1 维，它会运行，但是当我将其更改为 2 维（保持其他所有内容相同）时，就会出现错误。
这与具有 2 个输出的架构有关吗？我认为我在这里缺少一些东西，欢迎任何帮助。
我添加完整的错误日志以供参考：
Epoch 1/15
Traceback (most recent call last):
File "neuralnet_minimal.py", line 41, in <module>
model.fit( x=dataGenerator(),
File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 66, in _method_wrapper
return method(self, *args, **kwargs)
File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 848, in fit
tmp_logs = train_function(iterator)
File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 580, in __call__
result = self._call(*args, **kwds)
File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 505, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "/path/of/my/project/venv/lib/python3.8/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 "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2657, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "/path/of/my/project/venv/lib/python3.8/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 "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:571 train_function *
outputs = self.distribute_strategy.run(
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:532 train_step **
loss = self.compiled_loss(
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:143 __call__
losses = self.call(y_true, y_pred)
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:246 call
return self.fn(y_true, y_pred, **self._fn_kwargs)
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/path/of/my/project/venv/lib/python3.8/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, 17, 29) are incompatible
【问题讨论】：
标签： python python-3.x tensorflow keras