我正在尝试使用Keras中的Functional API创建具有以下结构的多输入模型：
有三个输入：Team_1_In，Team_2_In，Home_In。其中Team_1_In和Team_2_In穿过Embedding层，然后是BatchNormalization和Flatten层。问题是当我尝试在Flatten之后添加BatchNormalization层时出现此错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last) <ipython-input-46-8354b255cfd1> in <module>
15 batch_normalization_2 = BatchNormalization()(team_2_strength)
16
---> 17 flatten_1 = Flatten()(batch_normalization_1)
18 flatten_2 = Flatten()(batch_normalization_2)
19
~/conda/lib/python3.6/site-packages/keras/engine/topology.py in
__call__(self, inputs, **kwargs)
573 # Raise exceptions in case the input is not compatible
574 # with the input_spec specified in the layer constructor.
--> 575 self.assert_input_compatibility(inputs)
576
577 # Collect input shapes to build layer.
~/conda/lib/python3.6/site-packages/keras/engine/topology.py in assert_input_compatibility(self, inputs)
488 self.name + ': expected min_ndim=' +
489 str(spec.min_ndim) + ', found ndim=' +
--> 490 str(K.ndim(x)))
491 # Check dtype.
492 if spec.dtype is not None:
ValueError: Input 0 is incompatible with layer flatten_10: expected min_ndim=3, found ndim=2
我尝试使用BatchNormalization层的轴参数，但没有帮助。这是我的代码：
# create embedding layer
from keras.layers import Embedding
from keras.layers import BatchNormalization, Flatten, Dense
from numpy import unique
# Create an embedding layer
team_lookup = Embedding(input_dim=n_teams,
output_dim=1,
input_length=1,
name='Team-Strength')
# create model with embedding layer
from keras.layers import Input, Embedding, Flatten
from keras.models import Model
# Create an input layer for the team ID
teamid_in = Input(shape=(1,))
# Lookup the input in the team strength embedding layer
strength_lookup = team_lookup(teamid_in)
# Flatten the output
strength_lookup_flat = Flatten()(strength_lookup)
# Combine the operations into a single, re-usable model
team_strength_model = Model(teamid_in, strength_lookup_flat, name='Team-Strength-Model')
# Create an Input for each team
team_in_1 = Input(shape=(1,), name='Team-1-In')
team_in_2 = Input(shape=(1,), name='Team-2-In')
# Create an input for home vs away
home_in = Input(shape=(1,), name='Home-In')
# Lookup the team inputs in the team strength model
team_1_strength = team_strength_model(team_in_1)
team_2_strength = team_strength_model(team_in_2)
batch_normalization_1 = BatchNormalization()(team_1_strength)
batch_normalization_2 = BatchNormalization()(team_2_strength)
flatten_1 = Flatten()(batch_normalization_1)
flatten_2 = Flatten()(batch_normalization_2)
# Combine the team strengths with the home input using a Concatenate layer, then add a Dense layer
out = Concatenate()([flatten_1, flatten_2, home_in])
out = Dense(1)(out)
python大神给出的解决方案
如错误所示，Flatten层需要3D张量：
ValueError: Input 0 is incompatible with layer flatten_10: expected min_ndim=3, found ndim=2
在代码的第一部分中，您将输入传递给了嵌入层，一切都很好，并且编译成功：
team_lookup = Embedding(input_dim=1,
output_dim=1,
input_length=1,
name='Team-Strength')
strength_lookup = team_lookup(teamid_in)
batch_normalization_1 = BatchNormalization()(strength_lookup)
strength_lookup_flat = Flatten()(batch_normalization_1)
team_strength_model = Model(teamid_in, strength_lookup_flat, name='Team-Strength-Model')
team_strength_model.compile(optimizer='adam', loss='categorical_crossentropy')
但是在第二部分代码中，您将输入传递给team_strength_model，从而拉平了将其形状转换为(batch, flatten)的张量。当您将此2D张量传递给BatchNormalization时，它将引发此类异常。
要解决此问题：
1）将输入传递到Embedding层
2）做BatchNormalization
3）展平BatchNormalization的输出
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