我正在尝试建立一个暹罗网络模型。整个模型有两个输入。这两个输入将传递到两个孪生网络（换句话说，共享网络）中。它们的输出将在距离层中进行比较，然后连接到输出层。
对于我如何将输入层传递到共享网络，该错误似乎并不满意。这是构建模型的代码
def get_siamese_model(input_shape):
"""
Model architecture based on the one provided in: http://www.cs.utoronto.ca/~gkoch/files/msc-thesis.pdf
"""
#2 inputs
left_input = Input(input_shape)
right_input = Input(input_shape)
#currently not convolutional NN
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=input_shape),
tf.keras.layers.Dense(512, activation = tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation = tf.nn.softmax)
])
#encoding layer
encoded_l = model(left_input)
encoded_r = model(right_input)
#distance layer
L1_layer = Lambda(lambda tensors: K.abs(tensors[0]- tensors[1]))
L1_distance = L1_layer([encoded_l,encoded_r])
#prediction layer
prediction = Dense(1, activation = 'sigmoid', bias_initializer = initialize_bias)(L1_distance)
#connect the inputs with the outputs
siamese_net = Model(input=[left_input,right_input], outputs = prediction)
return siamese_net
model = get_siamese_model((28, 28,1))
model.summary()
这是一堆完整的错误：
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-43-655eb0c236b9> in <module>()
----> 1 model = get_siamese_model((28, 28,1))
2 model.summary()
10 frames
<ipython-input-42-af8702cd4619> in get_siamese_model(input_shape)
33 # kernel_initializer=initialize_weights,bias_initializer=initialize_bias))
34 #encoding layer
---> 35 encoded_l = model(left_input)
36 encoded_r = model(right_input)
37
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
632 outputs = base_layer_utils.mark_as_return(outputs, acd)
633 else:
--> 634 outputs = call_fn(inputs, *args, **kwargs)
635
636 except TypeError as e:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py in call(self, inputs, training, mask)
245 if not self.built:
246 self._init_graph_network(self.inputs, self.outputs, name=self.name)
--> 247 return super(Sequential, self).call(inputs, training=training, mask=mask)
248
249 outputs = inputs # handle the corner case where self.layers is empty
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in call(self, inputs, training, mask)
749 ' implement a `call` method.')
750
--> 751 return self._run_internal_graph(inputs, training=training, mask=mask)
752
753 def compute_output_shape(self, input_shape):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in _run_internal_graph(self, inputs, training, mask)
891
892 # Compute outputs.
--> 893 output_tensors = layer(computed_tensors, **kwargs)
894
895 # Update tensor_dict.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
661 kwargs.pop('training')
662 inputs, outputs = self._set_connectivity_metadata_(
--> 663 inputs, outputs, args, kwargs)
664 self._handle_activity_regularization(inputs, outputs)
665 self._set_mask_metadata(inputs, outputs, previous_mask)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _set_connectivity_metadata_(self, inputs, outputs, args, kwargs)
1706 kwargs.pop('mask', None) # `mask` should not be serialized.
1707 self._add_inbound_node(
-> 1708 input_tensors=inputs, output_tensors=outputs, arguments=kwargs)
1709 return inputs, outputs
1710
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _add_inbound_node(self, input_tensors, output_tensors, arguments)
1793 """
1794 inbound_layers = nest.map_structure(lambda t: t._keras_history.layer,
-> 1795 input_tensors)
1796 node_indices = nest.map_structure(lambda t: t._keras_history.node_index,
1797 input_tensors)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py in map_structure(func, *structure, **kwargs)
513
514 return pack_sequence_as(
--> 515 structure[0], [func(*x) for x in entries],
516 expand_composites=expand_composites)
517
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py in <listcomp>(.0)
513
514 return pack_sequence_as(
--> 515 structure[0], [func(*x) for x in entries],
516 expand_composites=expand_composites)
517
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in <lambda>(t)
1792 `call` method of the layer at the call that created the node.
1793 """
-> 1794 inbound_layers = nest.map_structure(lambda t: t._keras_history.layer,
1795 input_tensors)
1796 node_indices = nest.map_structure(lambda t: t._keras_history.node_index,
AttributeError: 'tuple' object has no attribute 'layer'
一篇可能与我有关的话题可能需要降低我的张量流。我没有尝试过，因为我想使用最新版本。我想知道在构建模型时是否做错了什么。