当我使用keras.models.Model（）创建模型时，出现以下错误：
AttributeError：“张量”对象没有属性“ _keras_history”
我在模型中创建了3个MLP，并且意图是形状为（6040,100）的张量。
代码和完整的回溯如下：
def get_model(num_users,num_items,layers_1=[10],
layers_2=[10],layers_3=[10],reg_layers_1=[0],
reg_layers_2=[0],reg_layers_3=[0]):
assert len(layers_1) == len(reg_layers_1)
assert len(layers_2) == len(reg_layers_2)
assert len(layers_3) == len(reg_layers_3)
num_layer_1 = len(layers_1)
num_layer_2 = len(layers_2)
num_layer_3 = len(layers_3)
intents = scaled_dotproduct_attention(user_bought_sorted[0],100)
for i in range(1,len(user_bought_sorted)):
temp = scaled_dotproduct_attention(user_bought_sorted[i],100)
intents = concatenate([intents,temp],0)
#intents = tf.reshape(intents,[-1,100])
user_input = Input(shape=(1,),dtype='int32',name='user_input')
item_input = Input(shape=(1,),dtype='int32',name='item_input')
mlp_embedding_user = Embedding(input_dim=num_users,output_dim=100,name='mlp_embedding_user',
embeddings_initializer=initializers.random_normal(),
embeddings_regularizer=l2(reg_layers_1[0]),input_length=1)
mlp_embedding_item = Embedding(input_dim=num_items,output_dim=100,name='mlp_embedding_items',
embeddings_initializer=initializers.random_normal(),
embeddings_regularizer=l2(reg_layers_1[0]),input_length=1)
attention_embedding_item = Embedding(input_dim=num_items,output_dim=100,name='attention_embedding_items',
embeddings_initializer=initializers.random_normal(),
embeddings_regularizer=l2(reg_layers_2[0]),input_length=1)
#attention_embedding_intent = Embedding(input_dim=num_users,output_dim=100,name='attention_embedding_intent',
# embeddings_initializer=initializers.random_normal(),
# embeddings_regularizer=l2(reg_layers_2[0]),input_length=1)
#MLP_1
mlp_user_latent = Flatten()(mlp_embedding_user(user_input))
mlp_item_latent = Flatten()(mlp_embedding_item(item_input))
mlp_vector = concatenate([mlp_user_latent,mlp_item_latent])
for idx in range(1,num_layer_1):
layer_1 = Dense(layers_1[idx],kernel_regularizer=l2(reg_layers_1[idx]),
activation='relu',name='layer_1%d' % idx)
mlp_vector = layer_1(mlp_vector)
#MLP_attention
attention_item_latent = Flatten()(attention_embedding_item(item_input))
#attention_intent_latent = Reshape((100,))(intents)
attention_vector = K.dot(attention_item_latent,K.transpose(intents))
for adx in range(1,num_layer_2):
layer_2 = Dense(layers_2[adx],kernel_regularizer=l2(reg_layers_2[adx]),
activation='relu',name='layer_2%d' % adx)
attention_vector = layer_2(attention_vector)
#MLP_intents
intents_vector = concatenate([mlp_vector,attention_vector])
for ndx in range(num_layer_3):
layer_3 = Dense(layers_3[ndx],kernel_regularizer=l2(reg_layers_3[ndx]),
activation='relu',name='layer_3%d' % ndx)
intents_vector = layer_3(intents_vector)
prediction = Dense(1,activation='sigmoid',kernel_initializer=initializers.lecun_normal(),
name='prediction')(intents_vector)
model = Model(inputs=[user_input, item_input], outputs=prediction)
return model
model = get_model(num_users,num_items,layers_1=[64,32,16,8],
layers_2=[64,32],layers_3=[64,32,16],
reg_layers_1=[0,0,0,0],reg_layers_2=[0,0],
reg_layers_3=[0,0,0])
和完整的追溯：
AttributeError Traceback (most recent call last)
<ipython-input-121-25ef0dd05d42> in <module>
2 layers_2=[64,32],layers_3=[64,32,16],
3 reg_layers_1=[0,0,0,0],reg_layers_2=[0,0],
----> 4 reg_layers_3=[0,0,0])
<ipython-input-120-d2a66d53e76f> in get_model(num_users, num_items, layers_1, layers_2, layers_3, reg_layers_1, reg_layers_2, reg_layers_3)
62 name='prediction')(intents_vector)
63
---> 64 model = Model(inputs=[user_input, item_input], outputs=prediction)
65
66 return model
~/Desktop/100_server_venv/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
85 warnings.warn('Update your `' + object_name +
86 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87 return func(*args, **kwargs)
88 wrapper._original_function = func
89 return wrapper
~/Desktop/100_server_venv/lib/python3.6/site-packages/keras/engine/topology.py in __init__(self, inputs, outputs, name)
1714 nodes_in_progress = set()
1715 for x in self.outputs:
-> 1716 build_map_of_graph(x, finished_nodes, nodes_in_progress)
1717
1718 for node in reversed(nodes_in_decreasing_depth):
~/Desktop/100_server_venv/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
1704 tensor_index = node.tensor_indices[i]
1705 build_map_of_graph(x, finished_nodes, nodes_in_progress,
-> 1706 layer, node_index, tensor_index)
1707
1708 finished_nodes.add(node)
~/Desktop/100_server_venv/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
1704 tensor_index = node.tensor_indices[i]
1705 build_map_of_graph(x, finished_nodes, nodes_in_progress,
-> 1706 layer, node_index, tensor_index)
1707
1708 finished_nodes.add(node)
~/Desktop/100_server_venv/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
1704 tensor_index = node.tensor_indices[i]
1705 build_map_of_graph(x, finished_nodes, nodes_in_progress,
-> 1706 layer, node_index, tensor_index)
1707
1708 finished_nodes.add(node)
~/Desktop/100_server_venv/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
1704 tensor_index = node.tensor_indices[i]
1705 build_map_of_graph(x, finished_nodes, nodes_in_progress,
-> 1706 layer, node_index, tensor_index)
1707
1708 finished_nodes.add(node)
~/Desktop/100_server_venv/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
1704 tensor_index = node.tensor_indices[i]
1705 build_map_of_graph(x, finished_nodes, nodes_in_progress,
-> 1706 layer, node_index, tensor_index)
1707
1708 finished_nodes.add(node)
~/Desktop/100_server_venv/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
1704 tensor_index = node.tensor_indices[i]
1705 build_map_of_graph(x, finished_nodes, nodes_in_progress,
-> 1706 layer, node_index, tensor_index)
1707
1708 finished_nodes.add(node)
~/Desktop/100_server_venv/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
1675 """
1676 if not layer or node_index is None or tensor_index is None:
-> 1677 layer, node_index, tensor_index = tensor._keras_history
1678 node = layer.inbound_nodes[node_index]
1679
AttributeError: 'Tensor' object has no attribute '_keras_history'
答案 0 :(得分：0)
您不能直接在Keras张量中使用后端函数，这些张量中的每个操作都必须是一层。您需要将每个自定义操作包装在Lambda层中，并为该层提供适当的输入。