我正在尝试训练一个模型，其中使用了一个共享的特征提取器，然后分成n个“头部”，每个“头部”由小层组成以产生不同的输出。

当我首先训练“头部a”时，一切正常，但当我切换到“头部b”时，python会抛出一个来自tensorflow的InvalidArgumentError。如果我从“头部b”开始然后训练“头部a”，也会出现相同的情况。

我尝试了在stackoverflow上找到的不同方法，比如这个，但没有效果。

我构建模型的方式如下

alphaLeaky=0.3 inputs =Input(shape=(state_shape[0],state_shape[1],state_shape[2]))outputs=ZeroPadding2D(padding=(1,1))(inputs)outputs=LocallyConnected2D(1, (6,6), activation='linear', padding='valid')(outputs) outputs=Flatten()(outputs) outputs=Dense(768,kernel_initializer='lecun_uniform',bias_initializer='zeros')(outputs)                        outputs=advanced_activations.LeakyReLU(alpha=alphaLeaky)(outputs)outputs=Dense(512,kernel_initializer='lecun_uniform',bias_initializer='zeros')(outputs)                  outputs=advanced_activations.LeakyReLU(alpha=alphaLeaky)(outputs)outputs1=Dense(256,kernel_initializer='lecun_uniform',bias_initializer='zeros')(outputs)outputs1=advanced_activations.LeakyReLU(alpha=alphaLeaky)(outputs1)outputs1=Dense(action_number,kernel_initializer='lecun_uniform',bias_initializer='zeros')(outputs1)     outputs1=Activation('linear')(outputs1)outputs2=Dense(256,kernel_initializer='lecun_uniform',bias_initializer='zeros')(outputs)outputs2=advanced_activations.LeakyReLU(alpha=alphaLeaky)(outputs2)outputs2=Dense(action_number,kernel_initializer='lecun_uniform',bias_initializer='zeros')(outputs2)     outputs2=Activation('linear')(outputs2)outputs3=Dense(256,kernel_initializer='lecun_uniform',bias_initializer='zeros')(outputs)                        outputs3=advanced_activations.LeakyReLU(alpha=alphaLeaky)(outputs3)outputs3=Dense(action_number,kernel_initializer='lecun_uniform',bias_initializer='zeros')(outputs3)outputs3=Activation('linear')(outputs3)model1= Model(inputs=inputs, outputs=outputs1)model2= Model(inputs=inputs, outputs=outputs2)model3= Model(inputs=inputs, outputs=outputs3)model1.compile(loss='mse', optimizer=Adamax(lr=PAS_INITIAL, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)model2.compile(loss='mse', optimizer=Adamax(lr=PAS_INITIAL, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)model3.compile(loss='mse', optimizer=Adamax(lr=PAS_INITIAL, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)

然后我使用fit方法来训练它们。

例如，如果我运行model1.fit(...)，它能正常工作，但随后，当我运行model2.fit(...)或model3.fit(...)时，我会收到一个错误消息：

W tensorflow/core/framework/op_kernel.cc:993] Invalid argument: You must feed a value for placeholder tensor 'activation_1_target' with dtype float         [[Node: activation_1_target = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'activation_1_target' with dtype float         [[Node: activation_1_target = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]         [[Node: dense_5/bias/read/_1075 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_60_dense_5/bias/read", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]Caused by op 'activation_1_target', defined at:  File "main.py", line 100, in <module>    agent.init_brain()  File "/dds/work/DQL/dql_last_version/8th_code_multi/agent_per.py", line 225, in init_brain    self.brain = Brain_2D(self.state_shape,self.action_number)  File "/dds/work/DQL/dql_last_version/8th_code_multi/brain.py", line 141, in __init__    Brain.__init__(self, action_number)  File "/dds/work/DQL/dql_last_version/8th_code_multi/brain.py", line 20, in __init__    self.models, self.full_model = self._create_model()  File "/dds/work/DQL/dql_last_version/8th_code_multi/brain.py", line 216, in _create_model    neuralNet1.compile(loss='mse', optimizer=Adamax(lr=PAS_INITIAL, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0))  File "/dds/miniconda/envs/dds/lib/python3.5/site-packages/keras/engine/training.py", line 755, in compile    dtype=K.dtype(self.outputs[i]))  File "/dds/miniconda/envs/dds/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 497, in placeholder    x = tf.placeholder(dtype, shape=shape, name=name)  File "/dds/miniconda/envs/dds/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1502, in placeholder    name=name)  File "/dds/miniconda/envs/dds/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 2149, in _placeholder    name=name)  File "/dds/miniconda/envs/dds/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op    op_def=op_def)  File "/dds/miniconda/envs/dds/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2327, in create_op    original_op=self._default_original_op, op_def=op_def)  File "/dds/miniconda/envs/dds/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1226, in __init__    self._traceback = _extract_stack()InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'activation_1_target' with dtype float         [[Node: activation_1_target = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]         [[Node: dense_5/bias/read/_1075 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_60_dense_5/bias/read", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

我想只优化我选择的“头部”的权重，但似乎一旦某些输入已经通过网络走了一条路径，它就在等待我再次通过同一个“头部”。即使我想训练其他权重也是如此。

我想过构建一个具有多个输出的单一模型

model= Model(inputs=inputs, outputs=[outputs1,outputs2,outputs3,outputs4]) 

但我想让每个“头部”在不同的数据批次上进行训练（我正在做一个强化学习项目）。

谢谢！

回答：

我解决了我的问题。

我最终只编译了一个模型，但它有n个输入和n个输出，n是“头部”的数量。我为每个输入关联了一个不同的批次，以便它们可以用不同的数据分布训练每个“头部”。

在测试部分，我只是将相同的输入复制n次并将其输入到模型中。这可能不是最好的方法，但它有效。

如果你对我的解决方案有任何想法或评论，请随时提出，我很乐意看到其他方法。

谢谢