InvalidArgumentError: 2 root error(s) found.(0) Invalid argument: You must feed a value for placeholder tensor 'Placeholder_22'

Question

def CTcnn(shape=(None,SIZE,SIZE,1)):

  x_input = tf.placeholder(tf.float32, shape=[None, 256,256, 1])

  #s = tf.keras.layers.Lambda(lambda x: x / 255)(x_input)

  encode_conv1 = Quad_conv_layer_same(x_input,shape=[3,3,1,15])

  encode_conv2 = Quad_conv_layer_same(encode_conv1,shape=[3,3,15,15])

  encode_conv3 = Quad_conv_layer_same(encode_conv2,shape=[3,3,15,15])

  encode_conv4 = Quad_conv_layer_same(encode_conv3,shape=[3,3,15,15])

  encode_conv5 = Quad_conv_layer_valid(encode_conv4,shape=[3,3,15,15])

  decode_conv4 = tf.nn.relu(Quad_deconv_layer_valid_linear(encode_conv5,shape=[3, 3,15,15],outputshape=tf.shape(encode_conv4))+encode_conv4)

  decode_conv3 = Quad_deconv_layer_same(decode_conv4,shape=[3, 3,15,15],outputshape=tf.shape(encode_conv3))

  decode_conv2 = tf.nn.relu(Quad_deconv_layer_same_linear(decode_conv3,shape=[3, 3,15,15],outputshape=tf.shape(encode_conv2))+encode_conv2)

  decode_conv1 = Quad_deconv_layer_same(decode_conv2,shape=[3, 3,15,15],outputshape=tf.shape(encode_conv1))

  x_output = tf.nn.relu(Quad_deconv_layer_same_linear(decode_conv1,shape=[3, 3,1,15],outputshape=tf.shape(x_input))+x_input)

  model=tf.keras.Model(inputs=x_input, outputs=x_output, name='CTcnn')
  return model

  cost = tf.reduce_mean(tf.square(tf.subtract(x_output, x_input)))
  opt = keras.optimizers.Adam(learning_rate=0.0004).minimize(cost)

函数调用时：

model = CTcnn(shape=(None,SIZE,SIZE,1))

错误信息：

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-77-4de3fa78340e> in <module>()

----> 1 model = CTcnn(shape=(None,SIZE,SIZE,1))

      2 #opt = keras.optimizers.Adam(learning_rate=0.0004)

      3 #model.compile(loss='categorical_crossentropy', optimizer=opt)

      4 model.compile(optimizer=opt, loss='mean_squared_error')

      5 model.summary()

<ipython-input-76-01dc6c972eba> in CTcnn(shape)

     17   x_output = tf.nn.relu(Quad_deconv_layer_same_linear(decode_conv1,shape=[3, 3,1,15],outputshape=tf.shape(x_input))+x_input)

     18 

---> 19   model=tf.keras.Model(inputs=x_input, outputs=x_output, name='CTcnn')

     20   return model

     21 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)

    515     self._self_setattr_tracking = False  # pylint: disable=protected-access

    516     try:

--> 517       result = method(self, *args, **kwargs)

    518     finally:

    519       self._self_setattr_tracking = previous_value  # pylint: 
disable=protected-access

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py in __init__(self, inputs, outputs, name, trainable, **kwargs)

    118     generic_utils.validate_kwargs(kwargs, {})

    119     super(Functional, self).__init__(name=name, trainable=trainable)

--> 120     self._init_graph_network(inputs, outputs)

    121 

    122   @trackable.no_automatic_dependency_tracking

/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)

    515     self._self_setattr_tracking = False  # pylint: disable=protected-access

    516     try:

--> 517       result = method(self, *args, **kwargs)

    518     finally:

    519       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py in _init_graph_network(self, inputs, outputs)

    153     if not keras_tensor.keras_tensors_enabled():

    154       if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):

--> 155         base_layer_utils.create_keras_history(self._nested_outputs)

    156 

    157     self._validate_graph_inputs_and_outputs()

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py in create_keras_history(tensors)

    186       the raw Tensorflow operations.

    187   """

--> 188   _, created_layers = _create_keras_history_helper(tensors, set(), [])

    189   return created_layers

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)

    248                 constants[i] = backend.eval_in_eager_or_function(op_input)

    249               else:

--> 250                 constants[i] = backend.function([], op_input)([])

    251       layer_inputs = unnest_if_single_tensor(layer_inputs)

    252       processed_ops, created_layers = _create_keras_history_helper(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py in __call__(self, inputs)

   3955 

   3956     fetched = self._callable_fn(*array_vals,

-> 3957                                 run_metadata=self.run_metadata)

   3958     self._call_fetch_callbacks(fetched[-len(self._fetches):])

   3959     output_structure = nest.pack_sequence_as(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)

   1480         ret = tf_session.TF_SessionRunCallable(self._session._session,

   1481                                                self._handle, args,

-> 1482                                                run_metadata_ptr)

   1483         if run_metadata:

   1484           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

InvalidArgumentError: 2 root error(s) found.

  (0) Invalid argument: You must feed a value for placeholder tensor 'Placeholder_22' with dtype float and shape [?,256,256,1]

     [[{{node Placeholder_22}}]]

  (1) Invalid argument: You must feed a value for placeholder tensor 'Placeholder_22' with dtype float and shape [?,256,256,1]

     [[{{node Placeholder_22}}]]

     [[add_902/_3]]

0 successful operations.

0 derived errors ignored.

Answer 1

看起来问题出在tf.placeholder。该错误表明，为 Placeholder_22 提供一些值。需要提供一些值才能使用占位符。

1. 为你的占位符命名：然后很清楚它们的定义位置

   示例：tf.placeholder(name="some Name")

2. 它会在第二个回溯中向您显示未指定的占位符

   即占位符_22

3. 它向您展示了 sess.run() 调用也未能在回溯中包含所有占位符。