将Tensorflow低级API（张量/占位符）与Keras模型结合使用

Question

根据张量流 。 使用tf.keras.Input提供占位符，使用tf.keras.layers.Dense提供张量。 因此，我想使用带有Tensorflow低级API的Tensor和Placeholders测试等效性，然后使用keras高级别API训练我的模型。 这是我的代码：

tf.reset_default_graph()  

inputs = tf.placeholder(tf.float32, shape=[None, 32])
W_h = tf.get_variable(name="W_h", shape=[32, 64], initializer=tf.truncated_normal_initializer(stddev=0.01))
W_out = tf.get_variable(name="W_out", shape=[64, 10], initializer=tf.truncated_normal_initializer(stddev=0.01))

h = tf.nn.relu(tf.matmul(inputs, W_h, name="MatMul"), name='relu')
predictions = tf.nn.relu(tf.matmul(h, W_out, name="MatMul"), name='relu')

model = tf.keras.Model(inputs=inputs, outputs=predictions)
model.compile(loss='mean_squared_error', optimizer='sgd') # sgd stands for stochastic gradient descent
model.fit(x_train, y_train, batch_size=32, epochs=5)

但是，在调用tf.keras.Model出现错误：

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,32]
     [[{{node Placeholder}}]]

我确实给输入占位符了吗？

---

PS：完整错误消息：

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-15-27f92d1f784d> in <module>
      8 predictions = tf.nn.relu(tf.matmul(h, W_out, name="MatMul"), name='relu')
      9 
---> 10 model = tf.keras.Model(inputs=inputs, outputs=predictions)
     11 model.compile(loss='mean_squared_error', optimizer='sgd') # sgd stands for stochastic gradient descent
     12 model.fit(x_train, y_train, batch_size=32, epochs=5)

~\.conda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\keras\engine\training.py in __init__(self, *args, **kwargs)
    127 
    128   def __init__(self, *args, **kwargs):
--> 129     super(Model, self).__init__(*args, **kwargs)
    130     # initializing _distribution_strategy here since it is possible to call
    131     # predict on a model without compiling it.

~\.conda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\keras\engine\network.py in __init__(self, *args, **kwargs)
    160         'inputs' in kwargs and 'outputs' in kwargs):
    161       # Graph network
--> 162       self._init_graph_network(*args, **kwargs)
    163     else:
    164       # Subclassed network

~\.conda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\.conda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\keras\engine\network.py in _init_graph_network(self, inputs, outputs, name, **kwargs)
    267 
    268     if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
--> 269       base_layer_utils.create_keras_history(self._nested_outputs)
    270 
    271     self._base_init(name=name, **kwargs)

~\.conda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\keras\engine\base_layer_utils.py in create_keras_history(tensors)
    198     keras_tensors: The Tensors found that came from a Keras Layer.
    199   """
--> 200   _, created_layers = _create_keras_history_helper(tensors, set(), [])
    201   return created_layers
    202 

~\.conda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\keras\engine\base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
    242             constants[i] = op_input
    243           else:
--> 244             constants[i] = backend.function([], op_input)([])
    245       processed_ops, created_layers = _create_keras_history_helper(
    246           layer_inputs, processed_ops, created_layers)

~\.conda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\keras\backend.py in __call__(self, inputs)
   3290 
   3291     fetched = self._callable_fn(*array_vals,
-> 3292                                 run_metadata=self.run_metadata)
   3293     self._call_fetch_callbacks(fetched[-len(self._fetches):])
   3294     output_structure = nest.pack_sequence_as(

~\.conda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args, **kwargs)
   1456         ret = tf_session.TF_SessionRunCallable(self._session._session,
   1457                                                self._handle, args,
-> 1458                                                run_metadata_ptr)
   1459         if run_metadata:
   1460           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,32]
     [[{{node Placeholder}}]]

Answer 1

您不能混合使用tf.placeholder和tf.keras.Input 。 换句话说，如果您想使用tf.keras API，请使用tf.keras.Input ，或者如果您想使用tf本机API，请使用tf.placeholder 。 此外，您的选择将反映代码的其他部分。 假设您想使用tf.keras API，则应采用以下方法：

class CustomLayer(tf.keras.layers.Layer):

    def __init__(self, output_dim, **kwargs):
        self.output_dim = output_dim
        super(CustomLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        # Create a trainable weight variable for this layer.
        self.kernel = self.add_weight(name='kernel', 
                                      shape=tf.TensorShape((input_shape[1], self.output_dim)),
                                      initializer=tf.truncated_normal_initializer(stddev=0.01),
                                      trainable=True)
        super(CustomLayer, self).build(input_shape)  # Be sure to call this at the end

    def call(self, x):
        return tf.nn.relu(tf.matmul(x, self.kernel))

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.output_dim)

inputs = tf.keras.Input(shape=(32,), dtype=tf.float32)
h = CustomLayer(output_dim=64)(inputs)
predictions = CustomLayer(output_dim=10)(h)
model = tf.keras.Model(inputs=inputs, outputs=predictions)
model.compile(loss='mean_squared_error', optimizer='sgd') # sgd stands for stochastic gradient descent
model.summary()
model.fit(np.random.rand(100,32), np.random.rand(100,10), batch_size=32, epochs=5)

请注意，如果我们认为您使用注释中提到的TF 1.14，则此方法很重要。 在TF 2.0上，这不那么复杂，而且更直观。 另一方面，如果要坚持使用TF 1.14本机API并使用tf.placeholder ，则应使用tf.placeholder作为将用于馈送数据的节点来构建图形。 此外，关于您的问题tf.keras.Input是否返回一个占位符-它确实返回一个占位符节点，您可以使用该节点来tf.keras.Input数据。 但它不会返回tf.placeholder 。 tf.placeholder的用法如下：

X = tf.placeholder(shape=None, dtype=tf.int32)
Y = tf.placeholder(shape=None, dtype=tf.int32)
add = X + Y
with tf.Session() as sess:
    print(sess.run(add, feed_dict={X: 2, Y: 3}))
    # 5
    print(sess.run(add, feed_dict={X: 10, Y: 9}))
    # 19

正如可以看到，静态图形被创建，然后将其节点与一个执行tf.Session同时使用在图中馈送数据tf.placeholder 。 另一方面， tf.keras.Input具有相同的目的（这就是为什么在文档中称为占位符的原因），但其用例与tf.keras API有关，而与tf本机API不相关。