tensorflow.placeholder()函数用法

tensorflow.placeholder()，placeholder是占位符的意思，在tensorflow中类似于函数参数，在执行的时候再赋具体的值。

参数含义：
dtype：数据类型。常用的是tf.float32,tf.float64等数值类型
shape：数据形状。默认是None，就是一维值，也可以是多维，比如[2,3], [None, 3]表示列是3，行不定
name：名称。

看一段源码：

@tf_export("placeholder")
def placeholder(dtype, shape=None, name=None):
  """Inserts a placeholder for a tensor that will be always fed.

  **Important**: This tensor will produce an error if evaluated. Its value must
  be fed using the `feed_dict` optional argument to `Session.run()`,
  `Tensor.eval()`, or `Operation.run()`.

  For example:

  ```python
  x = tf.placeholder(tf.float32, shape=(1024, 1024))
  y = tf.matmul(x, x)

  with tf.Session() as sess:
    print(sess.run(y))  # ERROR: will fail because x was not fed.

    rand_array = np.random.rand(1024, 1024)
    print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.
  ```

  @compatibility(eager)
  Placeholders are not compatible with eager execution.
  @end_compatibility

  Args:
    dtype: The type of elements in the tensor to be fed.
    shape: The shape of the tensor to be fed (optional). If the shape is not
      specified, you can feed a tensor of any shape.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` that may be used as a handle for feeding a value, but not
    evaluated directly.

  Raises:
    RuntimeError: if eager execution is enabled
  """
  if context.executing_eagerly():
    raise RuntimeError("tf.placeholder() is not compatible with "
                       "eager execution.")

  return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)

参数dtype是必选的，其它都是可选的。

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