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tf.placeholder

发布时间:2020-12-14 21:52:31 所属栏目:大数据 来源:网络整理
导读:tf.placeholder tf.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 ?o

tf.placeholder

tf.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().

在构建graph的过程中,tensor是没有实际数据的,只是表达计算过程,那么通过placeholder函数对tensor变量进行占位表示。然后在Session执行过程中,通过feed_dict对占位的tensor进行feed值

Args:

  • dtype: The type of elements in the tensor to be fed.指定数据类型
  • shape: The shape of the tensor to be fed (optional). If the shape is not specified,you can feed a tensor of any shape.指定tensor的维度,如果没有指定,可以feed任意维度的tensor
  • 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
?
 1 import tensorflow as tf
 2 import numpy as np
 3 
 4                                                                 
 5 x = tf.placeholder(tf.float32,shape=(1024,1024))
 6 y = tf.matmul(x,x)
 7 
 8 with tf.Session() as sess:
 9   #print(sess.run(y))  # ERROR: will fail because x was not fed.
10   rand_array = np.random.rand(1024,1024)
11   print(sess.run(y,feed_dict={x: rand_array}))  # Will succeed.

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