tensorflow 里面的占位符 placeholder

占位符比变量更基本。它只是我们在未来时间分配数据的变量。占位符是在执行时才输入值的节点。如果我们的网络输入依赖于一些外部数据，并且我们不希望我们的图在定义开发时依赖于任何实际值，那么占位符就是我们需要的数据类型。事实上，我们可以在没有任何数据的情况下构建图。因此，占位符不需要任何初始值；只有一个数据类型（例如 float32）和一个张量形状，因此即使它还没有任何存储值，图形仍然知道要计算什么。

创建占位符的一些示例如下：

a = tf.placeholder(tf.float32, shape=[5])
b = tf.placeholder(dtype=tf.float32, shape=None, name=None)
X = tf.placeholder(tf.float32, shape=[None, 784], name='input')
Y = tf.placeholder(tf.float32, shape=[None, 10], name='label') 

 
# importing packages
import tensorflow.compat.v1 as tf
  
# disabling eager mode
tf.compat.v1.disable_eager_execution()
  
# creating a placeholder
a = tf.placeholder(tf.float32, None)
  
# creating an operation
b = a + 10
  
# creating a session
with tf.Session() as session:
    
    # feeding data in the placeholder
    operation_res = session.run(b, feed_dict={a: [10, 20, 30, 40]})
    print("after executing the operation: " + str(operation_res))

after executing the operation: [20. 30. 40. 50.]

Explanation:

• Eager mode is disabled in case there are any errors. 
• A placeholder is created using tf.placeholder() method which has a dtype ‘tf.float32’, None says we didn’t specify any size. 
• Operation is created before feeding in data. 
• The operation adds 10 to the tensor. 
• A session is created and started using tf.Session(). 
• Session.run takes the operation we created and data to be fed as parameters and it returns the result.

 

# importing packages
import tensorflow.compat.v1 as tf
  
# disabling eager mode
tf.compat.v1.disable_eager_execution()
  
# creating a tensorflow graph
graph = tf.Graph()
with graph.as_default():
    
    # creating a placeholder
    a = tf.placeholder(tf.float64, shape=(3, 3), name='tensor1')
      
    # creating an operation
    b = a ** 2
  
# array1 will be fed into 'a'
array1 = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
  
# Creating a session, and running the graph
with tf.Session(graph=graph) as session:
      
    # run the session until it reaches node b,
    # then input an array of values into a
    operation_res = session.run(b, feed_dict={a: array1})
    print("after executing the operation: ")
    print(operation_res)

 

after executing the operation: 
[[ 1.  4.  9.]
 [16. 25. 36.]
 [49. 64. 81.]]