标签:hiera log detail operation style 十分 already time cal
with tf.Session() as sess:
/ tf.InteractiveSession()
初始化:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
命名空间与 operation name(oper.name 获取操作名):
c_0 = tf.constant(0, name="c") # => operation named "c"
print(c_0.name)
# Already-used names will be "uniquified".
c_1 = tf.constant(2, name="c") # => operation named "c_1"
# Name scopes add a prefix to all operations created in the same context.
with tf.name_scope("outer"):
c_2 = tf.constant(2, name="c") # => operation named "outer/c"
# Name scopes nest like paths in a hierarchical file system.
with tf.name_scope("inner"):
c_3 = tf.constant(3, name="c") # => operation named "outer/inner/c"
# Exiting a name scope context will return to the previous prefix.
c_4 = tf.constant(4, name="c") # => operation named "outer/c_1"
# Already-used name scopes will be "uniquified".
with tf.name_scope("inner"):
c_5 = tf.constant(5, name="c") # => operation named "outer/inner_1/c"
#CODING: UTF-8
import time
import tensorflow as tf
N = 100000
x = tf.constant([1.])
b = 1.
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
t1 = time.time()
for _ in range(N):
y = sess.run(x)
print(‘使用sess.run() 读取变量数据耗时‘, time.time()-t1)
t2 = time.time()
for _ in range(N):
a = b
print(‘直接赋值耗时‘, time.time()-t2)
假设 x 为 tf 下的一个 Tensor 对象,t.eval() 执行的动作就是 tf.Session().run(t) 。
import tensorflow as tf
x = tf.constant([5.])
print(tf.Session().run(x))
with tf.Session():
print(x.eval())
在第二个例子中,session的作用就象context manager,context manager在with块的生存期,将session作为默认的 session。对简单应用的情形(如单元测试),context manager的方法可以得到更简洁的代码;如果你的代码要处理多个graph和 session,更直白的方式可能是显式调用Session.run()。
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TensorFlow 学习(二)—— tf Graph tf Session 与 tf Session run
标签:hiera log detail operation style 十分 already time cal
原文地址:https://www.cnblogs.com/siwnhwxh/p/10185295.html