- session：
with tf.Session() as sess:/tf.InteractiveSession()初始化：
- tf.global_variables_initializer()
with tf.Session() as sess: sess.run(tf.global_variables_initializer())- 1
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0. tf.Graph
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"- 1
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1. 使用 tf.Session().run() 读取变量的值十分耗时
#CODING: UTF-8import timeimport tensorflow as tfN = 100000x = 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)
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2. tf.Session().run() 与 Tensor.eval()
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 tfx = tf.constant([5.])print(tf.Session().run(x))with tf.Session(): print(x.eval())
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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()。
再分享一下我老师大神的人工智能教程吧。零基础！通俗易懂！风趣幽默！还带黄段子！希望你也加入到我们人工智能的队伍中来！https://blog.csdn.net/jiangjunshow