tensorflow函数解析:Session.run和Tensor.eval的区别

时间:2023-03-08 17:21:29

tensorflow函数解析:Session.run和Tensor.eval

翻译 2017年04月20日 15:05:50
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原问题链接:

http://*.com/questions/33610685/in-tensorflow-what-is-the-difference-between-session-run-and-tensor-eval

译:

问题:

tensorflow有两种方式:Session.run和 Tensor.eval,这两者的区别在哪?

答:

如果你有一个Tensor t,在使用t.eval()时,等价于:tf.get_default_session().run(t).

举例:

t = tf.constant(42.0)
sess = tf.Session()
with sess.as_default(): # or `with sess:` to close on exit
assert sess is tf.get_default_session()
assert t.eval() == sess.run(t)

这其中最主要的区别就在于你可以使用sess.run()在同一步获取多个tensor中的值,

例如:

t = tf.constant(42.0)
u = tf.constant(37.0)
tu = tf.mul(t, u)
ut = tf.mul(u, t)
with sess.as_default():
tu.eval() # runs one step
ut.eval() # runs one step
sess.run([tu, ut]) # evaluates both tensors in a single step

注意到:每次使用 eval 和 run时,都会执行整个计算图,为了获取计算的结果,将它分配给tf.Variable,然后获取。

原文如下:

Question:

TensorFlow has two ways to evaluate part of graph: Session.run on a list of variables and Tensor.eval. Is there a difference between these two?

Answer:

If you have a Tensor t, calling t.eval() is equivalent to calling tf.get_default_session().run(t).

You can make a session the default as follows:

t = tf.constant(42.0)
sess = tf.Session()
with sess.as_default(): # or `with sess:` to close on exit
assert sess is tf.get_default_session()
assert t.eval() == sess.run(t)

The most important difference is that you can use sess.run() to fetch the values of many tensors in the same step:

t = tf.constant(42.0)
u = tf.constant(37.0)
tu = tf.mul(t, u)
ut = tf.mul(u, t)
with sess.as_default():
tu.eval() # runs one step
ut.eval() # runs one step
sess.run([tu, ut]) # evaluates both tensors in a single step

Note that each call to eval and run will execute the whole graph from scratch. To cache the result of a computation, assign it to a tf.Variable.