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

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.