tensorflow函数解析：Session.run和Tensor.eval
原问题链接：
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，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 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 .