In TensorFlow, what is the difference between Session.run() and Tensor.eval()?
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?
If you have a
Tensor t, calling
t.eval() is equivalent to calling
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
run will execute the whole graph from scratch. To cache the result of a computation, assign it to a
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