In TensorFlow, what is the difference between and Tensor.eval()?

TensorFlow has two ways to evaluate part of graph: 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 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() ==

The most important difference is that you can use 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[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.


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