# 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 `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`

.

From: stackoverflow.com/q/33610685

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