问题
In Tensorflow programming, can someone please tell what is the difference between ".eval()" and "sess.run()". What do each of them do and when to use them?
回答1:
A session object encapsulates the environment in which Tensor objects are evaluated.
If x is a tf.Tensor object, tf.Tensor.eval is shorthand for tf.Session.run, where sess is the current tf.get_default_session.
You can make session the default as below
x = tf.constant(5.0)
y = tf.constant(6.0)
z = x * y
with tf.Session() as sess:
print(sess.run(z)) # 30.0
print(z.eval()) # 30.0
The most important difference is you can use sess.run to fetch the values of many tensors in the same step as below
print(sess.run([x,y])) # [5.0, 6.0]
print(sess.run(z)) # 30.0
Where as eval fetch single tensor value at a time as below
print(x.eval()) # 5.0
print(z.eval()) # 3.0
TensorFlow computations define a computation graph that has no numerical value until evaluated as below
print(x) # Tensor("Const_1:0", shape=(), dtype=float32)
In Tensorflow 2.x (>= 2.0), You can use tf.compat.v1.Session() instead of tf.session()
来源：https://stackoverflow.com/questions/58888215/sess-run-and-eval-in-tensorflow-programming