In TensorFlow what is the difference between Session run and Tensor eval

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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

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Tensorflow 2 x Compatible Answer  Converting mrry s code to Tensorflow 2 x   gt   2 0  for the benefit of the community    pip install tensorflow  2 1 import tensorflow as tf  tf compat v1 disable eager execution        t   tf constant 42 0  sess   tf compat v1 Session   with sess as default        or  with sess   to close on exit     assert sess is tf compat v1 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 multiply t  u  ut   tf multiply 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

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eval   can not handle the list object  tf reset default graph    a   tf Variable 0 2  name  a   b   tf Variable 0 3  name  b   z   tf constant 0 0  name  z0   for i in range 100       z   a   tf cos z   i    z   tf sin b - i  grad   tf gradients z   a  b    init   tf global variables initializer    with tf Session   as sess      init run       print  z    z eval        print  grad   grad eval      but Session run   can  print  grad   sess run grad     correct me if I am wrong

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In tensorflow you create graphs and pass values to that graph  Graph does all the hardwork and generate the output based on the configuration that you have made in the graph  Now When you pass values to the graph then first you need to create a tensorflow session   tf Session     Once session is initialized then you are supposed to use that session because all the variables and settings are now part of the session  So  there are two ways to pass external values to the graph so that graph accepts them  One is to call the  run   while you are using the session being executed   Other way which is basically a shortcut to this is to use  eval    I said shortcut because the full form of  eval   is   tf get default session   run values    You can check that yourself  At the place of values eval   run tf get default session   run values   You must get the same behavior   what eval is doing is using the default session and then executing run

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The most important thing to remember      The only way to get a constant  variable  any result  from TenorFlow is the session    Knowing this everything else is easy      Both tf Session run   and tf Tensor eval   get results from the session where tf Tensor eval   is a shortcut for calling tf get default session   run t      I would also outline the method tf Operation run   as in here      After the graph has been launched in a session  an Operation can be executed by passing it to tf Session run    op run   is a shortcut for calling tf get default session   run op

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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

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The FAQ session on tensor flow has an answer to exactly the same question  I will just go ahead and leave it here     If t is a Tensor object  t eval   is shorthand for sess run t   where sess is the current default session  The two following snippets of code are equivalent   sess   tf Session   c   tf constant 5 0  print sess run c   c   tf constant 5 0  with tf Session      print c eval     In the second example  the session acts as a context manager  which has the effect of installing it as the default session for the lifetime of the with block  The context manager approach can lead to more concise code for simple use cases  like unit tests   if your code deals with multiple graphs and sessions  it may be more straightforward to explicit calls to Session run     I d recommend that you at least skim throughout the whole FAQ  as it might clarify a lot of things

[python] In TensorFlow, what is the difference between Session.run() and Tensor.eval()?

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