How to print the value of a Tensor object in TensorFlow

Question

I have been using the introductory example of matrix multiplication in TensorFlow   matrix1   tf constant   3   3     matrix2   tf constant   2    2     product   tf matmul matrix1  matrix2    When I print the product  it is displaying it as a Tensor object    lt tensorflow python framework ops Tensor object at 0x10470fcd0 gt    But how do I know the value of product   The following doesn t help   print product Tensor  MatMul 0   shape TensorShape  Dimension 1   Dimension 1     dtype float32    I know that graphs run on Sessions  but isn t there any way I can check the output of a Tensor object without running the graph in a session

User · Answer

While other answers are correct that you cannot print the value until you evaluate the graph, they do not talk about one easy way of actually printing a value inside the graph, once you evaluate it.

The easiest way to see a value of a tensor whenever the graph is evaluated (using run or eval) is to use the Print operation as in this example:

# Initialize session
import tensorflow as tf
sess = tf.InteractiveSession()

# Some tensor we want to print the value of
a = tf.constant([1.0, 3.0])

# Add print operation
a = tf.Print(a, [a], message="This is a: ")

# Add more elements of the graph using a
b = tf.add(a, a)

Now, whenever we evaluate the whole graph, e.g. using b.eval(), we get:

I tensorflow/core/kernels/logging_ops.cc:79] This is a: [1 3]

User · Answer

I didn t find it easy to understand what is required even after reading all the answers until I executed this  TensofFlow is new to me too   def printtest    x   tf constant  1 0  3 0   x   tf Print x  x  message  Test   init    tf global variables initializer    tf local variables initializer    b   tf add x  x  with tf Session   as sess      sess run init      print sess run b       sess close     But still you may need the value returned by executing the session   def printtest        x   tf constant  100 0       x   tf Print x  x  message  Test       init    tf global variables initializer    tf local variables initializer        b   tf add x  x      with tf Session   as sess          sess run init          c   sess run b          print c          sess close

User · Answer

Reiterating what others said  its not possible to check the values without running the graph   A simple snippet for anyone looking for an easy example to print values is as below  The code can be executed without any modification in ipython notebook  import tensorflow as tf   define a variable to hold normal random values  normal rv   tf Variable  tf truncated normal  2 3  stddev   0 1     initialize the variable init op   tf initialize all variables     run the graph with tf Session   as sess      sess run init op   execute init op      print the random values that we sample     print  sess run normal rv     Output     -0 16702934  0 07173464 -0 04512421    -0 02265321  0 06509651 -0 01419079

User · Answer

tf Print is now deprecated  here s how to use tf print  lowercase p  instead   While running a session is a good option  it is not always the way to go  For instance  you may want to print some tensor in a particular session   The new print method returns a print operation which has no output tensors   print op   tf print tensor to print    Since it has no outputs  you can t insert it in a graph the same way as you could with tf Print  Instead  you can you can add it to control dependencies in your session in order to make it print   sess   tf compat v1 Session   with sess as default      tensor to print   tf range 10    print op   tf print tensor to print  with tf control dependencies  print op      tripled tensor   tensor to print   3 sess run tripled tensor    Sometimes  in a larger graph  maybe created partly in subfunctions  it is cumbersome to propagate the print op to the session call  Then  tf tuple can be used to couple the print operation with another operation  which will then run with that operation whichever session executes the code  Here s how that is done    print op   tf print tensor to print  some tensor list   tf tuple  some tensor   control inputs  print op     Use some tensor list 0  instead of any tensor below

User · Answer

Question  How to print the value of a Tensor object in TensorFlow   Answer   import tensorflow as tf    Variable x   tf Variable   1 2 3       initialize init    tf global variables initializer    tf local variables initializer       Create a session sess   tf Session      run the session sess run init     print the value sess run x

User · Answer

Basically  in tensorflow when you create a tensor of any sort they are created and stored inside which can be accessed only when you run a tensorflow session  Say you have created a constant tensor  c   tf constant   1 0  2 0  3 0    4 0  5 0  6 0    Without running a session  you can get - op  An Operation  Operation that computes this tensor  - value index  An int  Index of the operation s endpoint that produces this tensor  - dtype  A DType  Type of elements stored in this tensor   To get the values you can run a session with the tensor you require as   with tf Session   as sess      print sess run c       sess close     The output will be something like this      array   1   2   3              4   5   6     dtype float32

User · Answer

Using tips provided in https   www tensorflow org api docs python tf print I use the log d function to print formatted strings   import tensorflow as tf  def log d fmt   args       op   tf py func func lambda fmt    args   print fmt   args                          inp  fmt    args   Tout         return tf control dependencies  op       actual code starts now     matrix1   tf constant   3   3     matrix2   tf constant   2    2     product   tf matmul matrix1  matrix2   with log d  MAT1   s  MAT2   s   matrix1  matrix2     this will print the log line     product   tf matmul matrix1  matrix2   with tf Session   as sess      sess run product

User · Answer

In Tensorflow 1 x  import tensorflow as tf tf enable eager execution   matrix1   tf constant   3   3     matrix2   tf constant   2    2     product   tf matmul matrix1  matrix2    print the product print product            tf Tensor   12     shape  1  1   dtype float32  print product numpy        12      With Tensorflow 2 x  eager mode is enabled by default  so the following code works with TF2 0   import tensorflow as tf matrix1   tf constant   3   3     matrix2   tf constant   2    2     product   tf matmul matrix1  matrix2    print the product print product            tf Tensor   12     shape  1  1   dtype float32  print product numpy        12

User · Answer

I think you need to get some fundamentals right  With the examples above you have created tensors  multi dimensional array   But for tensor flow to really work you have to initiate a  session  and run your  operation  in the session  Notice the word  session  and  operation   You need to know 4 things to work with  tensorflow     tensors Operations Sessions Graphs   Now from what you wrote out you have given the tensor  and the operation but you have no session running nor a graph  Tensor  edges of the graph  flow through graphs and are manipulated by operations  nodes of the graph   There is default graph but you can initiate yours in a session   When you say print   you only access the shape of the variable or constant you defined   So you can see what you are missing     with tf Session   as sess                  print sess run product              print  product eval      Hope it helps

User · Answer

In Tensorflow 2 0   or in Eager mode environment  you can call  numpy   method   import tensorflow as tf  matrix1   tf constant   3   3 0    matrix2   tf constant   2 0   2 0    product   tf matmul matrix1  matrix2   print product numpy

User · Answer

import tensorflow as tf sess   tf InteractiveSession   x     1  2  1    1  1  1        y   tf nn softmax x              matrix1   tf constant   3   3     matrix2   tf constant   2    2     product   tf matmul matrix1  matrix2   print product eval    tf reset default graph   sess close

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You should think of TensorFlow Core programs as consisting of two discrete sections    Building the computational graph  Running the computational graph    So for the code below you just Build the computational graph     matrix1   tf constant   3   3     matrix2   tf constant   2    2     product   tf matmul matrix1  matrix2    You need also To initialize all the variables in a TensorFlow program   you must explicitly call a special operation as follows   init   tf global variables initializer     Now you build the graph and initialized all variables  next step is to evaluate the nodes  you must run the computational graph within a session  A session encapsulates the control and state of the TensorFlow runtime   The following code creates a Session object and then invokes its run method to run enough of the computational graph to evaluate product    sess   tf Session      run variables initializer sess run init   print sess run  product

User · Answer

tf keras backend eval is useful for evaluating small expressions   tf keras backend eval op    TF 1 x and TF 2 0 compatible     Minimal Verifiable Example  from tensorflow keras backend import eval  m1   tf constant   3   3     m2   tf constant   2    2      eval tf matmul m1  m2     array   12     dtype float32    This is useful because you do not have to explicitly create a Session or InteractiveSession

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I am not sure if I am missing here  but I think the easiest and best way to do it is using tf keras backend get value API  print product   gt  gt tf Tensor   12     shape  1  1   dtype float32  print tf keras backend get value product    gt  gt   12

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You could print out the tensor value in session as follow  import tensorflow as tf  a   tf constant  1  1 5  2 5   dtype tf float32  b   tf constant  1  -2  3   dtype tf float32  c   a   b  with tf Session   as sess      result   c eval       print result

User · Answer

No  you can not see the content of the tensor without running the graph  doing session run     The only things you can see are    the dimensionality of the tensor  but I assume it is not hard to calculate it for the list of the operations that TF has  type of the operation that will be used to generate the tensor  transpose 1 0  random uniform 0  type of elements in the tensor  float32    I have not found this in documentation  but I believe that the values of the variables  and some of the constants are not calculated at the time of assignment      Take a look at this example   import tensorflow as tf from datetime import datetime dim   7000   The first example where I just initiate a constant Tensor of random numbers run approximately the same time irrespectibly of dim  0 00 00 003261   startTime   datetime now   m1   tf truncated normal  dim  dim   mean 0 0  stddev 0 02  dtype tf float32  seed 1  print datetime now   - startTime   In the second case  where the constant is actually gets evaluated and the values are assigned  the time clearly depends on dim  0 00 01 244642   startTime   datetime now   m1   tf truncated normal  dim  dim   mean 0 0  stddev 0 02  dtype tf float32  seed 1  sess   tf Session   sess run m1  print datetime now   - startTime   And you can make it more clear by calculating something  d   tf matrix determinant m1   keeping in mind that the time will run in O dim 2 8    P S  I found were it is explained in documentation      A Tensor object is a symbolic handle to the result of an operation    but does not actually hold the values of the operation s output

User · Answer

You can check the output of a TensorObject without running the graph in a session  by enabling eager execution   Simply add the following two lines of code   import tensorflow contrib eager as tfe tfe enable eager execution     right after you import tensorflow   The output of print product in your example will now be   tf Tensor    12     shape  1  1   dtype float32    Note that as of now  November 2017  you ll have to install a Tensorflow nightly build to enable eager execution  Pre-built wheels can be found here

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Try this simple code    it is self explanatory    import tensorflow as tf sess   tf InteractiveSession     see the answers above    x     1  2  1    1  1  1         a 2D matrix as input to softmax y   tf nn softmax x              this is the softmax function                                  you can have anything you like here u   y eval   print u

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Based on the answers above  with your particular code snippet you can print the product like this   import tensorflow as tf  Initialize the session sess   tf InteractiveSession    matrix1   tf constant   3   3     matrix2   tf constant   2    2     product   tf matmul matrix1  matrix2    print the product print product eval      close the session to release resources sess close

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Enable the eager execution which is introduced in tensorflow after version 1 10  It s very easy to use     Initialize session import tensorflow as tf tf enable eager execution       Some tensor we want to print the value of a   tf constant  1 0  3 0    print a

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Please note that tf Print   will change the tensor name  If the tensor you seek to print is a placeholder  feeding data to it will fail as the original name will not be found during feeding  For example   import tensorflow as tf tens   tf placeholder tf float32  None 2  name  placeholder   print eval  tens    tens   tf Print tens  tens  tf shape tens   summarize 10 message  tens    print eval  tens    res   tens   tens sess   tf Session   sess run tf global variables initializer     print sess run res     Output is   python test py Tensor  placeholder 0   shape     2   dtype float32  Tensor  Print 0   shape     2   dtype float32  Traceback  most recent call last         InvalidArgumentError  see above for traceback   You must feed a value for placeholder tensor  placeholder  with dtype float

User · Answer

You can use Keras  one-line answer will be to use eval method like so   import keras backend as K print K eval your tensor

User · Answer

In Tensorflow V2  Print value of tensor using  tf keras backend print tensor x  message

User · Answer

The easiest A  way to evaluate the actual value of a Tensor object is to pass it to the Session run   method  or call Tensor eval   when you have a default session  i e  in a with tf Session    block  or see below   In general B   you cannot print the value of a tensor without running some code in a session   If you are experimenting with the programming model  and want an easy way to evaluate tensors  the tf InteractiveSession lets you open a session at the start of your program  and then use that session for all Tensor eval    and Operation run    calls  This can be easier in an interactive setting  such as the shell or an IPython notebook  when it s tedious to pass around a Session object everywhere  For example  the following works in a Jupyter notebook   with tf Session   as sess   print product eval       This might seem silly for such a small expression  but one of the key ideas in Tensorflow 1 x is deferred execution  it s very cheap to build a large and complex expression  and when you want to evaluate it  the back-end  to which you connect with a Session  is able to schedule its execution more efficiently  e g  executing independent parts in parallel and using GPUs       A   To print the value of a tensor without returning it to your Python program  you can use the tf print   operator  as Andrzej suggests in another answer  According to the official documentation       To make sure the operator runs  users need to pass the produced op to tf compat v1 Session s run method  or to use the op as a control dependency for executed ops by specifying with tf compat v1 control dependencies  print op    which is printed to standard output     Also note that      In Jupyter notebooks and colabs  tf print prints to the notebook cell outputs  It will not write to the notebook kernel s console logs     B   You might be able to use the tf get static value   function to get the constant value of the given tensor if its value is efficiently calculable

[python] How to print the value of a Tensor object in TensorFlow?

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