We Are Going To Discuss About How to save the value of a tensor in Tensorflow. So lets Start this Python Article.
How to save the value of a tensor in Tensorflow
- How to solve How to save the value of a tensor in Tensorflow
Well, I suppose that you are running the function under graph mode, otherwise (eager mode execution) you can just use NumPy or normal pythonic file handling ways to save the values after accessing them via
.numpy()function of a tensor.
In graph mode, you can usetf.io.write_file()operation. Elaborating more on the previously mentioned solution,write_filefn takes a single string. Below example might help more:a = tf.constant([1,2,3,4,5] , dtype = tf.int32) b = tf.constant([53.44569] , dtype= tf.float32) c = tf.constant(0) # if u wish to write all these tensors on each line , # then create a single string out of these. one_string = tf.strings.format("{}\n{}\n{}\n", (a,b,c)) # {} is a placeholder for each element ina string and thus you would need n PH for n tensors. # send this string to write_file fn tf.io.write_file(filename, one_string)write_filefn only accepts strings, so you need to convert everything to string first.
Also if you call write_file fn n number of times in the same run, each call will override previous's output, thus file will contain last call content. - How to save the value of a tensor in Tensorflow
Well, I suppose that you are running the function under graph mode, otherwise (eager mode execution) you can just use NumPy or normal pythonic file handling ways to save the values after accessing them via
.numpy()function of a tensor.
In graph mode, you can usetf.io.write_file()operation. Elaborating more on the previously mentioned solution,write_filefn takes a single string. Below example might help more:a = tf.constant([1,2,3,4,5] , dtype = tf.int32) b = tf.constant([53.44569] , dtype= tf.float32) c = tf.constant(0) # if u wish to write all these tensors on each line , # then create a single string out of these. one_string = tf.strings.format("{}\n{}\n{}\n", (a,b,c)) # {} is a placeholder for each element ina string and thus you would need n PH for n tensors. # send this string to write_file fn tf.io.write_file(filename, one_string)write_filefn only accepts strings, so you need to convert everything to string first.
Also if you call write_file fn n number of times in the same run, each call will override previous's output, thus file will contain last call content.
Solution 1
Well, I suppose that you are running the function under graph mode, otherwise (eager mode execution) you can just use NumPy or normal pythonic file handling ways to save the values after accessing them via .numpy() function of a tensor.
In graph mode, you can use tf.io.write_file() operation. Elaborating more on the previously mentioned solution, write_file fn takes a single string. Below example might help more:
a = tf.constant([1,2,3,4,5] , dtype = tf.int32)
b = tf.constant([53.44569] , dtype= tf.float32)
c = tf.constant(0)
# if u wish to write all these tensors on each line ,
# then create a single string out of these.
one_string = tf.strings.format("{}\n{}\n{}\n", (a,b,c))
# {} is a placeholder for each element ina string and thus you would need n PH for n tensors.
# send this string to write_file fn
tf.io.write_file(filename, one_string)
write_file fn only accepts strings, so you need to convert everything to string first.
Also if you call write_file fn n number of times in the same run, each call will override previous’s output, thus file will contain last call content.
Original Author yogeesh agarwal Of This Content
Solution 2
Take a look at tf.io.write_file. It allows you to write a tensor to a file.
The corresponding function to read a saved tensor file is tf.io.read_file.
Original Author Susmit Agrawal Of This Content
Conclusion
So This is all About This Tutorial. Hope This Tutorial Helped You. Thank You.
