Better example code for the "Taking advantage of graphs" section#2424
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HsienChing wants to merge 1 commit intotensorflow:masterfrom
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Better example code for the "Taking advantage of graphs" section#2424HsienChing wants to merge 1 commit intotensorflow:masterfrom
HsienChing wants to merge 1 commit intotensorflow:masterfrom
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# Better example code for the "Taking advantage of graphs" section **Position:** "Taking advantage of graphs" section **Link:** https://www.tensorflow.org/guide/intro_to_graphs#taking_advantage_of_graphs **Condition:** The example code is OK, no bug. I am talking about a more detailed consideration about the tutorial. In the last line of the example code, the `assert` keyword is used to check the values between `a_regular_function()` and `tf.function(a_regular_function)`. ```Python assert(orig_value == tf_function_value) ``` However, when I run the code, nothing happens. As I learned the usage of `assert`, I understood that "noting happens" indicates "orig_value == tf_function_value". NOTE: In Python, the [`assert`](https://docs.python.org/3/reference/simple_stmts.html#grammar-token-python-grammar-assert_stmt) keyword is a debugging tool used to verify that a condition is true. If the condition evaluates to False, the program immediately raises an AssertionError and terminates, helping developers catch bugs during the development and testing phases. **Suggestion:** In my opinion, a tutorial should give readers an "explicit" guide instead of an "implicit" guide. Using `print` is a better choice than using `assert`. When using `print`, a `True` or `False` will be sent according to the conditional expression `orig_value == tf_function_value`, and readers will understand the result explicitly and immediately. ```Python print(orig_value == tf_function_value) ``` REF: https://github.com/HsienChing/ML_DL_project_State_Estimation_of_Li-ion_Batteries/blob/main/other/Issues_in_TensorFlow_official_doc_Introduction_to_graphs_and_tf.function.ipynb
PreviewPreview and run these notebook edits with Google Colab: Rendered notebook diffs available on ReviewNB.com.Format and styleUse the TensorFlow docs notebook tools to format for consistent source diffs and lint for style:$ python3 -m pip install -U --user git+https://github.com/tensorflow/docsIf commits are added to the pull request, synchronize your local branch: git pull origin patch-4
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Better example code for the "Taking advantage of graphs" section
Position:
"Taking advantage of graphs" section
Link:
https://www.tensorflow.org/guide/intro_to_graphs#taking_advantage_of_graphs
Condition:
The example code is OK, no bug. I am talking about a more detailed consideration about the tutorial.
In the last line of the example code, the
assertkeyword is used to check the values betweena_regular_function()andtf.function(a_regular_function).However, when I run the code, nothing happens. As I learned the usage of
assert, I understood that "noting happens" indicates "orig_value == tf_function_value".NOTE: In Python, the
assertkeyword is a debugging tool used to verify that a condition is true. If the condition evaluates to False, the program immediately raises an AssertionError and terminates, helping developers catch bugs during the development and testing phases.Suggestion:
In my opinion, a tutorial should give readers an "explicit" guide instead of an "implicit" guide. Using
printis a better choice than usingassert.When using
print, aTrueorFalsewill be sent according to the conditional expressionorig_value == tf_function_value, and readers will understand the result explicitly and immediately.REF: https://github.com/HsienChing/ML_DL_project_State_Estimation_of_Li-ion_Batteries/blob/main/other/Issues_in_TensorFlow_official_doc_Introduction_to_graphs_and_tf.function.ipynb