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Wrong x-axis label order #2212

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Wrong x-axis label order #2212

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FrankXu1
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Just take a look at this chapter's graph
www.tensorflow.org/tutorials/audio/simple_audio#run_inference_on_an_audio_file
You may find It is wrong actually. Result is not campatible with the words.
It seems you posted the wrong order of x-axis labels.
The correct order is followed by initial characters by alphabet order.
So I propose it should be ['down', 'go', 'left', 'no', 'right', 'stop', 'up', 'yes']
I am not so sure whether there are any hidden possible order I did not notice from this.
If have, please tell me and then take the correction order.

@FrankXu1 FrankXu1 requested a review from a team as a code owner March 29, 2023 09:30
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Preview

Preview and run these notebook edits with Google Colab: Rendered notebook diffs available on ReviewNB.com.

Format and style

Use 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/docs

$ python3 -m tensorflow_docs.tools.nbfmt notebook.ipynb
$ python3 -m tensorflow_docs.tools.nblint --arg=repo:tensorflow/docs notebook.ipynb
If commits are added to the pull request, synchronize your local branch: git pull origin master

@MaskedFaceDataSet
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I agreed to @FrankXu1.
After we reorder the label, the model predicted correctly.

I am not sure if we can put the label in this way or not. but seem the model work well after reorder the label and testing with other inferent data.

@vijayasarathib
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This looks good to me

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@markmcd markmcd left a comment

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LG, just one little request.

@@ -826,7 +826,7 @@
"x = x[tf.newaxis,...]\n",
"\n",
"prediction = model(x)\n",
"x_labels = ['no', 'yes', 'down', 'go', 'left', 'up', 'right', 'stop']\n",
"x_labels = ['down', 'go', 'left', 'no', 'right', 'stop', 'up', 'yes']\n",
"plt.bar(x_labels, tf.nn.softmax(prediction[0]))\n",
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Suggested change
"plt.bar(x_labels, tf.nn.softmax(prediction[0]))\n",
"plt.bar(label_names, tf.nn.softmax(prediction[0]))\n",

I think instead of hard-coding them here, we can just use the label_names variable that is created earlier, and used throughout this guide (so you need to delete the previous line too).

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