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chapter-4/1_feature_extraction.ipynb using default batch size 32 instead of defined batch_size 128 #164

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gitgithan opened this issue Feb 10, 2023 · 1 comment
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chapter-4/1_feature_extraction.ipynb using default batch size 32 instead of defined batch_size 128 #164

gitgithan opened this issue Feb 10, 2023 · 1 comment

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@gitgithan
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gitgithan commented Feb 10, 2023 •
edited 

batch_size = 128
datagen = tensorflow.keras.preprocessing.image.ImageDataGenerator(preprocessing_function=preprocess_input)

generator = datagen.flow_from_directory(root_dir,
                                        target_size=(224, 224),
                                        class_mode=None,
                                        shuffle=False)

uses default batch_size = 32.

num_epochs = int(math.ceil(num_images / batch_size))
calculates 68 epochs based on 128 batch size.
So generator only generates 68*32 = 2176 images for feature_list, mismatching the 8677 in standard_feature_list with features extracted from the non generator method.
@gitgithan gitgithan changed the title chapter-4/1_feature_extraction.ipynb using default batch size instead of defined variable chapter-4/1_feature_extraction.ipynb using default batch size 32 instead of defined batch_size 128 Feb 10, 2023
@gitgithan gitgithan mentioned this issue Mar 21, 2023
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@gitgithan gitgithan mentioned this issue Apr 3, 2023
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@havardox
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@PracticalDL This seems like a pretty big issue. The whole of chapter 4 uses the wrong number of training samples.

manavrmoorthy pushed a commit to manavrmoorthy/Practical-Deep-Learning-Book that referenced this issue Aug 28, 2023
@manavmoorthy
chapter 4:
90d5a6f 
- removing deprecated files
- the gcloud sessions are not using the same runtime (so we need to re-download data) so writing outputs to gdrive to make sure things are running and outputs are accessible for subsequent notebooks in colab
- renaming 4/1 notebook - since it had underscores instead of hyphens, the colab link in the notebook was not working
- xception added to imports for notebook 1, since it is part of the model_maker
- PracticalDL#163 - fixed
- PracticalDL#164 - fixed
- PracticalDL#169 - fixed
- metric='angular' added for annoy as default arg will be removed in subsequent releases
- removing a duplicate PCA + Annoy section
- PracticalDL#170 - fixed
- time is a negligible factor here, and we do not need it in the plots (since we are using optimised accuracy calculation using numpy from issue 170) - hence, modifying the plots
- removing matplotlib.style.use('seaborn') since it is deprecated
- the final fine-tuning notebook uses Caltech256 features (as per the book), which do not exist, since fine-tuning was done on Caltech101 - hence, renaming those files to caltech101. Can we retain caltech101 to test?
- PracticalDL#167 - fixed, if the above is okay
- formatted the code

chapter 5:
- write_grads and batch_size params have been removed from callback, or will be removed in subsequent releases
- PracticalDL#174 - not able to replicate this issue
- added a pointer to the notebook that suggests that for tensorboard to work without a 403 Forbidden error on Colab, cookies need to be allowed (I faced this issue)
- notebook 3 in chapter 5 is the exact same as notebook 2 in chapter 2 - replaced the file directly
- the autokeras notebook in Colab is named autokeras-error.ipynb - where can we change this to autokeras.ipynb?
- fixing accuracy score calculation in the autokeras notebook
- formatted the code

chapter 6:
- including the download_sample_image function
- formatted the code
@manavrmoorthy manavrmoorthy mentioned this issue Aug 28, 2023
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@havardox @gitgithan