Readme

classifierclass_usage.ipynb is a jupyter-notebook, it has tensorboard visualization and some links for other sources. Use this on a personal machine.
classifierclass_usage_keras_main_script.py and classifierclass_usage_numpy_main_script.py are script files to be used when running on a cluster.
data/ folder contains data for EMNIST and MNIST, if you don't find data in data folder then download it from internet and place it in the data folder, if the folder doesn't exist create one.
EMNSIT can be found at, use this link for .pkl.gz format.
MNIST can be found at

Suggested Exercises

Try to run the notebooks classifierclass_usage.ipynb, train_using_numpy_arrays.ipynb and complete the exrercises given within.

All the codes were tested with tensorflow 1.15 and Keras 2.2.4 in Python 2.7 and Python 3.7.5 on Linux however when you try to run on windows you might encounter issues like below:

If you don't have nbextension tab visible in your notebook then open anaconda prompt and run the following
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user
jupyter nbextension enable varInspector/main
Enable table of contents from nbextension tab once you start your notebook

Another problem you might have is:
AttributeError: module 'h5py' has no attribute 'Group' when you try to run something with keras in windows
So, run the following to fix this by opening anaconda command prompt in admin mode
pip uninstall h5py
pip install h5py==2.10.0

Go to the directory where you're logging your file by using the command cd "your/logging/directory". Replace your/logging/directory" with the path to your tensorflow log files.
And then run the following tensorboard --logdir "your logging directory name here"
And then tenosrboard will print a link to the screen that might looks like http://ENG40xxx:6006/
Copy the link and paste it in your browser then you should be able to see the various trends for the models that are being trained.

Name		Name	Last commit message	Last commit date
data		data
temporal_models		temporal_models
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
__init__.py		__init__.py
bare_bones_tensorflow_nn.ipynb		bare_bones_tensorflow_nn.ipynb
bare_bones_tfdata_nn.ipynb		bare_bones_tfdata_nn.ipynb
classifier_class_usage_keras_script.py		classifier_class_usage_keras_script.py
classifier_class_usage_numpy_script.py		classifier_class_usage_numpy_script.py
classifierclass_usage.ipynb		classifierclass_usage.ipynb
clr_callback.py		clr_callback.py
eager_vs_lazy.ipynb		eager_vs_lazy.ipynb
feature_reconstruction.py		feature_reconstruction.py
nn_classifierclass.py		nn_classifierclass.py
numpy_fcn.py		numpy_fcn.py
pandas_multi_level_df.ipynb		pandas_multi_level_df.ipynb
tensorflow_convolutions_times.ipynb		tensorflow_convolutions_times.ipynb
tf_recordization.ipynb		tf_recordization.ipynb
tf_recordization_parallel.ipynb		tf_recordization_parallel.ipynb
tfrecord_examination.ipynb		tfrecord_examination.ipynb
tfrecord_helpers.py		tfrecord_helpers.py
tfrecords_generator.ipynb		tfrecords_generator.ipynb
tfrecords_iterator.ipynb		tfrecords_iterator.ipynb
tfrecords_worker.py		tfrecords_worker.py
train_cnn_script.py		train_cnn_script.py
train_cnn_using_numpy_arrays.ipynb		train_cnn_using_numpy_arrays.ipynb
train_cnn_using_numpy_arrays2.ipynb		train_cnn_using_numpy_arrays2.ipynb
train_using_numpy_arrays.ipynb		train_using_numpy_arrays.ipynb
train_using_tfrecords.ipynb		train_using_tfrecords.ipynb