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As the retrained_graph.pb file was 88 MB, we have uploaded them in the following URL. The One Drive folder also consists of Bottlenecks that were created during the predictions

https://mcgill-my.sharepoint.com/personal/nirmal_kanagasabai_mail_mcgill_ca/_layouts/15/guestaccess.aspx?docid=19b10f61652704239a18eecd259fe3eba&authkey=Ad7ExX0ggXwPQ9hR5vDoyBc

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To ensure the Convolution Neural Networks program run, the following steps are to be followed:

1. Docker Installation
2. Tensorflow Installation
3. Docker Optimization
4. Image Extraction
5. Directory Structure Creation
6. Download Inceptionv3
7. Build Tensorflow (Retrain the model)
8. Make Predictions*

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Skipping the installation process

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Docker Optimization:

• To cater to the computational needs of the program, it is mandatory to tweak the parameters as the speed of the processor in the virtual machine is crucial.

• As our project involves a lot of number crunching, we had to optimize the configuration for speed.

The following steps were carried out:

• VirtualBox (ACPI Shutdown)
• Base Memory increased to 75% of Laptop's total Memory
• Processor increased to the maximum applicable
• Headless Start

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Image Extraction:

Please refer to 'Image_Extraction.ipynb' file

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Creating Tensorflow Docker Image:

docker run -it gcr.io/tensorflow/tensorflow:latest-devel

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Directory Structure Creation:

It is very important to ensure that the directory structure is obeyed when we are adopting Transfer learning approach.

tf_files |_ _ _ Train_Image (Directory containing sub-directories) |_ _ _ Test_Image (Directory containing all test images) |_ _ _ Bottlenecks (which will be created later) |_ _ _ Retrained_Labels.txt (which will be created later) |_ _ _ Retrained_Graph.pb (which will be created later) |_ _ _ Label_Image.py (The prediction file) |_ _ _ Inception (which will be downloaded from Github)

Train_Image |_ _ _ 0 |_ _ _ 1 |_ _ _ 2 |_ _ _ 3 |_ _ _ . |_ _ _ . |_ _ _ . |_ _ _ 39

0,1,2,3,..,39 represents the 40 classes of Tiny Imagenet Challenge

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Sharing the folders with the VM:

docker run -it -v $HOME/tf_files:/tf_files gcr.io/tensorflow/tensorflow:latest-devel

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Downloading Inception V3:

Allow us to retrain the Inception V3 classifier with our Tiny Imagenet dataset.

git config --global user.email "you@example.com"
git config --global user.name "Your Name"
git pull origin master
git checkout 6d46c0b370836698a3195a6d73398f15fa44bcb2

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Building the Code:

bazel build -c opt --copt=-mavx tensorflow/examples/image_retraining:retrain

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Running the Tensorflow Retraining process:

-> Bottleneck directory will be used to cache the outputs of the lower layers on disk so that they do not have to repeatedly be recalculated

-> Retraned_Graph.pb will be the retrained model for the Tiny Imagenet classifier

-> Retrained_Labels.txt will contain the list of all the classes that is involved and that we will use to correspond the test images to

bazel-bin/tensorflow/examples/image_retraining/retrain \
--bottleneck_dir=/tf_files/bottlenecks \
--model_dir=/tf_files/inception \
--output_graph=/tf_files/retrained_graph.pb \
--output_labels=/tf_files/retrained_labels.txt \
--image_dir /tf_files/Train_Image

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Prediction:

Label_Image.py

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