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Berkeley CS182/282 Designing, Visualizing and Understanding Deep Neural Networks

Self study on CS182/CS282 - Designing, Visualizing and Understanding Deep Neural Networks (Spring 2019) @ UC Berkeley. Includes assignments, lecture slides, and lecture notes. Solutions passed all the self-contained unit tests but were not submitted using student-only submission system.

Assignment1: ✔️

Based on Stanford CS 231n assignment 2. Only Python 2.7 supported. Migrated and completed using Python 3.7.

  • Implenentation of fully connected deep neural network for classification using numpy only.

Assignment2: ✔️

Based on Stanford CS 231n assignment 3. Python 3.5 and 3.6 supported. Done using Python 3.7. Network visualization and style transfer done using pytorch 1.2. Also implemented GAN notebook.

  • Implementation of image captioning neural network using numpy only.
  • Implementation of saliency map using Pytorch.
  • Implementation of image style transfer using Pytorch.
  • Implemnentation of GAN and DCGAN using using Pytorch.

Assignment3: ✔️

Done using Python 3.7 and tensorflow 2.0 in tensorflow.compat.v1 mode. Got 4.54 validation loss with default transformer hyper params vs. possible <= 4.5 indicated in the assignment notebook. Batch size limited by GPU memory.

Assignment4: ✔️

Based on assignment 2 and 3 of CS294-112 Deep Reinforcement Learning at UC Berkeley. Python 3.5 and 3.6 supported,tensorflow 1.10 code base.

Done using Python 3.7 and Tensorflow 2.0 in tensorflow.compat.v1 mode. Migraded codes in train_dqn.py from Tensorflow 1.10 to Tensorflow 1.15.

  • Implementation of vanilla Policy Gradient, DQN, DDQN.

OpenAI Gym FFMPEG issue prevented pong from training, causing ERROR: VideoRecorder encoder exited with status 1.

dd if=/dev/zero bs=750000 count=50 | ffmpeg -nostats -loglevel error -y -r 60 -f rawvideo -s:v 500x500 -pix_fmt 'rgb24' -i /dev/stdin -vcodec libx264 -pix_fmt yuv420p /tmp/foo.mp4
Unknown encoder 'libx264'
dd: error writing 'standard output': Broken pipe
2+0 records in
1+0 records out
815536 bytes (816 kB, 796 KiB) copied, 0.00551571 s, 148 MB/s

After above issue fixed, new error: Not enough memory.

MemoryError: Unable to allocate array with shape (1000000, 210, 160, 3) and data type uint8

The array shape from provided code is 210x160x3 but in atari.py the frames are downsampled to 84x84x1 using ProcessFrame84. My Ubuntu box can only fit at most (250000, 210, 160, 3) int8. Since DQN and DDQN looks fine on Cartpole, will save my energy bill and pospond running the training later.

LICENSE

Course Notes, homework assignments, and code scaffolds are owned by U.C. Berkeley.

You can use my solutions under MIT License.