A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
- Perceptron
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Logistic Regression
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Softmax Regression (Multinomial Logistic Regression)
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
- Multilayer Perceptron
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Multilayer Perceptron with Dropout
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Multilayer Perceptron with Batch Normalization
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Multilayer Perceptron with Backpropagation from Scratch
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
- Convolutional Neural Network
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Convolutional Neural Network with He Initialization
[PyTorch: GitHub | Nbviewer]
- LeNet-5 on MNIST
[PyTorch: GitHub | Nbviewer] - LeNet-5 on CIFAR-10
[PyTorch: GitHub | Nbviewer] - LeNet-5 on QuickDraw
[PyTorch: GitHub | Nbviewer]
- Convolutional Neural Network VGG-16
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - VGG-16 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer] - Convolutional Neural Network VGG-19
[PyTorch: GitHub | Nbviewer]
- DenseNet-121 Digit Classifier Trained on MNIST
[PyTorch: GitHub | Nbviewer] - DenseNet-121 Image Classifier Trained on CIFAR-10
[PyTorch: GitHub | Nbviewer]
- ResNet and Residual Blocks
[PyTorch: GitHub | Nbviewer] - ResNet-18 Digit Classifier Trained on MNIST
[PyTorch: GitHub | Nbviewer] - ResNet-18 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer] - ResNet-34 Digit Classifier Trained on MNIST
[PyTorch: GitHub | Nbviewer] - ResNet-34 Object Classifier Trained on QuickDraw
[PyTorch: GitHub | Nbviewer] - ResNet-34 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer] - ResNet-50 Digit Classifier Trained on MNIST
[PyTorch: GitHub | Nbviewer] - ResNet-50 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer] - ResNet-101 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer] - ResNet-101 Trained on CIFAR-10
[PyTorch: GitHub | Nbviewer] - ResNet-152 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer]
- BatchNorm before and after Activation for Network-in-Network CIFAR-10 Classifier
[PyTorch: GitHub | Nbviewer] - Filter Response Normalization for Network-in-Network CIFAR-10 Classifier
[PyTorch: GitHub | Nbviewer]
- Autoencoder (MNIST)
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Autoencoder (MNIST) + Scikit-Learn Random Forest Classifier
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
- Convolutional Autoencoder with Deconvolutions / Transposed Convolutions
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Convolutional Autoencoder with Deconvolutions and Continuous Jaccard Distance
[PyTorch: GitHub | Nbviewer] - Convolutional Autoencoder with Deconvolutions (without pooling operations)
[PyTorch: GitHub | Nbviewer] - Convolutional Autoencoder with Nearest-neighbor Interpolation
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on CelebA
[PyTorch: GitHub | Nbviewer] - Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on Quickdraw
[PyTorch: GitHub | Nbviewer]
- Variational Autoencoder
[PyTorch: GitHub | Nbviewer] - Convolutional Variational Autoencoder
[PyTorch: GitHub | Nbviewer]
- Conditional Variational Autoencoder (with labels in reconstruction loss)
[PyTorch: GitHub | Nbviewer] - Conditional Variational Autoencoder (without labels in reconstruction loss)
[PyTorch: GitHub | Nbviewer] - Convolutional Conditional Variational Autoencoder (with labels in reconstruction loss)
[PyTorch: GitHub | Nbviewer] - Convolutional Conditional Variational Autoencoder (without labels in reconstruction loss)
[PyTorch: GitHub | Nbviewer]
- Fully Connected GAN on MNIST
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Fully Connected Wasserstein GAN on MNIST
[PyTorch: GitHub | Nbviewer] - Convolutional GAN on MNIST
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Convolutional GAN on MNIST with Label Smoothing
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer] - Convolutional Wasserstein GAN on MNIST
[PyTorch: GitHub | Nbviewer]
- Most Basic Graph Neural Network with Gaussian Filter on MNIST
[PyTorch: GitHub | Nbviewer] - Basic Graph Neural Network with Edge Prediction on MNIST
[PyTorch: GitHub | Nbviewer] - Basic Graph Neural Network with Spectral Graph Convolution on MNIST
[PyTorch: GitHub | Nbviewer]
- A simple single-layer RNN (IMDB)
[PyTorch: GitHub | Nbviewer] - A simple single-layer RNN with packed sequences to ignore padding characters (IMDB)
[PyTorch: GitHub | Nbviewer] - RNN with LSTM cells (IMDB)
[PyTorch: GitHub | Nbviewer] - RNN with LSTM cells (IMDB) and pre-trained GloVe word vectors
[PyTorch: GitHub | Nbviewer] - RNN with LSTM cells and Own Dataset in CSV Format (IMDB)
[PyTorch: GitHub | Nbviewer] - RNN with GRU cells (IMDB)
[PyTorch: GitHub | Nbviewer] - Multilayer bi-directional RNN (IMDB)
[PyTorch: GitHub | Nbviewer] - Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (AG News)
[PyTorch: GitHub | Nbviewer] - Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (Yelp Review Polarity)
[PyTorch: GitHub | Nbviewer] - Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (Amazon Review Polarity)
[PyTorch: GitHub | Nbviewer]
- Ordinal Regression CNN -- CORAL w. ResNet34 on AFAD-Lite
[PyTorch: GitHub | Nbviewer] - Ordinal Regression CNN -- Niu et al. 2016 w. ResNet34 on AFAD-Lite
[PyTorch: GitHub | Nbviewer] - Ordinal Regression CNN -- Beckham and Pal 2016 w. ResNet34 on AFAD-Lite
[PyTorch: GitHub | Nbviewer]
- Cyclical Learning Rate
[PyTorch: GitHub | Nbviewer] - Annealing with Increasing the Batch Size (w. CIFAR-10 & AlexNet)
[PyTorch: GitHub | Nbviewer] - Gradient Clipping (w. MLP on MNIST)
[PyTorch: GitHub | Nbviewer]
- Transfer Learning Example (VGG16 pre-trained on ImageNet for Cifar-10)
- Using PyTorch Dataset Loading Utilities for Custom Datasets -- CSV files converted to HDF5
[PyTorch: GitHub | Nbviewer] - Using PyTorch Dataset Loading Utilities for Custom Datasets -- Face Images from CelebA
[PyTorch: GitHub | Nbviewer] - Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from Quickdraw
[PyTorch: GitHub | Nbviewer] - Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from the Street View House Number (SVHN) Dataset
[PyTorch: GitHub | Nbviewer] - Using PyTorch Dataset Loading Utilities for Custom Datasets -- Asian Face Dataset (AFAD)
[PyTorch: GitHub | Nbviewer] - Using PyTorch Dataset Loading Utilities for Custom Datasets -- Dating Historical Color Images
[PyTorch: GitHub | Nbviewer]
- Generating Validation Set Splits
[PyTorch]: GitHub | Nbviewer] - Dataloading with Pinned Memory
[PyTorch: GitHub | Nbviewer] - Standardizing Images
[PyTorch: GitHub | Nbviewer] - Image Transformation Examples
[PyTorch: GitHub | Nbviewer] - Char-RNN with Own Text File
[PyTorch: GitHub | Nbviewer] - Sentiment Classification RNN with Own CSV File
[PyTorch: GitHub | Nbviewer]
- Using Multiple GPUs with DataParallel -- VGG-16 Gender Classifier on CelebA
[PyTorch: GitHub | Nbviewer]
- Sequential API and hooks
[PyTorch: GitHub | Nbviewer] - Weight Sharing Within a Layer
[PyTorch: GitHub | Nbviewer] - Plotting Live Training Performance in Jupyter Notebooks with just Matplotlib
[PyTorch: GitHub | Nbviewer]
- Chunking an Image Dataset for Minibatch Training using NumPy NPZ Archives
[TensorFlow 1: GitHub | Nbviewer] - Storing an Image Dataset for Minibatch Training using HDF5
[TensorFlow 1: GitHub | Nbviewer] - Using Input Pipelines to Read Data from TFRecords Files
[TensorFlow 1: GitHub | Nbviewer] - Using Queue Runners to Feed Images Directly from Disk
[TensorFlow 1: GitHub | Nbviewer] - Using TensorFlow's Dataset API
[TensorFlow 1: GitHub | Nbviewer]