Deep Learning with PyTorch 1.X

Implementations of neural network models with torch (>=1.0)

See also implementations with TesorFlow 2.0 here.

Requirements

• PyTorch >= 1.0

$ pip install torch torchvision

Models

• Logistic Regression
• MLP
• LeNet
• ResNet (ResNet34, ResNet50)
• Encoder-Decoder (LSTM)
• Encoder-Decoder (Attention)
• Transformer
• Deep Q-Network
• Variational Autoencoder
• Generative Adversarial Network
• Conditional GAN

models/
├── conditonal_gan_mnist.py
├── dqn_cartpole.py
├── encoder_decoder_attention.py
├── encoder_decoder_lstm.py
├── gan_fashion_mnist.py
├── lenet_mnist.py
├── logistic_regression_mnist.py
├── mlp_mnist.py
├── resnet34_fashion_mnist.py
├── resnet50_fashion_mnist.py
├── transformer.py
├── vae_fashion_mnist.py
│
└── layers/
    ├── Attention.py
    ├── DotProductAttention.py
    ├── Flatten.py
    ├── GlobalAvgPool2d.py
    ├── MultiHeadAttention.py
    ├── PositionalEncoding.py
    └── ScaledDotProductAttention.py