Deep Learning with TensorFlow 2.X

Implementations of neural network models with tf (>=2.0)

See also implementations with PyTorch 1.0 here.

Requirements

• TensorFlow >= 2.0

$ pip install tensorflow

Models

• Logistic Regression
• MLP
• LeNet
• ResNet (ResNet34, ResNet50)
• DenseNet (DenseNet121)
• Encoder-Decoder (LSTM)
• EncoderDecoder (Attention)
• Transformer
• Deep Q-Network
• Variational Autoencoder
• Generative Adversarial Network

models/
├── densenet121_cifar10_beginner.py
├── dqn_cartpole.py
├── encoder_decoder_attention.py
├── encoder_decoder_lstm.py
├── gan_fashion_mnist.py
├── lenet_mnist.py
├── lenet_mnist_beginner.py
├── logistic_regression_mnist.py
├── logistic_regression_mnist_beginner.py
├── mlp_mnist.py
├── mlp_mnist_beginner.py
├── resnet34_fashion_mnist.py
├── resnet34_fashion_mnist_beginner.py
├── resnet50_fashion_mnist.py
├── resnet50_fashion_mnist_beginner.py
├── transformer.py
├── vae_fashion_mnist.py
│
└── layers/
    ├── Attention.py
    ├── DotProductAttention.py
    ├── LayerNormalization.py
    ├── MultiHeadAttention.py
    ├── PositionalEncoding.py
    └── ScaledDotProductAttention.py

*_beginner.py is the file using only Keras.