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/
โโโ densenet121_cifar10_beginner.py
โโโ dqn_cartpole.py
โโโ encoder_decoder_attention.py
โโโ encoder_decoder_lstm.py
โโโ gan_fashion_mnist.py
โโโ lenet_mnist.py
โโโ lenet_mnist_beginner.py
โโโ logistic_regression_mnist.py
โโโ logistic_regression_mnist_beginner.py
โโโ mlp_mnist.py
โโโ mlp_mnist_beginner.py
โโโ resnet34_fashion_mnist.py
โโโ resnet34_fashion_mnist_beginner.py
โโโ resnet50_fashion_mnist.py
โโโ resnet50_fashion_mnist_beginner.py
โโโ transformer.py
โโโ vae_fashion_mnist.py
โ
โโโ layers/
ย ย โโโ Attention.py
ย ย โโโ DotProductAttention.py
ย ย โโโ LayerNormalization.py
ย ย โโโ MultiHeadAttention.py
ย ย โโโ PositionalEncoding.py
ย ย โโโ ScaledDotProductAttention.py
*_beginner.py is the file using only Keras.