thunet 0.0.10.202209

A Deep learning framework for scientific and educational purpose

THUNET: A simple deep learning framework for scientific and education purpose.

1. Neural networks[1]

   • Layers / Layer-wise ops

     • Add

     • Flatten

     • Multiply

     • Softmax

     • Fully-connected/Dense

     • Sparse evolutionary connections

     • LSTM

     • Elman-style RNN

     • Max + average pooling

     • Dot-product attention

     • Embedding layer

     • Restricted Boltzmann machine (w. CD-n training)

     • 2D deconvolution (w. padding and stride)

     • 2D convolution (w. padding, dilation, and stride)

     • 1D convolution (w. padding, dilation, stride, and causality)

   • Modules

     • Bidirectional LSTM

     • ResNet-style residual blocks (identity and convolution)

     • WaveNet-style residual blocks with dilated causal convolutions

     • Transformer-style multi-headed scaled dot product attention

   • Regularizers

     • Dropout

   • Normalization

     • Batch normalization (spatial and temporal)

     • Layer normalization (spatial and temporal)

   • Optimizers

     • SGD w/ momentum

     • AdaGrad

     • RMSProp

     • Adam

   • Learning Rate Schedulers

     • Constant

     • Exponential

     • Noam/Transformer

     • Dlib scheduler

   • Weight Initializers

     • Glorot/Xavier uniform and normal

     • He/Kaiming uniform and normal

     • Standard and truncated normal

   • Losses

     • Cross entropy

     • Squared error

     • Bernoulli VAE loss

     • Wasserstein loss with gradient penalty

     • Noise contrastive estimation loss

   • Activations

     • ReLU

     • Tanh

     • Affine

     • Sigmoid

     • Leaky ReLU

     • ELU

     • SELU

     • Exponential

     • Hard Sigmoid

     • Softplus

   • Models

     • Bernoulli variational autoencoder

     • Wasserstein GAN with gradient penalty

     • word2vec encoder with skip-gram and CBOW architectures

   • Utilities

     • col2im (MATLAB port)

     • im2col (MATLAB port)

     • conv1D

     • conv2D

     • deconv2D

     • minibatch

2. BERT

   • Vanilla BERT

   • Simple BERT

3. REFERENCE

Our contribution is implementation of the vanilla BERT and simple BERT.

All other codes following the licence claimed by (ddbourgin)[https://github.com/ddbourgin] in his (Numpy_ML)![https://github.com/ddbourgin/numpy-ml] project.

4. Release Frequent Asked Questions

• Q: Python2.7: LookupError: unknown encoding: cp0

• A: Setting environment in the shell: set PYTHONIOENCODING=UTF-8

5. Product Release

Supported Python versions:

| Python |

|--------|

| 2.7 |

| 3.5 |

| 3.6 |

| 3.7 |

| 3.8 |

| 3.9 |

| 3.10 |

[1] David Bourgin. Machine learning, in numpy. https://github.com/ddbourgin/numpy-ml.