Neural network models

This module implements building-blocks for larger neural network models in the Keras-style. This module does not implement a general autograd system in order emphasize conceptual understanding over flexibility.

1. Activations. Common activation nonlinearities. Includes:

   • Rectified linear units (ReLU) (Hahnloser et al., 2000)
   • Leaky rectified linear units (Maas, Hannun, & Ng, 2013)
   • Exponential linear units (ELU) (Clevert, Unterthiner, & Hochreiter, 2016)
   • Scaled exponential linear units (Klambauer, Unterthiner, & Mayr, 2017)
   • Softplus units
   • Hard sigmoid units
   • Exponential units
   • Hyperbolic tangent (tanh)
   • Logistic sigmoid
   • Affine

2. Losses. Common loss functions. Includes:

   • Squared error
   • Categorical cross entropy
   • VAE Bernoulli loss (Kingma & Welling, 2014)
   • Wasserstein loss with gradient penalty (Gulrajani et al., 2017)
   • Noise contrastive estimation (NCE) loss (Gutmann & Hyvärinen; Minh & Teh, 2012)

3. Wrappers. Layer wrappers. Includes:

   • Dropout (Srivastava, et al., 2014)

4. Layers. Common layers / layer-wise operations that can be composed to create larger neural networks. Includes:

   • Fully-connected
   • Sparse evolutionary (Mocanu et al., 2018)
   • Dot-product attention (Luong, Pho, & Manning, 2015; Vaswani et al., 2017)
   • 1D and 2D convolution (with stride, padding, and dilation) (van den Oord et al., 2016; Yu & Kolton, 2016)
   • 2D "deconvolution" (with stride and padding) (Zeiler et al., 2010)
   • Restricted Boltzmann machines (with CD-n training) (Smolensky, 1996; Carreira-Perpiñán & Hinton, 2005)
   • Elementwise multiplication
   • Embedding
   • Summation
   • Flattening
   • Softmax
   • Max & average pooling
   • 1D and 2D batch normalization (Ioffe & Szegedy, 2015)
   • 1D and 2D layer normalization (Ba, Kiros, & Hinton, 2016)
   • Recurrent (Elman, 1990)
   • Long short-term memory (LSTM) (Hochreiter & Schmidhuber, 1997)

5. Optimizers. Common modifications to stochastic gradient descent. Includes:

   • SGD with momentum (Rummelhart, Hinton, & Williams, 1986)
   • AdaGrad (Duchi, Hazan, & Singer, 2011)
   • RMSProp (Tieleman & Hinton, 2012)
   • Adam (Kingma & Ba, 2015)

6. Learning Rate Schedulers. Common learning rate decay schedules.

   • Constant
   • Exponential decay
   • Noam/Transformer scheduler (Vaswani et al., 2017)
   • King/Dlib scheduler (King, 2018)

7. Initializers. Common weight initialization strategies.

   • Glorot/Xavier uniform and normal (Glorot & Bengio, 2010)
   • He/Kaiming uniform and normal (He et al., 2015)
   • Standard normal
   • Truncated normal

8. Modules. Common multi-layer blocks that appear across many deep networks. Includes:

   • Bidirectional LSTMs (Schuster & Paliwal, 1997)
   • ResNet-style "identity" (i.e., same-convolution) residual blocks (He et al., 2015)
   • ResNet-style "convolutional" (i.e., parametric) residual blocks (He et al., 2015)
   • WaveNet-style residual block with dilated causal convolutions (van den Oord et al., 2016)
   • Transformer-style multi-headed dot-product attention (Vaswani et al., 2017)

9. Models. Well-known network architectures. Includes:

   • vae.py: Bernoulli variational autoencoder (Kingma & Welling, 2014)
   • wgan_gp.py: Wasserstein generative adversarial network with gradient penalty (Gulrajani et al., 2017; Goodfellow et al., 2014)
   • w2v.py: word2vec model with CBOW and skip-gram architectures and training via noise contrastive estimation (Mikolov et al., 2012)

10. Utils. Common helper functions, primarily for dealing with CNNs. Includes:

    • im2col
    • col2im
    • conv1D
    • conv2D
    • dilate
    • deconv2D
    • minibatch
    • Various weight initialization utilities
    • Various padding and convolution arithmetic utilities