Worch

Worch is an implementation of a simple deep learning library based on pytorch. It supports MLPs with tanh, sigmoid or relu activations, MSE loss and SGD optimizer. It's been developed as a project for the EE-559 Deep Learning class at EPFL. The main goal was to implement backpropagation from scratch, i.e without using torch.autograd.

Design

The design is very similar to pytorch. The neural networks components and loss functions inherit from worch.nn.Module and the optimizers from worch.optim.Optimizer. Implementing a new module, however, requires to explicitly implement the backward pass in the backward method, as torch.autograd is not active.

Example

Let's create a MLP with MSE loss and SGD optimizer using worch and train it:

import worch
from data import Toy

# param
batch_size = 128
n_epochs = 20

# Get some data
toy = Toy()
X_train, Y_train = toy.get(test=False, normalize=True, onehot=True)

# Build sequential model
net = worch.nn.Sequential(
    worch.nn.Linear(in_features=X_train.shape[1], out_features=25),
    worch.nn.ReLU(),
    worch.nn.Linear(in_features=25, out_features=16),
    worch.nn.ReLU(),
    worch.nn.Linear(in_features=16, out_features=2),
    worch.nn.Sigmoid()
)

# Build loss
crit = worch.nn.LossMSE()
crit.register_previous_module(net) # Required additional boilerplate

# Build optimizer
optimizer = worch.optim.SGD(net.parameters(), lr=0.1)

# training loop
for epoch in range(n_epochs):
    for b in range(0, X_train.shape[0], batch_size):
        x = X_train[b:b+batch_size]
        y = Y_train[b:b+batch_size]    
        # forward pass
        yp = net(x)    
        # Compute loss
        loss = crit(yp, y)    
        # Backprop
        optimizer.zero_grad() # Reset gradients
        crit.backward() # Compute gradients: call on module, not `loss` tensor
        optimizer.step() # Compute gradient step

eliotwalt/worch

Worch

Design

Example