syaffers/deepy

Tiny and elegant deep learning library

deepy

Deep learning library written in python just for fun.

It uses numpy for computations. API is similar to PyTorch's one.

Examples:

• In examples directory there is a MNIST linear classifier, which scores over 96% accuracy.

• Sequential model creation:

from deepy.module import Linear, Sequential
from deepy.autograd.activations import Softmax, ReLU
my_model = Sequential(
    Linear(28 * 28, 300),
    ReLU(),
    Linear(300, 300),
    ReLU(),
    Linear(300, 10),
    Softmax()
    )

• Losses:

from deepy.module import Linear
from deepy.autograd.losses import CrossEntropyLoss, MSELoss
from deepy.variable import Variable
import numpy as np

my_model = Linear(10, 10)

loss1 = CrossEntropyLoss()
loss2 = MSELoss()


good_output = Variable(np.zeros((10,10)))
model_input = Variable(np.ones((10,10)))
model_output = my_model(model_input)

error = loss1(good_output, model_output)

# now you can propagate error backwards:
error.backward()

• Optimizers:

from deepy.module import Linear
from deepy.autograd.losses import CrossEntropyLoss, MSELoss
from deepy.variable import Variable
from deepy.autograd.optimizers import SGD
import numpy as np


my_model = Linear(10, 10)

loss1 = CrossEntropyLoss()
loss2 = MSELoss()

optimizer1 = SGD(my_model.get_variables_list())

good_output = Variable(np.zeros((10,10)))
model_input = Variable(np.ones((10,10)))
model_output = my_model(model_input)

error = loss1(good_output, model_output)

# now you can propagate error backwards:
error.backward()

# and then optimizer can update variables:
optimizer1.zero_grad()
optimizer1.step()