Slick-dnn

Deep learning library written in python just for fun.

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

Docs:

https://slick-dnn.readthedocs.io/en/latest/

Includes:

1. Activation functions:

   • ArcTan
   • ReLU
   • Sigmoid
   • Softmax
   • Softplus
   • Softsign
   • Tanh

2. Losses:

   • MSE
   • Cross Entropy

3. Optimizers:

   • SGD
   • Adam

4. Layers:

   • Linear
   • Conv2d
   • Sequential

5. Autograd operations:

   • Reshape
   • Flatten
   • SwapAxes
   • Img2Col
   • MaxPool2d
   • AvgPool2d
   • MatMul
   • Mul
   • Sub
   • Add

Examples:

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

• In examples directory there is also MNIST CNN classifier, which scored 99.19% accuracy on test set. One epoch of training takes about 290 seconds. It took 7 epochs to reach 99.19% accuracy (~30 min). Time measured on i5-4670k

• Sequential model creation:

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

• Losses:

from slick_dnn.module import Linear
from slick_dnn.autograd.losses import CrossEntropyLoss, MSELoss
from slick_dnn.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 slick_dnn.module import Linear
from slick_dnn.autograd.losses import CrossEntropyLoss, MSELoss
from slick_dnn.variable import Variable
from slick_dnn.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()