Deep learning library written in python just for fun.
It uses numpy for computations. API is similar to PyTorch's one.
https://slick-dnn.readthedocs.io/en/latest/
-
Activation functions:
- ArcTan
- ReLU
- Sigmoid
- Softmax
- Softplus
- Softsign
- Tanh
-
Losses:
- MSE
- Cross Entropy
-
Optimizers:
- SGD
- Adam
-
Layers:
- Linear
- Conv2d
- Sequential
-
Autograd operations:
- Reshape
- Flatten
- SwapAxes
- Img2Col
- MaxPool2d
- AvgPool2d
- MatMul
- Mul
- Sub
- Add
-
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()
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