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Project: slick-dnn (GitHub Link)

• slick-dnn-master
  • examples
    • mnist_linear.py
    • mnist_cnn.py
  • LICENSE
  • Pipfile
  • setup.py
  • Pipfile.lock
  • README.md
  • slick_dnn
    • module.py
    • optimizers.py
    • autograd
      • tensor_modifications.py
      • activations.py
      • losses.py
      • mathematical.py
      • __init__.py
    • variable.py
    • data
      • example_datasets.py
      • __init__.py
    • __init__.py
  • .gitignore
  • docs
    • Makefile
    • source
      • optimizers.rst
      • index.rst
      • conf.py
      • autograd.rst
    • requirements.txt

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:

```python
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()