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[P] OpenCL framework with Dense layers

[P] OpenCL framework with Dense layers

Hi everyone,

I made simple DL framework on OpenCL just with Dense layers and several activations and losses: https://github.com/Airplaneless/Hallgerd

Matrix multiplication performance

MLP performance

Syntax is similar to Keras:

In [1]: from hallgerd.core import Sequential gpu = Device(devices['GeForce GTX 660'], DTYPE=np.float32) model = Sequential(device=gpu, lr=1e-1, batch_size=1024, epochs=40, loss='cross_entropy') In [2]: model.add(Dense(200, 200, activation='relu')) model.add(Dense(200, 5, activation='softmax')) In [3]: model.fit(X, y) # here X.shape = (feature size, dataset size) # y.shape = (output size, dataset size) In [4]: yp = model(X) 

I think performance is quite sufficient on devices without PyTorch or Tensorflow support.

https://i.redd.it/4m3g6kfcmsh31.jpg

submitted by /u/airplanelesss
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.