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

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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.

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