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  1. SystemDS
  2. SYSTEMDS-618

Deep Learning DML Library

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    Details

    • Type: Epic
    • Status: In Progress
    • Priority: Major
    • Resolution: Unresolved
    • Affects Version/s: None
    • Fix Version/s: None
    • Component/s: Algorithms
    • Labels:
      None
    • Epic Name:
      Deep Learning DML Library

      Description

      This issue tracks the creation of a layers-based deep learning library in pure DML.

      The library contains layers with simple forward (function evaluation) and backward (gradient computation) functions for affine, convolution (start with 2D), max-pooling, non-linearities (relu, sigmoid, softmax, etc.), dropout, loss functions, other layers, optimizers, and gradient checks.

      Examples: Please see example scripts and notebooks in the examples folder: https://github.com/apache/systemml/tree/master/scripts/nn/examples.

      SystemML-NN: https://github.com/apache/systemml/tree/master/scripts/nn

      • Layers:
        • Core:
          • Affine
          • Batch Normalization 1D
          • Batch Normalization 2D ("Spatial Batch Normalization")
          • Convolution 2D ("Spatial Convolution")
          • LSTM
          • Max Pooling 2D ("Spatial Max Pooling")
          • RNN
        • Nonlinearities:
          • ReLU
          • Sigmoid
          • Softmax
          • Softmax 2D
          • Tanh
        • Loss:
          • Cross-entropy loss
          • Cross-entropy loss 2D
          • L1 loss
          • L2 loss
          • Log ("Logistic") loss
        • Regularization:
          • Dropout
          • L1 reg
          • L2 reg
      • Optimizers:
        • Adagrad
        • Adam
        • RMSprop
        • SGD
        • SGD w/ Momentum
        • SGD w/ Nesterov Momentum
      • Tests:
        • Gradient Checks
        • Unit Tests

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              • Assignee:
                dusenberrymw Mike Dusenberry
                Reporter:
                dusenberrymw Mike Dusenberry
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                Dates

                • Created:
                  Updated: