Repository: systemml Updated Branches: refs/heads/gh-pages f5ae0596d -> 0284f593f
[SYSTEMML-445] Added builtin functions for efficient computation of lstm_backward function - The current implementation treats lstm and lstm_backward as stateless function for simplicity. We can revisit this after performance testing. - Removed reserve parameter from lstm builtin function. - Updated the language reference and lstm_staging.dml file. - Added necessary kernels for transforming input to the format required by lstm_backward function. Project: http://git-wip-us.apache.org/repos/asf/systemml/repo Commit: http://git-wip-us.apache.org/repos/asf/systemml/commit/af4cf766 Tree: http://git-wip-us.apache.org/repos/asf/systemml/tree/af4cf766 Diff: http://git-wip-us.apache.org/repos/asf/systemml/diff/af4cf766 Branch: refs/heads/gh-pages Commit: af4cf766f6e5e03d9ff1cdfe1a212bcdeb962d37 Parents: f5ae059 Author: Niketan Pansare <npan...@us.ibm.com> Authored: Fri Jun 15 13:07:59 2018 -0700 Committer: Niketan Pansare <npan...@us.ibm.com> Committed: Fri Jun 15 13:07:59 2018 -0700 ---------------------------------------------------------------------- dml-language-reference.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/systemml/blob/af4cf766/dml-language-reference.md ---------------------------------------------------------------------- diff --git a/dml-language-reference.md b/dml-language-reference.md index 3212806..5bf9099 100644 --- a/dml-language-reference.md +++ b/dml-language-reference.md @@ -1520,7 +1520,7 @@ Hence, the images are internally represented as a matrix with dimension (N, C * | max_pool_backward, avg_pool_backward | input, dout | [batch_size X num_channels* height_image* width_image] | [batch_size X num_channels* height_out* width_out] | [batch_size X num_channels* height_image* width_image] | stride=[stride_h, stride_w], padding=[pad_h, pad_w], input_shape=[batch_size, num_channels, height_image, width_image], pool_size=[height_pool, width_pool] | Computes the gradients wrt input of 2D max pooling, average pooling | | bias_add | input, bias | [batch_size X num_channels* height_image* width_image] | [num_channels X 1] | [batch_size X num_channels* height_image* width_image] | | Adds the bias (row vector of size num_channels) to input with the given num_channels | | bias_multiply | input, bias | [batch_size X num_channels* height_image* width_image] | [num_channels X 1] | [batch_size X num_channels* height_image* width_image] | | Multiplies the bias (row vector of size num_channels) to input with the given num_channels | -| lstm | X, W, bias, out0, c0 | [batch_size X seq_length*num_features] | [num_features+hidden_size X 4*hidden_size] | [batch_size X seq_length*hidden_size] if return_sequences else [batch_size X hidden_size] | return_sequences | Perform computation for single-layer unidirectional LSTM (outputs: out, carryOut, reserveSpace) | +| lstm | X, W, bias, out0, c0 | [batch_size X seq_length*num_features] | [num_features+hidden_size X 4*hidden_size] | [batch_size X seq_length*hidden_size] if return_sequences else [batch_size X hidden_size] | return_sequences | Perform computation for single-layer unidirectional LSTM (outputs: out, carryOut) | | batch_norm2d | input | [batch_size X num_channels* height_image* width_image] | | [batch_size X num_channels* height_image* width_image] | scale, shift, exponentialMovingAverage_Mean, exponentialMovingAverage_Variance, mode, epsilon, momentum | Performs batch normalization operation (outputs: updated exponential moving average mean and variance, cache of the batch mean and variance) | | batch_norm2d_backward | input, dout | [batch_size X num_channels* height_image* width_image] | [batch_size X num_channels* height_image* width_image] | [batch_size X num_channels* height_image* width_image] | scale, epsilon, cache_mean (from forward), cache_inv_var (from forward) | Computed backpropagation error for batch normalization operation |
