[systemds] branch master updated: [SYSTEMDS-2714] Seeded NN layers

[baunsgaard]

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baunsgaard pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/systemds.git


The following commit(s) were added to refs/heads/master by this push:
     new 0bf5536  [SYSTEMDS-2714] Seeded NN layers
0bf5536 is described below

commit 0bf553627a7f2a55da05f355ac45d3c10457a822
Author: baunsgaard <baunsga...@tugraz.at>
AuthorDate: Thu Nov 5 00:06:55 2020 +0100

    [SYSTEMDS-2714] Seeded NN layers
    
    Add seeds to the initialization of affine and Conv2d layers
---
 scripts/nn/layers/affine.dml         | 5 +++--
 scripts/nn/layers/conv2d.dml         | 5 +++--
 scripts/nn/layers/conv2d_builtin.dml | 5 +++--
 3 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/scripts/nn/layers/affine.dml b/scripts/nn/layers/affine.dml
index c9a740b..a2d1043 100644
--- a/scripts/nn/layers/affine.dml
+++ b/scripts/nn/layers/affine.dml
@@ -64,7 +64,7 @@ backward = function(matrix[double] dout, matrix[double] X,
   db = colSums(dout)
 }
 
-init = function(int D, int M)
+init = function(int D, int M, int seed = -1 )
     return (matrix[double] W, matrix[double] b) {
   /*
    * Initialize the parameters of this layer.
@@ -81,12 +81,13 @@ init = function(int D, int M)
    * Inputs:
    *  - D: Dimensionality of the input features (number of features).
    *  - M: Number of neurons in this layer.
+   *  - seed: The seed to initialize the weights
    *
    * Outputs:
    *  - W: Weights, of shape (D, M).
    *  - b: Biases, of shape (1, M).
    */
-  W = rand(rows=D, cols=M, pdf="normal") * sqrt(2.0/D)
+  W = rand(rows=D, cols=M, pdf="normal", seed=seed) * sqrt(2.0/D)
   b = matrix(0, rows=1, cols=M)
 }
 
diff --git a/scripts/nn/layers/conv2d.dml b/scripts/nn/layers/conv2d.dml
index 4fb0335..1ff115f 100644
--- a/scripts/nn/layers/conv2d.dml
+++ b/scripts/nn/layers/conv2d.dml
@@ -153,7 +153,7 @@ backward = function(matrix[double] dout, int Hout, int Wout,
   }
 }
 
-init = function(int F, int C, int Hf, int Wf)
+init = function(int F, int C, int Hf, int Wf, int seed = -1)
     return (matrix[double] W, matrix[double] b) {
   /*
    * Initialize the parameters of this layer.
@@ -172,12 +172,13 @@ init = function(int F, int C, int Hf, int Wf)
    *  - C: Number of input channels (dimensionality of depth).
    *  - Hf: Filter height.
    *  - Wf: Filter width.
+   *  - seed: The seed to initialize the weights
    *
    * Outputs:
    *  - W: Weights, of shape (F, C*Hf*Wf).
    *  - b: Biases, of shape (F, 1).
    */
-  W = rand(rows=F, cols=C*Hf*Wf, pdf="normal") * sqrt(2.0/(C*Hf*Wf))
+  W = rand(rows=F, cols=C*Hf*Wf, pdf="normal", seed=seed ) * 
sqrt(2.0/(C*Hf*Wf))
   b = matrix(0, rows=F, cols=1)
 }
 
diff --git a/scripts/nn/layers/conv2d_builtin.dml 
b/scripts/nn/layers/conv2d_builtin.dml
index a2c2ad4..b30ed01 100644
--- a/scripts/nn/layers/conv2d_builtin.dml
+++ b/scripts/nn/layers/conv2d_builtin.dml
@@ -131,7 +131,7 @@ backward = function(matrix[double] dout, int Hout, int Wout,
   db = util::channel_sums(dout, F, Hout, Wout)
 }
 
-init = function(int F, int C, int Hf, int Wf)
+init = function(int F, int C, int Hf, int Wf, int seed = -1)
     return (matrix[double] W, matrix[double] b) {
   /*
    * Initialize the parameters of this layer.
@@ -150,12 +150,13 @@ init = function(int F, int C, int Hf, int Wf)
    *  - C: Number of input channels (dimensionality of depth).
    *  - Hf: Filter height.
    *  - Wf: Filter width.
+   *  - seed: The seed to initialize the weights
    *
    * Outputs:
    *  - W: Weights, of shape (F, C*Hf*Wf).
    *  - b: Biases, of shape (F, 1).
    */
-  W = rand(rows=F, cols=C*Hf*Wf, pdf="normal") * sqrt(2.0/(C*Hf*Wf))
+  W = rand(rows=F, cols=C*Hf*Wf, pdf="normal", seed=seed) * sqrt(2.0/(C*Hf*Wf))
   b = matrix(0, rows=F, cols=1)
 }