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Posted to commits@systemml.apache.org by ni...@apache.org on 2017/05/01 21:36:30 UTC
[2/2] incubator-systemml git commit: [SYSTEMML-540] Added the
documentation for convolution
[SYSTEMML-540] Added the documentation for convolution
Closes #464.
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/1efaa552
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/1efaa552
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/1efaa552
Branch: refs/heads/gh-pages
Commit: 1efaa552063a837ab7bb7f4a7e57e9efb088d7e8
Parents: 4874c4f
Author: Niketan Pansare <np...@us.ibm.com>
Authored: Sun Apr 30 12:39:12 2017 -0700
Committer: Niketan Pansare <np...@us.ibm.com>
Committed: Sun Apr 30 12:39:12 2017 -0700
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dml-language-reference.md | 32 +++++++++++++++++++
img/dml-language-reference/Conv2d.gif | Bin 0 -> 419930 bytes
img/dml-language-reference/Conv2d1.gif | Bin 0 -> 174964 bytes
.../Conv2d_backward_data.gif | Bin 0 -> 444715 bytes
.../Conv2d_backward_data1.gif | Bin 0 -> 427546 bytes
.../Conv2d_backward_data2.gif | Bin 0 -> 376145 bytes
6 files changed, 32 insertions(+)
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http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1efaa552/dml-language-reference.md
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diff --git a/dml-language-reference.md b/dml-language-reference.md
index 31f7d23..9273857 100644
--- a/dml-language-reference.md
+++ b/dml-language-reference.md
@@ -1494,6 +1494,38 @@ The following code snippet shows an example scenario of transforming a training
Note that the metadata generated during the training phase (located at `/user/ml/train_tf_metadata`) is used to apply the list of transformations (that were carried out on training data set) on the test data set. Since the second invocation of `transform()` does not really generate any new metadata data, the given metadata (`/user/ml/train_tf_metdata`) is copied to the target location (`/user/ml/test_tf_metdata`). Even though such a behavior creates redundant copies of transformation metadata, it is preferred as it allows the association of every data set with the corresponding transformation metadata.
+### Deep Learning Built-In Functions
+
+SystemML represent a tensor as a matrix stored in a row-major format,
+where first dimension of tensor and matrix are exactly the same. For example, a tensor (with all zeros)
+of shape [3, 2, 4, 5] can be instantiated by following DML statement:
+```sh
+A = matrix(0, rows=3, cols=2*4*5)
+```
+
+The images are assumed to be stored NCHW format, where N = batch size, C = #channels, H = height of image and W = width of image.
+Hence, the images are internally represented as a matrix with dimension (N, C * H * W).
+
+
+| Function name | Input matrices | Input Parameters | Notes |
+|------------------------|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|
+| conv2d | input, filter | stride=[stride_h, stride_w], padding=[pad_h, pad_w], input_shape=[batch_size, num_channels, height_image, width_image], filter_shape=[numFilters, numChannels, height_filter, width_filter] | Performs 2D convolution operation |
+| conv2d_backward_filter | input, dout | stride=[stride_h, stride_w], padding=[pad_h, pad_w], input_shape=[batch_size, num_channels, height_image, width_image], filter_shape=[numFilters, numChannels, height_filter, width_filter] | Computes the gradients wrt filter of 2D convolution |
+| conv2d_backward_data | filter, dout | stride=[stride_h, stride_w], padding=[pad_h, pad_w], input_shape=[batch_size, num_channels, height_image, width_image], filter_shape=[numFilters, numChannels, height_filter, width_filter] | Computes the gradients wrt input of 2D convolution |
+| max_pool | input | 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] | Performs max pooling operation |
+| max_pool_backward | input, dout | 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 maxpooling |
+
+
+Examples:
+
+| Function | Parameters | Visualization |
+|----------------------|-----------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| conv2d | stride=[1,1] |  |
+| conv2d | stride=[2,2] |  |
+| conv2d_backward_data | stride=[1,1] |  |
+| conv2d_backward_data | stride=[2,2] |  |
+| conv2d_backward_data | stride=[2,2] and 2x2 filter |  |
+
### Other Built-In Functions
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1efaa552/img/dml-language-reference/Conv2d.gif
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