Repository: systemml Updated Branches: refs/heads/gh-pages 3144d4b9d -> 88e70806f
[SYSTEMML-540] Include the memory requirement of each layer in the summary table of Caffe2DML - This helps the user to estimate the batch size she should set for optimal performance. Project: http://git-wip-us.apache.org/repos/asf/systemml/repo Commit: http://git-wip-us.apache.org/repos/asf/systemml/commit/e0951e17 Tree: http://git-wip-us.apache.org/repos/asf/systemml/tree/e0951e17 Diff: http://git-wip-us.apache.org/repos/asf/systemml/diff/e0951e17 Branch: refs/heads/gh-pages Commit: e0951e177608fb4d24268a0037a18d4f72d5d4da Parents: 3144d4b Author: Niketan Pansare <npan...@us.ibm.com> Authored: Wed Oct 25 15:40:21 2017 -0700 Committer: Niketan Pansare <npan...@us.ibm.com> Committed: Wed Oct 25 15:42:04 2017 -0700 ---------------------------------------------------------------------- beginners-guide-caffe2dml.md | 37 +++++++++++++++++++++---------------- 1 file changed, 21 insertions(+), 16 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/systemml/blob/e0951e17/beginners-guide-caffe2dml.md ---------------------------------------------------------------------- diff --git a/beginners-guide-caffe2dml.md b/beginners-guide-caffe2dml.md index 4d6b7fd..8814283 100644 --- a/beginners-guide-caffe2dml.md +++ b/beginners-guide-caffe2dml.md @@ -64,22 +64,27 @@ lenet.summary() Output: ``` -+-----+---------------+--------------+------------+---------+-----------+---------+ -| Name| Type| Output| Weight| Bias| Top| Bottom| -+-----+---------------+--------------+------------+---------+-----------+---------+ -|mnist| Data| (, 1, 28, 28)| | |mnist,mnist| | -|conv1| Convolution|(, 32, 28, 28)| [32 X 25]| [32 X 1]| conv1| mnist| -|relu1| ReLU|(, 32, 28, 28)| | | relu1| conv1| -|pool1| Pooling|(, 32, 14, 14)| | | pool1| relu1| -|conv2| Convolution|(, 64, 14, 14)| [64 X 800]| [64 X 1]| conv2| pool1| -|relu2| ReLU|(, 64, 14, 14)| | | relu2| conv2| -|pool2| Pooling| (, 64, 7, 7)| | | pool2| relu2| -| ip1| InnerProduct| (, 512, 1, 1)|[3136 X 512]|[1 X 512]| ip1| pool2| -|relu3| ReLU| (, 512, 1, 1)| | | relu3| ip1| -|drop1| Dropout| (, 512, 1, 1)| | | drop1| relu3| -| ip2| InnerProduct| (, 10, 1, 1)| [512 X 10]| [1 X 10]| ip2| drop1| -| loss|SoftmaxWithLoss| (, 10, 1, 1)| | | loss|ip2,mnist| -+-----+---------------+--------------+------------+---------+-----------+---------+ ++-----+---------------+--------------+------------+---------+-----------+---------+--------------------+ +| Name| Type| Output| Weight| Bias| Top| Bottom|Memory* (train/test)| ++-----+---------------+--------------+------------+---------+-----------+---------+--------------------+ +|mnist| Data| (, 1, 28, 28)| | |mnist,mnist| | 1/0| +|conv1| Convolution|(, 32, 28, 28)| [32 X 25]| [32 X 1]| conv1| mnist| 25/12| +|relu1| ReLU|(, 32, 28, 28)| | | relu1| conv1| 25/12| +|pool1| Pooling|(, 32, 14, 14)| | | pool1| relu1| 6/3| +|conv2| Convolution|(, 64, 14, 14)| [64 X 800]| [64 X 1]| conv2| pool1| 38/7| +|relu2| ReLU|(, 64, 14, 14)| | | relu2| conv2| 12/6| +|pool2| Pooling| (, 64, 7, 7)| | | pool2| relu2| 3/2| +| ip1| InnerProduct| (, 512, 1, 1)|[3136 X 512]|[1 X 512]| ip1| pool2| 797/13| +|relu3| ReLU| (, 512, 1, 1)| | | relu3| ip1| 1/0| +|drop1| Dropout| (, 512, 1, 1)| | | drop1| relu3| 1/0| +| ip2| InnerProduct| (, 10, 1, 1)| [512 X 10]| [1 X 10]| ip2| drop1| 3/0| +| loss|SoftmaxWithLoss| (, 10, 1, 1)| | | loss|ip2,mnist| 0/0| ++-----+---------------+--------------+------------+---------+-----------+---------+--------------------+ + +Total number of layer outputs/errors/weights/bias/gradients: 5568768/5568768/1662752/618/106455680 +Total memory requirements for parameters* for train/test: 910/55 +[Advanced] Key network statistics to compute intermediate CP overhead batchSize/maxThreads/1-thread im2col*(sum, max)/1-thread reshape_col*(sum, max): 64/48/(1, 1)/(0, 0). +* => memory in megabytes assuming the parameters are in double precision and in dense format. ``` To train the above lenet model, we use the MNIST dataset.
