Repository: incubator-singa Updated Branches: refs/heads/master 475d264f1 -> 2608d4166
Fix broken Markdown headings Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/2608d416 Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/2608d416 Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/2608d416 Branch: refs/heads/master Commit: 2608d4166d1b4547a6f618b3c5a46de918dcdd3b Parents: 475d264 Author: Santiago Castro <[email protected]> Authored: Sun Apr 16 23:36:40 2017 -0300 Committer: Santiago Castro <[email protected]> Committed: Sun Apr 16 23:36:40 2017 -0300 ---------------------------------------------------------------------- README.md | 4 +- doc/en/docs/cnn.md | 282 ++++++++++++++-------------- doc/en/releases/RELEASE_NOTES_0.1.0.md | 2 +- doc/en/releases/RELEASE_NOTES_0.2.0.md | 2 +- doc/en/releases/RELEASE_NOTES_0.3.0.md | 2 +- doc/en/releases/RELEASE_NOTES_1.0.0.md | 2 +- doc/en/releases/RELEASE_NOTES_1.1.0.md | 2 +- 7 files changed, 148 insertions(+), 148 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/2608d416/README.md ---------------------------------------------------------------------- diff --git a/README.md b/README.md index 5dc0c59..a2788e7 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -#Apache SINGA +# Apache SINGA [](https://travis-ci.org/apache/incubator-singa)  @@ -16,7 +16,7 @@ Distributed deep learning system * [JIRA tickets](https://issues.apache.org/jira/browse/SINGA) -##Mailing Lists +## Mailing Lists * [Development Mailing List](mailto:[email protected]) ([Archive](http://mail-archives.apache.org/mod_mbox/singa-dev/)) * [Commits Mailing List](mailto:[email protected]) ([Archive](http://mail-archives.apache.org/mod_mbox/singa-commits/)) http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/2608d416/doc/en/docs/cnn.md ---------------------------------------------------------------------- diff --git a/doc/en/docs/cnn.md b/doc/en/docs/cnn.md index 21ef1f7..6609137 100755 --- a/doc/en/docs/cnn.md +++ b/doc/en/docs/cnn.md @@ -1,141 +1,141 @@ -#Quickstart - Cifar10 example -Convolution neural network (CNN) is a type of feed-forward artificial neural network widely used for image classification. In this example, we will use a deep CNN model to do image classification for the [CIFAR10 dataset](http://www.cs.toronto.edu/~kriz/cifar.html). - -## Running instructions for CPP version -Please refer to [Installation](installation.html) page for how to install SINGA. Currently, we CNN requires CUDNN, hence both CUDA and CUDNN should be installed and SINGA should be compiled with CUDA and CUDNN. - -The Cifar10 dataset could be downloaded by running - - # switch to cifar10 directory - $ cd ../examples/cifar10 - # download data for CPP version - $ python download_data.py bin - -'bin' is for downloading binary version of Cifar10 data. - -During downloading, you should see the detailed output like - - Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz - The tar file does exist. Extracting it now.. - Finished! - -Now you have prepared the data for this Cifar10 example, the final step is to execute the `run.sh` script, - - # in SINGA_ROOT/examples/cifar10/ - $ ./run.sh - -You should see the detailed output as follows: first read the data files in order, show the statistics of training and testing data, then show the details of neural net structure with some parameter information, finally illustrate the performance details during training and validation process. The number of epochs can be specified in `run.sh` file. - - Start training - Reading file cifar-10-batches-bin/data_batch_1.bin - Reading file cifar-10-batches-bin/data_batch_2.bin - Reading file cifar-10-batches-bin/data_batch_3.bin - Reading file cifar-10-batches-bin/data_batch_4.bin - Reading file cifar-10-batches-bin/data_batch_5.bin - Reading file cifar-10-batches-bin/test_batch.bin - Training samples = 50000, Test samples = 10000 - conv1(32, 32, 32, ) - pool1(32, 16, 16, ) - relu1(32, 16, 16, ) - lrn1(32, 16, 16, ) - conv2(32, 16, 16, ) - relu2(32, 16, 16, ) - pool2(32, 8, 8, ) - lrn2(32, 8, 8, ) - conv3(64, 8, 8, ) - relu3(64, 8, 8, ) - pool3(64, 4, 4, ) - flat(1024, ) - ip(10, ) - conv1_weight : 8.09309e-05 - conv1_bias : 0 - conv2_weight : 0.00797731 - conv2_bias : 0 - conv3_weight : 0.00795888 - conv3_bias : 0 - ip_weight : 0.00798683 - ip_bias : 0 - Messages will be appended to an existed file: train_perf - Messages will be appended to an existed file: val_perf - Epoch 0, training loss = 1.828369, accuracy = 0.329420, lr = 0.001000 - Epoch 0, val loss = 1.561823, metric = 0.420600 - Epoch 1, training loss = 1.465898, accuracy = 0.469940, lr = 0.001000 - Epoch 1, val loss = 1.361778, metric = 0.513300 - Epoch 2, training loss = 1.320708, accuracy = 0.529000, lr = 0.001000 - Epoch 2, val loss = 1.242080, metric = 0.549100 - Epoch 3, training loss = 1.213776, accuracy = 0.571620, lr = 0.001000 - Epoch 3, val loss = 1.175346, metric = 0.582000 - -The training details are stored in `train_perf` file in the same directory and the validation details in `val_perf` file. - - -## Running instructions for Python version -To run CNN example in Python version, we need to compile SINGA with Python binding, - - $ mkdir build && cd build - $ cmake -DUSE_PYTHON=ON .. - $ make - -Now download the Cifar10 dataset, - - # switch to cifar10 directory - $ cd ../examples/cifar10 - # download data for Python version - $ python download_data.py py - -During downloading, you should see the detailed output like - - Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz - The tar file does exist. Extracting it now.. - Finished! - -Then execute the `train.py` script to build the model - - $ python train.py - -You should see the output as follows including the details of neural net structure with some parameter information, reading data files, and the performance details during training and testing process. - - (32L, 32L, 32L) - (32L, 16L, 16L) - (32L, 16L, 16L) - (32L, 16L, 16L) - (32L, 16L, 16L) - (32L, 16L, 16L) - (32L, 8L, 8L) - (32L, 8L, 8L) - (64L, 8L, 8L) - (64L, 8L, 8L) - (64L, 4L, 4L) - (1024L,) - Start intialization............ - conv1_weight gaussian 7.938460476e-05 - conv1_bias constant 0.0 - conv2_weight gaussian 0.00793507322669 - conv2_bias constant 0.0 - conv3_weight gaussian 0.00799657031894 - conv3_bias constant 0.0 - dense_weight gaussian 0.00804364029318 - dense_bias constant 0.0 - Loading data .................. - Loading data file cifar-10-batches-py/data_batch_1 - Loading data file cifar-10-batches-py/data_batch_2 - Loading data file cifar-10-batches-py/data_batch_3 - Loading data file cifar-10-batches-py/data_batch_4 - Loading data file cifar-10-batches-py/data_batch_5 - Loading data file cifar-10-batches-py/test_batch - Epoch 0 - training loss = 1.881866, training accuracy = 0.306360 accuracy = 0.420000 - test loss = 1.602577, test accuracy = 0.412200 - Epoch 1 - training loss = 1.536011, training accuracy = 0.441940 accuracy = 0.500000 - test loss = 1.378170, test accuracy = 0.507600 - Epoch 2 - training loss = 1.333137, training accuracy = 0.519960 accuracy = 0.520000 - test loss = 1.272205, test accuracy = 0.540600 - Epoch 3 - training loss = 1.185212, training accuracy = 0.574120 accuracy = 0.540000 - test loss = 1.211573, test accuracy = 0.567600 - -This script will call `alexnet.py` file to build the alexnet model. After the training is finished, SINGA will save the model parameters into a checkpoint file `model.bin` in the same directory. Then we can use this `model.bin` file for prediction. - - $ python predict.py +# Quickstart - Cifar10 example +Convolution neural network (CNN) is a type of feed-forward artificial neural network widely used for image classification. In this example, we will use a deep CNN model to do image classification for the [CIFAR10 dataset](http://www.cs.toronto.edu/~kriz/cifar.html). + +## Running instructions for CPP version +Please refer to [Installation](installation.html) page for how to install SINGA. Currently, we CNN requires CUDNN, hence both CUDA and CUDNN should be installed and SINGA should be compiled with CUDA and CUDNN. + +The Cifar10 dataset could be downloaded by running + + # switch to cifar10 directory + $ cd ../examples/cifar10 + # download data for CPP version + $ python download_data.py bin + +'bin' is for downloading binary version of Cifar10 data. + +During downloading, you should see the detailed output like + + Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz + The tar file does exist. Extracting it now.. + Finished! + +Now you have prepared the data for this Cifar10 example, the final step is to execute the `run.sh` script, + + # in SINGA_ROOT/examples/cifar10/ + $ ./run.sh + +You should see the detailed output as follows: first read the data files in order, show the statistics of training and testing data, then show the details of neural net structure with some parameter information, finally illustrate the performance details during training and validation process. The number of epochs can be specified in `run.sh` file. + + Start training + Reading file cifar-10-batches-bin/data_batch_1.bin + Reading file cifar-10-batches-bin/data_batch_2.bin + Reading file cifar-10-batches-bin/data_batch_3.bin + Reading file cifar-10-batches-bin/data_batch_4.bin + Reading file cifar-10-batches-bin/data_batch_5.bin + Reading file cifar-10-batches-bin/test_batch.bin + Training samples = 50000, Test samples = 10000 + conv1(32, 32, 32, ) + pool1(32, 16, 16, ) + relu1(32, 16, 16, ) + lrn1(32, 16, 16, ) + conv2(32, 16, 16, ) + relu2(32, 16, 16, ) + pool2(32, 8, 8, ) + lrn2(32, 8, 8, ) + conv3(64, 8, 8, ) + relu3(64, 8, 8, ) + pool3(64, 4, 4, ) + flat(1024, ) + ip(10, ) + conv1_weight : 8.09309e-05 + conv1_bias : 0 + conv2_weight : 0.00797731 + conv2_bias : 0 + conv3_weight : 0.00795888 + conv3_bias : 0 + ip_weight : 0.00798683 + ip_bias : 0 + Messages will be appended to an existed file: train_perf + Messages will be appended to an existed file: val_perf + Epoch 0, training loss = 1.828369, accuracy = 0.329420, lr = 0.001000 + Epoch 0, val loss = 1.561823, metric = 0.420600 + Epoch 1, training loss = 1.465898, accuracy = 0.469940, lr = 0.001000 + Epoch 1, val loss = 1.361778, metric = 0.513300 + Epoch 2, training loss = 1.320708, accuracy = 0.529000, lr = 0.001000 + Epoch 2, val loss = 1.242080, metric = 0.549100 + Epoch 3, training loss = 1.213776, accuracy = 0.571620, lr = 0.001000 + Epoch 3, val loss = 1.175346, metric = 0.582000 + +The training details are stored in `train_perf` file in the same directory and the validation details in `val_perf` file. + + +## Running instructions for Python version +To run CNN example in Python version, we need to compile SINGA with Python binding, + + $ mkdir build && cd build + $ cmake -DUSE_PYTHON=ON .. + $ make + +Now download the Cifar10 dataset, + + # switch to cifar10 directory + $ cd ../examples/cifar10 + # download data for Python version + $ python download_data.py py + +During downloading, you should see the detailed output like + + Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz + The tar file does exist. Extracting it now.. + Finished! + +Then execute the `train.py` script to build the model + + $ python train.py + +You should see the output as follows including the details of neural net structure with some parameter information, reading data files, and the performance details during training and testing process. + + (32L, 32L, 32L) + (32L, 16L, 16L) + (32L, 16L, 16L) + (32L, 16L, 16L) + (32L, 16L, 16L) + (32L, 16L, 16L) + (32L, 8L, 8L) + (32L, 8L, 8L) + (64L, 8L, 8L) + (64L, 8L, 8L) + (64L, 4L, 4L) + (1024L,) + Start intialization............ + conv1_weight gaussian 7.938460476e-05 + conv1_bias constant 0.0 + conv2_weight gaussian 0.00793507322669 + conv2_bias constant 0.0 + conv3_weight gaussian 0.00799657031894 + conv3_bias constant 0.0 + dense_weight gaussian 0.00804364029318 + dense_bias constant 0.0 + Loading data .................. + Loading data file cifar-10-batches-py/data_batch_1 + Loading data file cifar-10-batches-py/data_batch_2 + Loading data file cifar-10-batches-py/data_batch_3 + Loading data file cifar-10-batches-py/data_batch_4 + Loading data file cifar-10-batches-py/data_batch_5 + Loading data file cifar-10-batches-py/test_batch + Epoch 0 + training loss = 1.881866, training accuracy = 0.306360 accuracy = 0.420000 + test loss = 1.602577, test accuracy = 0.412200 + Epoch 1 + training loss = 1.536011, training accuracy = 0.441940 accuracy = 0.500000 + test loss = 1.378170, test accuracy = 0.507600 + Epoch 2 + training loss = 1.333137, training accuracy = 0.519960 accuracy = 0.520000 + test loss = 1.272205, test accuracy = 0.540600 + Epoch 3 + training loss = 1.185212, training accuracy = 0.574120 accuracy = 0.540000 + test loss = 1.211573, test accuracy = 0.567600 + +This script will call `alexnet.py` file to build the alexnet model. After the training is finished, SINGA will save the model parameters into a checkpoint file `model.bin` in the same directory. Then we can use this `model.bin` file for prediction. + + $ python predict.py http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/2608d416/doc/en/releases/RELEASE_NOTES_0.1.0.md ---------------------------------------------------------------------- diff --git a/doc/en/releases/RELEASE_NOTES_0.1.0.md b/doc/en/releases/RELEASE_NOTES_0.1.0.md index 2674d90..f0de7a5 100644 --- a/doc/en/releases/RELEASE_NOTES_0.1.0.md +++ b/doc/en/releases/RELEASE_NOTES_0.1.0.md @@ -1,4 +1,4 @@ -#singa-incubating-0.1.0 Release Notes +# singa-incubating-0.1.0 Release Notes --- http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/2608d416/doc/en/releases/RELEASE_NOTES_0.2.0.md ---------------------------------------------------------------------- diff --git a/doc/en/releases/RELEASE_NOTES_0.2.0.md b/doc/en/releases/RELEASE_NOTES_0.2.0.md index 38f498a..f2133e3 100644 --- a/doc/en/releases/RELEASE_NOTES_0.2.0.md +++ b/doc/en/releases/RELEASE_NOTES_0.2.0.md @@ -1,4 +1,4 @@ -#singa-incubating-0.2.0 Release Notes +# singa-incubating-0.2.0 Release Notes --- http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/2608d416/doc/en/releases/RELEASE_NOTES_0.3.0.md ---------------------------------------------------------------------- diff --git a/doc/en/releases/RELEASE_NOTES_0.3.0.md b/doc/en/releases/RELEASE_NOTES_0.3.0.md index d863e14..4298aa6 100644 --- a/doc/en/releases/RELEASE_NOTES_0.3.0.md +++ b/doc/en/releases/RELEASE_NOTES_0.3.0.md @@ -1,4 +1,4 @@ -#singa-incubating-0.3.0 Release Notes +# singa-incubating-0.3.0 Release Notes --- http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/2608d416/doc/en/releases/RELEASE_NOTES_1.0.0.md ---------------------------------------------------------------------- diff --git a/doc/en/releases/RELEASE_NOTES_1.0.0.md b/doc/en/releases/RELEASE_NOTES_1.0.0.md index 32a839f..724f47c 100644 --- a/doc/en/releases/RELEASE_NOTES_1.0.0.md +++ b/doc/en/releases/RELEASE_NOTES_1.0.0.md @@ -1,4 +1,4 @@ -#singa-incubating-1.0.0 Release Notes +# singa-incubating-1.0.0 Release Notes --- http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/2608d416/doc/en/releases/RELEASE_NOTES_1.1.0.md ---------------------------------------------------------------------- diff --git a/doc/en/releases/RELEASE_NOTES_1.1.0.md b/doc/en/releases/RELEASE_NOTES_1.1.0.md index 612fb97..75d086d 100644 --- a/doc/en/releases/RELEASE_NOTES_1.1.0.md +++ b/doc/en/releases/RELEASE_NOTES_1.1.0.md @@ -1,4 +1,4 @@ -#singa-incubating-1.1.0 Release Notes +# singa-incubating-1.1.0 Release Notes ---
