Modified: incubator/singa/site/trunk/en/docs/cnn.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/cnn.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/cnn.html (original) +++ incubator/singa/site/trunk/en/docs/cnn.html Thu Dec 29 09:46:24 2016 @@ -150,7 +150,7 @@ Convolution neural network (CNN) is a ty <span id="running-instructions-for-cpp-version"></span><h1>Running instructions for CPP version<a class="headerlink" href="#running-instructions-for-cpp-version" title="Permalink to this headline">¶</a></h1> <p>Please refer to <a class="reference external" href="installation.html">Installation</a> 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.</p> <p>The Cifar10 dataset could be downloaded by running</p> -<div class="highlight-default"><div class="highlight"><pre># switch to cifar10 directory +<div class="highlight-python"><div class="highlight"><pre># switch to cifar10 directory $ cd ../examples/cifar10 # download data for CPP version $ python download_data.py bin @@ -158,56 +158,56 @@ $ python download_data.py bin </div> <p>‘bin’ is for downloading binary version of Cifar10 data.</p> <p>During downloading, you should see the detailed output like</p> -<div class="highlight-default"><div class="highlight"><pre> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz +<div class="highlight-python"><div class="highlight"><pre> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz The tar file does exist. Extracting it now.. Finished! </pre></div> </div> <p>Now you have prepared the data for this Cifar10 example, the final step is to execute the <code class="docutils literal"><span class="pre">run.sh</span></code> script,</p> -<div class="highlight-default"><div class="highlight"><pre># in SINGA_ROOT/examples/cifar10/ +<div class="highlight-python"><div class="highlight"><pre># in SINGA_ROOT/examples/cifar10/ $ ./run.sh </pre></div> </div> <p>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 <code class="docutils literal"><span class="pre">run.sh</span></code> file.</p> -<div class="highlight-default"><div class="highlight"><pre><span class="n">Start</span> <span class="n">training</span> -<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_1</span><span class="o">.</span><span class="n">bin</span> -<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_2</span><span class="o">.</span><span class="n">bin</span> -<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_3</span><span class="o">.</span><span class="n">bin</span> -<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_4</span><span class="o">.</span><span class="n">bin</span> -<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_5</span><span class="o">.</span><span class="n">bin</span> -<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">test_batch</span><span class="o">.</span><span class="n">bin</span> -<span class="n">Training</span> <span class="n">samples</span> <span class="o">=</span> <span class="mi">50000</span><span class="p">,</span> <span class="n">Test</span> <span class="n">samples</span> <span class="o">=</span> <span class="mi">10000</span> -<span class="n">conv1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="p">)</span> -<span class="n">pool1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> -<span class="n">relu1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> -<span class="n">lrn1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> -<span class="n">conv2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> -<span class="n">relu2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> -<span class="n">pool2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span> -<span class="n">lrn2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span> -<span class="n">conv3</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span> -<span class="n">relu3</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span> -<span class="n">pool3</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="p">)</span> -<span class="n">flat</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="p">)</span> -<span class="n">ip</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="p">)</span> -<span class="n">conv1_weight</span> <span class="p">:</span> <span class="mf">8.09309e-05</span> -<span class="n">conv1_bias</span> <span class="p">:</span> <span class="mi">0</span> -<span class="n">conv2_weight</span> <span class="p">:</span> <span class="mf">0.00797731</span> -<span class="n">conv2_bias</span> <span class="p">:</span> <span class="mi">0</span> -<span class="n">conv3_weight</span> <span class="p">:</span> <span class="mf">0.00795888</span> -<span class="n">conv3_bias</span> <span class="p">:</span> <span class="mi">0</span> -<span class="n">ip_weight</span> <span class="p">:</span> <span class="mf">0.00798683</span> -<span class="n">ip_bias</span> <span class="p">:</span> <span class="mi">0</span> -<span class="n">Messages</span> <span class="n">will</span> <span class="n">be</span> <span class="n">appended</span> <span class="n">to</span> <span class="n">an</span> <span class="n">existed</span> <span class="n">file</span><span class="p">:</span> <span class="n">train_perf</span> -<span class="n">Messages</span> <span class="n">will</span> <span class="n">be</span> <span class="n">appended</span> <span class="n">to</span> <span class="n">an</span> <span class="n">existed</span> <span class="n">file</span><span class="p">:</span> <span class="n">val_perf</span> -<span class="n">Epoch</span> <span class="mi">0</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.828369</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.329420</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span> -<span class="n">Epoch</span> <span class="mi">0</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.561823</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.420600</span> -<span class="n">Epoch</span> <span class="mi">1</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.465898</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.469940</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span> -<span class="n">Epoch</span> <span class="mi">1</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.361778</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.513300</span> -<span class="n">Epoch</span> <span class="mi">2</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.320708</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.529000</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span> -<span class="n">Epoch</span> <span class="mi">2</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.242080</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.549100</span> -<span class="n">Epoch</span> <span class="mi">3</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.213776</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.571620</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span> -<span class="n">Epoch</span> <span class="mi">3</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.175346</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.582000</span> +<div class="highlight-python"><div class="highlight"><pre>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 </pre></div> </div> <p>The training details are stored in <code class="docutils literal"><span class="pre">train_perf</span></code> file in the same directory and the validation details in <code class="docutils literal"><span class="pre">val_perf</span></code> file.</p> @@ -215,73 +215,73 @@ $ ./run.sh <div class="section" id="running-instructions-for-python-version"> <span id="running-instructions-for-python-version"></span><h1>Running instructions for Python version<a class="headerlink" href="#running-instructions-for-python-version" title="Permalink to this headline">¶</a></h1> <p>To run CNN example in Python version, we need to compile SINGA with Python binding,</p> -<div class="highlight-default"><div class="highlight"><pre>$ mkdir build && cd build +<div class="highlight-python"><div class="highlight"><pre>$ mkdir build && cd build $ cmake -DUSE_PYTHON=ON .. $ make </pre></div> </div> <p>Now download the Cifar10 dataset,</p> -<div class="highlight-default"><div class="highlight"><pre># switch to cifar10 directory +<div class="highlight-python"><div class="highlight"><pre># switch to cifar10 directory $ cd ../examples/cifar10 # download data for Python version $ python download_data.py py </pre></div> </div> <p>During downloading, you should see the detailed output like</p> -<div class="highlight-default"><div class="highlight"><pre> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz +<div class="highlight-python"><div class="highlight"><pre> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz The tar file does exist. Extracting it now.. Finished! </pre></div> </div> <p>Then execute the <code class="docutils literal"><span class="pre">train.py</span></code> script to build the model</p> -<div class="highlight-default"><div class="highlight"><pre>$ python train.py +<div class="highlight-python"><div class="highlight"><pre>$ python train.py </pre></div> </div> <p>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.</p> -<div class="highlight-default"><div class="highlight"><pre><span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">32</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">64</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">64</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">64</span><span class="n">L</span><span class="p">,</span> <span class="mi">4</span><span class="n">L</span><span class="p">,</span> <span class="mi">4</span><span class="n">L</span><span class="p">)</span> -<span class="p">(</span><span class="mi">1024</span><span class="n">L</span><span class="p">,)</span> -<span class="n">Start</span> <span class="n">intialization</span><span class="o">............</span> -<span class="n">conv1_weight</span> <span class="n">gaussian</span> <span class="mf">7.938460476e-05</span> -<span class="n">conv1_bias</span> <span class="n">constant</span> <span class="mf">0.0</span> -<span class="n">conv2_weight</span> <span class="n">gaussian</span> <span class="mf">0.00793507322669</span> -<span class="n">conv2_bias</span> <span class="n">constant</span> <span class="mf">0.0</span> -<span class="n">conv3_weight</span> <span class="n">gaussian</span> <span class="mf">0.00799657031894</span> -<span class="n">conv3_bias</span> <span class="n">constant</span> <span class="mf">0.0</span> -<span class="n">dense_weight</span> <span class="n">gaussian</span> <span class="mf">0.00804364029318</span> -<span class="n">dense_bias</span> <span class="n">constant</span> <span class="mf">0.0</span> -<span class="n">Loading</span> <span class="n">data</span> <span class="o">..................</span> -<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_1</span> -<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_2</span> -<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_3</span> -<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_4</span> -<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_5</span> -<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">test_batch</span> -<span class="n">Epoch</span> <span class="mi">0</span> -<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.881866</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.306360</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.420000</span> -<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.602577</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.412200</span> -<span class="n">Epoch</span> <span class="mi">1</span> -<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.536011</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.441940</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.500000</span> -<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.378170</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.507600</span> -<span class="n">Epoch</span> <span class="mi">2</span> -<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.333137</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.519960</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.520000</span> -<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.272205</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.540600</span> -<span class="n">Epoch</span> <span class="mi">3</span> -<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.185212</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.574120</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.540000</span> -<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.211573</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.567600</span> +<div class="highlight-python"><div class="highlight"><pre>(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 </pre></div> </div> <p>This script will call <code class="docutils literal"><span class="pre">alexnet.py</span></code> file to build the alexnet model. After the training is finished, SINGA will save the model parameters into a checkpoint file <code class="docutils literal"><span class="pre">model.bin</span></code> in the same directory. Then we can use this <code class="docutils literal"><span class="pre">model.bin</span></code> file for prediction.</p> -<div class="highlight-default"><div class="highlight"><pre>$ python predict.py +<div class="highlight-python"><div class="highlight"><pre>$ python predict.py </pre></div> </div> </div>
Modified: incubator/singa/site/trunk/en/docs/converter.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/converter.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/converter.html (original) +++ incubator/singa/site/trunk/en/docs/converter.html Thu Dec 29 09:46:24 2016 @@ -104,7 +104,7 @@ <li class="toctree-l2"><a class="reference internal" href="data.html">Data</a></li> <li class="toctree-l2"><a class="reference internal" href="image_tool.html">Image Tool</a></li> <li class="toctree-l2"><a class="reference internal" href="snapshot.html">Snapshot</a></li> -<li class="toctree-l2 current"><a class="current reference internal" href="#">Caffe Converter</a></li> +<li class="toctree-l2 current"><a class="current reference internal" href="">Caffe Converter</a></li> <li class="toctree-l2"><a class="reference internal" href="utils.html">Utils</a></li> <li class="toctree-l2"><a class="reference internal" href="examples/index.html">Examples</a></li> </ul> Modified: incubator/singa/site/trunk/en/docs/data.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/data.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/data.html (original) +++ incubator/singa/site/trunk/en/docs/data.html Thu Dec 29 09:46:24 2016 @@ -101,7 +101,7 @@ <li class="toctree-l2"><a class="reference internal" href="loss.html">Loss</a></li> <li class="toctree-l2"><a class="reference internal" href="metric.html">Metric</a></li> <li class="toctree-l2"><a class="reference internal" href="optimizer.html">Optimizer</a></li> -<li class="toctree-l2 current"><a class="current reference internal" href="#">Data</a></li> +<li class="toctree-l2 current"><a class="current reference internal" href="">Data</a></li> <li class="toctree-l2"><a class="reference internal" href="image_tool.html">Image Tool</a></li> <li class="toctree-l2"><a class="reference internal" href="snapshot.html">Snapshot</a></li> <li class="toctree-l2"><a class="reference internal" href="converter.html">Caffe Converter</a></li> @@ -171,8 +171,8 @@ <span id="data"></span><h1>Data<a class="headerlink" href="#module-singa.data" title="Permalink to this headline">¶</a></h1> <p>This module includes classes for loading and prefetching data batches.</p> <p>Example usage:</p> -<div class="highlight-default"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">image_tool</span> -<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span> +<div class="highlight-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">image_tool</span> +<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> <span class="n">tool</span> <span class="o">=</span> <span class="n">image_tool</span><span class="o">.</span><span class="n">ImageTool</span><span class="p">()</span> @@ -182,26 +182,26 @@ <span class="p">(</span><span class="mi">112</span><span class="p">,</span> <span class="mi">128</span><span class="p">))</span><span class="o">.</span><span class="n">random_crop</span><span class="p">(</span> <span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">96</span><span class="p">))</span><span class="o">.</span><span class="n">flip</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> -<span class="n">data</span> <span class="o">=</span> <span class="n">ImageBatchIter</span><span class="p">(</span><span class="s">'train.txt'</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> - <span class="n">image_transform</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="k">True</span><span class="p">,</span> <span class="n">delimeter</span><span class="o">=</span><span class="s">','</span><span class="p">,</span> - <span class="n">image_folder</span><span class="o">=</span><span class="s">'images/'</span><span class="p">,</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">ImageBatchIter</span><span class="p">(</span><span class="s1">'train.txt'</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> + <span class="n">image_transform</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">','</span><span class="p">,</span> + <span class="n">image_folder</span><span class="o">=</span><span class="s1">'images/'</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> <span class="n">data</span><span class="o">.</span><span class="n">start</span><span class="p">()</span> -<span class="c"># imgs is a numpy array for a batch of images,</span> -<span class="c"># shape: batch_size, 3 (RGB), height, width</span> +<span class="c1"># imgs is a numpy array for a batch of images,</span> +<span class="c1"># shape: batch_size, 3 (RGB), height, width</span> <span class="n">imgs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">next</span><span class="p">()</span> -<span class="c"># convert numpy array back into images</span> +<span class="c1"># convert numpy array back into images</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">imgs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span> <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> - <span class="s">'RGB'</span><span class="p">)</span> - <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s">'img%d.png'</span> <span class="o">%</span> <span class="n">idx</span><span class="p">)</span> + <span class="s1">'RGB'</span><span class="p">)</span> + <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'img</span><span class="si">%d</span><span class="s1">.png'</span> <span class="o">%</span> <span class="n">idx</span><span class="p">)</span> <span class="n">data</span><span class="o">.</span><span class="n">end</span><span class="p">()</span> </pre></div> </div> <dl class="class"> <dt id="singa.data.ImageBatchIter"> -<em class="property">class </em><code class="descclassname">singa.data.</code><code class="descname">ImageBatchIter</code><span class="sig-paren">(</span><em>img_list_file</em>, <em>batch_size</em>, <em>image_transform</em>, <em>shuffle=True</em>, <em>delimeter=' '</em>, <em>image_folder=None</em>, <em>capacity=10</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.data.ImageBatchIter" title="Permalink to this definition">¶</a></dt> +<em class="property">class </em><code class="descclassname">singa.data.</code><code class="descname">ImageBatchIter</code><span class="sig-paren">(</span><em>img_list_file</em>, <em>batch_size</em>, <em>image_transform</em>, <em>shuffle=True</em>, <em>delimiter=' '</em>, <em>image_folder=None</em>, <em>capacity=10</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.data.ImageBatchIter" title="Permalink to this definition">¶</a></dt> <dd><p>Utility for iterating over an image dataset to get mini-batches.</p> <table class="docutils field-list" frame="void" rules="none"> <col class="field-name" /> @@ -209,12 +209,12 @@ <tbody valign="top"> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> <li><strong>img_list_file</strong> (<em>str</em>) – name of the file containing image meta data; each -line consists of image_path_suffix delimeter label</li> +line consists of image_path_suffix delimiter label</li> <li><strong>batch_size</strong> (<em>int</em>) – num of samples in one mini-batch</li> <li><strong>image_transform</strong> – a function for image augmentation; it accepts the full image path and outputs a list of augmented images.</li> <li><strong>shuffle</strong> (<em>boolean</em>) – True for shuffling images in the list</li> -<li><strong>delimeter</strong> (<em>char</em>) – delimeter between image_path_suffix and label, e.g., +<li><strong>delimiter</strong> (<em>char</em>) – delimiter between image_path_suffix and label, e.g., space or comma</li> <li><strong>image_folder</strong> (<em>boolean</em>) – prefix of the image path</li> <li><strong>capacity</strong> (<em>int</em>) – the max num of mini-batches in the internal queue.</li> Modified: incubator/singa/site/trunk/en/docs/dependencies.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/dependencies.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/dependencies.html (original) +++ incubator/singa/site/trunk/en/docs/dependencies.html Thu Dec 29 09:46:24 2016 @@ -196,7 +196,7 @@ OpenBLAS with version 0.2.18 as test:</p <p>Step 1: Download and decompress the source code.</p> <p>Step 2: Start a cmd window under the OpenBLAS folder then run the following commands to generate the solution:</p> -<div class="highlight-default"><div class="highlight"><pre>$ md build $$ cd build +<div class="highlight-python"><div class="highlight"><pre>$ md build $$ cd build $ cmake -G "Visual Studio 14" .. </pre></div> </div> Modified: incubator/singa/site/trunk/en/docs/device.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/device.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/device.html (original) +++ incubator/singa/site/trunk/en/docs/device.html Thu Dec 29 09:46:24 2016 @@ -93,7 +93,7 @@ <li class="toctree-l1 current"><a class="reference internal" href="index.html">Documentation</a><ul class="current"> <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li> <li class="toctree-l2"><a class="reference internal" href="software_stack.html">Software Stack</a></li> -<li class="toctree-l2 current"><a class="current reference internal" href="#">Device</a><ul> +<li class="toctree-l2 current"><a class="current reference internal" href="">Device</a><ul> <li class="toctree-l3"><a class="reference internal" href="#specific-devices">Specific devices</a></li> <li class="toctree-l3"><a class="reference internal" href="#module-singa.device">Python API</a></li> <li class="toctree-l3"><a class="reference internal" href="#cpp-api">CPP API</a></li> @@ -231,11 +231,11 @@ to call singa::Device and its methods.</ </dd></dl> <p>The following code provides examples of creating devices:</p> -<div class="highlight-default"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">device</span> -<span class="n">cuda</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu_on</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c"># use GPU card of ID 0</span> -<span class="n">host</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">get_default_device</span><span class="p">()</span> <span class="c"># get the default host device (a CppCPU)</span> -<span class="n">ary1</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="c"># create 2 devices, starting from ID 0</span> -<span class="n">ary2</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span> <span class="c"># create 2 devices on ID 0 and 2</span> +<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">device</span> +<span class="n">cuda</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu_on</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># use GPU card of ID 0</span> +<span class="n">host</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">get_default_device</span><span class="p">()</span> <span class="c1"># get the default host device (a CppCPU)</span> +<span class="n">ary1</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># create 2 devices, starting from ID 0</span> +<span class="n">ary2</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span> <span class="c1"># create 2 devices on ID 0 and 2</span> </pre></div> </div> </div> Added: incubator/singa/site/trunk/en/docs/examples/caffe/README.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/examples/caffe/README.html?rev=1776389&view=auto ============================================================================== --- incubator/singa/site/trunk/en/docs/examples/caffe/README.html (added) +++ incubator/singa/site/trunk/en/docs/examples/caffe/README.html Thu Dec 29 09:46:24 2016 @@ -0,0 +1,278 @@ + + + +<!DOCTYPE html> +<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]--> +<head> + <meta charset="utf-8"> + + <meta name="viewport" content="width=device-width, initial-scale=1.0"> + + <title>Use parameters pre-trained from Caffe in SINGA — incubator-singa 1.0.0 documentation</title> + + + + + + + + + + + + + + + + <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" /> + + + + + + <link rel="top" title="incubator-singa 1.0.0 documentation" href="../../../index.html"/> + <link href="../../../_static/style.css" rel="stylesheet" type="text/css"> + + + + <script src="../../../_static/js/modernizr.min.js"></script> + +</head> + +<body class="wy-body-for-nav" role="document"> + + <div class="wy-grid-for-nav"> + + + <nav data-toggle="wy-nav-shift" 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id="use-parameters-pre-trained-from-caffe-in-singa"> +<span id="use-parameters-pre-trained-from-caffe-in-singa"></span><h1>Use parameters pre-trained from Caffe in SINGA<a class="headerlink" href="#use-parameters-pre-trained-from-caffe-in-singa" title="Permalink to this headline">¶</a></h1> +<p>In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.</p> +<div class="section" id="run-this-example"> +<span id="run-this-example"></span><h2>Run this example<a class="headerlink" href="#run-this-example" title="Permalink to this headline">¶</a></h2> +<p>You can run this example by simply executing <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg16</span></code> or <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg19</span></code> +The script does the following work.</p> +<div class="section" id="obtain-the-caffe-model"> +<span id="obtain-the-caffe-model"></span><h3>Obtain the Caffe model<a class="headerlink" href="#obtain-the-caffe-model" title="Permalink to this headline">¶</a></h3> +<ul class="simple"> +<li>Download caffe model prototxt and parameter binary file.</li> +<li>Currently we only support the latest caffe format, if your model is in +previous version of caffe, please update it to current format.(This is +supported by caffe)</li> +<li>After updating, we can obtain two files, i.e., the prototxt and parameter +binary file.</li> +</ul> +</div> +<div class="section" id="prepare-test-images"> +<span id="prepare-test-images"></span><h3>Prepare test images<a class="headerlink" href="#prepare-test-images" title="Permalink to this headline">¶</a></h3> +<p>A few sample images are downloaded into the <code class="docutils literal"><span class="pre">test</span></code> folder.</p> +</div> +<div class="section" id="predict"> +<span id="predict"></span><h3>Predict<a class="headerlink" href="#predict" title="Permalink to this headline">¶</a></h3> +<p>The <code class="docutils literal"><span class="pre">predict.py</span></code> script creates the VGG model and read the parameters,</p> +<div class="highlight-python"><div class="highlight"><pre>usage: predict.py [-h] model_txt model_bin imgclass +</pre></div> +</div> +<p>where <code class="docutils literal"><span class="pre">imgclass</span></code> refers to the synsets of imagenet dataset for vgg models. +You can start the prediction program by executing the following command:</p> +<div class="highlight-python"><div class="highlight"><pre>python predict.py vgg16.prototxt vgg16.caffemodel synset_words.txt +</pre></div> +</div> +<p>Then you type in the image path, and the program would output the top-5 labels.</p> +<p>More Caffe models would be tested soon.</p> +</div> +</div> +</div> + + + </div> + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2016 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners.. + + </p> + </div> + Built with <a href="http://sphinx-doc.org/";>Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme";>theme</a> provided by <a href="https://readthedocs.org";>Read the Docs</a>. + +</footer> + + </div> + </div> + + </section> + + </div> + + + + + + <script type="text/javascript"> + var DOCUMENTATION_OPTIONS = { + URL_ROOT:'../../../', + VERSION:'1.0.0', + COLLAPSE_INDEX:false, + FILE_SUFFIX:'.html', + HAS_SOURCE: true + }; + </script> + <script type="text/javascript" src="../../../_static/jquery.js"></script> + <script type="text/javascript" src="../../../_static/underscore.js"></script> + <script type="text/javascript" src="../../../_static/doctools.js"></script> + + + + + + <script type="text/javascript" src="../../../_static/js/theme.js"></script> + + + + + <script type="text/javascript"> + jQuery(function () { + SphinxRtdTheme.StickyNav.enable(); + }); + </script> + + +<div class="rst-versions shift-up" data-toggle="rst-versions" role="note" aria-label="versions"> +<a href="http://incubator.apache.org/";> +<img src= "../../../_static/apache.jpg"> +</a> + + <span class="rst-current-version" data-toggle="rst-current-version"> + <span class="fa fa-book"> incubator-singa </span> + v: 1.0.0 + <span class="fa fa-caret-down"></span> + </span> + <div class="rst-other-versions"> + <dl> + <dt>Languages</dt> + <dd><a href="../../../../en/index.html">English</a></dd> + <dd><a href="../../../../zh/index.html">ä¸æ</a></dd> + </dl> + <dl> + <dt>Versions</dt> + <dd><a href="http://singa.apache.org/v0.3.0/";>0.3</a></dd> + </dl> + + </div> +</div> + + <a href="https://github.com/apache/incubator-singa";> + <img style="position: absolute; top: 0; right: 0; border: 0; z-index: 10000;" + src="https://s3.amazonaws.com/github/ribbons/forkme_right_orange_ff7600.png"; + alt="Fork me on GitHub"> +</a> + + + + +</body> +</html> \ No newline at end of file Modified: incubator/singa/site/trunk/en/docs/examples/char-rnn/README.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/examples/char-rnn/README.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/examples/char-rnn/README.html (original) +++ incubator/singa/site/trunk/en/docs/examples/char-rnn/README.html Thu Dec 29 09:46:24 2016 @@ -108,7 +108,7 @@ <li class="toctree-l2"><a class="reference internal" href="../../utils.html">Utils</a></li> <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current"> <li class="toctree-l3"><a class="reference internal" href="../cifar10/README.html">Train CNN over Cifar-10</a></li> -<li class="toctree-l3 current"><a class="current reference internal" href="#">Train Char-RNN over plain text</a><ul> +<li class="toctree-l3 current"><a class="current reference internal" href="">Train Char-RNN over plain text</a><ul> <li class="toctree-l4"><a class="reference internal" href="#instructions">Instructions</a></li> </ul> </li> @@ -196,16 +196,16 @@ generate meaningful code from the model. Other plain text files can also be used.</p> </li> <li><p class="first">Start the training,</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">linux_input</span><span class="o">.</span><span class="n">txt</span> +<div class="highlight-python"><div class="highlight"><pre> python train.py linux_input.txt </pre></div> </div> <p>Some hyper-parameters could be set through command line,</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="o">-</span><span class="n">h</span> +<div class="highlight-python"><div class="highlight"><pre> python train.py -h </pre></div> </div> </li> <li><p class="first">Sample characters from the model by providing the number of characters to sample and the seed string.</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="s">'model.bin'</span> <span class="mi">100</span> <span class="o">--</span><span class="n">seed</span> <span class="s">'#include <std'</span> +<div class="highlight-python"><div class="highlight"><pre> python sample.py 'model.bin' 100 --seed '#include <std' </pre></div> </div> <p>Please replace ‘model.bin’ with the path to one of the checkpoint paths.</p> Modified: incubator/singa/site/trunk/en/docs/examples/cifar10/README.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/examples/cifar10/README.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/examples/cifar10/README.html (original) +++ incubator/singa/site/trunk/en/docs/examples/cifar10/README.html Thu Dec 29 09:46:24 2016 @@ -107,7 +107,7 @@ <li class="toctree-l2"><a class="reference internal" href="../../converter.html">Caffe Converter</a></li> <li class="toctree-l2"><a class="reference internal" href="../../utils.html">Utils</a></li> <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current"> -<li class="toctree-l3 current"><a class="current reference internal" href="#">Train CNN over Cifar-10</a><ul> +<li class="toctree-l3 current"><a class="current reference internal" href="">Train CNN over Cifar-10</a><ul> <li class="toctree-l4"><a class="reference internal" href="#instructions">Instructions</a></li> </ul> </li> @@ -200,11 +200,11 @@ are required. Please refer to the instal <div class="section" id="data-preparation"> <span id="data-preparation"></span><h3>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">¶</a></h3> <p>The binary Cifar-10 dataset could be downloaded by</p> -<div class="highlight-default"><div class="highlight"><pre><span class="n">python</span> <span class="n">download_data</span><span class="o">.</span><span class="n">py</span> <span class="nb">bin</span> +<div class="highlight-python"><div class="highlight"><pre>python download_data.py bin </pre></div> </div> <p>The Python version could be downloaded by</p> -<div class="highlight-default"><div class="highlight"><pre><span class="n">python</span> <span class="n">download_data</span><span class="o">.</span><span class="n">py</span> <span class="n">py</span> +<div class="highlight-python"><div class="highlight"><pre>python download_data.py py </pre></div> </div> </div> @@ -214,26 +214,26 @@ are required. Please refer to the instal <ol> <li><p class="first">train.py. The following command would train the VGG model using the python version of the Cifar-10 dataset in ‘cifar-10-batches-py’ folder.</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span> +<div class="highlight-python"><div class="highlight"><pre> python train.py vgg cifar-10-batches-py </pre></div> </div> <p>To train other models, please replace ‘vgg’ to ‘alexnet’, ‘resnet’ or ‘caffe’, where ‘caffe’ refers to the alexnet model converted from Caffe. By default the training would run on a CudaGPU device, to run it on CppCPU, add an additional argument</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span> <span class="o">--</span><span class="n">use_cpu</span> +<div class="highlight-python"><div class="highlight"><pre> python train.py vgg cifar-10-batches-py --use_cpu </pre></div> </div> </li> <li><p class="first">alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="o">./</span><span class="n">run</span><span class="o">.</span><span class="n">sh</span> +<div class="highlight-python"><div class="highlight"><pre> ./run.sh </pre></div> </div> </li> <li><p class="first">alexnet-parallel.cc. It trains the AlexNet model using the CPP APIs on two CudaGPU devices. The two devices run synchronously to compute the gradients of the mode parameters, which are averaged on the host CPU device and then be applied to update the parameters.</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="o">./</span><span class="n">run</span><span class="o">-</span><span class="n">parallel</span><span class="o">.</span><span class="n">sh</span> +<div class="highlight-python"><div class="highlight"><pre> ./run-parallel.sh </pre></div> </div> </li> @@ -244,7 +244,7 @@ averaged on the host CPU device and then <div class="section" id="prediction"> <span id="prediction"></span><h3>Prediction<a class="headerlink" href="#prediction" title="Permalink to this headline">¶</a></h3> <p>predict.py includes the prediction function</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">images</span><span class="p">,</span> <span class="n">dev</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> +<div class="highlight-python"><div class="highlight"><pre> def predict(net, images, dev, topk=5) </pre></div> </div> <p>The net is created by loading the previously trained model; Images consist of @@ -252,7 +252,7 @@ a numpy array of images (one row per ima a CudaGPU device or the host CppCPU device; It returns the topk labels for each instance.</p> <p>The predict.py file’s main function provides an example of using the pre-trained alexnet model to do prediction for new images. The ‘model.bin’ file generated by the training program should be placed at the cifar10 folder to run</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span> +<div class="highlight-python"><div class="highlight"><pre> python predict.py </pre></div> </div> </div> Modified: incubator/singa/site/trunk/en/docs/examples/imagenet/README.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/examples/imagenet/README.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/examples/imagenet/README.html (original) +++ incubator/singa/site/trunk/en/docs/examples/imagenet/README.html Thu Dec 29 09:46:24 2016 @@ -109,7 +109,7 @@ <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current"> <li class="toctree-l3"><a class="reference internal" href="../cifar10/README.html">Train CNN over Cifar-10</a></li> <li class="toctree-l3"><a class="reference internal" href="../char-rnn/README.html">Train Char-RNN over plain text</a></li> -<li class="toctree-l3 current"><a class="current reference internal" href="#">Train AlexNet over ImageNet</a><ul> +<li class="toctree-l3 current"><a class="current reference internal" href="">Train AlexNet over ImageNet</a><ul> <li class="toctree-l4"><a class="reference internal" href="#instructions">Instructions</a></li> </ul> </li> @@ -206,7 +206,7 @@ or from <a class="reference external" hr <ul> <li><p class="first">Assuming you have downloaded the data and the list. Now we should transform the data into binary files. You can run:</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">sh</span> <span class="n">create_data</span><span class="o">.</span><span class="n">sh</span> +<div class="highlight-python"><div class="highlight"><pre> sh create_data.sh </pre></div> </div> <p>The script will generate a test file(<code class="docutils literal"><span class="pre">test.bin</span></code>), a mean file(<code class="docutils literal"><span class="pre">mean.bin</span></code>) and @@ -229,7 +229,7 @@ The script will generate these files in <span id="training"></span><h3>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h3> <ul> <li><p class="first">After preparing data, you can run the following command to train the Alexnet model.</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">sh</span> <span class="n">run</span><span class="o">.</span><span class="n">sh</span> +<div class="highlight-python"><div class="highlight"><pre> sh run.sh </pre></div> </div> </li> Modified: incubator/singa/site/trunk/en/docs/examples/index.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/examples/index.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/examples/index.html (original) +++ incubator/singa/site/trunk/en/docs/examples/index.html Thu Dec 29 09:46:24 2016 @@ -106,7 +106,7 @@ <li class="toctree-l2"><a class="reference internal" href="../snapshot.html">Snapshot</a></li> <li class="toctree-l2"><a class="reference internal" href="../converter.html">Caffe Converter</a></li> <li class="toctree-l2"><a class="reference internal" href="../utils.html">Utils</a></li> -<li class="toctree-l2 current"><a class="current reference internal" href="#">Examples</a><ul> +<li class="toctree-l2 current"><a class="current reference internal" href="">Examples</a><ul> <li class="toctree-l3"><a class="reference internal" href="cifar10/README.html">Train CNN over Cifar-10</a></li> <li class="toctree-l3"><a class="reference internal" href="char-rnn/README.html">Train Char-RNN over plain text</a></li> <li class="toctree-l3"><a class="reference internal" href="imagenet/README.html">Train AlexNet over ImageNet</a></li> Modified: incubator/singa/site/trunk/en/docs/examples/mnist/README.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/examples/mnist/README.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/examples/mnist/README.html (original) +++ incubator/singa/site/trunk/en/docs/examples/mnist/README.html Thu Dec 29 09:46:24 2016 @@ -155,14 +155,14 @@ MNIST dataset. The RBM model and its hyp <li><p class="first">Download the pre-processed <a class="reference external" href="https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz";>MNIST dataset</a></p> </li> <li><p class="first">Start the training</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">mnist</span><span class="o">.</span><span class="n">pkl</span><span class="o">.</span><span class="n">gz</span> +<div class="highlight-python"><div class="highlight"><pre> python train.py mnist.pkl.gz </pre></div> </div> </li> </ol> <p>By default the training code would run on CPU. To run it on a GPU card, please start the program with an additional argument</p> -<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">mnist</span><span class="o">.</span><span class="n">pkl</span><span class="o">.</span><span class="n">gz</span> <span class="o">--</span><span class="n">use_gpu</span> +<div class="highlight-python"><div class="highlight"><pre> python train.py mnist.pkl.gz --use_gpu </pre></div> </div> </div> Modified: incubator/singa/site/trunk/en/docs/image_tool.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/image_tool.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/image_tool.html (original) +++ incubator/singa/site/trunk/en/docs/image_tool.html Thu Dec 29 09:46:24 2016 @@ -102,7 +102,7 @@ <li class="toctree-l2"><a class="reference internal" href="metric.html">Metric</a></li> <li class="toctree-l2"><a class="reference internal" href="optimizer.html">Optimizer</a></li> <li class="toctree-l2"><a class="reference internal" href="data.html">Data</a></li> -<li class="toctree-l2 current"><a class="current reference internal" href="#">Image Tool</a></li> +<li class="toctree-l2 current"><a class="current reference internal" href="">Image Tool</a></li> <li class="toctree-l2"><a class="reference internal" href="snapshot.html">Snapshot</a></li> <li class="toctree-l2"><a class="reference internal" href="converter.html">Caffe Converter</a></li> <li class="toctree-l2"><a class="reference internal" href="utils.html">Utils</a></li> @@ -171,12 +171,12 @@ <span id="image-tool"></span><h1>Image Tool<a class="headerlink" href="#module-singa.image_tool" title="Permalink to this headline">¶</a></h1> <p>An utility model for image augmentation.</p> <p>Example usage:</p> -<div class="highlight-default"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">image_tool</span> +<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">image_tool</span> <span class="n">tool</span> <span class="o">=</span> <span class="n">image_tool</span><span class="o">.</span><span class="n">ImageTool</span><span class="p">()</span> -<span class="n">imgs</span> <span class="o">=</span> <span class="n">tool</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">'input.png'</span><span class="p">)</span><span class="o">.</span> <span class="n">resize_by_list</span><span class="p">([</span><span class="mi">112</span><span class="p">])</span><span class="o">.</span><span class="n">crop5</span><span class="p">((</span><span class="mi">96</span><span class="p">,</span> <span class="mi">96</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">enhance</span><span class="p">()</span><span class="o">.</span><span class="n">flip</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> +<span class="n">imgs</span> <span class="o">=</span> <span class="n">tool</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'input.png'</span><span class="p">)</span><span class="o">.</span> <span class="n">resize_by_list</span><span class="p">([</span><span class="mi">112</span><span class="p">])</span><span class="o">.</span><span class="n">crop5</span><span class="p">((</span><span class="mi">96</span><span class="p">,</span> <span class="mi">96</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">enhance</span><span class="p">()</span><span class="o">.</span><span class="n">flip</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">img</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">imgs</span><span class="p">):</span> - <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s">'%d.png'</span> <span class="o">%</span> <span class="n">idx</span><span class="p">)</span> + <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'</span><span class="si">%d</span><span class="s1">.png'</span> <span class="o">%</span> <span class="n">idx</span><span class="p">)</span> </pre></div> </div> <dl class="class"> Modified: incubator/singa/site/trunk/en/docs/index.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/index.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/index.html (original) +++ incubator/singa/site/trunk/en/docs/index.html Thu Dec 29 09:46:24 2016 @@ -89,7 +89,7 @@ <ul class="current"> <li class="toctree-l1"><a class="reference internal" href="../downloads.html">Download SINGA</a></li> -<li class="toctree-l1 current"><a class="current reference internal" href="#">Documentation</a><ul> +<li class="toctree-l1 current"><a class="current reference internal" href="">Documentation</a><ul> <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li> <li class="toctree-l2"><a class="reference internal" href="software_stack.html">Software Stack</a></li> <li class="toctree-l2"><a class="reference internal" href="device.html">Device</a></li> Modified: incubator/singa/site/trunk/en/docs/initializer.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/initializer.html?rev=1776389&r1=1776388&r2=1776389&view=diff ============================================================================== --- incubator/singa/site/trunk/en/docs/initializer.html (original) +++ incubator/singa/site/trunk/en/docs/initializer.html Thu Dec 29 09:46:24 2016 @@ -97,7 +97,7 @@ <li class="toctree-l2"><a class="reference internal" href="tensor.html">Tensor</a></li> <li class="toctree-l2"><a class="reference internal" href="layer.html">Layer</a></li> <li class="toctree-l2"><a class="reference internal" href="net.html">FeedForward Net</a></li> -<li class="toctree-l2 current"><a class="current reference internal" href="#">Initializer</a><ul> +<li class="toctree-l2 current"><a class="current reference internal" href="">Initializer</a><ul> <li class="toctree-l3"><a class="reference internal" href="#module-singa.initializer">Python API</a></li> <li class="toctree-l3"><a class="reference internal" href="#cpp-api">CPP API</a></li> </ul> @@ -177,12 +177,12 @@ <span id="python-api"></span><h2>Python API<a class="headerlink" href="#module-singa.initializer" title="Permalink to this headline">¶</a></h2> <p>Popular initialization methods for parameter values (Tensor objects).</p> <p>Example usages:</p> -<div class="highlight-default"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">tensor</span> -<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">initializer</span> +<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">initializer</span> <span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span> -<span class="n">initializer</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="c"># use both fan_in and fan_out</span> -<span class="n">initializer</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c"># use only fan_in</span> +<span class="n">initializer</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="c1"># use both fan_in and fan_out</span> +<span class="n">initializer</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># use only fan_in</span> </pre></div> </div> <dl class="function">
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