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   <p>#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 <a class="reference external" href="http://www.cs.toronto.edu/~kriz/cifar.html">CIFAR10 dataset</a>.</p>
 <div class="section" id="running-instructions-for-cpp-version">
 <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-python"><div class="highlight"><pre># switch to cifar10 directory
 $ cd ../examples/cifar10
 # download data for CPP version
 $ python download_data.py bin
 </pre></div>
 </div>
 <p>&#8216;bin&#8217; is for downloading binary version of Cifar10 data.</p>
 <p>During downloading, you should see the detailed output like</p>
 <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-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-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>
 </div>
 <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-python"><div class="highlight"><pre>$ mkdir build &amp;&amp; cd build
 $ cmake -DUSE_PYTHON=ON ..
 $ make
 </pre></div>
 </div>
 <p>Now download the Cifar10 dataset,</p>
 <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-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-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-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-python"><div class="highlight"><pre>$ python predict.py
 </pre></div>
 </div>
 </div>


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	<p>#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 <a class="reference external" href="http://www.cs.toronto.edu/~kriz/cifar.html">CIFAR10 dataset</a>.</p>
	<div class="section" id="running-instructions-for-cpp-version">
	<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-python"><div class="highlight"><pre># switch to cifar10 directory
	$ cd ../examples/cifar10
	# download data for CPP version
	$ python download_data.py bin
	</pre></div>
	</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-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-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-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>
	</div>
	<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-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-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-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-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-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>
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	<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-python"><div class="highlight"><pre>$ python predict.py
	</pre></div>
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