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.
Please refer to Installation 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.
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