examples/mnist/train.py

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import numpy as np
import os
import gzip
import argparse
import cPickle
from singa import initializer
from singa import utils
from singa import optimizer
from singa import device
from singa import tensor


from singa.proto import core_pb2



def load_train_data(file_path):
    f = gzip.open(file_path, 'rb')
    train_set, valid_set, test_set = cPickle.load(f)
    traindata = train_set[0].astype(np.float32)
    validdata = valid_set[0].astype(np.float32)
    print traindata.shape, validdata.shape
    return traindata, validdata



def train(data_file, use_gpu, num_epoch=10, batch_size=100):
    print 'Start intialization............'
    lr = 0.1   # Learning rate
    weight_decay  = 0.0002
    hdim = 1000
    vdim = 784
    opt = optimizer.SGD(momentum=0.8, weight_decay=weight_decay)

    tweight = tensor.Tensor((vdim, hdim))
    tweight.gaussian(0.0, 0.1)
    tvbias = tensor.from_numpy(np.zeros(vdim, dtype = np.float32))
    thbias = tensor.from_numpy(np.zeros(hdim, dtype = np.float32))
    opt = optimizer.SGD(momentum=0.5, weight_decay=weight_decay)

    print 'Loading data ..................'
    train_x, valid_x = load_train_data(data_file)

    if use_gpu:
        dev = device.create_cuda_gpu()
    else:
        dev = device.get_default_device()

    for t in [tweight, tvbias, thbias]:
        t.to_device(dev)

    num_train_batch = train_x.shape[0] / batch_size
    print "num_train_batch = %d " % (num_train_batch)
    for epoch in range(num_epoch):
        trainerrorsum = 0.0
        print 'Epoch %d' % epoch
        for b in range(num_train_batch):
            # positive phase
            tdata = tensor.from_numpy(
                    train_x[(b * batch_size):((b + 1) * batch_size), : ])
            tdata.to_device(dev)
            tposhidprob = tensor.mult(tdata, tweight)
            tposhidprob.add_row(thbias)
            tposhidprob = tensor.sigmoid(tposhidprob)
            tposhidrandom = tensor.Tensor(tposhidprob.shape, dev)
            tposhidrandom.uniform(0.0, 1.0)
            tposhidsample = tensor.gt(tposhidprob, tposhidrandom)

            # negative phase
            tnegdata = tensor.mult(tposhidsample, tweight.T())
            tnegdata.add_row(tvbias)
            tnegdata = tensor.sigmoid(tnegdata)

            tneghidprob = tensor.mult(tnegdata, tweight)
            tneghidprob.add_row(thbias)
            tneghidprob = tensor.sigmoid(tneghidprob)
            error = tensor.sum(tensor.square((tdata - tnegdata)))
            trainerrorsum = error + trainerrorsum

            tgweight = tensor.mult(tnegdata.T(), tneghidprob) -\
                    tensor.mult(tdata.T(), tposhidprob)
            tgvbias = tensor.sum(tnegdata, 0) - tensor.sum(tdata, 0)
            tghbias = tensor.sum(tneghidprob, 0) - tensor.sum(tposhidprob, 0)

            opt.apply_with_lr(epoch, lr / batch_size, tgweight, tweight, 'w')
            opt.apply_with_lr(epoch, lr / batch_size, tgvbias, tvbias, 'vb')
            opt.apply_with_lr(epoch, lr / batch_size, tghbias, thbias, 'hb')

        print 'training errorsum = %f' % (trainerrorsum)

        tvaliddata = tensor.from_numpy(valid_x)
        tvaliddata.to_device(dev)
        tvalidposhidprob = tensor.mult(tvaliddata, tweight)
        tvalidposhidprob.add_row(thbias)
        tvalidposhidprob = tensor.sigmoid(tvalidposhidprob)
        tvalidposhidrandom = tensor.Tensor(tvalidposhidprob.shape, dev)
        initializer.uniform(tvalidposhidrandom, 0.0, 1.0)
        tvalidposhidsample = tensor.gt(tvalidposhidprob, tvalidposhidrandom)

        tvalidnegdata = tensor.mult(tvalidposhidsample, tweight.T())
        tvalidnegdata.add_row(tvbias)
        tvalidnegdata = tensor.sigmoid(tvalidnegdata)

        validerrorsum = tensor.sum(tensor.square((tvaliddata - tvalidnegdata)))
        print 'valid errorsum = %f' % (validerrorsum)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Train RBM over MNIST')
    parser.add_argument('file', type=str, help='the dataset path')
    parser.add_argument('--use_gpu', action='store_true')
    args = parser.parse_args()

    assert os.path.exists(args.file), 'Pls download the MNIST dataset from' \
            'https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz'
    train(args.file, args.use_gpu)