glove.py 文件源码

def train_glove(infile, inputSize=20000, batchSize=100, dimensionSize=100, maxEpochs=1000, outfile='result', x_max=100, alpha=0.75):
    options = locals().copy()
    print 'initializing parameters'
    params = init_params(options)
    tparams = init_tparams(params)

    print 'loading data'
    I, J, Weight = load_data(infile)
    n_batches = int(np.ceil(float(I.get_value(borrow=True).shape[0]) / float(batchSize)))

    print 'building models'
    weightVector, iVector, jVector, cost = build_model(tparams, options)
    grads = T.grad(cost, wrt=tparams.values())
    f_grad_shared, f_update = adadelta(tparams, grads, weightVector, iVector, jVector, cost)

    logFile = outfile + '.log'
    print 'training start'
    for epoch in xrange(maxEpochs):
        costVector = []
        iteration = 0
        for batchIndex in random.sample(range(n_batches), n_batches):
            cost = f_grad_shared(Weight.get_value(borrow=True, return_internal_type=True)[batchIndex*batchSize:(batchIndex+1)*batchSize],
                                I.get_value(borrow=True, return_internal_type=True)[batchIndex*batchSize: (batchIndex+1)*batchSize],
                                J.get_value(borrow=True, return_internal_type=True)[batchIndex*batchSize: (batchIndex+1)*batchSize])
            f_update()
            costVector.append(cost)

            if (iteration % 1000 == 0):
                buf = 'epoch:%d, iteration:%d/%d, cost:%f' % (epoch, iteration, n_batches, cost)
                print buf
                print2file(buf, logFile)
            iteration += 1
        trainCost = np.mean(costVector)
        buf = 'epoch:%d, cost:%f' % (epoch, trainCost)
        print buf
        print2file(buf, logFile)
        tempParams = unzip(tparams)
        np.savez_compressed(outfile + '.' + str(epoch), **tempParams)