def test(theta, mo_create=dft_mo_create, data_load=dft_data_load):
print 'loading data...'
_, _, dataTe = data_load()
print 'building the graph...'
x, f = mo_create(theta, is_tr=False)
# fprop zero-one loss
y = T.ivector('y')
ell = loss.create_zeroone(f, y)
# all in one graph
fg_te = graph_mgr(
inputs=[],
outputs=ell,
givens={x: dataTe[0], y: dataTe[1]}
)
print 'fire the graph...'
er = fg_te()
print 'error rate = %5.4f' % (er,)
def train(itMax=100, szBatch=256, lr=0.01, vaFreq=10,
pa_init=dft_pa_init,
mo_create=dft_mo_create,
data_load=dft_data_load):
print 'loading data...'
dataTr, dataVa, _ = data_load()
print 'building graph...'
# initialize parameters
theta = pa_init()
# fprop: the prediction model for MLP
x, F = mo_create(theta, is_tr=True)
# fprop: the loss
y = T.ivector('y')
ell = loss.create_logistic(F, y)
# bprop
dtheta = T.grad(ell, wrt=theta)
# the graph for training
ibat = T.lscalar('ibat')
fg_tr = graph_mgr(
inputs=[ibat],
outputs=ell,
updates=zip(theta, optim.update_gd(theta, dtheta)),
givens={
x: dataset.get_batch(ibat, dataTr[0], szBatch=szBatch),
y: dataset.get_batch(ibat, dataTr[1], szBatch=szBatch)
}
)
# the graph for validation
ell_zo = loss.create_zeroone(F, y)
fg_va = graph_mgr(
inputs=[],
outputs=ell_zo,
givens={
x: dataVa[0],
y: dataVa[1]
}
)
print 'Fire the graph...'
trLoss, er_va = [], []
N = dataTr[0].get_value(borrow=True).shape[0]
numBatch = (N + szBatch) / szBatch
print '#batch = %d' % (numBatch,)
for i in xrange(itMax):
ibat = i % numBatch
tmpLoss = fg_tr(ibat)
print 'training: iteration %d, ibat = %d, loss = %6.5f' % (i, ibat, tmpLoss)
trLoss.append(tmpLoss)
if i%vaFreq == 0:
tmp_er = fg_va()
print 'validation: iteration %d, error rate = %6.5f' % (i, tmp_er)
er_va.append(tmp_er)
# plot
import matplotlib.pyplot as plt
plt.subplot(1, 2, 1)
plt.plot(range(1, len(trLoss)+1), trLoss, 'ro-')
plt.subplot(1, 2, 2)
plt.plot([i*vaFreq for i in range(len(er_va))], er_va, 'bx-')
plt.show(block=True)
# return the parameters
return theta