def main():
pl.init_cl(1)
rng = np.random.RandomState(1234)
mlp = MLP(rng, 784, 32, 10)
tvX, tvY, testX, testY = get_mnist_data()
tvX.shape = (60000, 784)
testX.shape = (10000, 784)
tvX = (tvX.astype(np.float32) / 255.0).astype(np.float32)
testX = (testX / 255.0).astype(np.float32)
tvYoh = pd.get_dummies(tvY).values.astype(np.float32)
testYoh = pd.get_dummies(testY).values.astype(np.float32)
vsplit = int(0.9 * tvX.shape[0])
trainX = tvX[:vsplit, :]
validX = tvX[vsplit:, :]
trainY = tvYoh[:vsplit, :]
validY = tvY[vsplit:].astype(np.float32)
n_epochs = 30 # 000
batch_size = 512
n_train_batches = trainY.shape[0] / batch_size
epoch = 0
while epoch < n_epochs:
epoch += 1
for minibatch_index in xrange(n_train_batches):
mlp.train(
trainX[minibatch_index * batch_size:(minibatch_index + 1) *
batch_size, :],
trainY[minibatch_index * batch_size:(minibatch_index + 1) *
batch_size, :])
if (minibatch_index
== n_train_batches - 1) and minibatch_index > 0:
err = mlp.test(validX, validY)
print '>>>>>>>>validation error:', err, 'batch:', minibatch_index, '/', n_train_batches
err = mlp.test(testX, testY)
print 'TEST ERROR:', err
for param, value in mlp.params:
val = value.get()
print '>PARAM', param, val.shape
print val
print '-' * 79
def setUpClass(cls):
plat.init_cl(1)
def main():
pl.init_cl(1)
q = pl.qs[0]
argparser = argparse.ArgumentParser()
argparser.add_argument('--params', help='saved network parameters')
argparser.add_argument('-v',
'--validate',
action='store_true',
help='use last batch as validation set')
args = argparser.parse_args()
net_params = None
if args.params:
net_params = load_net(q, args.params)
rng = np.random.RandomState(1234)
anet = Alexnet(rng, 10, params=net_params)
with open('/home/petar/datasets/cifar-10.pkl', 'rb') as f:
[(X, Y), (testX, testY)] = cPickle.load(f)
n_epochs = 10
batch_size = 128
n_train_batches = Y.shape[0] / batch_size
n_valid_batches = 0
if args.validate:
n_train_batches = int(0.8 * n_train_batches)
n_valid_batches = int(0.2 * Y.shape[0] / batch_size)
n_test_batches = testY.shape[0] / batch_size
print 'starting training...'
start_time = timeit.default_timer()
# preload batches into GPU memory
X_batches = []
Y_batches = []
X_validbs = []
Y_validbs = []
X_test = []
Y_test = []
for minibatch_index in xrange(n_train_batches):
X_batches.append(
clarray.to_device(
pl.qs[0], X[minibatch_index *
batch_size:(minibatch_index + 1) * batch_size]))
Y_batches.append(
clarray.to_device(
pl.qs[0],
pd.get_dummies(
Y[minibatch_index * batch_size:(minibatch_index + 1) *
batch_size]).values.astype(np.float32)))
for minibatch_index in xrange(n_train_batches,
n_train_batches + n_valid_batches):
X_validbs.append(
clarray.to_device(
pl.qs[0], X[minibatch_index *
batch_size:(minibatch_index + 1) * batch_size]))
Y_validbs.append(
clarray.to_device(
pl.qs[0], Y[minibatch_index *
batch_size:(minibatch_index + 1) * batch_size]))
for minibatch_index in xrange(n_test_batches):
X_test.append(
clarray.to_device(
pl.qs[0],
testX[minibatch_index * batch_size:(minibatch_index + 1) *
batch_size]))
Y_test.append(
clarray.to_device(
pl.qs[0],
testY[minibatch_index * batch_size:(minibatch_index + 1) *
batch_size]))
epoch = 0
lrn_rate = 0.01
momentum = 0.0
while epoch < n_epochs:
epoch += 1
print 'epoch:', epoch, 'of', n_epochs, 'lr:', lrn_rate, 'm:', momentum
for mbi in xrange(n_train_batches):
print '\r>training batch', mbi, 'of', n_train_batches,
sys.stdout.flush()
anet.train(X_batches[mbi], Y_batches[mbi], lrn_rate, momentum)
if args.validate and mbi % 65 == 0:
verr = np.mean([
float(anet.test(X_validbs[vbi], Y_validbs[vbi]))
for vbi in range(n_valid_batches)
])
print '\rvalidation error:', verr
if epoch % 8 == 0 and epoch > 0:
lrn_rate /= 10.0
# momentum += 0.1
print
print '=' * 70
# print '>>final wc:\n', anet.conv1.params[0][1]
print 'test error:',
es = []
for mbi in xrange(n_test_batches):
er = anet.test(X_test[mbi], Y_test[mbi])
# print 'test batch', mbi, 'error:', er
es.append(er)
print np.mean([float(e) for e in es])
end_time = timeit.default_timer()
print 'ran for %.2fm' % ((end_time - start_time) / 60.)
save_net(anet, 'alexnet_cifar.pkl')
def setUpClass(cls):
clplatf.init_cl(1)
