valid_set.y = np.float32(np.eye(num_outputs)[valid_set.y])
test_set.y = np.float32(np.eye(num_outputs)[test_set.y])
# for hinge loss
train_set.y = 2 * train_set.y - 1.
valid_set.y = 2 * valid_set.y - 1.
test_set.y = 2 * test_set.y - 1.
print('Building the CNN...')
# Prepare Theano variables for inputs and targets
input = T.tensor4('inputs')
target = T.matrix('targets')
LR = T.scalar('LR', dtype=theano.config.floatX)
cnn = cnv.genCnv(input, num_outputs, learning_parameters)
train_output = lasagne.layers.get_output(cnn, deterministic=False)
# squared hinge loss
loss = T.mean(T.sqr(T.maximum(0., 1. - target * train_output)))
# W updates
W = lasagne.layers.get_all_params(cnn, binary=True)
W_grads = binary_net.compute_grads(loss, cnn)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = binary_net.clipping_scaling(updates, cnn)
# other parameters updates
# for hinge loss
train_set.y = 2 * train_set.y - 1.
valid_set.y = 2 * valid_set.y - 1.
test_set.y = 2 * test_set.y - 1.
print('Building Network...')
# Prepare Theano variables for inputs and targets
input = T.tensor4('inputs')
target = T.matrix('targets')
LR = T.scalar('LR', dtype=theano.config.floatX)
if args.model == 'cnv':
import cnv
cnn = cnv.genCnv(input, classes, learning_parameters)
elif args.model == 'resnet':
import resnet
cnn = resnet.genCnv(input, classes, learning_parameters)
elif args.model == 'lenet':
import lenet
cnn = lenet.genCnv(input, classes, learning_parameters)
elif args.model == 'inception':
import inception
cnn = inception.genCnv(input, classes, learning_parameters)
train_output = lasagne.layers.get_output(cnn, deterministic=False)
# squared hinge loss
loss = T.mean(T.sqr(T.maximum(0., 1. - target * train_output)))
valid_sety = np.float32(np.eye(30)[valid_sety])
test_sety = np.float32(np.eye(30)[test_sety])
# for hinge loss
train_sety = 2 * train_sety - 1.
valid_sety = 2 * valid_sety - 1.
test_sety = 2 * test_sety - 1.
print('Building the CNN...')
# Prepare Theano variables for inputs and targets
input = T.tensor4('inputs')
target = T.matrix('targets')
LR = T.scalar('LR', dtype=theano.config.floatX)
cnn = cnv.genCnv(input, 30, learning_parameters)
train_output = lasagne.layers.get_output(cnn, deterministic=False)
# squared hinge loss
loss = T.mean(T.sqr(T.maximum(0., 1. - target * train_output)))
# W updates
W = lasagne.layers.get_all_params(cnn, binary=True)
W_grads = binary_net.compute_grads(loss, cnn)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = binary_net.clipping_scaling(updates, cnn)
# other parameters updates
