def train():
args = get_args()
# Get context.
from nnabla.contrib.context import extension_context
extension_module = args.context
if args.context is None:
extension_module = 'cpu'
logger.info("Running in %s" % extension_module)
ctx = extension_context(extension_module, device_id=args.device_id)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
if args.net == "cifar10_resnet23_prediction":
model_prediction = cifar10_resnet23_prediction
# TRAIN
maps = 64
data_iterator = data_iterator_cifar10
c = 3
h = w = 32
n_train = 50000
n_valid = 10000
# Create input variables.
image = nn.Variable([args.batch_size, c, h, w])
label = nn.Variable([args.batch_size, 1])
# Create model_prediction graph.
pred = model_prediction(image, maps=maps, test=False)
pred.persistent = True
# Create loss function.
loss = F.mean(F.softmax_cross_entropy(pred, label))
# SSL Regularization
loss += ssl_regularization(nn.get_parameters(), args.filter_decay,
args.channel_decay)
# TEST
# Create input variables.
vimage = nn.Variable([args.batch_size, c, h, w])
vlabel = nn.Variable([args.batch_size, 1])
# Create predition graph.
vpred = model_prediction(vimage, maps=maps, test=True)
# Create Solver.
solver = S.Adam(args.learning_rate)
solver.set_parameters(nn.get_parameters())
# Create monitor.
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
monitor_verr = MonitorSeries("Test error", monitor, interval=1)
# Initialize DataIterator
data = data_iterator(args.batch_size, True)
vdata = data_iterator(args.batch_size, False)
best_ve = 1.0
ve = 1.0
# Training loop.
for i in range(args.max_iter):
if i % args.val_interval == 0:
# Validation
ve = 0.0
for j in range(int(n_valid / args.batch_size)):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
ve /= int(n_valid / args.batch_size)
monitor_verr.add(i, ve)
if ve < best_ve:
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % i))
best_ve = ve
# Training forward
image.d, label.d = data.next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
loss.backward(clear_buffer=True)
solver.weight_decay(args.weight_decay)
solver.update()
e = categorical_error(pred.d, label.d)
monitor_loss.add(i, loss.d.copy())
monitor_err.add(i, e)
monitor_time.add(i)
ve = 0.0
for j in range(int(n_valid / args.batch_size)):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
ve /= int(n_valid / args.batch_size)
monitor_verr.add(i, ve)
parameter_file = os.path.join(args.model_save_path,
'params_{:06}.h5'.format(args.max_iter))
nn.save_parameters(parameter_file)
def classification_svd():
args = get_args()
# Get context.
from nnabla.ext_utils import get_extension_context
logger.info("Running in %s" % args.context)
ctx = get_extension_context(args.context,
device_id=args.device_id,
type_config=args.type_config)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
mnist_cnn_prediction = mnist_lenet_prediction_slim
# TRAIN
reference = "reference"
slim = "slim"
rrate = 0.5 # reduction rate
# Create input variables.
image = nn.Variable([args.batch_size, 1, 28, 28])
label = nn.Variable([args.batch_size, 1])
# Create `reference` and "slim" prediction graph.
model_load_path = args.model_load_path
pred = mnist_cnn_prediction(image, scope=slim, rrate=rrate, test=False)
pred.persistent = True
# Decompose and set parameters
decompose_network_and_set_params(model_load_path, reference, slim, rrate)
loss = F.mean(F.softmax_cross_entropy(pred, label))
# TEST
# Create input variables.
vimage = nn.Variable([args.batch_size, 1, 28, 28])
vlabel = nn.Variable([args.batch_size, 1])
# Create reference prediction graph.
vpred = mnist_cnn_prediction(vimage, scope=slim, rrate=rrate, test=True)
# Create Solver.
solver = S.Adam(args.learning_rate)
with nn.parameter_scope(slim):
solver.set_parameters(nn.get_parameters())
# Create monitor.
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
monitor_verr = MonitorSeries("Test error", monitor, interval=10)
# Initialize DataIterator for MNIST.
data = data_iterator_mnist(args.batch_size, True)
vdata = data_iterator_mnist(args.batch_size, False)
best_ve = 1.0
# Training loop.
for i in range(args.max_iter):
if i % args.val_interval == 0:
# Validation
ve = 0.0
for j in range(args.val_iter):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
monitor_verr.add(i, ve / args.val_iter)
if ve < best_ve:
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % i))
best_ve = ve
# Training forward
image.d, label.d = data.next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
loss.backward(clear_buffer=True)
solver.weight_decay(args.weight_decay)
solver.update()
e = categorical_error(pred.d, label.d)
monitor_loss.add(i, loss.d.copy())
monitor_err.add(i, e)
monitor_time.add(i)
ve = 0.0
for j in range(args.val_iter):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
monitor_verr.add(i, ve / args.val_iter)
parameter_file = os.path.join(args.model_save_path,
'params_{:06}.h5'.format(args.max_iter))
nn.save_parameters(parameter_file)
def train():
"""
Main script.
Steps:
* Parse command line arguments.
* Instantiate a communicator and set parameter variables.
* Specify contexts for computation.
* Initialize DataIterator.
* Construct a computation graph for training and one for validation.
* Initialize solver and set parameter variables to that.
* Create monitor instances for saving and displaying training stats.
* Training loop
* Computate error rate for validation data (periodically)
* Get a next minibatch.
* Execute forwardprop
* Set parameter gradients zero
* Execute backprop.
* Solver updates parameters by using gradients computed by backprop.
* Compute training error
"""
# Parse args
args = get_args()
n_train_samples = 50000
bs_valid = args.batch_size
extension_module = args.context
ctx = get_extension_context(extension_module,
device_id=args.device_id,
type_config=args.type_config)
nn.set_default_context(ctx)
if args.net == "cifar10_resnet23":
prediction = functools.partial(resnet23_prediction,
ncls=10,
nmaps=64,
act=F.relu)
data_iterator = data_iterator_cifar10
if args.net == "cifar100_resnet23":
prediction = functools.partial(resnet23_prediction,
ncls=100,
nmaps=384,
act=F.elu)
data_iterator = data_iterator_cifar100
# Create training graphs
test = False
image_train = nn.Variable((args.batch_size, 3, 32, 32))
label_train = nn.Variable((args.batch_size, 1))
pred_train = prediction(image_train, test)
loss_train = loss_function(pred_train, label_train)
input_image_train = {"image": image_train, "label": label_train}
# Create validation graph
test = True
image_valid = nn.Variable((bs_valid, 3, 32, 32))
pred_valid = prediction(image_valid, test)
input_image_valid = {"image": image_valid}
# Solvers
solver = S.Adam()
solver.set_parameters(nn.get_parameters())
# Create monitor
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=10)
monitor_verr = MonitorSeries("Test error", monitor, interval=1)
# Data Iterator
tdata = data_iterator(args.batch_size, True)
vdata = data_iterator(args.batch_size, False)
# Training-loop
for i in range(args.max_iter):
# Validation
if i % int(n_train_samples / args.batch_size) == 0:
ve = 0.
for j in range(args.val_iter):
image, label = vdata.next()
input_image_valid["image"].d = image
pred_valid.forward()
ve += categorical_error(pred_valid.d, label)
ve /= args.val_iter
monitor_verr.add(i, ve)
if int(i % args.model_save_interval) == 0:
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % i))
# Forward/Zerograd/Backward
image, label = tdata.next()
input_image_train["image"].d = image
input_image_train["label"].d = label
loss_train.forward()
solver.zero_grad()
loss_train.backward()
# Solvers update
solver.update()
e = categorical_error(pred_train.d, input_image_train["label"].d)
monitor_loss.add(i, loss_train.d.copy())
monitor_err.add(i, e)
monitor_time.add(i)
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % (args.max_iter)))
def train():
"""
Naive Multi-Device Training
NOTE: the communicator exposes low-level interfaces
* Parse command line arguments.
* Instantiate a communicator and set parameter variables.
* Specify contexts for computation.
* Initialize DataIterator.
* Construct a computation graph for training and one for validation.
* Initialize solver and set parameter variables to that.
* Create monitor instances for saving and displaying training stats.
* Training loop
* Computate error rate for validation data (periodically)
* Get a next minibatch.
* Execute forwardprop
* Set parameter gradients zero
* Execute backprop.
* Solver updates parameters by using gradients computed by backprop.
* Compute training error
"""
# Parse args
args = get_args()
n_train_samples = 50000
bs_valid = args.batch_size
rng = np.random.RandomState(313)
if args.net == "cifar10_resnet23":
prediction = functools.partial(resnet23_prediction,
rng=rng,
ncls=10,
nmaps=64,
act=F.relu)
data_iterator = data_iterator_cifar10
if args.net == "cifar100_resnet23":
prediction = functools.partial(resnet23_prediction,
rng=rng,
ncls=100,
nmaps=384,
act=F.elu)
data_iterator = data_iterator_cifar100
# Communicator and Context
extension_module = "cuda.cudnn"
ctx = extension_context(extension_module)
comm = C.MultiProcessDataParalellCommunicator(ctx)
comm.init()
n_devices = comm.size
mpi_rank = comm.rank
mpi_local_rank = comm.local_rank
device_id = mpi_local_rank
ctx = extension_context(extension_module, device_id=device_id)
nn.set_default_context(ctx)
# Create training graphs
test = False
image_train = nn.Variable((args.batch_size, 3, 32, 32))
label_train = nn.Variable((args.batch_size, 1))
pred_train = prediction(image_train, test)
loss_train = loss_function(pred_train, label_train)
input_image_train = {"image": image_train, "label": label_train}
# add parameters to communicator
comm.add_context_and_parameters((ctx, nn.get_parameters()))
# Create validation graph
test = True
image_valid = nn.Variable((bs_valid, 3, 32, 32))
pred_valid = prediction(image_valid, test)
input_image_valid = {"image": image_valid}
# Solvers
solver = S.Adam()
solver.set_parameters(nn.get_parameters())
base_lr = args.learning_rate
warmup_iter = int(
1. * n_train_samples / args.batch_size / n_devices) * args.warmup_epoch
warmup_slope = base_lr * (n_devices - 1) / warmup_iter
solver.set_learning_rate(base_lr)
# Create monitor
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=10)
monitor_verr = MonitorSeries("Test error", monitor, interval=10)
# Data Iterator
rng = np.random.RandomState(device_id)
tdata = data_iterator(args.batch_size, True, rng)
vdata = data_iterator(args.batch_size, False)
# Training-loop
for i in range(int(args.max_iter / n_devices)):
# Validation
if device_id == 0:
if i % int(n_train_samples / args.batch_size / n_devices) == 0:
ve = 0.
for j in range(args.val_iter):
image, label = vdata.next()
input_image_valid["image"].d = image
pred_valid.forward()
ve += categorical_error(pred_valid.d, label)
ve /= args.val_iter
monitor_verr.add(i * n_devices, ve)
if i % int(args.model_save_interval / n_devices) == 0:
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % i))
# Forward/Zerograd/Backward
image, label = tdata.next()
input_image_train["image"].d = image
input_image_train["label"].d = label
loss_train.forward()
solver.zero_grad()
loss_train.backward()
# Allreduce
comm.allreduce(division=False, inplace=False)
# Solvers update
solver.update()
# Linear Warmup
if i <= warmup_iter:
lr = base_lr + warmup_slope * i
solver.set_learning_rate(lr)
if device_id == 0:
e = categorical_error(pred_train.d, input_image_train["label"].d)
monitor_loss.add(i * n_devices, loss_train.d.copy())
monitor_err.add(i * n_devices, e)
monitor_time.add(i * n_devices)
if device_id == 0:
nn.save_parameters(
os.path.join(args.model_save_path,
'params_%06d.h5' % (args.max_iter / n_devices)))
def train(args):
# Get context.
from nnabla.ext_utils import get_extension_context
logger.info("Running in %s" % args.context)
ctx = get_extension_context(args.context,
device_id=args.device_id,
type_config=args.type_config)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
if args.net == "cifar10_resnet23_prediction":
model_prediction = cifar10_resnet23_prediction
elif args.net == 'cifar10_binary_connect_resnet23_prediction':
model_prediction = cifar10_binary_connect_resnet23_prediction
elif args.net == 'cifar10_binary_net_resnet23_prediction':
model_prediction = cifar10_binary_net_resnet23_prediction
elif args.net == 'cifar10_binary_weight_resnet23_prediction':
model_prediction = cifar10_binary_weight_resnet23_prediction
elif args.net == 'cifar10_fp_connect_resnet23_prediction':
model_prediction = functools.partial(
cifar10_fp_connect_resnet23_prediction,
n=args.bit_width,
delta=args.delta)
elif args.net == 'cifar10_fp_net_resnet23_prediction':
model_prediction = functools.partial(
cifar10_fp_net_resnet23_prediction,
n=args.bit_width,
delta=args.delta)
elif args.net == 'cifar10_pow2_connect_resnet23_prediction':
model_prediction = functools.partial(
cifar10_pow2_connect_resnet23_prediction,
n=args.bit_width,
m=args.upper_bound)
elif args.net == 'cifar10_pow2_net_resnet23_prediction':
model_prediction = functools.partial(
cifar10_pow2_net_resnet23_prediction,
n=args.bit_width,
m=args.upper_bound)
elif args.net == 'cifar10_inq_resnet23_prediction':
model_prediction = functools.partial(cifar10_inq_resnet23_prediction,
num_bits=args.bit_width)
elif args.net == 'cifar10_min_max_resnet23_prediction':
model_prediction = functools.partial(
cifar10_min_max_resnet23_prediction,
ql_min=args.ql_min,
ql_max=args.ql_max,
p_min_max=args.p_min_max,
a_min_max=args.a_min_max,
a_ema=args.a_ema,
ste_fine_grained=args.ste_fine_grained)
# TRAIN
maps = 64
data_iterator = data_iterator_cifar10
c = 3
h = w = 32
n_train = 50000
n_valid = 10000
# Create input variables.
image = nn.Variable([args.batch_size, c, h, w])
label = nn.Variable([args.batch_size, 1])
# Create model_prediction graph.
pred = model_prediction(image, maps=maps, test=False)
pred.persistent = True
# Create loss function.
loss = F.mean(F.softmax_cross_entropy(pred, label))
# TEST
# Create input variables.
vimage = nn.Variable([args.batch_size, c, h, w])
vlabel = nn.Variable([args.batch_size, 1])
# Create prediction graph.
vpred = model_prediction(vimage, maps=maps, test=True)
# Create Solver.
solver = S.Adam(args.learning_rate)
solver.set_parameters(nn.get_parameters())
# Create monitor.
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
monitor_verr = MonitorSeries("Test error", monitor, interval=1)
# Initialize DataIterator
data = data_iterator(args.batch_size, True)
vdata = data_iterator(args.batch_size, False)
best_ve = 1.0
ve = 1.0
# Training loop.
for i in range(args.max_iter):
if i % args.val_interval == 0:
# Validation
ve = 0.0
for j in range(int(n_valid / args.batch_size)):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
ve /= int(n_valid / args.batch_size)
monitor_verr.add(i, ve)
if ve < best_ve:
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % i))
best_ve = ve
# Training forward
image.d, label.d = data.next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
loss.backward(clear_buffer=True)
solver.weight_decay(args.weight_decay)
solver.update()
e = categorical_error(pred.d, label.d)
monitor_loss.add(i, loss.d.copy())
monitor_err.add(i, e)
monitor_time.add(i)
ve = 0.0
for j in range(int(n_valid / args.batch_size)):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
ve /= int(n_valid / args.batch_size)
monitor_verr.add(i, ve)
parameter_file = os.path.join(args.model_save_path,
'params_{:06}.h5'.format(args.max_iter))
nn.save_parameters(parameter_file)
def distil():
args = get_args()
# Get context.
from nnabla.ext_utils import get_extension_context
logger.info("Running in %s" % args.context)
ctx = get_extension_context(args.context,
device_id=args.device_id,
type_config=args.type_config)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
if args.net == "cifar10_resnet23_prediction":
model_prediction = cifar10_resnet23_prediction
data_iterator = data_iterator_cifar10
c = 3
h = w = 32
n_train = 50000
n_valid = 10000
# TRAIN
teacher = "teacher"
student = "student"
maps = args.maps
rrate = args.reduction_rate
# Create input variables.
image = nn.Variable([args.batch_size, c, h, w])
image.persistent = True # not clear the intermediate buffer re-used
label = nn.Variable([args.batch_size, 1])
label.persistent = True # not clear the intermediate buffer re-used
# Create `teacher` and "student" prediction graph.
model_load_path = args.model_load_path
nn.load_parameters(model_load_path)
pred_label = model_prediction(image,
net=teacher,
maps=maps,
test=not args.use_batch)
pred_label.need_grad = False # no need backward through teacher graph
pred = model_prediction(image,
net=student,
maps=int(maps * (1. - rrate)),
test=False)
pred.persistent = True # not clear the intermediate buffer used
loss_ce = F.mean(F.softmax_cross_entropy(pred, label))
loss_ce_soft = ce_soft(pred, pred_label)
loss = args.weight_ce * loss_ce + args.weight_ce_soft * loss_ce_soft
# TEST
# Create input variables.
vimage = nn.Variable([args.batch_size, c, h, w])
vlabel = nn.Variable([args.batch_size, 1])
# Create teacher prediction graph.
vpred = model_prediction(vimage,
net=student,
maps=int(maps * (1. - rrate)),
test=True)
# Create Solver.
solver = S.Adam(args.learning_rate)
with nn.parameter_scope(student):
solver.set_parameters(nn.get_parameters())
# Create monitor.
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
monitor_verr = MonitorSeries("Test error", monitor, interval=1)
# Initialize DataIterator for MNIST.
data = data_iterator(args.batch_size, True)
vdata = data_iterator(args.batch_size, False)
best_ve = 1.0
# Training loop.
for i in range(args.max_iter):
if i % args.val_interval == 0:
# Validation
ve = 0.0
for j in range(int(n_valid / args.batch_size)):
vimage.d, vlabel.d = vdata[1].next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
ve /= int(n_valid / args.batch_size)
monitor_verr.add(i, ve)
if ve < best_ve:
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % i))
best_ve = ve
# Training forward
image.d, label.d = data[1].next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
loss.backward(clear_buffer=True)
solver.weight_decay(args.weight_decay)
solver.update()
e = categorical_error(pred.d, label.d)
monitor_loss.add(i, loss.d.copy())
monitor_err.add(i, e)
monitor_time.add(i)
ve = 0.0
for j in range(int(n_valid / args.batch_size)):
vimage.d, vlabel.d = vdata[1].next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
ve /= int(n_valid / args.batch_size)
monitor_verr.add(i, ve)
parameter_file = os.path.join(args.model_save_path,
'params_{:06}.h5'.format(args.max_iter))
nn.save_parameters(parameter_file)
def distil():
args = get_args()
# Get context.
from nnabla.contrib.context import extension_context
extension_module = args.context
if args.context is None:
extension_module = 'cpu'
logger.info("Running in %s" % extension_module)
ctx = extension_context(extension_module, device_id=args.device_id)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
mnist_cnn_prediction = mnist_resnet_prediction
# TRAIN
teacher = "teacher"
student = "student"
# Create input variables.
image = nn.Variable([args.batch_size, 1, 28, 28])
image.persistent = True # not clear the intermediate buffer re-used
label = nn.Variable([args.batch_size, 1])
label.persistent = True # not clear the intermediate buffer re-used
# Create `teacher` and "student" prediction graph.
model_load_path = args.model_load_path
nn.load_parameters(model_load_path)
pred_label = mnist_cnn_prediction(image, net=teacher, maps=64, test=False)
pred_label.need_grad = False # no need backward through teacher graph
pred = mnist_cnn_prediction(image, net=student, maps=32, test=False)
pred.persistent = True # not clear the intermediate buffer used
loss_ce = F.mean(F.softmax_cross_entropy(pred, label))
loss_kl = kl_divergence(pred, pred_label)
loss = args.weight_ce * loss_ce + args.weight_kl * loss_kl
# TEST
# Create input variables.
vimage = nn.Variable([args.batch_size, 1, 28, 28])
vlabel = nn.Variable([args.batch_size, 1])
# Create teacher predition graph.
vpred = mnist_cnn_prediction(vimage, net=student, maps=32, test=True)
# Create Solver.
solver = S.Adam(args.learning_rate)
with nn.parameter_scope(student):
solver.set_parameters(nn.get_parameters())
# Create monitor.
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
monitor_verr = MonitorSeries("Test error", monitor, interval=10)
# Initialize DataIterator for MNIST.
data = data_iterator_mnist(args.batch_size, True)
vdata = data_iterator_mnist(args.batch_size, False)
best_ve = 1.0
# Training loop.
for i in range(args.max_iter):
if i % args.val_interval == 0:
# Validation
ve = 0.0
for j in range(args.val_iter):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
monitor_verr.add(i, ve / args.val_iter)
if ve < best_ve:
nn.save_parameters(os.path.join(
args.model_save_path, 'params_%06d.h5' % i))
best_ve = ve
# Training forward
image.d, label.d = data.next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
loss.backward(clear_buffer=True)
solver.weight_decay(args.weight_decay)
solver.update()
e = categorical_error(pred.d, label.d)
monitor_loss.add(i, loss.d.copy())
monitor_err.add(i, e)
monitor_time.add(i)
ve = 0.0
for j in range(args.val_iter):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
monitor_verr.add(i, ve / args.val_iter)
parameter_file = os.path.join(
args.model_save_path, 'params_{:06}.h5'.format(args.max_iter))
nn.save_parameters(parameter_file)
