def main():
# INPUT DATA
source = dn.loaders.mnist()
source.read_data()
source.partition()
# SPECIFY ARCHITECTURE
mod = dn.stack()
mod.set_arch(arch)
mod.set_transfn(transfn)
for i in range(len(arch)):
j = len(arch) - i - 1
if i < len(arch) // 2:
mod[i].set_weights(weights)
else:
mod[i].set_weights(mod[j].ret_params('weights'), **weights_kwds)
# SPECIFY NETWORK
net = dn.network(net_name)
net.set_subnets(mod)
net.set_inputs(input_dims)
# SPECIFY SUPERVISOR AND TRAINING
sup = dn.supervisor()
sup.set_labels(dtype='float32')
sup.set_errorq('mse')
sup.set_work(net)
sup.set_optimiser(optimiser, **optimiser_kwds)
sup.add_schedule(learning_rate)
# TRAIN AND TEST
now = datetime.datetime.utcnow().strftime("%Y%m%d%H%M%S")
t0 = time()
with sup.call_session(write_dir + net_name + "_" + now):
for i in range(n_epochs):
while True:
data = source.next_batch('train', batch_size)
if data:
data = data[0].reshape([batch_size, -1])
sup.train(data, data)
else:
break
data = source.next_batch('test')
data = data[0].reshape([len(data[0]), -1])
summary_str = sup.test(data, data)
print("".join(["Epoch {} ({} s): ",
summary_str]).format(str(i),
str(round(time() - t0))))
def main():
# INPUT DATA
source = dn.loaders.mnist()
source.read_data()
source.partition(seed=seed)
# SPECIFY ARCHITECTURE
mod = dn.stack()
mod.set_arch(arch)
mod.set_transfn(transfn)
mod.set_dropout(dropout)
mod.set_normal(normal, **normal_kwds)
mod.set_reguln(reguln, **reguln_kwds)
# SPECIFY NETWORK
net = dn.network(net_name)
net.set_subnets(mod)
net.set_inputs(input_dims)
# SPECIFY SUPERVISOR AND TRAINING
sup = dn.supervisor()
sup.set_optimiser(optimiser, **optimiser_kwds)
sup.set_work(net)
sup.add_schedule(learning_rate)
sup.add_schedule(0.1*learning_rate)
sup.add_schedule(0.01*learning_rate)
index = sup.add_schedule(0.01*learning_rate)
sup.set_schedule(index, False) # disable dropout
# TRAIN AND TEST
now = datetime.datetime.utcnow().strftime("%Y%m%d%H%M%S")
t0 = time()
with sup.call_session(write_dir+net_name+"_"+now, seed=seed):
for i in range(n_epochs):
if not (i % epochs_per_schedule):
sup.use_schedule(i // epochs_per_schedule)
while True:
data = source.next_batch('train', batch_size)
if data:
sup.train(*data)
else:
break
data = source.next_batch('test')
summary_str = sup.test(*data)
print("".join(["Epoch {} ({} s): ", summary_str]).format(str(i), str(round(time()-t0))))
def main():
# INPUT DATA
source = dn.loaders.mnist()
source.read_data()
source.partition()
# SPECIFY ARCHITECTURE
mod = dn.level()
mod.set_arch(arch)
mod.set_transfn(transfn)
mod.set_opverge(True)
# SPECIFY NETWORK
net = dn.network(net_name)
net.set_subnets(mod)
net.set_inputs(input_dims)
# SPECIFY SUPERVISOR AND TRAINING
sup = dn.supervisor()
sup.set_work(net)
sup.add_schedule(learning_rate)
sup.set_costfn('mse')
# TRAIN AND TEST
now = datetime.datetime.utcnow().strftime("%Y%m%d%H%M%S")
t0 = time()
with sup.call_session(write_dir + net_name + "_" + now):
for i in range(n_epochs):
while True:
data = source.next_batch('train', batch_size)
if data:
sup.train(*data)
else:
break
data = source.next_batch('test')
summary_str = sup.test(*data)
print("".join(["Epoch {} ({} s): ",
summary_str]).format(str(i),
str(round(time() - t0))))
def main():
seed = 42
# INPUT DATA
source = dn.loaders.cifar10()
source.read_data(gcn=gcn, zca=zca, gcn_within_depth=gcn_within_depth)
source.partition(seed=seed)
# SPECIFY ARCHITECTURE
mod = dn.stack()
mod.set_arch(arch)
mod.set_transfn(transfn)
mod.set_dropout(dropout)
mod.set_normal(normal, **normal_kwds)
mod.set_reguln(reguln, **reguln_kwds)
# SPECIFY NETWORK
net = dn.network(net_name)
net.set_subnets(mod)
net.set_inputs(input_dims)
# SPECIFY SUPERVISOR AND TRAINING
sup = dn.supervisor()
sup.set_optimiser(optimiser, **optimiser_kwds)
sup.set_work(net)
for epoch in sorted(list(learning_rates.keys())):
index = sup.add_schedule(learning_rates[epoch])
if index == len(learning_rates) - 1:
sup.set_schedule(index, False) # disable dropout
# CHECK FOR RESTOREPOINT
modfiler = dn.helpers.model_filer(write_dir, net_name)
restore_point = modfiler.interview()
if restore_point is not None:
seed = restore_point
# TRAIN AND TEST
now = datetime.datetime.utcnow().strftime("%Y%m%d%H%M%S")
log_out = None if write_dir is None else write_dir+net_name+"_"+now
mod_out = None if write_dir is None else log_out + "/" + net_name
schedule = -1
epoch_0 = 0
t0 = time()
with sup.call_session(log_out, seed=seed):
if restore_point is not None:
epoch_0 = int(np.ceil(float(sup.progress[1])/float(source.sets['train']['support'])))
for i in range(epoch_0):
if i in learning_rates:
schedule += 1
sup.use_schedule(schedule)
for i in range(epoch_0, n_epochs):
if i in learning_rates:
schedule += 1
sup.use_schedule(schedule)
while True:
data = source.next_batch('train', batch_size, \
rand_flip=rand_flip, rand_crop=rand_crop)
if not data:
break
sup.train(*data)
data = source.next_batch('test')
summary_str = sup.test(*data, split=test_split)
print("".join(["Epoch {} ({} s): ", summary_str]).format(str(i), str(round(time()-t0))))
if i and mod_out is not None:
if not(i % save_interval) or i == n_epochs -1:
sup.save(mod_out)