本文整理汇总了Python中models.py方法的典型用法代码示例。如果您正苦于以下问题:Python models.py方法的具体用法?Python models.py怎么用?Python models.py使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类models
的用法示例。
在下文中一共展示了models.py方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: import models [as 别名]
# 或者: from models import py [as 别名]
def __init__(self, args):
self.args = args
self.vocab = dict()
self.unkdict = dict()
self.counter = 0
self.maxSeqLen = 0
# consistent with models.py
self.use_sourcelang = args.source_vectors is not None
self.use_image = not args.no_image
self.model = None
self.prepare_datagenerator()
# this results in two file handlers for dataset (here and
# data_generator)
if not self.args.dataset:
logger.warn("No dataset given, using flickr8k")
self.dataset = h5py.File("flickr8k/dataset.h5", "r")
else:
self.dataset = h5py.File("%s/dataset.h5" % self.args.dataset, "r")
if self.args.debug:
theano.config.optimizer = 'None'
theano.config.exception_verbosity = 'high'
示例2: __init__
# 需要导入模块: import models [as 别名]
# 或者: from models import py [as 别名]
def __init__(self, args):
self.args = args
self.vocab = dict()
self.unkdict = dict()
self.counter = 0
self.maxSeqLen = 0
# consistent with models.py
# maybe use_sourcelang isn't applicable here?
self.use_sourcelang = args.source_vectors is not None
self.use_image = not args.no_image
if self.args.debug:
theano.config.optimizer = 'None'
theano.config.exception_verbosity = 'high'
示例3: __init__
# 需要导入模块: import models [as 别名]
# 或者: from models import py [as 别名]
def __init__(self, args):
self.args = args
self.args.generate_from_N_words = 0 # Default 0
self.vocab = dict()
self.unkdict = dict()
self.counter = 0
self.maxSeqLen = 0
self.MAX_HT = self.args.generation_timesteps - 1
# consistent with models.py
# maybe use_sourcelang isn't applicable here?
self.use_sourcelang = args.source_vectors is not None
self.use_image = not args.no_image
if self.args.debug:
theano.config.optimizer = 'None'
theano.config.exception_verbosity = 'high'
self.source_type = "predicted" if self.args.use_predicted_tokens else "gold"
self.source_encoder = "mt_enc" if self.args.no_image else "vis_enc"
self.source_dim = self.args.hidden_size
self.h5_dataset_str = "%s-hidden_feats-%s-%d" % (self.source_type,
self.source_encoder,
self.source_dim)
logger.info("Serialising into %s" % self.h5_dataset_str)
示例4: eval_trained_dnn
# 需要导入模块: import models [as 别名]
# 或者: from models import py [as 别名]
def eval_trained_dnn(main_dir, _iter, egs_dir, run_opts):
input_model_dir = "{dir}/model_{iter}".format(dir=main_dir, iter=_iter)
# we assume that there are just one tar file for validation
tar_file = ("{0}/valid_egs.1.tar".format(egs_dir))
_command = '{command} "{main_dir}/log/compute_prob_valid.{iter}.log" ' \
'local/tf/eval_dnn.py ' \
'--tar-file="{tar_file}" --use-gpu=no ' \
'--log-file="{main_dir}/log/compute_prob_valid.{iter}.log" ' \
'--input-dir="{input_model_dir}"'.format(command=run_opts.command,
main_dir=main_dir,
iter=_iter,
tar_file=tar_file,
input_model_dir=input_model_dir)
utils.background_command(_command)
# we assume that there are just one tar file for train diagnostics
tar_file = ("{0}/train_subset_egs.1.tar".format(egs_dir))
_command = '{command} "{main_dir}/log/compute_prob_train_subset.{iter}.log" ' \
'local/tf/eval_dnn.py ' \
'--tar-file="{tar_file}" --use-gpu=no ' \
'--log-file="{main_dir}/log/compute_prob_train_subset.{iter}.log" ' \
'--input-dir="{input_model_dir}"'.format(command=run_opts.command,
main_dir=main_dir,
iter=_iter,
tar_file=tar_file,
input_model_dir=input_model_dir)
utils.background_command(_command)
示例5: delete
# 需要导入模块: import models [as 别名]
# 或者: from models import py [as 别名]
def delete(self, scenario_id=None):
if scenario_id is None:
return {'message': "Requests to delete a scenario must specify the id in the URI."}, 400
scenario = fetch_owned_scenario(scenario_id)
# We don't allow built-in scenarios to be deleted via the API (even by an admin) because it may be unsafe.
# See comment on demand_case and supply_case relationships for Scenario in models.py for discussion.
if scenario.is_built_in():
return {'message': "Built-in scenarios cannot be deleted via this API."}, 400
models.db.session.delete(scenario)
models.db.session.commit()
return {'message': 'Deleted'}, 200
示例6: finetune
# 需要导入模块: import models [as 别名]
# 或者: from models import py [as 别名]
def finetune(model, dataloaders, optimizer, criterion, best_model_path, use_lr_schedule=False):
N_EPOCH = args.epoch
best_model_wts = copy.deepcopy(model.state_dict())
since = time.time()
best_acc = 0.0
acc_hist = []
for epoch in range(1, N_EPOCH + 1):
if use_lr_schedule:
lr_schedule(optimizer, epoch)
for phase in ['train', 'val']:
if phase == 'train':
model.train()
else:
model.eval()
total_loss, correct = 0, 0
for inputs, labels in dataloaders[phase]:
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
loss = criterion(outputs, labels)
preds = torch.max(outputs, 1)[1]
if phase == 'train':
loss.backward()
optimizer.step()
total_loss += loss.item() * inputs.size(0)
correct += torch.sum(preds == labels.data)
epoch_loss = total_loss / len(dataloaders[phase].dataset)
epoch_acc = correct.double() / len(dataloaders[phase].dataset)
acc_hist.append([epoch_loss, epoch_acc])
print('Epoch: [{:02d}/{:02d}]---{}, loss: {:.6f}, acc: {:.4f}'.format(epoch, N_EPOCH, phase, epoch_loss,
epoch_acc))
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
torch.save(model.state_dict(
), 'save_model/best_{}_{}-{}.pth'.format(args.model_name, args.source, epoch))
time_pass = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_pass // 60, time_pass % 60))
print('------Best acc: {}'.format(best_acc))
model.load_state_dict(best_model_wts)
torch.save(model.state_dict(), best_model_path)
print('Best model saved!')
return model, best_acc, acc_hist
# Extract features for given intermediate layers
# Currently, this only works for ResNet since AlexNet and VGGNET only have features and classifiers modules.
# You will need to manually define a function in the forward function to extract features
# (by letting it return features and labels).
# Please follow digit_deep_network.py for reference.