python data_Python data.Data方法代码示例

本文整理汇总了Python中data.Data方法的典型用法代码示例。如果您正苦于以下问题：Python data.Data方法的具体用法？Python data.Data怎么用？Python data.Data使用的例子？那么恭喜您, 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模块data的用法示例。

在下文中一共展示了data.Data方法的11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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: __init__

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def __init__(self):

super(Predictor, self).__init__()

num_units = 512

num_layer = 2

batch_size = 1

data_dir = 'data/'

input_file = 'poetry.txt'

vocab_file = 'vocab.pkl'

tensor_file = 'tensor.npy'

self.data = Data(data_dir, input_file, vocab_file, tensor_file,

is_train=False, batch_size=batch_size)

self.model = Net(self.data, num_units, num_layer, batch_size)

self.sess = tf.Session()

saver = tf.train.Saver(tf.global_variables())

saver.restore(self.sess, 'model/model')

print('Load model done.' + '\n')

开发者ID:stardut，项目名称:Text-Generate-RNN，代码行数:20，

示例2: main

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def main():

global model

if args.data_test == ['video']:

from videotester import VideoTester

model = model.Model(args, checkpoint)

t = VideoTester(args, model, checkpoint)

t.test()

else:

if checkpoint.ok:

loader = data.Data(args)

_model = model.Model(args, checkpoint)

_loss = loss.Loss(args, checkpoint) if not args.test_only else None

t = Trainer(args, loader, _model, _loss, checkpoint)

while not t.terminate():

t.train()

t.test()

checkpoint.done()

开发者ID:thstkdgus35，项目名称:EDSR-PyTorch，代码行数:20，

示例3: test

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def test():

from data import Data

from config import Config

conf = Config()

usecuda = True

we = torch.load('./data/processed/ji/we.pkl')

char_table = None

sub_table = None

if conf.need_char or conf.need_elmo:

char_table = torch.load('./data/processed/ji/char_table.pkl')

if conf.need_sub:

sub_table = torch.load('./data/processed/ji/sub_table.pkl')

model = IDRCModel(conf, we, char_table, sub_table, usecuda)

if usecuda:

model.cuda()

d = Data(usecuda, conf)

for a1, a2, sense, conn in d.train_loader:

if usecuda:

a1, a2 = a1.cuda(), a2.cuda()

a1, a2 = Variable(a1), Variable(a2)

break

model.eval()

out = model(a1, a2)

print(out)

开发者ID:hxbai，项目名称:Deep_Enhanced_Repr_for_IDRR，代码行数:26，

示例4: createdata

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def createdata(path):

""" Create training data by calling the Data class

:type path: string

:param path: path to the training document folder

"""

data = Data()

data.builddata(path)

# Change the threshold if you want to filter

# out the low-frequency features

data.buildvocab(thresh=1)

data.buildmatrix()

data.savematrix("training-data.pickle.gz")

data.savevocab("vocab.pickle.gz")

开发者ID:jiyfeng，项目名称:RSTParser，代码行数:16，

示例5: main

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def main():

data = Data(dataname='kosarak', limit=20000)

finder = SVSM(data, top_k=32, epsilon=4)

cand_dict = finder.find()

print(cand_dict)

开发者ID:vvv214，项目名称:LDP_Protocols，代码行数:7，

示例6: main

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def main():

data = Data()

finder = PEM(data, top_k=32, epsilon=4)

cand_dict = finder.find()

print(cand_dict)

开发者ID:vvv214，项目名称:LDP_Protocols，代码行数:7，

示例7: _pre_data

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def _pre_data(self):

print('pre data...')

self.data = Data(self.cuda, self.conf)

开发者ID:hxbai，项目名称:Deep_Enhanced_Repr_for_IDRR，代码行数:5，

示例8: update_pickle_file

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def update_pickle_file(file_name, eps=0, k=0, v=0):

d_old = data_old.Data(file_name)

d_old.load()

print(file_name, 'loaded')

# d_old.print_fields()

d_new = data.Data()

d_new.set_agent('Wolp',

int(d_old.get_data('max_actions')[0]),

k,

v)

d_new.set_experiment(d_old.get_data('experiment')[0],

[-3],

[3],

eps)

space = action_space.Space([-3], [3], int(d_old.get_data('max_actions')[0]))

# print(space.get_space())

# d_new.print_data()

done = d_old.get_data('done')

actors_result = d_old.get_data('actors_result')

actions = d_old.get_data('actions')

state_0 = d_old.get_data('state_0').tolist()

state_1 = d_old.get_data('state_1').tolist()

state_2 = d_old.get_data('state_2').tolist()

state_3 = d_old.get_data('state_3').tolist()

rewards = d_old.get_data('rewards').tolist()

ep = 0

temp = 0

l = len(done)

for i in range(l):

d_new.set_action(space.import_point(actions[i]).tolist())

d_new.set_actors_action(space.import_point(actors_result[i]).tolist())

d_new.set_ndn_action(space.import_point(

space.search_point(actors_result[i], 1)[0]).tolist())

state = [state_0[i], state_1[i], state_2[i], state_3[i]]

d_new.set_state(state)

d_new.set_reward(1)

if done[i] > 0:

# print(ep, i - temp, 'progress', i / l)

temp = i

ep += 1

# if ep % 200 == 199:

# d_new.finish_and_store_episode()

# else:

d_new.end_of_episode()

d_new.save()

开发者ID:jimkon，项目名称:Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces，代码行数:52，

示例9: run

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def run(args):

save_dir = '{}/'.format(args.experiment_name)

if not os.path.exists(save_dir):

os.mkdir(save_dir)

query = args.query

k = args.k

trained_prefix = args.trained_filename

untrained_prefix = args.untrained_filename

threshold = args.threshold

search_space = Data('darts')

# if it's the first iteration, choose k arches at random to train

if query == 0:

print('about to generate {} random'.format(k))

data = search_space.generate_random_dataset(num=k, train=False)

arches = [d['spec'] for d in data]

next_arches = []

for arch in arches:

d = {}

d['spec'] = arch

next_arches.append(d)

else:

# get the data from prior iterations from pickle files

data = []

for i in range(query):

filepath = '{}{}_{}.pkl'.format(save_dir, trained_prefix, i)

with open(filepath, 'rb') as f:

arch = pickle.load(f)

data.append(arch)

print('Iteration {}'.format(query))

print('Data from last round')

print(data)

# run the meta neural net to output the next arches

next_arches = run_meta_neuralnet(search_space, data, k=k)

print('next batch')

print(next_arches)

# output the new arches to pickle files

for i in range(k):

index = query + i

filepath = '{}{}_{}.pkl'.format(save_dir, untrained_prefix, index)

next_arches[i]['index'] = index

next_arches[i]['filepath'] = filepath

with open(filepath, 'wb') as f:

pickle.dump(next_arches[i], f)

开发者ID:naszilla，项目名称:bananas，代码行数:56，

示例10: run_experiments

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def run_experiments(args, save_dir):

os.environ['search_space'] = args.search_space

from nas_algorithms import run_nas_algorithm

from data import Data

trials = args.trials

out_file = args.output_filename

save_specs = args.save_specs

metann_params = meta_neuralnet_params(args.search_space)

algorithm_params = algo_params(args.algo_params)

num_algos = len(algorithm_params)

logging.info(algorithm_params)

# set up search space

mp = copy.deepcopy(metann_params)

ss = mp.pop('search_space')

dataset = mp.pop('dataset')

search_space = Data(ss, dataset=dataset)

for i in range(trials):

results = []

walltimes = []

run_data = []

for j in range(num_algos):

# run NAS algorithm

print('\n* Running algorithm: {}'.format(algorithm_params[j]))

starttime = time.time()

algo_result, run_datum = run_nas_algorithm(algorithm_params[j], search_space, mp)

algo_result = np.round(algo_result, 5)

# remove unnecessary dict entries that take up space

for d in run_datum:

if not save_specs:

d.pop('spec')

for key in ['encoding', 'adjacency', 'path', 'dist_to_min']:

if key in d:

d.pop(key)

# add walltime, results, run_data

walltimes.append(time.time()-starttime)

results.append(algo_result)

run_data.append(run_datum)

# print and pickle results

filename = os.path.join(save_dir, '{}_{}.pkl'.format(out_file, i))

print('\n* Trial summary: (params, results, walltimes)')

print(algorithm_params)

print(metann_params)

print(results)

print(walltimes)

print('\n* Saving to file {}'.format(filename))

with open(filename, 'wb') as f:

pickle.dump([algorithm_params, metann_params, results, walltimes, run_data], f)

f.close()

开发者ID:naszilla，项目名称:bananas，代码行数:59，

示例11: build_pdf

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# 需要导入模块: import data [as 别名]

# 或者: from data import Data [as 别名]

def build_pdf(self, source, texinputs=[]):

with TempDir() as tmpdir,\

source.temp_saved(suffix='.latex', dir=tmpdir) as tmp:

# close temp file, so other processes can access it also on Windows

tmp.close()

base_fn = os.path.splitext(tmp.name)[0]

output_fn = base_fn + '.pdf'

latex_cmd = [shlex_quote(self.pdflatex),

'-interaction=batchmode',

'-halt-on-error',

'-no-shell-escape',

'-file-line-error',

'%O',

'%S', ]

if self.variant == 'pdflatex':

args = [self.latexmk,

'-pdf',

'-pdflatex={}'.format(' '.join(latex_cmd)),

tmp.name, ]

elif self.variant == 'xelatex':

args = [self.latexmk,

'-xelatex',

tmp.name, ]

else:

raise ValueError('Invalid LaTeX variant: {}'.format(

self.variant))

# create environment

newenv = os.environ.copy()

newenv['TEXINPUTS'] = os.pathsep.join(texinputs) + os.pathsep

try:

subprocess.check_call(args,

cwd=tmpdir,

env=newenv,

stdin=open(os.devnull, 'r'),

stdout=open(os.devnull, 'w'),

stderr=open(os.devnull, 'w'), )

except CalledProcessError as e:

raise_from(LatexBuildError(base_fn + '.log'), e)

return I(open(output_fn, 'rb').read(), encoding=None)

开发者ID:mbr，项目名称:latex，代码行数:48，

注：本文中的data.Data方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。