Python setup_path примеры использования

Пример #1

Файл: DA_RNN.py Проект: spacegoing/tutorial_pt

from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F

import matplotlib
# matplotlib.use('Agg')
get_ipython().magic(u'matplotlib inline')

import datetime as dt, itertools, pandas as pd, matplotlib.pyplot as plt, numpy as np

import utility as util

global logger

util.setup_log()
util.setup_path()
logger = util.logger

use_cuda = torch.cuda.is_available()
logger.info("Is CUDA available? %s.", use_cuda)

# In[2]:


class encoder(nn.Module):

  def __init__(self, input_size, hidden_size, T, logger):
    # input size: number of underlying factors (81)
    # T: number of time steps (10)
    # hidden_size: dimension of the hidden state
    super(encoder, self).__init__()

Пример #2

Файл: DA_RNN.py Проект: chandlerzuo/chandlerzuo.github.io

from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F

import matplotlib
# matplotlib.use('Agg')
get_ipython().magic(u'matplotlib inline')

import datetime as dt, itertools, pandas as pd, matplotlib.pyplot as plt, numpy as np

import utility as util

global logger

util.setup_log()
util.setup_path()
logger = util.logger

use_cuda = torch.cuda.is_available()
logger.info("Is CUDA available? %s.", use_cuda)


# In[2]:

class encoder(nn.Module):
    def __init__(self, input_size, hidden_size, T, logger):
        # input size: number of underlying factors (81)
        # T: number of time steps (10)
        # hidden_size: dimension of the hidden state
        super(encoder, self).__init__()
        self.input_size = input_size

Пример #3

from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F

import matplotlib
# matplotlib.use('Agg')
#get_ipython().magic(u'matplotlib inline')

import datetime as dt, pandas as pd, matplotlib.pyplot as plt, numpy as np

import utility as util

global logger

util.setup_log()
util.setup_path('/home/wang/Wang/da_rnn3/s3_prefix','/home/wang/Wang/da_rnn3/data_dir')
logger = util.logger

use_cuda = torch.cuda.is_available()
logger.info("Is CUDA available? %s.", use_cuda)


# In[2]:

class encoder(nn.Module):
    def __init__(self, input_size, hidden_size, T, logger):
        # input size: number of underlying factors (81)
        # T: number of time steps (10)
        # hidden_size: dimension of the hidden state
        super(encoder, self).__init__()
        self.input_size = input_size

Пример #4

import torch.nn.functional as F

#import matplotlib
# matplotlib.use('Agg')
#get_ipython().magic(u'matplotlib inline')

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

import utility as util

global logger

util.setup_log()
util.setup_path(s3_prefix='prefix', data_dir='~/nasdaq100')
logger = util.logger

use_cuda = torch.cuda.is_available()
logger.info("Is CUDA available? %s.", use_cuda)

# In[2]:


class encoder(nn.Module):
    def __init__(self, input_size, hidden_size, T, logger):
        # input size: number of underlying factors (81)
        # T: number of time steps (10)
        # hidden_size: dimension of the hidden state
        super(encoder, self).__init__()
        self.input_size = input_size