applenob

class ModelWrapper(object):
    """
    在原始模型之外进行一层外包封装。
    动态地替换原模型的属性。
    """
    def __init__(self, execute_model):
        self.execute_model = execute_model  # 实际运行的模型
        self.overwrite_model_attr()

    def overwrite_model_attr(self):

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import threading
import contextlib

import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.contrib.distribute.python import collective_all_reduce_strategy
from tensorflow.python.distribute import multi_worker_test_base
from tensorflow.python.training import coordinator
from tensorflow.python.training import server_lib
from tensorflow.python.eager import context

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"""Stack tracer for multi-threaded applications.


Usage:

import stacktracer
stacktracer.start_trace("trace.html",interval=5,auto=True) # Set auto flag to always update file!
....
stacktracer.stop_trace()
"""

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#-*- coding:utf-8 -*- 
import Queue
from contextlib import contextmanager

class ObjectPool(object):
  """A simple object pool with thread safe"""
  def __init__(self,objectFn,*args,**kwargs):
    super(ObjectPool, self).__init__()
    self.objectFn = objectFn
    self.objectCls = None

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"""
Tensorflow estimator API example

References:
- <https://www.tensorflow.org/guide/custom_estimators>
- <https://github.com/tensorflow/models/blob/master/samples/core/get_started/custom_estimator.py>
- <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/distribute/README.md>
"""
import numpy as np
import tensorflow as tf

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import tensorflow as tf
from tensorflow.python.platform import gfile
with tf.Session() as sess:
    model_filename ='PATH_TO_PB.pb'
    with gfile.FastGFile(model_filename, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        g_in = tf.import_graph_def(graph_def)
LOGDIR='/logs/tests/1/'
train_writer = tf.summary.FileWriter(LOGDIR)

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import importlib


def find_name_path(root, name):
  res = []
  try:
    m = importlib.import_module(root)
  except (AttributeError, ModuleNotFoundError) as e:
    return res
  if name in dir(m):

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"""
训练向量
python w2v.py corpus.txt w2v_char.txt
"""

import logging
import os.path
import sys
import multiprocessing

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"""
from https://www.kaggle.com/shonenkov/tpu-training-super-fast-xlmroberta
"""

import torch.nn as nn

class LabelSmoothing(nn.Module):
    def __init__(self, smoothing = 0.1):
        super(LabelSmoothing, self).__init__()
        self.confidence = 1.0 - smoothing

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import requests

headers = {"token": "API TOKEN"}
params = {"something": "SOMETHING"}

response = requests.get("https://www.something.com", headers=headers, params=params)
json_data = response.json()
status = response.status_code