小编pet*_*rux的帖子

我有一个三维张量的形状[batch, None, dim],其中第二个维度,即时间步长,是未知的.我dynamic_rnn用来处理这样的输入,如下面的代码片段所示:

import numpy as np
import tensorflow as tf

batch = 2
dim = 3
hidden = 4

lengths = tf.placeholder(dtype=tf.int32, shape=[batch])
inputs = tf.placeholder(dtype=tf.float32, shape=[batch, None, dim])
cell = tf.nn.rnn_cell.GRUCell(hidden)
cell_state = cell.zero_state(batch, tf.float32)
output, _ = tf.nn.dynamic_rnn(cell, inputs, lengths, initial_state=cell_state)

实际上,运行这个剪切了一些实际数字,我有一些合理的结果:

inputs_ = np.asarray([[[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3]],
                    [[6, 6, 6], [7, 7, 7], [8, 8, 8], [9, 9, 9]]],
                    dtype=np.int32)
lengths_ = np.asarray([3, 1], …

我有一个在azure上运行的Ubuntu VM.我需要ssh连接到它,但我丢失了我的私钥,因为我的home分区已经消失.所以,我在我的本地机器上重新安装了操作系统,并重新生成了一对新的私有/公共密钥 - 我已经在github,bitbucket,ecc中使用了它.有没有办法从Azure门户在Ubuntu VM上设置我的新公钥？

我正在尝试在PySpark应用程序中广播用户定义的变量，但始终出现以下错误：

 File "/usr/local/spark-2.1.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 174, in main
    process()
  File "/usr/local/spark-2.1.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 169, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/usr/local/spark-2.1.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/serializers.py", line 268, in dump_stream
    vs = list(itertools.islice(iterator, batch))
  File "/home/.../sparkbroad.py", line 29, in <lambda>
    output = input_.map(lambda item: b.value.map(item))
  File "/usr/local/spark-2.1.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/broadcast.py", line 106, in value
    self._value = self.load(self._path)
  File "/usr/local/spark-2.1.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/broadcast.py", line 97, in load
    return pickle.load(f)

AttributeError: 'module' object has no attribute 'FooMap'

模块中的代码sparkbrad.py如下：

import random
import pyspark as spark

class FooMap(object):

    def __init__(self):
        keys = list(range(10))
        values = …

我对搜索API有点困惑.我们假设我查询"foobar",代码如下:

from twython import Twython
api = Twython(...)
r = api.search(q="foobar")

通过这种方式,我有15个状态和一个"next_results"in r["metadata"].有什么办法超生这些元数据到Twython API并具有下列状态更新为好,或者我应该得到下一个until_id用手从"next_results"和执行全新的查询？