问socket.gaierror：[Errno -2]名称或服务未知urllib.error.URLError:打开错误[Errno -2]名称或服务不是known>

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function  
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)    
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))   
import tensorflow as tf    
from tensorflow.contrib import learn
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib

tf.logging.set_verbosity(tf.logging.INFO)

def cnn_model_fn(features, labels, mode):
  """Model function for CNN."""
  # Input Layer
  input_layer = tf.reshape(features, [-1, 28, 28, 1])

  # Convolutional Layer #1
  conv1 = tf.layers.conv2d(
      inputs=input_layer,
      filters=32,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)

  # Pooling Layer #1
  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

  # Convolutional Layer #2 and Pooling Layer #2
  conv2 = tf.layers.conv2d(
      inputs=pool1,
      filters=64,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)
  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

  # Dense Layer
  pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
  dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
  dropout = tf.layers.dropout(
      inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)

  # Logits Layer
  logits = tf.layers.dense(inputs=dropout, units=10)

  loss = None
  train_op = None

  # Calculate Loss (for both TRAIN and EVAL modes)
  if mode != learn.ModeKeys.INFER:
    onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
    loss = tf.losses.softmax_cross_entropy(
        onehot_labels=onehot_labels, logits=logits)

  # Configure the Training Op (for TRAIN mode)
  if mode == learn.ModeKeys.TRAIN:
    train_op = tf.contrib.layers.optimize_loss(
        loss=loss,
        global_step=tf.contrib.framework.get_global_step(),
        learning_rate=0.001,
        optimizer="SGD")

  # Generate Predictions
  predictions = {
      "classes": tf.argmax(
          input=logits, axis=1),
      "probabilities": tf.nn.softmax(
          logits, name="softmax_tensor")
  }

  # Return a ModelFnOps object
  return model_fn_lib.ModelFnOps(
      mode=mode, predictions=predictions, loss=loss, train_op=train_op)


def main():
    print("In main")
    # Load training and eval data
    mnist = learn.datasets.load_dataset("mnist")
    train_data = tf.train.string_input_producer(tf.train.match_filenames_once("../inputs/train/*.jpg")) # Returns np.array
    train_labels = np.asarray(train_labels.csv, dtype=np.float32)
    test_data = tf.train.string_input_producer(tf.train.match_filenames_once("../inputs/test/*.jpg")) # Returns np.array
    # eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)


    # Create the Estimator
    mnist_classifier = learn.Estimator(
          model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")

    # Set up logging for predictions
    tensors_to_log = {"probabilities": "softmax_tensor"}
    logging_hook = tf.train.LoggingTensorHook(
      tensors=tensors_to_log, every_n_iter=50)


    mnist_classifier.fit(
        x=train_data,
        y=train_labels,
        batch_size=100,
        steps=20000,
        monitors=[logging_hook])

    # Configure the accuracy metric for evaluation
    metrics = {
        "accuracy":
            learn.MetricSpec(
                metric_fn=tf.metrics.accuracy, prediction_key="classes"),
    }


    # Evaluate the model and print results
    eval_results = mnist_classifier.evaluate(
        x=eval_data, y=eval_labels, metrics=metrics)
    print(eval_results)

main()

Traceback (most recent call last):
  File "/opt/conda/lib/python3.6/urllib/request.py", line 1318, in do_open
10.0s
3
    encode_chunked=req.has_header('Transfer-encoding'))
  File "/opt/conda/lib/python3.6/http/client.py", line 1239, in request
    self._send_request(method, url, body, headers, encode_chunked)
  File "/opt/conda/lib/python3.6/http/client.py", line 1285, in _send_request
    self.endheaders(body, encode_chunked=encode_chunked)
  File "/opt/conda/lib/python3.6/http/client.py", line 1234, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "/opt/conda/lib/python3.6/http/client.py", line 1026, in _send_output
    self.send(msg)
  File "/opt/conda/lib/python3.6/http/client.py", line 964, in send
    self.connect()
  File "/opt/conda/lib/python3.6/http/client.py", line 1392, in connect
    super().connect()
  File "/opt/conda/lib/python3.6/http/client.py", line 936, in connect
    (self.host,self.port), self.timeout, self.source_address)
  File "/opt/conda/lib/python3.6/socket.py", line 704, in create_connection
    for res in getaddrinfo(host, port, 0, SOCK_STREAM):
  File "/opt/conda/lib/python3.6/socket.py", line 743, in getaddrinfo
    for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
socket.gaierror: [Errno -2] Name or service not known

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "../src/script.py", line 130, in <module>
    main()
  File "../src/script.py", line 93, in main
    mnist = learn.datasets.load_dataset("mnist")
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/__init__.py", line 73, in load_dataset
10.1s
4
    return DATASETS[name]()
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py", line 279, in load_mnist
10.2s
5
    return read_data_sets(train_dir)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py", line 235, in read_data_sets
    SOURCE_URL + TRAIN_IMAGES)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 208, in maybe_download
10.2s
6
    temp_file_name, _ = urlretrieve_with_retry(source_url)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 165, in wrapped_fn
    return fn(*args, **kwargs)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 190, in urlretrieve_with_retry
    return urllib.request.urlretrieve(url, filename)
  File "/opt/conda/lib/python3.6/urllib/request.py", line 248, in urlretrieve
    with contextlib.closing(urlopen(url, data)) as fp:
  File "/opt/conda/lib/python3.6/urllib/request.py", line 223, in urlopen
10.2s
7
    return opener.open(url, data, timeout)
  File "/opt/conda/lib/python3.6/urllib/request.py", line 526, in open
    response = self._open(req, data)
  File "/opt/conda/lib/python3.6/urllib/request.py", line 544, in _open
    '_open', req)
  File "/opt/conda/lib/python3.6/urllib/request.py", line 504, in _call_chain
    result = func(*args)
  File "/opt/conda/lib/python3.6/urllib/request.py", line 1361, in https_open
    context=self._context, check_hostname=self._check_hostname)
  File "/opt/conda/lib/python3.6/urllib/request.py", line 1320, in do_open
    raise URLError(err)
urllib.error.URLError: <urlopen error [Errno -2] Name or service not known>