我正在寻找关于如何实现卷积神经网络的this tutorial。
我按照那里的说明进行操作，现在我有了这段代码：
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()
对于此代码，我收到此错误：
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>
这条消息对我来说似乎很不清楚。知道这可能是什么原因吗？
答案 0 :(得分：2)
错误是由于此行mnist = learn.datasets.load_dataset("mnist")尝试下载mnist数据集。但我没有在您的代码中看到mnist。因此，如果您不使用数据集，则可以对其进行评论。或者，如果您想使用它，可以从http://yann.lecun.com/exdb/mnist/（所有四个文件）下载它，并将目录文件的路径放在：mnist = learn.datasets.load_dataset("/path/to/mnist")中。
答案 1 :(得分：0)
好像你的脚本在这行中失败了：
mnist = learn.datasets.load_dataset("mnist")
因为它无法在本地（在磁盘上）找到mnist数据集，尝试下载它，但它（由于某种原因）不能。
尝试将downloading数据集发送到MNIST-data目录（检查load_dataset()的来源，使用默认train_dir='MNIST-data'调用load_mnist()。最后，{{3}尝试从磁盘加载火车/测试图像/标签，但如果在./MNIST-data中找不到，则尝试从read_data_sets等下载它们。）