这个例子，是学习tensorflow的人员通常会用到的，也是基本的学习曲线中的一环。我也是！
这个例子很简单，这里，就是简单的说下，不同的tensorflow版本，相关的接口函数，可能会有不一样哟。在TensorFlow的中文介绍文档中的内容，有些可能与你使用的tensorflow的版本不一致了，我这里用到的tensorflow的版本就有这个问题。 另外，还给大家说下，例子中的MNIST所用到的资源图片，在原始的官网上，估计很多人都下载不到了。我也提供一下下载地址。
我的tensorflow的版本信息：
>>> import tensorflow as tf
>>> print tf.VERSION
1.0.
>>> print tf.GIT_VERSION
v1.0.0--g4763edf-dirty
>>> print tf.COMPILER_VERSION
4.8.
下面，就看看，我参考的中文tensorflow网站的代码，在自己的环境里，运行的结果。
[root@bogon tensorflow]# python
Python 2.7. (default, Nov , ::)
[GCC 4.8. (Red Hat 4.8.-)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow.examples.tutorials.mnist.input_data as input_data
>>> mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
Traceback (most recent call last):
File "<stdin>", line , in <module>
File "/usr/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py", line , in read_data_sets
SOURCE_URL + TRAIN_IMAGES)
File "/usr/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line , in maybe_download
temp_file_name, _ = urlretrieve_with_retry(source_url)
File "/usr/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line , in wrapped_fn
return fn(*args, **kwargs)
File "/usr/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line , in urlretrieve_with_retry
return urllib.request.urlretrieve(url, filename)
File "/usr/lib64/python2.7/urllib.py", line , in urlretrieve
return _urlopener.retrieve(url, filename, reporthook, data)
File "/usr/lib64/python2.7/urllib.py", line , in retrieve
fp = self.open(url, data)
File "/usr/lib64/python2.7/urllib.py", line , in open
return self.open_unknown_proxy(proxy, fullurl, data)
File "/usr/lib64/python2.7/urllib.py", line , in open_unknown_proxy
raise IOError, ('url error', 'invalid proxy for %s' % type, proxy)
IOError: [Errno url error] invalid proxy for http: '10.90.1.101:8080'
>>>
>>> mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
28 Extracting MNIST_data/train-images-idx3-ubyte.gz
29 Extracting MNIST_data/train-labels-idx1-ubyte.gz
30 Extracting MNIST_data/t10k-images-idx3-ubyte.gz
31 Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
>>> import tensorflow as tf
>>> x = tf.placeholder(tf.float32, [None, ])
>>> W = tf.Variable(tf.zeros([,]))
>>> b = tf.Variable(tf.zeros([]))
>>> y = tf.nn.softmax(tf.matmul(x,W) + b)
>>> y_ = tf.placeholder("float", [None,])
>>> cross_entropy = -tf.reduce_sum(y_*tf.log(y))
>>> train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
40 >>> init = tf.initialize_all_variables()
WARNING:tensorflow:From <stdin>:: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after --.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
44 >>> init = tf.global_variables_initializer()
>>> sess = tf.Session()
W tensorflow/core/platform/cpu_feature_guard.cc:] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
>>> sess.run(init)
>>> for i in range():
... batch_xs, batch_ys = mnist.train.next_batch()
... sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
...
>>> correct_prediction = tf.equal(tf.argmax(y,), tf.argmax(y_,))
>>> accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
>>> print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
0.9088
>>>
上述日志，是我的测试全过程记录，上面反映的信息有如下几点：
1. 红色部分的错误，因为我本地机器是通过代理上网的，这个过程中，tensorflow会用urllib进行MNIST的图片资源的下载，由于网络问题，资源文件下载失败。
2. 都有哪些资源文件要下载呢？追踪日志中的文件/usr/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py第211行前后：
def read_data_sets(train_dir,
fake_data=False,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
validation_size=):
if fake_data: def fake():
return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype) train = fake()
validation = fake()
test = fake()
return base.Datasets(train=train, validation=validation, test=test) TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz' local_file = base.maybe_download(TRAIN_IMAGES, train_dir,
SOURCE_URL + TRAIN_IMAGES)
with open(local_file, 'rb') as f:
train_images = extract_images(f) local_file = base.maybe_download(TRAIN_LABELS, train_dir,
SOURCE_URL + TRAIN_LABELS)
with open(local_file, 'rb') as f:
train_labels = extract_labels(f, one_hot=one_hot) local_file = base.maybe_download(TEST_IMAGES, train_dir,
SOURCE_URL + TEST_IMAGES)
with open(local_file, 'rb') as f:
test_images = extract_images(f) local_file = base.maybe_download(TEST_LABELS, train_dir,
SOURCE_URL + TEST_LABELS)
with open(local_file, 'rb') as f:
test_labels = extract_labels(f, one_hot=one_hot) if not <= validation_size <= len(train_images):
raise ValueError(
'Validation size should be between 0 and {}. Received: {}.'
.format(len(train_images), validation_size)) validation_images = train_images[:validation_size]
validation_labels = train_labels[:validation_size]
train_images = train_images[validation_size:]
train_labels = train_labels[validation_size:] train = DataSet(train_images, train_labels, dtype=dtype, reshape=reshape)
validation = DataSet(validation_images,
validation_labels,
dtype=dtype,
reshape=reshape)
test = DataSet(test_images, test_labels, dtype=dtype, reshape=reshape) return base.Datasets(train=train, validation=validation, test=test)
看到上面红色的部分，就是这里需要下载的图片资源文件。这个，我的网络环境是下载不了的。我通过其他途径下载到了这里需要的资源。我将下载的图片资源，放在了我进入python时所在的路径下。虽然直接下载没有成功，但是在当前路径下还是创建了MNIST_data的目录的。如下图，红色圈目录就是程序创建的目录。我将下载的train-images-idx3-ubyte.gz，train-labels-idx1-ubyte.gz，t10k-images-idx3-ubyte.gz，t10k-labels-idx1-ubyte.gz放在MNIST_data目录了。
然后，再次执行mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)，就ok了，不会报错。得到28-31行的输出信息。
3. 执行到第40行的代码时，爆出WARNING，提示用新的函数，按照提示信息，执行了第41行的代码，OK。说明版本兼容性，在tensorflow中需要注意。
4. 执行后，得到结果，如60行显示，识别率为0.9088。
关于MNIST的这个例子的手写识别性能的理论，不是本博文的重点，读者可以参照MNIST相关的文章自行学习。
最后，附上MNIST这个例子中，用到的资源图片下载地址，点击进行下载。（说明：需要积分才能下载的，谅解）