基于tensorflow的MNIST手写识别

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）