NO.3：自学tensorflow之路MNIST识别，神经网络拓展

引言

　　最近自学GRU神经网络，感觉真的不简单。为了能够快速跑完程序，给我的渣渣笔记本(GT650M)也安装了一个GPU版的tensorflow。顺便也更新了版本到了tensorflow-gpu 1.7。之前相关的程序代码依然兼容，可以运行。刚好遇到五一假期，一个人在实验室发霉，就顺便随手做了一下MNIST手写体数字的BP神经网络识别程序。做的比较简单，日后可能会扩充这一篇随笔，所以大概算是个草稿版。

正文

MNIST数据准备

　　MNIST手写体数字识别，在人工智能中的地位有点像’hello world‘在编程中的地位，算是一个入门程序。从这个程序中其实可以扩展出很多tensorflow的使用方法。然而由于最近犯春困，就简单写一下。准备数据可以使用Google已经提供好的input_data.py文件。这里也一并提供一下源代码。

# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import tensorflow.python.platform
import numpy
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
def maybe_download(filename, work_directory):
  """Download the data from Yann's website, unless it's already here."""
  if not os.path.exists(work_directory):
    os.mkdir(work_directory)
  filepath = os.path.join(work_directory, filename)
  if not os.path.exists(filepath):
    filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
    statinfo = os.stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
  return filepath
def _read32(bytestream):
  dt = numpy.dtype(numpy.uint32).newbyteorder('>')
  return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
def extract_images(filename):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
  print('Extracting', filename)
  with gzip.open(filename) as bytestream:
    magic = _read32(bytestream)
    if magic != 2051:
      raise ValueError(
          'Invalid magic number %d in MNIST image file: %s' %
          (magic, filename))
    num_images = _read32(bytestream)
    rows = _read32(bytestream)
    cols = _read32(bytestream)
    buf = bytestream.read(rows * cols * num_images)
    data = numpy.frombuffer(buf, dtype=numpy.uint8)
    data = data.reshape(num_images, rows, cols, 1)
    return data
def dense_to_one_hot(labels_dense, num_classes=10):
  """Convert class labels from scalars to one-hot vectors."""
  num_labels = labels_dense.shape[0]
  index_offset = numpy.arange(num_labels) * num_classes
  labels_one_hot = numpy.zeros((num_labels, num_classes))
  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
  return labels_one_hot
def extract_labels(filename, one_hot=False):
  """Extract the labels into a 1D uint8 numpy array [index]."""
  print('Extracting', filename)
  with gzip.open(filename) as bytestream:
    magic = _read32(bytestream)
    if magic != 2049:
      raise ValueError(
          'Invalid magic number %d in MNIST label file: %s' %
          (magic, filename))
    num_items = _read32(bytestream)
    buf = bytestream.read(num_items)
    labels = numpy.frombuffer(buf, dtype=numpy.uint8)
    if one_hot:
      return dense_to_one_hot(labels)
    return labels
class DataSet(object):
  def __init__(self, images, labels, fake_data=False, one_hot=False,
               dtype=tf.float32):
    """Construct a DataSet.
    one_hot arg is used only if fake_data is true.  `dtype` can be either
    `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
    `[0, 1]`.
    """
    dtype = tf.as_dtype(dtype).base_dtype
    if dtype not in (tf.uint8, tf.float32):
      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
                      dtype)
    if fake_data:
      self._num_examples = 10000
      self.one_hot = one_hot
    else:
      assert images.shape[0] == labels.shape[0], (
          'images.shape: %s labels.shape: %s' % (images.shape,
                                                 labels.shape))
      self._num_examples = images.shape[0]
      # Convert shape from [num examples, rows, columns, depth]
      # to [num examples, rows*columns] (assuming depth == 1)
      assert images.shape[3] == 1
      images = images.reshape(images.shape[0],
                              images.shape[1] * images.shape[2])
      if dtype == tf.float32:
        # Convert from [0, 255] -> [0.0, 1.0].
        images = images.astype(numpy.float32)
        images = numpy.multiply(images, 1.0 / 255.0)
    self._images = images
    self._labels = labels
    self._epochs_completed = 0
    self._index_in_epoch = 0
  @property
  def images(self):
    return self._images
  @property
  def labels(self):
    return self._labels
  @property
  def num_examples(self):
    return self._num_examples
  @property
  def epochs_completed(self):
    return self._epochs_completed
  def next_batch(self, batch_size, fake_data=False):
    """Return the next `batch_size` examples from this data set."""
    if fake_data:
      fake_image = [1] * 784
      if self.one_hot:
        fake_label = [1] + [0] * 9
      else:
        fake_label = 0
      return [fake_image for _ in xrange(batch_size)], [
          fake_label for _ in xrange(batch_size)]
    start = self._index_in_epoch
    self._index_in_epoch += batch_size
    if self._index_in_epoch > self._num_examples:
      # Finished epoch
      self._epochs_completed += 1
      # Shuffle the data
      perm = numpy.arange(self._num_examples)
      numpy.random.shuffle(perm)
      self._images = self._images[perm]
      self._labels = self._labels[perm]
      # Start next epoch
      start = 0
      self._index_in_epoch = batch_size
      assert batch_size <= self._num_examples
    end = self._index_in_epoch
    return self._images[start:end], self._labels[start:end]
def read_data_sets(train_dir, fake_data=False, one_hot=False, dtype=tf.float32):
  class DataSets(object):
    pass
  data_sets = DataSets()
  if fake_data:
    def fake():
      return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
    data_sets.train = fake()
    data_sets.validation = fake()
    data_sets.test = fake()
    return data_sets
  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'
  VALIDATION_SIZE = 5000
  local_file = maybe_download(TRAIN_IMAGES, train_dir)
  train_images = extract_images(local_file)
  local_file = maybe_download(TRAIN_LABELS, train_dir)
  train_labels = extract_labels(local_file, one_hot=one_hot)
  local_file = maybe_download(TEST_IMAGES, train_dir)
  test_images = extract_images(local_file)
  local_file = maybe_download(TEST_LABELS, train_dir)
  test_labels = extract_labels(local_file, one_hot=one_hot)
  validation_images = train_images[:VALIDATION_SIZE]
  validation_labels = train_labels[:VALIDATION_SIZE]
  train_images = train_images[VALIDATION_SIZE:]
  train_labels = train_labels[VALIDATION_SIZE:]
  data_sets.train = DataSet(train_images, train_labels, dtype=dtype)
  data_sets.validation = DataSet(validation_images, validation_labels,
                                 dtype=dtype)
  data_sets.test = DataSet(test_images, test_labels, dtype=dtype)
  return data_sets

　　下载保存后，将input_data.py文件放入工程目录中，然后新建工程文件，使用以下两行代码，就可以完成整个MNIST数据的准备。

import input_data

mnist =  input_data.read_data_sets('MNIST_data/', one_hot=True)

　　这样，就会自动下载好数据文件到工程目录下的‘/MNIST_data/’中。如果已经下载就会跳过下载，然后将train,valiation和test三个数据集保存在mnist变量之中。

神经网络的扩展

　　这一部分，以后慢慢填补，现在就用最简单的BP实现，BP的内容可以参考上一篇随笔。

损失函数

　　神经网络模型的效果以及优化的目标是通过损失函数来定义的。不同的优化目标就对应需要采用不同的损失函数。分类问题中，交叉熵是判断输出向量和期望的向量接近程度的一种指标。

　　摸了

优化算法

　　摸了

过拟合

　　摸了

滑动平均模型

　　摸了

模型保存

　　摸了

作业

　　完成手写体数字识别程序，并尽可能提高识别的准确率。

#-*- coding:utf-8 -*-
#The MNIST database of handwritten digits
#Author:Kai Z

import tensorflow as tf
import numpy as np
import input_data

#创建MNIST数据，存储于/MNIST_data目录下
#mnist.train mnist.test
mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)

#神经网络超参数
input_node = 784
output_node = 10
hide_node = 100
batch_size = 100
learning_rate = 1e-3
training_steps = 5000


x = tf.placeholder(tf.float32,[None,input_node])
y = tf.placeholder(tf.float32,[None,output_node])

hidden_weight = tf.Variable(tf.random_normal([input_node,hide_node],stddev = 1,seed = 1))
hidden_bias = tf.Variable(tf.zeros([1,hide_node],tf.float32))
output_weight = tf.Variable(tf.random_normal([hide_node,output_node],stddev = 1,seed = 1))
output_bias = tf.Variable(tf.zeros([1,output_node],tf.float32))

h = tf.nn.tanh(tf.matmul(x,hidden_weight)+hidden_bias)
y_pred = tf.nn.sigmoid(tf.matmul(h,output_weight)+output_bias)

correct_predict = tf.equal(tf.argmax(y_pred,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_predict,tf.float32))

cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y_pred,labels=tf.argmax(y,1))
cross_entropy_mean = tf.reduce_mean(cross_entropy)

train_op = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy_mean)
init_op = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init_op)

    for i in range(training_steps):
        input_batch,output_batch = mnist.train.next_batch(batch_size)
        sess.run(train_op,feed_dict={x:input_batch,y:output_batch})

        if i%100 == 0:
            right_rate = sess.run(accuracy,feed_dict = {x:mnist.validation.images,y:mnist.validation.labels})
            print('训练%d次后，训练正确率为百分之%f'%(i,right_rate*100))
    right_rate = sess.run(accuracy,feed_dict = {x:mnist.test.images,y:mnist.test.labels})
    print('训练%d次后，测试正确率为百分之%f'%(i,right_rate*100))

　　最终，结果为，测试准确率达到了91%。仍然有改进的空间。