MNIST数据训练入门

通过该训练来进行第一个机器学习的数据训练过程，类似于编程语言中的helloworld.
MNIST是入门级的CV数据集，包含手写的数字图片，从0~9.任务就是训练以使得机器能够识别图像中的数字内容。

数据集介绍

数据中包含60k的训练数据集（mnist.train）和10k的测试数据集(minst.test)，这两个集合中每个又包括一个用于存储手写数字图像的集合（mnist.xxx.image）和一个用于标志图像对应编号的标签的集合（mnist.xxx.label）
每张图片像素为28×28，用一个数字数组来表示图片。将数组展开成一个向量，容量为28x28 = 784。如何展开这个数组（数字间的顺序）不重要，只要保持各个图片采用相同的方式展开。

在数组中，空白的的地方用0来标注，完全黑的地方用1标注，在其之间灰色的地方用(0，1)范围内的的数字进行标注。对于整个数据集来说，mnist.train.images整体是一个大小为 [60000, 784] 的张量，第一个维度数字是图片的序号，第二维度表示每张图片中的像素点，点中的值代表该像素的强度大小（是否足够黑），范围为[0,1]
mnist.train.label整体是一个大小为[60000,10]的矩阵，第一个维度是图片序号，第二个维度是表示图片中的数字是多少，比如3，表示成（[0,0,0,1,0,0,0,0,0,0]）

训练方法

使用softmax方法来计算每一张照片会有更大的概率是数字几，即计算照片中图像属于某个特定数字类的证据（evidence），其实际计算过程
evidence[i] = ∑[j] W[i,j]×x[j]+b[i]
b[i]为偏置量
该方法的具体分析和详细内容作为下一学习步骤的理论学习内容，这里暂时跳过

代码实现

在上一次工作的基础上(https://blog.csdn.net/zzyincsdn/article/details/83342304)，在代码中导入tensorflow，以及用于下载数据集的文件input_data.py（与工程文件放于同一文件夹目录下即可）

import tensorflow as tf
import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder("float", [None, 784])
'''
x是一个占位符placeholder，在TensorFlow运行计算时输入这个值。我们希望能够输入任意数量的MNIST图像，
（这里的None表示此张量的第一个维度可以是任何长度的。） 
'''
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)

y_ = tf.placeholder("float", [None,10])

#为了计算交叉熵，我们首先需要添加一个新的占位符用于输入正确值：

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
'''
要求TensorFlow用梯度下降算法（gradient descent algorithm）以0.01的学习速率最小化交叉熵。
TensorFlow在这里实际上所做的是，它会在后台给描述你的计算的那张图里面，增加一系列新的计算操作单元用于实现反向传播算法和梯度下降算法。
然后，它返回给你的只是一个单一的操作，当运行这个操作时，它用梯度下降算法训练你的模型，微调你的变量，不断减少成本。
'''
init = tf.global_variables_initializer()
#初始化我们创建的变量，此处使用新版本tensorflow的global_variables_initializer()去初始化变量，原版的initialize_all_variables()已经停止使用了
sess = tf.Session()
sess.run(init)
#启动模型，并且初始化变量
for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print (sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

在pycharm中运行这一段代码，最终输出了模型训练之后进行测试的准确率，约为90%左右

在我实际操作过程中，出现了图片上红色字体的提示信息
查看该信息内容

2018-10-28 20:44:15.949794: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2018-10-28 20:44:15.951330: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance.

该段提示信息是说明正在进行计算训练的CPU支持AVX（Advanced Vector Extensions），运算速度可以扩展提升，所以建议开启更好更快的模式。该段提示对我们的训练不会产生影响。如果想要取消该段提示信息，可以在代码起始位置插入

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

即可取消信息的显示

下一步学习计划

学习的下一目标：学习本次测试中的相关公式以及机器学习知识，如softmax函数，梯度下降算法等等理论知识

实验资料引用：
http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html

实验时关于input_data.py

ps：在以上资料中，发现其input_data.py已经无法正常访问，通过其他网站找到了能使用的版本并进行了修改。input_data.py文件代码如下

# 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