TensorFlow笔记之MNIST例程详解

MNIST被称为这方面的HelloWorld，此程序修改自github

程序如下

# -*- coding:utf-8 -*-
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

dir='/home/kaka/Documents/input_data'
# 1.Import data
mnist = input_data.read_data_sets(dir, one_hot=True)

#Print the shape of mist
print (mnist.train.images.shape,mnist.train.labels.shape)
print(mnist.test.images.shape, mnist.train.labels.shape)
print(mnist.validation.images.shape, mnist.validation.labels.shape)

# 2.Create the model
# y=wx+b
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])

# The raw formulation of cross-entropy,
#
#   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
#                                 reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
# outputs of 'y', and then average across the batch.
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

# Init model
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
for i in range(100000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    if(i%10000==0):
        print(i)

# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                    y_: mnist.test.labels}))

整个程序分为一下几个部分：

1.导入tensorflow包

值得注意的是

from tensorflow.examples.tutorials.mnist import input_data

里面包含了一些输入的方法

2.导入数据

mnist = input_data.read_data_sets(dir, one_hot=True)

来看看read_data_sets()的定义

def read_data_sets(train_dir,
                   fake_data=False,
                   one_hot=False,
                   dtype=dtypes.float32,
                   reshape=True,
                   validation_size=5000,
                   seed=None):
  if fake_data:

    def fake():
      return DataSet(
          [], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed)

    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 0 <= 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, seed=seed)
  validation = DataSet(
      validation_images,
      validation_labels,
      dtype=dtype,
      reshape=reshape,
      seed=seed)
  test = DataSet(
      test_images, test_labels, dtype=dtype, reshape=reshape, seed=seed)

  return base.Datasets(train=train, validation=validation, test=test)

大致过程就是，检查目录下有没有想要的数据，没有的话下载，然后进行解压，返回一个Datasets包含train, validation, test
mnist数据如下

可以看出mnist包含三个数据集，其中train有55000条数据，其每条数据为28*28的图片转换成以为数组并归一化的数据。

3.定义模型

# Create the model
# y=wx+b
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b

wx相乘如图所示，然后对结果的每一列加上一个偏执bi。

4.设置优化方法

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])

# The raw formulation of cross-entropy,
#
#   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
#                                 reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
# outputs of 'y', and then average across the batch.
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

4.1 y_用于存储误差。
4.2 计算平均交叉熵

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

这里面的操作包括
1.对某一张图片的输出作softmax
关于softmax有一个很形象的图片：

然后根据公式计算交叉熵

2.交叉熵函数，表示真实值，yi为预测值，label为有十个元素的一维数组，属于某一类，某个值就为1，-log函数的特性为，越接近0数值越大，越接近1，数值越小。然后对所有元素的值进行相加，在计算中使用的loss为交叉熵的平均值。此部分更多详见

5.初始化模型

# Init model
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

新版本使用global_variables_initializer().run()来对数据进行初始化

6.训练模型

# Train
for i in range(100000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    if(i%10000==0):
        print(i)

训练时选择100个数据为一组输入
这里选择了100000次训练= =
#####7.评估模型

# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                    y_: mnist.test.labels}))

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))

可以看做一个比较预测值和真实值的函数，如果相等，则返回1

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

用计算准确度函数代替上面的优化方法。
计算输出。