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：

[b]1.导入tensorflow包[/b]

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

[b]2.导入数据[/b]

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。

[b]3.定义模型[/b]

# 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。



[b]4.设置优化方法[/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)

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

[b]5.初始化模型[/b]

# 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

[b]6.训练模型[/b]

# 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。