版权声明：站在巨人的肩膀上学习。 https://blog.csdn.net/zgcr654321/article/details/82958075
我们用一个简单的实例来说明分类学习。
我们使用mnist数据集，用下面的分类学习神经网络来学习这个数据库，并计算对手写体数字图片识别的准确率。
首先我们要下载一个数据库。
我们使用MNIST库，这是一个手写体数字库，总共有 60000 张图片，其中 50000 张训练图片，10000 张测试图片。
输出层的激励函数我们使用softmax激励函数，这是一个常用在分类问题上的激励函数。
loss函数我们使用交叉熵函数（cross_entropy）。
训练函数仍使用梯度下降法，交叉熵函数作为训练指引方向。
由于训练集中的图片很多，我们只取100张图片来训练。每训练20次，我们打印一次准确度。准确度即我们搭建的模型的预测值和真实值是否相同的概率，用百分比表示。
代码如下：
import tensorflow as tf
import numpy as np
import os
from tensorflow.examples.tutorials.mnist import input_data
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# 准备数据库（MNIST库，这是一个手写体数据库）
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# 这一步是如果电脑上没有这个数据库则会下载这个数据库到.py文件所在目录并创建一个MNIST_data文件夹
# 注意运行时这个数据库可能要翻墙才能下载下来
def add_layer(inputs, in_size, out_size, activation_function=None):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
# activation_function is None时没有激励函数，是线性关系
else:
outputs = activation_function(Wx_plus_b)
# activation_function不为None时，得到的Wx_plus_b再传入activation_function再处理一下
return outputs
def compute_accuracy(v_xs, v_ys):
global prediction
# 使用global则对全局变量prediction进行操作
y_pre = sess.run(prediction, feed_dict={xs: v_xs})
# 使用xs输入数据生成预测值prediction
correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
# 对于预测值和真实值的差别
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 计算预测的准确率
result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})
# run得到结果，结果是个百分比
return result
xs = tf.placeholder(tf.float32, [None, 784])
# None表示不规定样本的数量，784表示每个样本的大小为28X28=784个像素点
ys = tf.placeholder(tf.float32, [None, 10])
# 每张图片表示一个数字，我们的输出是数字0到9，所以是10个输出
prediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)
# 调用add_layer定义输出层，输入数据是784个特征，输出数据是10个特征，激励采用softmax函数
# softmax激励函数一般用于classification
# 搭建分类模型时，loss函数（即最优化目标函数）选用交叉熵函数（cross_entropy）
# 交叉熵用来衡量预测值和真实值的相似程度，如果完全相同，它们的交叉熵等于零。
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))
# 定义训练函数,使用梯度下降法训练,0.5是学习效率，通常小于1,minimize(cross_entropy)指要将cross_entropy减小
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# 创建会话，并开始将网络初始化
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
# 训练时只从数据集中取100张图片来训练
sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})
if i % 50 == 0:
# 每训练50次打印准确度
print(compute_accuracy(mnist.test.images, mnist.test.labels))
# 对比mnist中的training data和testing data的准确度运行结果如下：
WARNING:tensorflow:From C:/Users/1234/Desktop/test/src/test.py:9: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Please write your own downloading logic.
WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.one_hot on tensors.
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
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Process finished with exit code 0前面的warning提示一般不影响结果，可忽略。不过有时提示会告诉你有个别包无法下载，这时请更换新的梯子再重新运行。
可以看到我们训练的准确度是在不断提高的。