tensorflow基础模型之逻辑回归

接上一篇 tensorflow基础模型之线性回归，这篇是基础模型中的逻辑回归模型。其中数据来自经典的MNIST手写数字数据集。

from __future__ import print_function

import tensorflow as tf
# 导入MNIST数据
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("./tmp/data/", one_hot=True)

# 参数
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

# 图输入
x = tf.placeholder(tf.float32, [None, 784])  # mnist图像数据（28*28=784）
y = tf.placeholder(tf.float32, [None, 10])  # 0-9 数字识别（10类）

# 设置模型权重
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# 构造模型
pred = tf.nn.softmax(tf.matmul(x, W) + b)  # Softmax

# 使用交叉熵（cross entropy）最小化误差
cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))
# 梯度下降
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# 初始化全局的变量
init = tf.global_variables_initializer()

# 开始训练
with tf.Session() as sess:
    # 执行初始化
    sess.run(init)

    # 周期训练
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples / batch_size)
        # 全部批次循环
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # 执行优化与计算损失率
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
                                                          y: batch_ys})
            # 计算平均损失率
            avg_cost += c / total_batch
        # 间隔打印日志
        if (epoch + 1) % display_step == 0:
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))

    print("Optimization Finished!")

    # 测试模型
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # 计算准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))