机器学习训练手写数字识别（一）

以下场景为，通过机器学习的线性模型和卷积模型，训练数据，最后在浏览器中进行结果测试。
以下为代码，感兴趣的同学可直接配置好环境复用代码.
图片为项目结构：

卷积模型

import os
import model
import tensorflow as tf
import input_data

data = input_data.read_data_sets('MNIST_data', one_hot=True)

# 定义模型
with tf.variable_scope('convolutional'):
    x = tf.placeholder(tf.float32, [None, 784], name='x')
    keep_prob = tf.placeholder(tf.float32)
    y, variables = model.convolutional(x, keep_prob)

# 训练
y_ = tf.placeholder('float', [None, 10])
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.arg_max(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver(variables)

with tf.Session() as sess:
    merged_summary_op = tf.summary.merge_all()
    summary_write = tf.summary.FileWriter('tmp/mnist_log/1', sess.graph)
    summary_write.add_graph(sess.graph)
    sess.run(tf.global_variables_initializer())

    for i in range(20000):
        batch = data.train.next_batch(50)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
            print("Step %d, training accuracy %g" % (i, train_accuracy))
        sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
    print(sess.run(accuracy, feed_dict={x: data.test.images, y_: data.test.labels, keep_prob: 1.0}))

    path = saver.save(
        sess,
        os.path.join(os.path.dirname(__file__), 'data', 'convolutional.ckpt'),
        write_meta_graph=False,
        write_state=False
    )
    print("Saved:", path)

线性回归模型：

import os
import input_data
import tensorflow as tf
import model

# 下载数据
data = input_data.read_data_sets('MNIST_data/', one_hot=True)

# 建立回归模型
with tf.variable_scope("regression"):
    x = tf.placeholder(tf.float32, [None, 784])
    y, variables = model.regression(x)

# 训练
y_ = tf.placeholder('float', [None, 10])
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 保存训练好的参数
saver = tf.train.Saver(variables)

# 开始训练
with tf.Session() as sess:
    # 初始化所有的变量
    sess.run(tf.global_variables_initializer())
    # 定义训练1000次
    for _ in range(1000):
        batch_xs, batch_ys = data.train.next_batch(100)
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

    print((sess.run(accuracy, feed_dict={x: data.test.images, y_: data.test.labels})))

    path = saver.save(
        sess,
        os.path.join(os.path.dirname(__file__), 'data', 'regression.ckpt'),
        write_meta_graph=False,
        write_state=False
    )

    print("Saved:", path)