TensorFlow------单层(全连接层)实现手写数字识别训练及测试实例

TensorFlow之单层(全连接层)实现手写数字识别训练及测试实例：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('is_train',1,'指定程序是预测还是训练')

def full_connected():
    # 获取真实的数据
    mnist = input_data.read_data_sets('./data/mnists/', one_hot=True)

    # 1.建立数据的占位符 X [None,784]  y_true [None,10]
    # 创建一个作用域
    with tf.variable_scope('data'):
        # 特征值
        x = tf.placeholder(tf.float32, [None, 784])

        # 目标值(真实值)
        y_true = tf.placeholder(tf.int32, [None, 10])

    # 2. 建立一个全连接层的神经网络 W [784,10]  b [10]
    with tf.variable_scope('fc_model'):
        # 随机初始化权重和偏置
        weight = tf.Variable(tf.random_normal([784, 10], mean=0.0, stddev=1.0), name='w')

        bias = tf.Variable(tf.constant(0.0, shape=[10]))

        # 预测None个样本的输出结果matrix [None,784]*[784,10]+[10] = [None,10]
        y_predict = tf.matmul(x, weight) + bias

    # 3. 求出所有样本的损失，然后求平均值
    with tf.variable_scope('soft_cross'):
        # 求平均交叉熵损失
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_predict))

    # 4. 梯度下降求出损失(优化)
    with tf.variable_scope('optimizer'):
        train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

    # 5. 计算准确率
    with tf.variable_scope('acc'):
        equal_list = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_predict, 1))

        # equal_list None个样本  [1,0,1,0,1,1....]
        accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32))

    # 收集变量，单个数字值收集
    tf.summary.scalar('losses',loss)
    tf.summary.scalar('acc',accuracy)

    # 高纬度变量收集
    tf.summary.histogram('weightes',weight)
    tf.summary.histogram('biases',bias)

    # 定义一个初始化变量的op
    init_op = tf.global_variables_initializer()

    # 定义一个合并变量的op
    merged = tf.summary.merge_all()

    # 创建一个saver
    saver = tf.train.Saver()

    # 开启会话去训练
    with tf.Session() as sess:
        # 初始化变量
        sess.run(init_op)

        # 建立events文件，然后写入
        filewriter = tf.summary.FileWriter('./tmp/summary/test/',graph=sess.graph)

        if FLAGS.is_train == 1:
            # 迭代步数去训练，更新参数预测
            for i in range(2000):
                # 取出真实存在的特征值和目标值
                mnist_x, mnist_y = mnist.train.next_batch(50)

                # 运行train_op训练
                sess.run(train_op, feed_dict={x: mnist_x, y_true: mnist_y})

                # 写入每步训练的值
                summary = sess.run(merged,feed_dict={x: mnist_x, y_true: mnist_y})

                filewriter.add_summary(summary,i)

                print('训练第%d步，准确率为：%f' % (i, sess.run(accuracy, feed_dict={x: mnist_x, y_true: mnist_y})))

            # 保存模型
            saver.save(sess,'./tmp/summary/model/fc_model')
        else:
            # 加载模型
            saver.restore(sess,'./tmp/summary/model/fc_model')

            # 如果是0，做出预测
            for i in range(100):

                # 每次测试一张图片，[0,0,0,0,0,1,0,0,0]
                x_test,y_test = mnist.test.next_batch(1)

                print('第%d章图片，手写数字目标是:%d,预测结果是:%d' % (
                    i,
                    tf.argmax(y_test,1).eval(),
                    tf.argmax(sess.run(y_predict,feed_dict={x: x_test,y_true: y_test}),1).eval()
                ))

    return None

if __name__ == '__main__':
    full_connected()