import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
class MNISTModel(object):
def __init__(self, lr, batch_size, iter_num):
self.lr = lr
self.batch_size = batch_size
self.iter_num = iter_num
# 定义模型结构
# 输入张量,这里还没有数据,先占个地方,所以叫“placeholder”
self.x = tf.placeholder(tf.float32, [None, 784]) # 图像是28*28的大小
self.y = tf.placeholder(tf.float32, [None, 10]) # 输出是0-9的one-hot向量
self.h = tf.layers.dense(self.x, 100, activation=tf.nn.relu, use_bias=True, kernel_initializer=tf.truncated_normal_initializer) # 一个全连接层
self.y_ = tf.layers.dense(self.h, 10, use_bias=True, kernel_initializer=tf.truncated_normal_initializer) # 全连接层
# 使用交叉熵损失函数
self.loss = tf.losses.softmax_cross_entropy(self.y, self.y_)
self.optimizer = tf.train.AdamOptimizer()
self.train_step = self.optimizer.minimize(self.loss)
# 用于模型训练
self.correct_prediction = tf.equal(tf.argmax(self.y, axis=1), tf.argmax(self.y_, axis=1))
self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))
# 用于保存训练好的模型
self.saver = tf.train.Saver()
def train(self):
with tf.Session() as sess: # 打开一个会话。可以想象成浏览器打开一个标签页一样,直观地理解一下
sess.run(tf.global_variables_initializer()) # 先初始化所有变量。
for i in range(self.iter_num):
batch_x, batch_y = mnist.train.next_batch(self.batch_size) # 读取一批数据
loss, _ = sess.run([self.loss, self.train_step], feed_dict={self.x: batch_x, self.y: batch_y}) # 每调用一次sess.run,就像拧开水管一样,所有self.loss和self.train_step涉及到的运算都会被调用一次。
if i%1000 == 0:
train_accuracy = sess.run(self.accuracy, feed_dict={self.x: batch_x, self.y: batch_y}) # 把训练集数据装填进去
test_x, test_y = mnist.test.next_batch(self.batch_size)
test_accuracy = sess.run(self.accuracy, feed_dict={self.x: test_x, self.y: test_y}) # 把测试集数据装填进去
print( 'iter\t%i\tloss\t%f\ttrain_accuracy\t%f\ttest_accuracy\t%f' % (i,loss,train_accuracy,test_accuracy))
self.saver.save(sess, 'model/mnistModel') # 保存模型
def test(self):
with tf.Session() as sess:
self.saver.restore(sess, 'model/mnistModel')
Accuracy = []
for i in range(150):
test_x, test_y = mnist.test.next_batch(self.batch_size)
test_accuracy = sess.run(self.accuracy, feed_dict={self.x: test_x, self.y: test_y})
Accuracy.append(test_accuracy)
print ('==' * 15)
print ('Test Accuracy: ', np.mean(np.array(Accuracy)))
model = MNISTModel(0.001, 64, 40000) # 学习率为0.001,每批传入64张图,训练40000次
model.train() # 训练模型
model.test() #测试模型
本站文章如无特殊说明,均为本站原创,如若转载,请注明出处:基于多层感知机的手写数字识别(Tensorflow实现) - Python技术站