内容源于曹建老师的tensorflow笔记课程
源码链接:https://github.com/cj0012/AI-Practice-Tensorflow-Notes
测试图片下载:https://github.com/cj0012/AI-Practice-Tensorflow-Notes/blob/master/num.zip
主要包含四个文件,主要是mnist_forward.py,mnist_backward.py,mnist_test.py,mnist_app.py
定义前向传播过程 mnist_forward.py:
import tensorflow as tf INPUT_NODE = 784 OUTPUT_NODE = 10 LAYER_NODE = 500 # 定义神经网络参数,传入两个参数,一个是shape一个是正则化参数大小 def get_weight(shape,regularizer): # tf.truncated_normal截断的正态分布函数,超过标准差的重新生成 w = tf.Variable(tf.truncated_normal(shape,stddev=0.1)) if regularizer != None: # 将正则化结果存入losses中 tf.add_to_collection("losses",tf.contrib.layers.l2_regularizer(regularizer)(w)) return w # 定义偏置b,传入shape参数 def get_bias(shape): # 初始化为0 b = tf.Variable(tf.zeros(shape)) return b # 定义前向传播过程,两个参数,一个是输入数据,一个是正则化参数 def forward(x,regularizer): # w1的维度就是[输入神经元大小,第一层隐含层神经元大小] w1 = get_weight([INPUT_NODE,LAYER_NODE],regularizer) # 偏置b参数,与w的后一个参数相同 b1 = get_bias(LAYER_NODE) # 激活函数 y1 = tf.nn.relu(tf.matmul(x,w1)+b1) w2 = get_weight([LAYER_NODE,OUTPUT_NODE],regularizer) b2 = get_bias(OUTPUT_NODE) y = tf.matmul(y1,w2)+b2 return y
定义反向传播过程 mnist_backward.py:
#coding:utf-8 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import mnist_forward import os BATCH_SIZE = 200 #学习率衰减的原始值 LEARNING_RATE_BASE = 0.1 # 学习率衰减率 LEARNING_RATE_DECAY = 0.99 # 正则化参数 REGULARIZER = 0.0001 # 训练轮数 STEPS = 50000 #这个使用滑动平均的衰减率 MOVING_AVERAGE_DECAY = 0.99 MODEL_SAVE_PATH = "./model/" MODEL_NAME = "mnist_model" def backward(mnist): #一共有多少个特征,784行,一列 x = tf.placeholder(tf.float32,[None,mnist_forward.INPUT_NODE]) y_ = tf.placeholder(tf.float32,[None,mnist_forward.OUTPUT_NODE]) # 给前向传播传入参数x和正则化参数计算出y的值 y = mnist_forward.forward(x,REGULARIZER) # 初始化global—step,它会随着训练轮数增加 global_step = tf.Variable(0,trainable=False) # softmax和交叉商一起运算的函数，logits传入是x*w,也就是y ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1)) cem = tf.reduce_mean(ce) loss = cem + tf.add_n(tf.get_collection("losses")) learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train.num_examples/BATCH_SIZE, LEARNING_RATE_DECAY, staircase = True) train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step = global_step) # 滑动平均处理,可以提高泛华能力 ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step) ema_op = ema.apply(tf.trainable_variables()) # 将train_step和滑动平均计算ema_op放在同一个节点 with tf.control_dependencies([train_step,ema_op]): train_op = tf.no_op(name="train") saver = tf.train.Saver() with tf.Session() as sess: init_op = tf.global_variables_initializer() sess.run(init_op) for i in range(STEPS): # mnist.train.next_batch()函数包含一个参数BATCH_SIZE,表示随机从训练集中抽取BATCH_SIZE个样本输入到神经网络 # next_batch函数返回的是image的像素和标签label xs,ys = mnist.train.next_batch(BATCH_SIZE) # _,表示后面不使用这个变量 _,loss_value,step = sess.run([train_op,loss,global_step],feed_dict={x:xs,y_:ys}) if i % 1000 == 0: print("Ater {} training step(s),loss on training batch is {} ".format(step,loss_value)) saver.save(sess,os.path.join(MODEL_SAVE_PATH,MODEL_NAME),global_step=global_step) def main(): mnist = input_data.read_data_sets("./data",one_hot = True) backward(mnist) if __name__ == "__main__": main()
定义测试部分 mnist_test.py:
#coding:utf-8 import time import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import mnist_forward import mnist_backward TEST_INTERVAL_SECS = 5 def test(mnist): with tf.Graph().as_default() as g: # 占位符,第一个参数是tf.float32数据类型,第二个参数是shape,shape[0]=None表示输入维度任意,shpe[1]表示输入数据特征数 x = tf.placeholder(tf.float32,shape = [None,mnist_forward.INPUT_NODE]) y_ = tf.placeholder(tf.float32,shape = [None,mnist_forward.OUTPUT_NODE]) """注意这里没有传入正则化参数,需要明确的是,在测试的时候不要正则化,不要dropout""" y = mnist_forward.forward(x,None) # 实例化可还原的滑动平均模型 ema = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY) ema_restore = ema.variables_to_restore() saver = tf.train.Saver(ema_restore) # y计算的过程：x是mnist.test.images是10000×784的，最后输出的y仕10000×10的，y_:mnist.test.labels也是10000×10的 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1)) # tf.cast可以师兄数据类型的转换,tf.equal返回的只有True和False accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) while True: with tf.Session() as sess: # 加载训练好的模型 ckpt = tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: # 恢复模型到当前会话 saver.restore(sess,ckpt.model_checkpoint_path) # 恢复轮数 global_step = ckpt.model_checkpoint_path.split("/")[-1].split("-")[-1] # 计算准确率 accuracy_score = sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels}) print("After {} training step(s),test accuracy is: {} ".format(global_step,accuracy_score)) else: print("No chekpoint file found") print(sess.run(y,feed_dict={x:mnist.test.images})) return time.sleep(TEST_INTERVAL_SECS) def main(): mnist = input_data.read_data_sets("./data",one_hot=True) test(mnist) if __name__== "__main__": main()
定义使用手写图片部分mnist_app.py:
import tensorflow as tf import numpy as np from PIL import Image import mnist_forward import mnist_backward # 定义加载使用模型进行预测的函数 def restore_model(testPicArr): with tf.Graph().as_default() as tg: x = tf.placeholder(tf.float32,[None,mnist_forward.INPUT_NODE]) y = mnist_forward.forward(x,None) preValue = tf.argmax(y,1) # 加载滑动平均模型 variable_averages = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY) variables_to_restore = variable_averages.variables_to_restore() saver = tf.train.Saver(variables_to_restore) with tf.Session() as sess: ckpt = tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: # 恢复当前会话,将ckpt中的值赋值给w和b saver.restore(sess,ckpt.model_checkpoint_path) # 执行图计算 preValue = sess.run(preValue,feed_dict={x:testPicArr}) return preValue else: print("No checkpoint file found") return -1 # 图片预处理函数 def pre_pic(picName): # 先打开传入的原始图片 img = Image.open(picName) # 使用消除锯齿的方法resize图片 reIm = img.resize((28,28),Image.ANTIALIAS) # 变成灰度图，转换成矩阵 im_arr = np.array(reIm.convert("L")) threshold = 50#对图像进行二值化处理，设置合理的阈值，可以过滤掉噪声，让他只有纯白色的点和纯黑色点 for i in range(28): for j in range(28): im_arr[i][j] = 255-im_arr[i][j] if (im_arr[i][j]<threshold): im_arr[i][j] = 0 else: im_arr[i][j] = 255 # 将图像矩阵拉成1行784列，并将值变成浮点型（像素要求的仕0-1的浮点型输入） nm_arr = im_arr.reshape([1,784]) nm_arr = nm_arr.astype(np.float32) img_ready = np.multiply(nm_arr,1.0/255.0) return img_ready def application(): # input函数可以从控制台接受数字 testNum = int(input("input the number of test images:")) # 使用循环来历遍需要测试的图片才结束 for i in range(testNum): # input可以实现从控制台接收字符格式,图片存储路径 testPic = input("the path of test picture:") # 将图片路径传入图像预处理函数中 testPicArr = pre_pic(testPic) # 将处理后的结果输入到预测函数最后返回预测结果 preValue = restore_model(testPicArr) print("The prediction number is :",preValue) def main(): application() if __name__ == "__main__": main()
output:
The end.