TensorFlow实现手写数字识别应用

2024-01-17 05:01| 来源: 网络整理| 查看: 265

本程序使用TensorFlow实现输入手写数字识别结果，IDE为Pycharm。实现的主要功能是实现断点续训，输入真实图片，输出预测值。有完整代码。分为四个文件： forward.py backward.py test.py:测试已经训练好的神经网络，查看正确率 app.py：实现应用，输入图片，实现识别技术。

神经网络结构

在这里插入图片描述本NN采用两层的全连接网络，输入节点数为784,中间节点数为500，输出10分类。全连接层结构： [1,784] ->[1,500]->[1,10] 前向传播过程，其中INPUT_NODE=784， LAYER1_NODE=500，OUTPUT_NODE=10. 前向传播代码：

# -*-coding:gbk-*- import tensorflow as tf INPUT_NODE = 784 OUTPUT_NODE = 10 LAYER1_NODE = 500 def get_weight(shape, regularizer): w = tf.Variable(tf.truncated_normal(shape, stddev=0.1)) if regularizer != None: # collection容器可以保存很多值，这里使用L2正则化，在w的损失加入到losses中 tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w)) return w def get_bias(shape): print (shape) b = tf.Variable(tf.zeros(shape)) return b def forward(x, regularizer): w1 = get_weight([INPUT_NODE, LAYER1_NODE], regularizer) b1 = get_bias([LAYER1_NODE]) y1 = tf.nn.relu(tf.matmul(x, w1) + b1) w2 = get_weight([LAYER1_NODE, OUTPUT_NODE], regularizer) b2 = get_bias([OUTPUT_NODE]) y = tf.matmul(y1, w2) + b2 # 输出层不过激活 return y 训练模型，保存训练的计算图

在方向传播中，把模型保存指定路径，注意路径文件夹要先创建文件，否则可能出错。

断点续训技术

断点训练可以是把训练好的模型保存下来，再次使用不需要从头开始训练，而是从之前断开的位置开始，使用ckpt可以实现复现的计算图。

# tf.train.get_checkpoint_state(checkpoint_dir,latest_filename=None) # 函数表示如果断点文件夹中包含有效断点状态文件,则返回该文件。 # 参数说明: # checkpoint_dir:表示存储断点文件的目录 # latest_filename=None:断点文件的可选名称,默认为“checkpoint” ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) # saver.restore(sess, ckpt.model_checkpoint_path) # 该函数表示恢复当前会话,将 ckpt 中的值赋给 w 和 b。 # 参数说明: # sess:表示当前会话,之前保存的结果将被加载入这个会话 # ckpt.model_checkpoint_path:表示模型存储的位置,不需要提供模型的名字,它会去查看 checkpoint 文件反向传播代码（包括断点续训）： #coding:utf-8 # -*-coding:gbk-*- # 0导入模块，生成数据集 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import os import forward STEPS = 50000 BATCH_SIZE = 200 LEARNING_RATE_BASE = 0.1 LEARNING_RATE_DECAY = 0.99 GEGULARIZER = 0.0001 MOVING_AVERAGE_DECAY = 0.99 MODEL_SAVE_PATH = "./model/" MODEL_NAME = "mnist_model" def backward(mnist): x = tf.placeholder(tf.float32, [None, forward.INPUT_NODE]) # y_->labels y->logist y_ = tf.placeholder(tf.float32, [None, forward.OUTPUT_NODE]) y = forward.forward(x, GEGULARIZER) global_step = tf.Variable(0, trainable=False) # 定义loss函数 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')) # 加上w的损失 learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE, global_step, # 为样本个数 mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY, staircase=True) # 定义backward 方法，包括正则化 #train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss) train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) # 在模型训练时候使用滑动平均，模型更加健壮 ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) ema_op = ema.apply(tf.trainable_variables()) with tf.control_dependencies([train_step, ema_op]): train_op = tf.no_op(name='train') saver = tf.train.Saver() # 实例化saver对象 with tf.Session() as sess: init_op = tf.initialize_all_variables() #init_op = tf.global_variables_initializer() sess.run(init_op)#执行训练过程 ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess,ckpt.model_checkpoint_path) # 训练模型 for i in range(STEPS): xs, ys = mnist.train.next_batch(BATCH_SIZE) # 随机抽取BATCH_SIZE数据输入NN,xs:(200,784),ys:(200,10) _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys}) if i % 1000 == 0: print("After %d step(s),loss on all data is %g" % (step, loss_value)) saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step) # NN每隔1000轮，将参数信息保存到指定路径，并注明训练轮数 def practice(mnist): print "train data size:",mnist.train.num_examples print("validation data size:",mnist.validation.num_examples) print ("test data size:",mnist.test.num_examples) print mnist.train.labels[0] print mnist.train.images[0] def main(): mnist = input_data.read_data_sets("./data/", one_hot=True) #practice(mnist) backward(mnist) if __name__ == '__main__': main()

执行反向传播函数，训练神经网络模型：在这里插入图片描述

test.py代码： # coding:utf-8 import sys sys.path.append('/usr/local/lib/python2.7/dist-packages') # 0导入模块，生成数据集 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import os import forward import time import backward TEST_INTERVAL_SECS = 5 def test(mnist): # 复现计算图 with tf.Graph().as_default() as g: x = tf.placeholder(tf.float32, [None, forward.INPUT_NODE]) y_ = tf.placeholder(tf.float32, [None, forward.OUTPUT_NODE]) y = forward.forward(x, None) # 实例化可还原滑动平均的saver ema = tf.train.ExponentialMovingAverage(backward.MOVING_AVERAGE_DECAY) ema_restore = ema.variables_to_restore() saver = tf.train.Saver(ema_restore) correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) 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(backward.MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: # 恢复回话 saver.restore(sess, ckpt.model_checkpoint_path) # 恢复轮数,使用split函数获得已经训练的轮数 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 %s training step(s),test accuracy = %g " % (global_step, accuracy_score)) else: print("NO checkpoint file found") return time.sleep(TEST_INTERVAL_SECS) def main(): mnist = input_data.read_data_sets("./data/", one_hot=True) test(mnist) if __name__ == '__main__': main() app.py代码： #coding:utf-8 import tensorflow as tf import numpy as np import forward import backward from PIL import Image # 图片预处理 def pre_pic(testPic): img = Image.open(testPic) img.show() # 改变图片规格，适应神经网络的输入规格 reIm = img.resize((28,28),Image.ANTIALIAS) im_arr = np.array(reIm.convert('L')) threshold = 50 # 设定阈值，进行二值化 for i in range(28): for j in range(28): im_arr[i][j] = 255- im_arr[i][j] if(im_arr[i][j]

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