内容简介:训练20次结果如下:训练结果如下:训练50次结果如下:
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #载入数据集 mnist = input_data.read_data_sets('MNIST_data/', one_hot=True) #每个批次的大小 batch_size=100 #计算一共有多少批次 n_batch = mnist.train.num_examples // batch_size #定义两个placehold x = tf.placeholder(tf.float32,[None,784]) y = tf.placeholder(tf.float32,[None,10]) #创建一个简单的神经网络 W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) prediction = tf.nn.softmax(tf.matmul(x,W)+b) #二次代价函数 loss = tf.reduce_mean(tf.square(y-prediction)) #使用梯度下降法 train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) #初始化变量 init = tf.global_variables_initializer() #存放结果到一个布尔型变量中 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1)) #求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) with tf.Session() as sess: sess.run(init) for epoch in range(20): for batch in range(n_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys}) acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}) print("Iter "+str(epoch) + ",Testing Accuracy "+str(acc)) 复制代码
训练20次结果如下:
Iter 0,Testing Accuracy 0.8303 Iter 1,Testing Accuracy 0.8712 Iter 2,Testing Accuracy 0.8808 Iter 3,Testing Accuracy 0.8887 Iter 4,Testing Accuracy 0.8944 Iter 5,Testing Accuracy 0.8973 Iter 6,Testing Accuracy 0.8992 Iter 7,Testing Accuracy 0.9032 Iter 8,Testing Accuracy 0.9034 Iter 9,Testing Accuracy 0.905 Iter 10,Testing Accuracy 0.9068 Iter 11,Testing Accuracy 0.9074 Iter 12,Testing Accuracy 0.9083 Iter 13,Testing Accuracy 0.9096 Iter 14,Testing Accuracy 0.9101 Iter 15,Testing Accuracy 0.9109 Iter 16,Testing Accuracy 0.9118 Iter 17,Testing Accuracy 0.9121 Iter 18,Testing Accuracy 0.9127 Iter 19,Testing Accuracy 0.9133 复制代码
1-2. 使用交叉熵函数
# #二次代价函数 # loss = tf.reduce_mean(tf.square(y-prediction)) #交叉熵定义二次代价函数作用是加快模型收敛速度 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) 复制代码
训练结果如下:
Iter 0,Testing Accuracy 0.8239 Iter 1,Testing Accuracy 0.892 Iter 2,Testing Accuracy 0.9011 Iter 3,Testing Accuracy 0.9052 Iter 4,Testing Accuracy 0.9086 Iter 5,Testing Accuracy 0.9098 Iter 6,Testing Accuracy 0.912 …… Iter 15,Testing Accuracy 0.921 Iter 16,Testing Accuracy 0.9207 Iter 17,Testing Accuracy 0.9221 Iter 18,Testing Accuracy 0.9213 Iter 19,Testing Accuracy 0.922 复制代码
2-1. 具有3个隐层的神经网络
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #载入数据集 mnist = input_data.read_data_sets('MNIST_data/', one_hot=True) #每个批次的大小 batch_size=100 #计算一共有多少批次 n_batch = mnist.train.num_examples // batch_size #定义两个placehold x = tf.placeholder(tf.float32,[None,784]) y = tf.placeholder(tf.float32,[None,10]) keep_prob = tf.placeholder(tf.float32) #创建一个简单的神经网络 W1 = tf.Variable(tf.truncated_normal([784,500],stddev=0.1)) b1 = tf.Variable(tf.zeros([500])+0.1) L1 = tf.nn.tanh(tf.matmul(x,W1)+b1) L1_drop = tf.nn.dropout(L1,keep_prob) W2 = tf.Variable(tf.truncated_normal([500,300],stddev=0.1)) b2 = tf.Variable(tf.zeros([300])+0.1) L2 = tf.nn.tanh(tf.matmul(L1_drop,W2)+b2) L2_drop = tf.nn.dropout(L2,keep_prob) W3 = tf.Variable(tf.truncated_normal([300,10],stddev=0.1)) b3 = tf.Variable(tf.zeros([10])+0.1) prediction = tf.nn.softmax(tf.matmul(L2_drop,W3)+b3) #二次代价函数 loss = tf.reduce_mean(tf.square(y-prediction)) #使用梯度下降法 train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) #初始化变量 init = tf.global_variables_initializer() #存放结果到一个布尔型变量中 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1)) #求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) with tf.Session() as sess: sess.run(init) for epoch in range(51): for batch in range(n_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0}) test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0}) train_acc = sess.run(accuracy,feed_dict={x:mnist.train.images,y:mnist.train.labels,keep_prob:1.0}) print("Iter "+str(epoch) + ",Testing Accuracy "+str(test_acc)) 复制代码
训练50次结果如下:
Iter 0,Testing Accuracy 0.8852 Iter 1,Testing Accuracy 0.9103 Iter 2,Testing Accuracy 0.9179 Iter 3,Testing Accuracy 0.9257 …… Iter 13,Testing Accuracy 0.9502 Iter 14,Testing Accuracy 0.9504 …… Iter 24,Testing Accuracy 0.9605 Iter 25,Testing Accuracy 0.9603 …… Iter 49,Testing Accuracy 0.9699 Iter 50,Testing Accuracy 0.9698 复制代码
2-2 改用交叉熵函数
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) 复制代码
训练五十次后:
Iter 0,Testing Accuracy 0.9239 …… Iter 4,Testing Accuracy 0.9529 …… Iter 7,Testing Accuracy 0.9619 …… Iter 14,Testing Accuracy 0.9714 …… Iter 49,Testing Accuracy 0.9769 Iter 50,Testing Accuracy 0.9765 复制代码
2-3 进一步改变优化器优化模型
- 使用AdamOptimizer
#train_step = tf.train.AdamOptimizer(1e-2).minimize(loss) Iter 0,Testing Accuracy 0.9255 …… Iter 50,Testing Accuracy 0.9255 复制代码
- 使用MomentumOptimizer
#train_step = tf.train.MomentumOptimizer(learning_rate=0.1,momentum=0.9).minimize(loss) Iter 0,Testing Accuracy 0.9422 Iter 1,Testing Accuracy 0.9573 …… Iter 5,Testing Accuracy 0.9708 …… Iter 50,Testing Accuracy 0.9793 复制代码
2-4 优化到98%
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #载入数据集 mnist = input_data.read_data_sets('MNIST_data/', one_hot=True) #每个批次的大小 batch_size=100 #计算一共有多少批次 n_batch = mnist.train.num_examples // batch_size #定义两个placehold x = tf.placeholder(tf.float32,[None,784]) y = tf.placeholder(tf.float32,[None,10]) keep_prob = tf.placeholder(tf.float32) lr = tf.Variable(0.001,dtype=tf.float32) #学习率 #创建一个简单的神经网络 W1 = tf.Variable(tf.truncated_normal([784,500],stddev=0.1)) b1 = tf.Variable(tf.zeros([500])+0.1) L1 = tf.nn.tanh(tf.matmul(x,W1)+b1) L1_drop = tf.nn.dropout(L1,keep_prob) W2 = tf.Variable(tf.truncated_normal([500,300],stddev=0.1)) b2 = tf.Variable(tf.zeros([300])+0.1) L2 = tf.nn.tanh(tf.matmul(L1_drop,W2)+b2) L2_drop = tf.nn.dropout(L2,keep_prob) W3 = tf.Variable(tf.truncated_normal([300,10],stddev=0.1)) b3 = tf.Variable(tf.zeros([10])+0.1) prediction = tf.nn.softmax(tf.matmul(L2_drop,W3)+b3) #交叉熵代价函数 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) #训练 train_step = tf.train.AdamOptimizer(lr).minimize(loss) #初始化变量 init = tf.global_variables_initializer() #存放结果到一个布尔型变量中 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1)) #求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) with tf.Session() as sess: sess.run(init) for epoch in range(51): sess.run(tf.assign(lr,0.001 * (0.95 ** epoch))) for batch in range(n_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0}) learning_rate = sess.run(lr) acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0}) print("Iter "+str(epoch) + ",Testing Accuracy= "+str(acc)+" Learning Rate= "+str(learning_rate)) 复制代码
训练结果如下:
Iter 0,Testing Accuracy= 0.9497 Learning Rate= 0.001 Iter 1,Testing Accuracy= 0.9629 Learning Rate= 0.00095 Iter 2,Testing Accuracy= 0.971 Learning Rate= 0.0009025 Iter 3,Testing Accuracy= 0.9664 Learning Rate= 0.000857375 Iter 4,Testing Accuracy= 0.973 Learning Rate= 0.00081450626 Iter 5,Testing Accuracy= 0.9766 Learning Rate= 0.0007737809 Iter 6,Testing Accuracy= 0.9756 Learning Rate= 0.0007350919 Iter 7,Testing Accuracy= 0.9753 Learning Rate= 0.0006983373 Iter 8,Testing Accuracy= 0.9767 Learning Rate= 0.0006634204 Iter 9,Testing Accuracy= 0.9786 Learning Rate= 0.0006302494 Iter 10,Testing Accuracy= 0.9782 Learning Rate= 0.0005987369 Iter 11,Testing Accuracy= 0.9781 Learning Rate= 0.0005688001 Iter 12,Testing Accuracy= 0.9767 Learning Rate= 0.0005403601 Iter 13,Testing Accuracy= 0.9796 Learning Rate= 0.0005133421 Iter 14,Testing Accuracy= 0.9807 Learning Rate= 0.000487675 Iter 15,Testing Accuracy= 0.9809 Learning Rate= 0.00046329122 …… Iter 49,Testing Accuracy= 0.9818 Learning Rate= 8.099471e-05 Iter 50,Testing Accuracy= 0.9825 Learning Rate= 7.6944976e-05 复制代码
3使用inception-v3进行图像识别
daima import tensorflow as tf import os import numpy as np import re from PIL import Image import matplotlib.pyplot as plt class NodeLookup(object): def __init__(self): label_lookup_path = 'inception_model/imagenet_2012_challenge_label_map_proto.pbtxt' uid_lookup_path = 'inception_model/imagenet_synset_to_human_label_map.txt' self.node_lookup = self.load(label_lookup_path, uid_lookup_path) def load(self, label_lookup_path, uid_lookup_path): proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines() uid_to_human = {} #一行一行读取数据 for line in proto_as_ascii_lines : #去掉换行符 line=line.strip('\n') #按照'\t'分割 parsed_items = line.split('\t') #获取分类编号 uid = parsed_items[0] #获取分类名称 human_string = parsed_items[1] #保存编号字符串n********与分类名称映射关系 uid_to_human[uid] = human_string # 加载分类字符串n********对应分类编号1-1000的文件 proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines() node_id_to_uid = {} for line in proto_as_ascii: if line.startswith(' target_class:'): #获取分类编号1-1000 target_class = int(line.split(': ')[1]) if line.startswith(' target_class_string:'): #获取编号字符串n******** target_class_string = line.split(': ')[1] #保存分类编号1-1000与编号字符串n********映射关系 node_id_to_uid[target_class] = target_class_string[1:-2] #建立分类编号1-1000对应分类名称的映射关系 node_id_to_name = {} for key, val in node_id_to_uid.items(): #获取分类名称 name = uid_to_human[val] #建立分类编号1-1000到分类名称的映射关系 node_id_to_name[key] = name return node_id_to_name #传入分类编号1-1000返回分类名称 def id_to_string(self, node_id): if node_id not in self.node_lookup: return '' return self.node_lookup[node_id] #创建一个图来存放google训练好的模型 with tf.gfile.FastGFile('inception_model/classify_image_graph_def.pb', 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, name='') with tf.Session() as sess: softmax_tensor = sess.graph.get_tensor_by_name('softmax:0') #遍历目录 for root,dirs,files in os.walk('images/'): for file in files: #载入图片 image_data = tf.gfile.FastGFile(os.path.join(root,file), 'rb').read() predictions = sess.run(softmax_tensor,{'DecodeJpeg/contents:0': image_data})#图片格式是jpg格式 predictions = np.squeeze(predictions)#把结果转为1维数据 #打印图片路径及名称 image_path = os.path.join(root,file) print(image_path) #显示图片 img=Image.open(image_path) plt.imshow(img) plt.axis('off') plt.show() #排序 top_k = predictions.argsort()[-5:][::-1] node_lookup = NodeLookup() for node_id in top_k: #获取分类名称 human_string = node_lookup.id_to_string(node_id) #获取该分类的置信度 score = predictions[node_id] print('%s (score = %.5f)' % (human_string, score)) print() 复制代码
识别结果
images/man.jpg Windsor tie (score = 0.73132) suit, suit of clothes (score = 0.19451) stole (score = 0.01027) jean, blue jean, denim (score = 0.00458) wool, woolen, woollen (score = 0.00434) 复制代码
airliner (score = 0.80073) space shuttle (score = 0.05414) wing (score = 0.02091) airship, dirigible (score = 0.00550) warplane, military plane (score = 0.00169) 复制代码
lakeside, lakeshore (score = 0.93102) breakwater, groin, groyne, mole, bulwark, seawall, jetty (score = 0.01073) goose (score = 0.00414) boathouse (score = 0.00362) valley, vale (score = 0.00271) 复制代码
acoustic guitar (score = 0.97403) banjo (score = 0.00399) electric guitar (score = 0.00208) pick, plectrum, plectron (score = 0.00061) garter snake, grass snake (score = 0.00035) 复制代码
sports car, sport car (score = 0.62812) convertible (score = 0.14632) racer, race car, racing car (score = 0.13355) car wheel (score = 0.00704) limousine, limo (score = 0.00574) 复制代码
golden retriever (score = 0.94793) Labrador retriever (score = 0.00335) flat-coated retriever (score = 0.00066) Tibetan mastiff (score = 0.00063) Leonberg (score = 0.00061) 复制代码
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Understanding Machine Learning
Shai Shalev-Shwartz、Shai Ben-David / Cambridge University Press / 2014 / USD 48.51
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it of......一起来看看 《Understanding Machine Learning》 这本书的介绍吧!