这份数据集来源于Kaggle,数据集有12500只猫和12500只狗。在这里简单介绍下整体思路
1. 处理数据
2. 设计神经网络
3. 进行训练测试
1. 数据处理将图片数据处理为 tf 能够识别的数据格式,并将数据设计批次。
- 第一步get_files() 方法读取图片,然后根据图片名,添加猫狗 label,然后再将 image和label 放到 数组中,打乱顺序返回
- 将第一步处理好的图片 和label 数组 转化为 tensorflow 能够识别的格式,然后将图片裁剪和补充进行标准化处理,分批次返回。
import tensorflow as tfimport os import numpy as npdef get_files(file_dir): cats = [] label_cats = [] dogs = [] label_dogs = [] for file in os.listdir(file_dir): name = file.split(sep='.') if 'cat' in name[0]: cats.append(file_dir + file) label_cats.append(0) else: if 'dog' in name[0]: dogs.append(file_dir + file) label_dogs.append(1) image_list = np.hstack((cats,dogs)) label_list = np.hstack((label_cats,label_dogs)) # print('There are %d cats\nThere are %d dogs' %(len(cats), len(dogs))) # 多个种类分别的时候需要把多个种类放在一起,打乱顺序,这里不需要 # 把标签和图片都放倒一个 temp 中 然后打乱顺序,然后取出来 temp = np.array([image_list,label_list]) temp = temp.transpose() # 打乱顺序 np.random.shuffle(temp) # 取出第一个元素作为 image 第二个元素作为 label image_list = list(temp[:,0]) label_list = list(temp[:,1]) label_list = [int(i) for i in label_list] return image_list,label_list# 测试 get_files# imgs , label = get_files('/Users/yangyibo/GitWork/pythonLean/AI/猫狗识别/testImg/')# for i in imgs:# print("img:",i)# for i in label:# print('label:',i)# 测试 get_files end# image_W ,image_H 指定图片大小,batch_size 每批读取的个数 ,capacity队列中 最多容纳元素的个数def get_batch(image,label,image_W,image_H,batch_size,capacity): # 转换数据为 ts 能识别的格式 image = tf.cast(image,tf.string) label = tf.cast(label, tf.int32) # 将image 和 label 放倒队列里 input_queue = tf.train.slice_input_producer([image,label]) label = input_queue[1] # 读取图片的全部信息 image_contents = tf.read_file(input_queue[0]) # 把图片解码,channels =3 为彩色图片, r,g ,b 黑白图片为 1 ,也可以理解为图片的厚度 image = tf.image.decode_jpeg(image_contents,channels =3) # 将图片以图片中心进行裁剪或者扩充为 指定的image_W,image_H image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H) # 对数据进行标准化,标准化,就是减去它的均值,除以他的方差 image = tf.image.per_image_standardization(image) # 生成批次 num_threads 有多少个线程根据电脑配置设置 capacity 队列中 最多容纳图片的个数 tf.train.shuffle_batch 打乱顺序, image_batch, label_batch = tf.train.batch([image, label],batch_size = batch_size, num_threads = 64, capacity = capacity) # 重新定义下 label_batch 的形状 label_batch = tf.reshape(label_batch , [batch_size]) # 转化图片 image_batch = tf.cast(image_batch,tf.float32) return image_batch, label_batch# test get_batch# import matplotlib.pyplot as plt# BATCH_SIZE = 2# CAPACITY = 256 # IMG_W = 208# IMG_H = 208# train_dir = '/Users/yangyibo/GitWork/pythonLean/AI/猫狗识别/testImg/'# image_list, label_list = get_files(train_dir)# image_batch, label_batch = get_batch(image_list, label_list, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)# with tf.Session() as sess:# i = 0# # Coordinator 和 start_queue_runners 监控 queue 的状态,不停的入队出队# coord = tf.train.Coordinator()# threads = tf.train.start_queue_runners(coord=coord)# # coord.should_stop() 返回 true 时也就是 数据读完了应该调用 coord.request_stop()# try: # while not coord.should_stop() and i<1:# # 测试一个步# img, label = sess.run([image_batch, label_batch])# for j in np.arange(BATCH_SIZE):# print('label: %d' %label[j])# # 因为是个4D 的数据所以第一个为 索引 其他的为冒号就行了# plt.imshow(img[j,:,:,:])# plt.show()# i+=1# # 队列中没有数据# except tf.errors.OutOfRangeError:# print('done!')# finally:# coord.request_stop()# coord.join(threads) # sess.close()- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
- 54
- 55
- 56
- 57
- 58
- 59
- 60
- 61
- 62
- 63
- 64
- 65
- 66
- 67
- 68
- 69
- 70
- 71
- 72
- 73
- 74
- 75
- 76
- 77
- 78
- 79
- 80
- 81
- 82
- 83
- 84
- 85
- 86
- 87
- 88
- 89
- 90
- 91
- 92
- 93
- 94
- 95
- 96
- 97
- 98
- 99
- 100
- 101
- 102
- 103
- 104
- 105
- 106
- 107
- 108
- 109
- 110
2. 设计神经网络利用卷积神经网路处理,网络结构为
# conv1 卷积层 1# pooling1_lrn 池化层 1# conv2 卷积层 2# pooling2_lrn 池化层 2# local3 全连接层 1# local4 全连接层 2# softmax 全连接层 3#coding=utf-8 import tensorflow as tf def inference(images, batch_size, n_classes): with tf.variable_scope('conv1') as scope: # 卷积盒的为 3*3 的卷积盒,图片厚度是3,输出是16个featuremap weights = tf.get_variable('weights', shape=[3, 3, 3, 16], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[16], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME') pre_activation = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(pre_activation, name=scope.name) with tf.variable_scope('pooling1_lrn') as scope: pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1') norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') with tf.variable_scope('conv2') as scope: weights = tf.get_variable('weights', shape=[3, 3, 16, 16], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[16], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME') pre_activation = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(pre_activation, name='conv2') # pool2 and norm2 with tf.variable_scope('pooling2_lrn') as scope: norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2') pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2') with tf.variable_scope('local3') as scope: reshape = tf.reshape(pool2, shape=[batch_size, -1]) dim = reshape.get_shape()[1].value weights = tf.get_variable('weights', shape=[dim, 128], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[128], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) # local4 with tf.variable_scope('local4') as scope: weights = tf.get_variable('weights', shape=[128, 128], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[128], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4') # softmax with tf.variable_scope('softmax_linear') as scope: weights = tf.get_variable('softmax_linear', shape=[128, n_classes], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[n_classes], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear') return softmax_linear def losses(logits, labels): with tf.variable_scope('loss') as scope: cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \ (logits=logits, labels=labels, name='xentropy_per_example') loss = tf.reduce_mean(cross_entropy, name='loss') tf.summary.scalar(scope.name + '/loss', loss) return loss def trainning(loss, learning_rate): with tf.name_scope('optimizer'): optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate) global_step = tf.Variable(0, name='global_step', trainable=False) train_op = optimizer.minimize(loss, global_step= global_step) return train_op def evaluation(logits, labels): with tf.variable_scope('accuracy') as scope: correct = tf.nn.in_top_k(logits, labels, 1) correct = tf.cast(correct, tf.float16) accuracy = tf.reduce_mean(correct) tf.summary.scalar(scope.name + '/accuracy', accuracy) return accuracy- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
- 54
- 55
- 56
- 57
- 58
- 59
- 60
- 61
- 62
- 63
- 64
- 65
- 66
- 67
- 68
- 69
- 70
- 71
- 72
- 73
- 74
- 75
- 76
- 77
- 78
- 79
- 80
- 81
- 82
- 83
- 84
- 85
- 86
- 87
- 88
- 89
- 90
- 91
- 92
- 93
- 94
- 95
- 96
- 97
- 98
- 99
- 100
- 101
- 102
- 103
- 104
3. 训练数据,并将训练的模型存储import os import numpy as np import tensorflow as tf import input_data import model N_CLASSES = 2 # 2个输出神经元,[1,0] 或者 [0,1]猫和狗的概率IMG_W = 208 # 重新定义图片的大小,图片如果过大则训练比较慢 IMG_H = 208 BATCH_SIZE = 32 #每批数据的大小CAPACITY = 256 MAX_STEP = 15000 # 训练的步数,应当 >= 10000learning_rate = 0.0001 # 学习率,建议刚开始的 learning_rate <= 0.0001def run_training(): # 数据集 train_dir = '/Users/yangyibo/GitWork/pythonLean/AI/猫狗识别/img/' #My dir--20170727-csq #logs_train_dir 存放训练模型的过程的数据,在tensorboard 中查看 logs_train_dir = '/Users/yangyibo/GitWork/pythonLean/AI/猫狗识别/saveNet/' # 获取图片和标签集 train, train_label = input_data.get_files(train_dir) # 生成批次 train_batch, train_label_batch = input_data.get_batch(train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY) # 进入模型 train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES) # 获取 loss train_loss = model.losses(train_logits, train_label_batch) # 训练 train_op = model.trainning(train_loss, learning_rate) # 获取准确率 train__acc = model.evaluation(train_logits, train_label_batch) # 合并 summary summary_op = tf.summary.merge_all() sess = tf.Session() # 保存summary train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph) saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) try: for step in np.arange(MAX_STEP): if coord.should_stop(): break _, tra_loss, tra_acc = sess.run([train_op, train_loss, train__acc]) if step % 50 == 0: print('Step %d, train loss = %.2f, train accuracy = %.2f%%' %(step, tra_loss, tra_acc*100.0)) summary_str = sess.run(summary_op) train_writer.add_summary(summary_str, step) if step % 2000 == 0 or (step + 1) == MAX_STEP: # 每隔2000步保存一下模型,模型保存在 checkpoint_path 中 checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt') saver.save(sess, checkpoint_path, global_step=step) except tf.errors.OutOfRangeError: print('Done training -- epoch limit reached') finally: coord.request_stop() coord.join(threads) sess.close() # trainrun_training()- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
- 54
- 55
- 56
- 57
- 58
- 59
- 60
- 61
- 62
- 63
- 64
- 65
- 66
- 67
- 68
- 69
- 70
- 71
- 72
- 73
- 74
- 75
- 76
关于保存的模型怎么使用将在下一片博客中 展示。
TensorFlow 卷积神经网络之使用训练好的模型识别猫狗图片
如果需要训练数据集可以评论留下联系方式。
原文完整代码地址:
https://github.com/527515025/My-TensorFlow-tutorials/tree/master/%E7%8C%AB%E7%8B%97%E8%AF%86%E5%88%AB
欢迎 star 欢迎提问。