卷积神经网络python实现
Python通过TensorFlow卷积神经网络实现猫狗识别这份数据集来源于Kaggle，数据集有12500只猫和12500只狗。在这里简单介绍下整体思路
- 处理数据
- 设计神经网络
- 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。
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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。
新建数据处理文件 ，文件名 input_data.py
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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() |
2. 设计神经网络
利用卷积神经网路处理，网络结构为
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# 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 |
新建神经网络文件 ，文件名 model.py
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#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 |
3. 训练数据，并将训练的模型存储
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