猫狗大战（分出验证集的代码）下
1._input_data.py
import numpy as np
import tensorflow as tf
import os
import cv2
import matplotlib.pyplot as plt
import os
from PIL import Imagedef get_files(file_dir):cats = []dogs = []cats_label = []dogs_label = []img_dirs = os.listdir(file_dir)#读取文件名下所有！目录名(列表形式)for img_name in img_dirs:# cat.0.jpgname = img_name.split(".")# ['cat', '0', 'jpg']if name[0] == "cat":cats.append(file_dir + img_name)cats_label.append(0)else:if name[0] == "dog":dogs.append(file_dir + img_name)dogs_label.append(1)img_list = np.hstack((cats, dogs))label_list = np.hstack((cats_label, dogs_label))temp = np.array([img_list, label_list]) # 列表转化为矩阵temp = temp.transpose() # transpose的操作对象是矩阵，转置一下np.random.shuffle(temp) # 打乱顺序image_list = list(temp[:, 0])label_list = list(temp[:, 1])label_list = [int(i) for i in label_list]train_image_list = list(image_list[0:int(len(image_list) * 0.7)])train_label_list = list(label_list[0:int(len(image_list) * 0.7)])valid_image_list = list(image_list[int(len(image_list) * 0.7):len(image_list)])valid_label_list = list(label_list[int(len(image_list) * 0.7):len(image_list)])return train_image_list,train_label_list,valid_image_list,valid_label_list#############################################def get_batch(image, label, image_w, image_h, batch_size, capacity):#capacity: 队列中 最多容纳图片的个数input_queue = tf.train.slice_input_producer([image, label])#tf.train.slice_input_producer是一个tensor生成器，作用是# 按照设定，每次从一个tensor列表中按顺序或者随机抽取出一个tensor放入文件名队列。label = input_queue[1]img_contents = tf.read_file(input_queue[0])#一维image = tf.image.decode_jpeg(img_contents, channels=3)#解码成三维矩阵image = tf.image.resize_image_with_crop_or_pad(image, image_w, image_h)image = tf.cast(image, tf.float32)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image_batch, label_batch = tf.train.batch([image, label], batch_size=batch_size, num_threads=64, capacity=capacity)return image_batch, label_batch #shape = (32, 208, 208, 3) (32,)# img_list, label_list = get_files("F:/mytest/2.cat_dog/train/train/")
# image_batch, label_batch = get_batch(img_list, label_list,208,208,32,256)
# print(image_batch.shape)
# print(label_batch.shape)
2._model.py
import tensorflow as tfdef inference(image, batch_size, n_classes):with tf.variable_scope("conv1") as scope:#课本108，variable_scope控制get_variable是获取（reuse=True）还是创建变量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(image, 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")#局部响应归一化??????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=scope.name)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=[1,2,2,1], padding="SAME", name="pooling2")pool2_shape = pool2.get_shape().as_list()nodes = pool2_shape[1] * pool2_shape[2] * pool2_shape[3]dense = tf.reshape(pool2, [batch_size, nodes])with tf.variable_scope("local3") as scope:weights = tf.get_variable("weights", shape=[nodes, 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(dense, weights) + biases, name=scope.name)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")with tf.variable_scope("softmax_linear") as scope:weights = tf.get_variable("weights", 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.matmul(local4, weights) + biasesreturn softmax_linear"""
top_1_op取样本的最大预测概率的索引与实际标签对比，top_2_op取样本的最大和仅次最大的两个预测概率与实际标签对比，
如果实际标签在其中则为True，否则为False。3.train.py
import tensorflow as tf
import numpy as np
import os
import _input_data
import _modelN_CLASSES = 2
IMG_W = 208
IMG_H = 208
BATCH_SIZE = 32
CAPACITY = 256
STEP = 150 #训练步数应当大于10000
LEARNING_RATE = 0.0001train_dir = "E:/mytest/2.cat_dog/train/train/"
log_train_dir = "E:/mytest/2.cat_dog/train_savenet/"train_image_list, train_label_list,valid_image_list, valid_label_list = _input_data.get_files(train_dir)X_train,Y_train = _input_data.get_batch(train_image_list, train_label_list, IMG_W,IMG_H,BATCH_SIZE,CAPACITY)
X_valid,Y_valid = _input_data.get_batch(valid_image_list, valid_label_list, IMG_W,IMG_H,BATCH_SIZE,CAPACITY)x = tf.placeholder(tf.float32, shape=[None, 208,208,3])
y_ = tf.placeholder(tf.int64, shape=[None, ])out = _model.inference(x,BATCH_SIZE, N_CLASSES )
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits= out, labels= y_, name="entropy_per_example")
loss = tf.reduce_mean(cross_entropy)
global_step = tf.Variable(0, name="global_step", trainable=False) # 定义训练的轮数，为不可训练的参数
train_op = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE).minimize(loss, global_step=global_step)
correct = tf.equal(tf.argmax(out, 1), y_)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float16))out1 = tf.nn.softmax(out)
correct1 = tf.equal(tf.argmax(out1, 1), y_)
accuracy1 = tf.reduce_mean(tf.cast(correct1, tf.float16))saver = tf.train.Saver()
with tf.Session() as sess:sess.run(tf.global_variables_initializer())# Coordinator 和 start_queue_runners 监控 queue 的状态，不停的入队出队coord = tf.train.Coordinator()#https://blog.csdn.net/weixin_42052460/article/details/80714539threads = tf.train.start_queue_runners(sess=sess, coord=coord)try:for step in np.arange(STEP):if coord.should_stop():breakimg_train,label_train = sess.run([X_train,Y_train])#注意：1）feed喂的不可以是张量。2）接收的参数名和run（）里面的参数名不要一样_, tra_loss, tra_acc = sess.run([train_op, loss, accuracy],feed_dict={x: img_train, y_: label_train})if step % 50 == 0:#%.2f表示输出浮点数并保留两位小数。%%表示直接输出一个%print("step %d, train loss = %.2f, train accuracy = %.2f%%" %(step, tra_loss, tra_acc*100.0))if step % 2000 == 0 or (step+1) ==STEP:# 每隔2000步保存一下模型，模型保存在 checkpoint_path 中checkpoint_path = os.path.join(log_train_dir, "model.ckpt")saver.save(sess, checkpoint_path, global_step=step)img_valid, label_valid = sess.run([X_valid, Y_valid])valid_accuracy = sess.run(accuracy1, feed_dict={x: img_valid, y_: label_valid})# out2 = sess.run(out1, feed_dict = {x: img_valid})# out3 = sess.run(tf.argmax(out2, 1))# print(out2)# print(out3)# correct2 = sess.run(correct1, feed_dict={x: img_valid, y_: label_valid})# print(correct2)print(" valid accuracy %g" % valid_accuracy)except tf.errors.OutOfRangeError:print('Done training -- epoch limit reached')finally:coord.request_stop()coord.join(threads)4.test.py
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