更新完整Github代码：https://github.com/maples1993/Cats_vs_Dogs
2. 卷积神经网络模型的构造——model.py 关于神经网络模型不想说太多，视频中使用的模型是仿照TensorFlow的官方例程cifar-10的网络结构来写的。就是两个卷积层（每个卷积层后加一个池化层），两个全连接层，最后一个softmax输出分类结果。
import tensorflow as tf def inference(images, batch_size, n_classes): # conv1, shape = [kernel_size, kernel_size, channels, kernel_numbers] with tf.variable_scope("conv1") as scope: 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="conv1") # pool1 && norm1 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') # conv2 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 && norm2 with tf.variable_scope("pooling2_lrn") as scope: pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="SAME", name="pooling2") norm2 = tf.nn.lrn(pool2, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm2') # full-connect1 with tf.variable_scope("fc1") as scope: reshape = tf.reshape(norm2, 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)) fc1 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name="fc1") # full_connect2 with tf.variable_scope("fc2") 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)) fc2 = tf.nn.relu(tf.matmul(fc1, weights) + biases, name="fc2") # softmax 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.add(tf.matmul(fc2, weights), biases, name="softmax_linear") softmax_linear = tf.nn.softmax(softmax_linear) return softmax_linear 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384发现程序里面有很多with tf.variable_scope("name")的语句，这其实是TensorFlow中的变量作用域机制，目的是有效便捷地管理需要的变量。 变量作用域机制在TensorFlow中主要由两部分组成：
tf.get_variable(<name>, <shape>, <initializer>): 创建一个变量tf.variable_scope(<scope_name>): 指定命名空间如果需要共享变量，需要通过reuse_variables()方法来指定，详细的例子去官方文档中看就好了。（链接在博客参考部分）
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 123456789101112131415161718192021222324函数losses(logits, labels)用于计算训练过程中的loss，这里输入参数logtis是函数inference()的输出，代表图片对猫和狗的预测概率，labels则是图片对应的标签。 通过在程序中设置断点，查看logtis的值，结果如下图所示，根据这个就很好理解了，一个数值代表属于猫的概率，一个数值代表属于狗的概率，两者的和为1。
而函数tf.nn.sparse_sotfmax_cross_entropy_with_logtis从名字就很好理解，是将稀疏表示的label与输出层计算出来结果做对比。然后因为训练的时候是16张图片一个batch，所以再用tf.reduce_mean求一下平均值，就得到了这个batch的平均loss。 training(loss, learning_rate)就没什么好说的了，loss是训练的loss，learning_rate是学习率，使用AdamOptimizer优化器来使loss朝着变小的方向优化。 evaluation(logits, labels)功能是在训练过程中实时监测验证数据的准确率，达到反映训练效果的作用。
本来是自己之前犯懒，最后一篇关于训练的博客没写=0=，鉴于不少人想要训练代码，这里我就从简贴一下代码好了，大伙将就着看看，最近自己的事比较多，不想再把最开始的代码拿来翻了（刚开始写的太丑了）。
import os import numpy as np import tensorflow as tf import input_data import model N_CLASSES = 2 IMG_H = 208 IMG_W = 208 BATCH_SIZE = 32 CAPACITY = 2000 MAX_STEP = 15000 learning_rate = 0.0001 def run_training(): train_dir = "data\\train\\" logs_train_dir = "logs\\" train, train_label = input_data.get_files(train_dir) train_batch, train_label_batch = input_data.get_batch(train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY) train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES) train_loss = model.losses(train_logits, train_label_batch) train_op = model.trainning(train_loss, learning_rate) train_acc = model.evaluation(train_logits, train_label_batch) summary_op = tf.summary.merge_all() sess = tf.Session() 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 % 100 == 0: print("Step %d, train loss = %.2f, train accuracy = %.2f%%" % (step, tra_loss, tra_acc)) summary_str = sess.run(summary_op) train_writer.add_summary(summary_str, step) if step % 2000 == 0 or (step + 1) == MAX_STEP: 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() # 评估模型 from PIL import Image import matplotlib.pyplot as plt def get_one_image(train): n = len(train) ind = np.random.randint(0, n) img_dir = train[ind] image = Image.open(img_dir) plt.imshow(image) plt.show() image = image.resize([208, 208]) image = np.array(image) return image def evaluate_one_image(): train_dir = "C:\\Users\\panch\\Documents\\PycharmProjects\\Cats_vs_Dogs\\data\\train\\" train, train_label = input_data.get_files(train_dir) image_array = get_one_image(train) with tf.Graph().as_default(): BATCH_SIZE = 1 N_CLASSES = 2 image = tf.cast(image_array, tf.float32) image = tf.reshape(image, [1, 208, 208, 3]) logit = model.inference(image, BATCH_SIZE, N_CLASSES) logit = tf.nn.softmax(logit) x = tf.placeholder(tf.float32, shape=[208, 208, 3]) logs_train_dir = "C:\\Users\\panch\\Documents\\PycharmProjects\\Cats_vs_Dogs\\logs\\" saver = tf.train.Saver() with tf.Session() as sess: print("Reading checkpoints...") ckpt = tf.train.get_checkpoint_state(logs_train_dir) if ckpt and ckpt.model_checkpoint_path: global_step = ckpt.model_checkpoint_path.split("/")[-1].split("-")[-1] saver.restore(sess, ckpt.model_checkpoint_path) print("Loading success, global_step is %s" % global_step) else: print("No checkpoint file found") prediction = sess.run(logit, feed_dict={x: image_array}) max_index = np.argmax(prediction) if max_index == 0: print("This is a cat with possibility %.6f" % prediction[:, 0]) else: print("This is a dog with possibility %.6f" % prediction[:, 1]) run_training() # evaluate_one_image() 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120