TensorFlow——训练自己的数据（二）模型设计

参考：Tensorflow教程-猫狗大战数据集

文件：model.py

网络结构定义 一个简单的卷积神经网络，卷积+池化层x2，全连接层x2，最后一个softmax层做分类。 函数：def inference(images, batch_size, n_classes):

输入参数： images，image batch、4D tensor、tf.float32、[batch_size, width, height, channels]返回参数： logits, float、 [batch_size, n_classes]

卷积层1 16个3x3的卷积核（3通道），padding=’SAME’，表示padding后卷积的图与原图尺寸一致，激活函数relu()

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= scope.name)

池化层1 3x3最大池化，步长strides为2，池化后执行lrn()操作，局部响应归一化，对训练有利。

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')

卷积层2 16个3x3的卷积核（16通道），padding=’SAME’，表示padding后卷积的图与原图尺寸一致，激活函数relu()

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')

池化层2 3x3最大池化，步长strides为2，池化后执行lrn()操作，

#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')

全连接层3 128个神经元，将之前pool层的输出reshape成一行，激活函数relu()

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)

全连接层4 128个神经元，激活函数relu()

#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回归层 将前面的FC层输出，做一个线性回归，计算出每一类的得分，在这里是2类，所以这个层输出的是两个得分。

# 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

loss计算 将网络计算得出的每类得分与真实值进行比较，得出一个loss损失值，这个值代表了计算值与期望值的差距。这里使用的loss函数是交叉熵。一批loss取平均数。最后调用了summary.scalar()记录下这个标量数据，在TensorBoard中进行可视化。 函数：def losses(logits, labels):

传入参数：logits，网络计算输出值。labels，真实值，在这里是0或者1返回参数：loss，损失值 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

loss损失值优化 目的就是让loss越小越好，使用的是AdamOptimizer优化器 函数：def trainning(loss, learning_rate):

输入参数：loss。learning_rate，学习速率。返回参数：train_op，训练op，这个参数要输入sess.run中让模型去训练。 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

评价/准确率计算 计算出平均准确率来评价这个模型，在训练过程中按批次计算（每隔N步计算一次），可以看到准确率的变换情况。 函数：def evaluation(logits, labels):

输入参数：logits，网络计算值。labels，标签，也就是真实值，在这里是0或者1。返回参数：accuracy，当前step的平均准确率，也就是在这些batch中多少张图片被正确分类了。 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