企业官网建设流程全解析

之前已经介绍过了AlexNet的网络构建了，这次主要不是为了训练数据，而是为了对每个batch的前馈（Forward）和反馈（backward）的平均耗时进行计算。在设计网络的过程中，分类的结果很重要，但是运算速率也相当重要。尤其是在跟踪（Tracking）
的任务中，如果使用的网络太深，那么也会导致实时性不好。

fromdatetimeimportdatetimeimportmathimporttimeimporttensorflowastf batch_size=32num_batches=100defprint_activations(t):print(t.op.name,'',t.get_shape().as_list())definference(images):parameters=[]withtf.name_scope('conv1')asscope:kernel=tf.Variable(tf.truncated_normal([11,11,3,64],dtype=tf.float32,stddev=1e-1),name='weights')conv=tf.nn.conv2d(images,kernel,[1,4,4,1],padding='SAME')biases=tf.Variable(tf.constant(0.0,shape=[64],dtype=tf.float32),trainable=True,name='biases')bias=tf.nn.bias_add(conv,biases)conv1=tf.nn.relu(bias,name=scope)print_activations(conv1)parameters+=[kernel,biases]lrn1=tf.nn.lrn(conv1,4,bias=1.0,alpha=0.001/9,beta=0.75,name='lrn1')pool1=tf.nn.max_pool(lrn1,ksize=[1,3,3,1],strides=[1,2,2,1],padding='VALID',name='pool1')print_activations(pool1)withtf.name_scope('conv2')asscope:kernel=tf.Variable(tf.truncated_normal([5,5,64,192],dtype=tf.float32,stddev=1e-1),name='weights')conv=tf.nn.conv2d(pool1,kernel,[1,1,1,1],padding='SAME')biases=tf.Variable(tf.constant(0.0,shape=[192],dtype=tf.float32),trainable=True,name='biases')bias=tf.nn.bias_add(conv,biases)conv2=tf.nn.relu(bias,name=scope)parameters+=[kernel,biases]print_activations(conv2)lrn2=tf.nn.lrn(conv2,4,bias=1.0,alpha=0.001/9,beta=0.75,name='lrn2')pool2=tf.nn.max_pool(lrn2,ksize=[1,3,3,1],strides=[1,2,2,1],padding='VALID',name='pool2')print_activations(pool2)withtf.name_scope('conv3')asscope:kernel=tf.Variable(tf.truncated_normal([3,3,192,384],dtype=tf.float32,stddev=1e-1),name='weights')conv=tf.nn.conv2d(pool2,kernel,[1,1,1,1],padding='SAME')biases=tf.Variable(tf.constant(0.0,shape=[384],dtype=tf.float32),trainable=True,name='biases')bias=tf.nn.bias_add(conv,biases)conv3=tf.nn.relu(bias,name=scope)parameters+=[kernel,biases]print_activations(conv3)withtf.name_scope('conv4')asscope:kernel=tf.Variable(tf.truncated_normal([3,3,384,256],dtype=tf.float32,stddev=1e-1),name='weights')conv=tf.nn.conv2d(conv3,kernel,[1,1,1,1],padding='SAME')biases=tf.Variable(tf.constant(0.0,shape=[256],dtype=tf.float32),trainable=True,name='biases')bias=tf.nn.bias_add(conv,biases)conv4=tf.nn.relu(bias,name=scope)parameters+=[kernel,biases]print_activations(conv4)withtf.name_scope('conv5')asscope:kernel=tf.Variable(tf.truncated_normal([3,3,256,256],dtype=tf.float32,stddev=1e-1),name='weights')conv=tf.nn.conv2d(conv4,kernel,[1,1,1,1],padding='SAME')biases=tf.Variable(tf.constant(0.0,shape=[256],dtype=tf.float32),trainable=True,name='biases')bias=tf.nn.bias_add(conv,biases)conv5=tf.nn.relu(bias,name=scope)parameters+=[kernel,biases]print_activations(conv5)pool5=tf.nn.max_pool(conv5,ksize=[1,3,3,1],strides=[1,2,2,1],padding='VALID',name='pool5')print_activations(pool5)returnpool5,parametersdeftime_tensorflow_run(session,target,info_string):num_steps_burn_in=10total_duration=0.0total_duration_squared=0.0foriinrange(num_batches+num_steps_burn_in):start_time=time.time()_=session.run(target)duration=time.time()-start_timeifi>=num_steps_burn_in:ifnoti%10:print('%s: step %d, duration = %.3f'%(datetime.now(),i-num_steps_burn_in,duration))total_duration+=duration total_duration_squared+=duration*duration mn=total_duration/num_batches vr=total_duration_squared/num_batches-mn*mn sd=math.sqrt(vr)print('%s: %s across %d steps, %.3f +/- %.3f sec / batch'%(datetime.now(),info_string,num_batches,mn,sd))defrun_benchmark():withtf.Graph().as_default():image_size=224images=tf.Variable(tf.random_normal([batch_size,image_size,image_size,3],dtype=tf.float32,stddev=1e-1))pool5,parameters=inference(images)init=tf.global_variables_initializer()sess=tf.Session()sess.run(init)time_tensorflow_run(sess,pool5,"Forward")objective=tf.nn.l2_loss(pool5)grad=tf.gradients(objective,parameters)time_tensorflow_run(sess,grad,"Forward-backward")run_benchmark()

这里的代码都是之前讲过的，只是加了一个计算时间和现实网络的卷积核的函数，应该很容易就看懂了，就不多赘述了。我在GTX TITAN X上前馈大概需要0.024s, 反馈大概需要0.079s。哈哈，自己动手试一试哦O(∩_∩)O