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。
from datetime import datetimeimport mathimport timeimport tensorflow as tfbatch_size = 32num_batches = 100def print_activations(t): print(t.op.name, '', t.get_shape().as_list())def inference(images): parameters = [] with tf.name_scope('conv1') as scope: 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) with tf.name_scope('conv2') as scope: 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) with tf.name_scope('conv3') as scope: 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) with tf.name_scope('conv4') as scope: 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) with tf.name_scope('conv5') as scope: 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) return pool5, parametersdef time_tensorflow_run(session, target, info_string): num_steps_burn_in = 10 total_duration = 0.0 total_duration_squared = 0.0 for i in range(num_batches + num_steps_burn_in): start_time = time.time() _ = session.run(target) duration = time.time() - start_time if i >= num_steps_burn_in: if not i % 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))def run_benchmark(): with tf.Graph().as_default(): image_size = 224 images = 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。
