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from datetime import datetime
import math
import time
import tensorflow as tf
batch_size = 32
num_batches = 100
def print_activatitions(t):
print(t.op.name,' ',t.get_shape().as_list())
def inference(images):
parameters = [] #AlexNet中所有需要训练的模型，存入parameter
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_activatitions(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_activatitions(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_activatitions(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_activatitions(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_activatitions(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_activatitions(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_activatitions(conv5)
pool5 = tf.nn.max_pool(conv5,ksize=[1,3,3,1],strides=[1,2,2,1],padding='VALID',name='pool5')
print_activatitions(pool5)
return pool5, parameters
def time_tensorflow_run(session,target,info_string):
num_step_burn_in = 10 #10轮热身迭代
total_duration = 0.0
total_duration_squared = 0.0
for i in range(num_batches + num_step_burn_in):
start_time = time.time()
_ = session.run(target)
duration = time.time() - start_time
if i >= num_step_burn_in:
if not i%10:
print('%s: step %d, duration = %.3f' % (datetime.now(),i - num_step_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")
print(parameters[:])
run_benchmark()
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