from datetime
import datetime
import math
import time
import tensorflow
as tf
batch_size =
32
num_batches =
100
def print_activations(t):
print(t.op.name,
' ', t.get_shape().as_list())
def infrence(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, parameters
def 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 = infrence(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()
'''
conv1 [32, 56, 56, 64]
pool1 [32, 27, 27, 64]
conv2 [32, 27, 27, 192]
pool2 [32, 13, 13, 192]
conv3 [32, 13, 13, 384]
conv4 [32, 13, 13, 256]
conv5 [32, 13, 13, 256]
pool5 [32, 6, 6, 256]
2017-04-13 16:28:25.177867: step 0, duration = 0.059
2017-04-13 16:28:25.769441: step 10, duration = 0.059
2017-04-13 16:28:26.361012: step 20, duration = 0.059
2017-04-13 16:28:26.951583: step 30, duration = 0.059
2017-04-13 16:28:27.543156: step 40, duration = 0.059
2017-04-13 16:28:28.131719: step 50, duration = 0.059
2017-04-13 16:28:28.724311: step 60, duration = 0.059
2017-04-13 16:28:29.314866: step 70, duration = 0.059
2017-04-13 16:28:29.905437: step 80, duration = 0.059
2017-04-13 16:28:30.496006: step 90, duration = 0.059
2017-04-13 16:28:31.033435: Forward across 100 steps, 0.059 +/- 0.001 sec / batch
2017-04-13 16:28:33.830872: step 0, duration = 0.204
2017-04-13 16:28:35.877314: step 10, duration = 0.203
2017-04-13 16:28:37.911570: step 20, duration = 0.204
2017-04-13 16:28:39.954005: step 30, duration = 0.205
2017-04-13 16:28:41.987445: step 40, duration = 0.205
2017-04-13 16:28:44.026828: step 50, duration = 0.203
2017-04-13 16:28:46.063242: step 60, duration = 0.204
2017-04-13 16:28:48.103667: step 70, duration = 0.204
2017-04-13 16:28:50.141083: step 80, duration = 0.202
2017-04-13 16:28:52.172485: step 90, duration = 0.203
2017-04-13 16:28:54.002348: Forward-backward across 100 steps, 0.204 +/- 0.001 sec / batch
'''
