5.3tensorflow 진급을 실현하는 권적 신경 네트워크

#  CIFAR10   
#git clone https://github.com/tensorflow/models.git
#cd models/tutorials/images/cifar10

import cifar10,cifar10_input
import tensorflow as tf
import numpy as np
import time
max_steps = 3000 #    
batch_size = 128
data_dir = 'tmp/cifar10_data/cifar-10-batches-bin'

#   w
def variable_with_weight_loss(shape, stddev, wl):
    var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))
    if wl is not None:
        weight_loss = tf.multiply(tf.nn.l2_loss(var),wl,name='weight_loss')
        tf.add_to_collection('losses', weight_loss)
    return var

#     
cifar10.maybe_download_and_extract()

#             ，           
images_train, labels_train = cifar10_input.distorted_inputs(data_dir=data_dir, batch_size=batch_size)
images_test, labels_test = cifar10_input.inputs(eval_data=True, data_dir=data_dir, batch_size=batch_size)

#     placeholder
image_holder = tf.placeholder(tf.float32, [batch_size,24,24,3])
label_holder = tf.placeholder(tf.int32, [batch_size])

#      
weight1 = variable_with_weight_loss(shape=[5,5,3,64], stddev=5e-2, wl=0.0)
kernel1 = tf.nn.conv2d(image_holder, weight1, [1,1,1,1], padding='SAME')
bias1 = tf.Variable(tf.constant(0.0, shape=[64]))
conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bias1))
pool1 = tf.nn.max_pool(conv1, ksize=[1,3,3,1], strides=[1,2,2,1], padding='SAME')
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)

#      
weight2 = variable_with_weight_loss(shape=[5,5,64,64], stddev=5e-2, wl=0.0)
kernel2 = tf.nn.conv2d(norm1, weight2, [1,1,1,1], padding='SAME')
bias2 = tf.Variable(tf.constant(0.1, shape=[64]))
conv2 = tf.nn.relu(tf.nn.bias_add(kernel2, bias2))
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)
pool2 = tf.nn.max_pool(norm2, ksize=[1,3,3,1], strides=[1,2,2,1], padding='SAME')

#    
reshape = tf.reshape(pool2, [batch_size, -1])
dim = reshape.get_shape()[1],value
weight3 = variable_with_weight_loss(shape=[dim,384], stddev=0.04, wl=0.004)
bias3 = tf.Variable(tf.constant(0.1, shape=[384]))
local3 = tf.nn.relu(tf.matmul(reshape, weight3) + bias3)

#    
weight4 = variable_with_weight_loss(shape=[384,192], stddev=0.04, wl=0.004)
bias4 = tf.Variable(tf.constant(0.1, shape=[192]))
local4 = tf.nn.relu(tf.matmul(local3, weight4) + bias4)

#    
weight5 = variable_with_weight_loss(shape=[192,10], stddev=1/192.0, wl=0.0)
bias5 = tf.Variable(tf.constant(0.0, shape=[10]))
logits = tf.add(tf.matmul(local4, weight5), bias5)

#    
def loss(logits, labels):
    labels = tf.cast(labels, tf.int64)
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='cross_entropy_per_example')
    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
    tf.add_to_collection('losses', cross_entropy_mean)
    return tf.add_n(tf.get_collection('losses'), name='total_loss')

loss = loss(logits, label_holder)

#   
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)

#top k   
top_k_op = tf.nn.in_top_k(logits, label_holder, 1)

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
tf.train.start_queue_runners()

#    
for step in range(max_steps):
    start_time = time.time()
    image_batch, label_batch = sess.run([image_train, label_train])
    _, loss_value = sess.run([train_op, loss], feed_dict={image_holder:image_batch, label_holder:label_batch})
    duration = time.time() - start_time
    if step%10 == 0:
        examples_per_sec = batch_size / duration
        sec_per_batch = float(duration)
        
        format_str = ('step %d,loss=%.2f (%.1f examples/sec; %.3f sec/batch)')
        print(format_str % (step, loss_value, examples_per_sec, sec_per_batch))
        
#     
num_examples = 10000
import math
num_iter = int(math.ceil(num_examples / batch_size))
true_count = 0
total_sample_count = num_iter * batch_size
step = 0
while step < num_iter:
    image_batch, label_batch = sess.run([images_test, labels_test])
    predictions = sess.run([top_k_op], feed_dict={image_holder:image_batch, label_holder:label_batch})
    
    true_count += np.sum(predictions)
    step += 1


precision = true_count / total_sample_count
print('precision @1 = %.3f' % precision)