import tensorflow as tf
from tflearn.layers.conv import global_avg_pool
from tensorflow.contrib.layers import batch_norm, flatten
from tensorflow.contrib.framework import arg_scope
from cifar10 import *
import numpy as np
weight_decay = 0.0005
momentum = 0.9
init_learning_rate = 0.1
reduction_ratio = 4
batch_size = 128
iteration = 391
# 128 * 391 ~ 50,000
test_iteration = 10
total_epochs = 100
def conv_layer(input, filter, kernel, stride=1, padding='SAME', layer_name="conv", activation=True):
with tf.name_scope(layer_name):
network = tf.layers.conv2d(inputs=input, use_bias=True, filters=filter, kernel_size=kernel, strides=stride, padding=padding)
if activation :
network = Relu(network)
return network
def Fully_connected(x, units=class_num, layer_name='fully_connected') :
with tf.name_scope(layer_name) :
return tf.layers.dense(inputs=x, use_bias=True, units=units)
def Relu(x):
return tf.nn.relu(x)
def Sigmoid(x):
return tf.nn.sigmoid(x)
def Global_Average_Pooling(x):
return global_avg_pool(x, name='Global_avg_pooling')
def Max_pooling(x, pool_size=[3,3], stride=2, padding='VALID') :
return tf.layers.max_pooling2d(inputs=x, pool_size=pool_size, strides=stride, padding=padding)
def Batch_Normalization(x, training, scope):
with arg_scope([batch_norm],
scope=scope,
updates_collections=None,
decay=0.9,
center=True,
scale=True,
zero_debias_moving_mean=True) :
return tf.cond(training,
lambda : batch_norm(inputs=x, is_training=training, reuse=None),
lambda : batch_norm(inputs=x, is_training=training, reuse=True))
def Concatenation(layers) :
return tf.concat(layers, axis=3)
def Dropout(x, rate, training) :
return tf.layers.dropout(inputs=x, rate=rate, training=training)
def Evaluate(sess):
test_acc = 0.0
test_loss = 0.0
test_pre_index = 0
add = 1000
for it in range(test_iteration):
test_batch_x = test_x[test_pre_index: test_pre_index + add]
test_batch_y = test_y[test_pre_index: test_pre_index + add]
test_pre_index = test_pre_index + add
test_feed_dict = {
x: test_batch_x,
label: test_batch_y,
learning_rate: epoch_learning_rate,
training_flag: False
}
loss_, acc_ = sess.run([cost, accuracy], feed_dict=test_feed_dict)
test_loss += loss_
test_acc += acc_
test_loss /= test_iteration # average loss
test_acc /= test_iteration # average accuracy
summary = tf.Summary(value=[tf.Summary.Value(tag='test_loss', simple_value=test_loss),
tf.Summary.Value(tag='test_accuracy', simple_value=test_acc)])
return test_acc, test_loss, summary
class SE_Inception_resnet_v2():
def __init__(self, x, training):
self.training = training
self.model = self.Build_SEnet(x)
def Stem(self, x, scope):
with tf.name_scope(scope) :
x = conv_layer(x, filter=32, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_conv1')
x = conv_layer(x, filter=32, kernel=[3,3], padding='VALID', layer_name=scope+'_conv2')
block_1 = conv_layer(x, filter=64, kernel=[3,3], layer_name=scope+'_conv3')
split_max_x = Max_pooling(block_1)
split_conv_x = conv_layer(block_1, filter=96, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv1')
x = Concatenation([split_max_x,split_conv_x])
split_conv_x1 = conv_layer(x, filter=64, kernel=[1,1], layer_name=scope+'_split_conv2')
split_conv_x1 = conv_layer(split_conv_x1, filter=96, kernel=[3,3], padding='VALID', layer_name=scope+'_split_conv3')
split_conv_x2 = conv_layer(x, filter=64, kernel=[1,1], layer_name=scope+'_split_conv4')
split_conv_x2 = conv_layer(split_conv_x2, filter=64, kernel=[7,1], layer_name=scope+'_split_conv5')
split_conv_x2 = conv_layer(split_conv_x2, filter=64, kernel=[1,7], layer_name=scope+'_split_conv6')
split_conv_x2 = conv_layer(split_conv_x2, filter=96, kernel=[3,3], padding='VALID', layer_name=scope+'_split_conv7')
x = Concatenation([split_conv_x1,split_conv_x2])
split_conv_x = conv_layer(x, filter=192, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv8')
split_max_x = Max_pooling(x)
x = Concatenation([split_conv_x, split_max_x])
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
return x
def Inception_resnet_A(self, x, scope):
with tf.name_scope(scope) :
init = x
split_conv_x1 = conv_layer(x, filter=32, kernel=[1,1], layer_name=scope+'_split_conv1')
split_conv_x2 = conv_layer(x, filter=32, kernel=[1,1], layer_name=scope+'_split_conv2')
split_conv_x2 = conv_layer(split_conv_x2, filter=32, kernel=[3,3], layer_name=scope+'_split_conv3')
split_conv_x3 = conv_layer(x, filter=32, kernel=[1,1], layer_name=scope+'_split_conv4')
split_conv_x3 = conv_layer(split_conv_x3, filter=48, kernel=[3,3], layer_name=scope+'_split_conv5')
split_conv_x3 = conv_layer(split_conv_x3, filter=64, kernel=[3,3], layer_name=scope+'_split_conv6')
x = Concatenation([split_conv_x1,split_conv_x2,split_conv_x3])
x = conv_layer(x, filter=384, kernel=[1,1], layer_name=scope+'_final_conv1', activation=False)
x = x*0.1
x = init + x
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
return x
def Inception_resnet_B(self, x, scope):
with tf.name_scope(scope) :
init = x
split_conv_x1 = conv_layer(x, filter=192, kernel=[1,1], layer_name=scope+'_split_conv1')
split_conv_x2 = conv_layer(x, filter=128, kernel=[1,1], layer_name=scope+'_split_conv2')
split_conv_x2 = conv_layer(split_conv_x2, filter=160, kernel=[1,7], layer_name=scope+'_split_conv3')
split_conv_x2 = conv_layer(split_conv_x2, filter=192, kernel=[7,1], layer_name=scope+'_split_conv4')
x = Concatenation([split_conv_x1, split_conv_x2])
x = conv_layer(x, filter=1152, kernel=[1,1], layer_name=scope+'_final_conv1', activation=False)
# 1154
x = x * 0.1
x = init + x
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
return x
def Inception_resnet_C(self, x, scope):
with tf.name_scope(scope) :
init = x
split_conv_x1 = conv_layer(x, filter=192, kernel=[1,1], layer_name=scope+'_split_conv1')
split_conv_x2 = conv_layer(x, filter=192, kernel=[1, 1], layer_name=scope + '_split_conv2')
split_conv_x2 = conv_layer(split_conv_x2, filter=224, kernel=[1, 3], layer_name=scope + '_split_conv3')
split_conv_x2 = conv_layer(split_conv_x2, filter=256, kernel=[3, 1], layer_name=scope + '_split_conv4')
x = Concatenation([split_conv_x1,split_conv_x2])
x = conv_layer(x, filter=2144, kernel=[1,1], layer_name=scope+'_final_conv2', activation=False)
# 2048
x = x * 0.1
x = init + x
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
return x
def Reduction_A(self, x, scope):
with tf.name_scope(scope) :
k = 256
l = 256
m = 384
n = 384
split_max_x = Max_pooling(x)
split_conv_x1 = conv_layer(x, filter=n, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv1')
split_conv_x2 = conv_layer(x, filter=k, kernel=[1,1], layer_name=scope+'_split_conv2')
split_conv_x2 = conv_layer(split_conv_x2, filter=l, kernel=[3,3], layer_name=scope+'_split_conv3')
split_conv_x2 = conv_layer(split_conv_x2, filter=m, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv4')
x = Concatenation([split_max_x, split_conv_x1, split_conv_x2])
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
return x
def Reduction_B(self, x, scope):
with tf.name_scope(scope) :
split_max_x = Max_pooling(x)
split_conv_x1 = conv_layer(x, filter=256, kernel=[1,1], layer_name=scope+'_split_conv1')
split_conv_x1 = conv_layer(split_conv_x1, filter=384, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv2')
split_conv_x2 = conv_layer(x, filter=256, kernel=[1,1], layer_name=scope+'_split_conv3')
split_conv_x2 = conv_layer(split_conv_x2, filter=288, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv4')
split_conv_x3 = conv_layer(x, filter=256, kernel=[1,1], layer_name=scope+'_split_conv5')
split_conv_x3 = conv_layer(split_conv_x3, filter=288, kernel=[3,3], layer_name=scope+'_split_conv6')
split_conv_x3 = conv_layer(split_conv_x3, filter=320, kernel=[3,3], stride=2, padding='VALID', layer_name=scope+'_split_conv7')
x = Concatenation([split_max_x, split_conv_x1, split_conv_x2, split_conv_x3])
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
return x
def Squeeze_excitation_layer(self, input_x, out_dim, ratio, layer_name):
with tf.name_scope(layer_name) :
squeeze = Global_Average_Pooling(input_x)
excitation = Fully_connected(squeeze, units=out_dim / ratio, layer_name=layer_name+'_fully_connected1')
excitation = Relu(excitation)
excitation = Fully_connected(excitation, units=out_dim, layer_name=layer_name+'_fully_connected2')
excitation = Sigmoid(excitation)
excitation = tf.reshape(excitation, [-1,1,1,out_dim])
scale = input_x * excitation
return scale
def Build_SEnet(self, input_x):
input_x = tf.pad(input_x, [[0, 0], [32, 32], [32, 32], [0, 0]])
# size 32 -> 96
print(np.shape(input_x))
# only cifar10 architecture
x = self.Stem(input_x, scope='stem')
for i in range(5) :
x = self.Inception_resnet_A(x, scope='Inception_A'+str(i))
channel = int(np.shape(x)[-1])
x = self.Squeeze_excitation_layer(x, out_dim=channel, ratio=reduction_ratio, layer_name='SE_A'+str(i))
x = self.Reduction_A(x, scope='Reduction_A')
channel = int(np.shape(x)[-1])
x = self.Squeeze_excitation_layer(x, out_dim=channel, ratio=reduction_ratio, layer_name='SE_A')
for i in range(10) :
x = self.Inception_resnet_B(x, scope='Inception_B'+str(i))
channel = int(np.shape(x)[-1])
x = self.Squeeze_excitation_layer(x, out_dim=channel, ratio=reduction_ratio, layer_name='SE_B'+str(i))
x = self.Reduction_B(x, scope='Reduction_B')
channel = int(np.shape(x)[-1])
x = self.Squeeze_excitation_layer(x, out_dim=channel, ratio=reduction_ratio, layer_name='SE_B')
for i in range(5) :
x = self.Inception_resnet_C(x, scope='Inception_C'+str(i))
channel = int(np.shape(x)[-1])
x = self.Squeeze_excitation_layer(x, out_dim=channel, ratio=reduction_ratio, layer_name='SE_C'+str(i))
# channel = int(np.shape(x)[-1])
# x = self.Squeeze_excitation_layer(x, out_dim=channel, ratio=reduction_ratio, layer_name='SE_C')
x = Global_Average_Pooling(x)
x = Dropout(x, rate=0.2, training=self.training)
x = flatten(x)
x = Fully_connected(x, layer_name='final_fully_connected')
return x
train_x, train_y, test_x, test_y = prepare_data()
train_x, test_x = color_preprocessing(train_x, test_x)
# image_size = 32, img_channels = 3, class_num = 10 in cifar10
x = tf.placeholder(tf.float32, shape=[None, image_size, image_size, img_channels])
label = tf.placeholder(tf.float32, shape=[None, class_num])
training_flag = tf.placeholder(tf.bool)
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
logits = SE_Inception_resnet_v2(x, training=training_flag).model
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=label, logits=logits))
l2_loss = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum, use_nesterov=True)
train = optimizer.minimize(cost + l2_loss * weight_decay)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(label, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state('./model')
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
saver.restore(sess, ckpt.model_checkpoint_path)
else:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter('./logs', sess.graph)
epoch_learning_rate = init_learning_rate
for epoch in range(1, total_epochs + 1):
if epoch % 30 == 0 :
epoch_learning_rate = epoch_learning_rate / 10
pre_index = 0
train_acc = 0.0
train_loss = 0.0
for step in range(1, iteration + 1):
if pre_index + batch_size < 50000:
batch_x = train_x[pre_index: pre_index + batch_size]
batch_y = train_y[pre_index: pre_index + batch_size]
else:
batch_x = train_x[pre_index:]
batch_y = train_y[pre_index:]
batch_x = data_augmentation(batch_x)
train_feed_dict = {
x: batch_x,
label: batch_y,
learning_rate: epoch_learning_rate,
training_flag: True
}
_, batch_loss = sess.run([train, cost], feed_dict=train_feed_dict)
batch_acc = accuracy.eval(feed_dict=train_feed_dict)
train_loss += batch_loss
train_acc += batch_acc
pre_index += batch_size
train_loss /= iteration # average loss
train_acc /= iteration # average accuracy
train_summary = tf.Summary(value=[tf.Summary.Value(tag='train_loss', simple_value=train_loss),
tf.Summary.Value(tag='train_accuracy', simple_value=train_acc)])
test_acc, test_loss, test_summary = Evaluate(sess)
summary_writer.add_summary(summary=train_summary, global_step=epoch)
summary_writer.add_summary(summary=test_summary, global_step=epoch)
summary_writer.flush()
line = "epoch: %d/%d, train_loss: %.4f, train_acc: %.4f, test_loss: %.4f, test_acc: %.4f \n" % (
epoch, total_epochs, train_loss, train_acc, test_loss, test_acc)
print(line)
with open('logs.txt', 'a') as f:
f.write(line)
saver.save(sess=sess, save_path='./model/Inception_resnet_v2.ckpt')
