目錄
在下面的結構圖中，每一個inception模塊中都有一個1∗1的沒有激活層的卷積層，用來擴展通道數，從而補償因為inception模塊導致的維度約間。其中Inception-ResNet-V1的結果與Inception v3相當；Inception-ResNet-V1與Inception v4結果差不多，不過實際過程中Inception v4會明顯慢於Inception-ResNet-v2，這也許是因為層數太多了。且在Inception-ResNet結構中，只在傳統層的上面使用BN層，而不在合並層上使用BN，雖然處處使用BN是有好處，不過更希望能夠將一個完整的組件放入單獨的GPU中。因為具有大量激活單元的層會占用過多的顯存，所以希望這些地方丟棄BN，從而總體增加Inception模塊的數量。使得不需要去解決計算資源和模塊什么的權衡問題。
1. inception v4
**圖1.1 inception v4 網絡結構圖**
**圖1.2 圖1.1的stem和Inception-A部分結構圖**
**圖1.3 圖1.1的Reduction-A和Inception-B部分結構圖**
**圖1.4 圖1.1的Reduction-B和Inception-C部分結構圖**
2. Inception-resnet-v1 & Inception-resnet-v2
**圖2.1 Inception-resnet-v1 & Inception-resnet-v2的結構圖**
2.1 Inception-resnet-v1的組成模塊
**圖2.1.1 圖2.1的stem和Inception-ResNet-A部分結構圖**
**圖2.1.2 圖2.1的Reduction-A和Inception-ResNet-B部分結構圖**
**圖2.1.3 圖2.1的Reduction-B和Inception-ResNet-C部分結構圖**
2.2 Inception-resnet-v2的組成模塊
**圖2.2.1 圖2.1的stem和Inception-ResNet-A部分結構圖**
**圖2.2.2 圖2.1的Reduction-A和Inception-ResNet-B部分結構圖**
**圖2.2.3 圖2.1的Reduction-B和Inception-ResNet-C部分結構圖**
3. 模型訓練
在上述的Inception V4，Inception-Resnet-V1，Inception-ResNet-v2這三個模型中都用到了Reduction-A，他們各自的具體參數如下：
圖3.1 不同模型下Reduction-A的模型超參數
作者們在訓練的過程中發現，如果通道數超過1000，那么Inception-resnet等網絡都會開始變得不穩定，並且過早的就“死掉了”，即在迭代幾萬次之后，平均池化的前面一層就會生成很多的0值。作者們通過調低學習率，增加BN都沒有任何改善。
不過他們發現如果在將殘差匯入之前，對殘差進行縮小，可以讓模型穩定訓練，值通常選擇[0,1.0.3]，如圖3.2
**圖3.2 對inception-resnet模塊進行最后輸出值的等比例縮小**
同樣的在ResNet-v1中，何凱明等人也在cifar-10中發現了模型的不穩定現象：即在特別深的網絡基礎上去訓cifar-10，需要先以0.01的學習率去訓練，然后在以0.1的學習率訓練。
不過這里的作者們認為如果通道數特別多的話，即使以特別低的學習率（0.00001）訓練也無法讓模型收斂，如果之后再用大學習率，那么就會輕松的破壞掉之前的成果。然而簡單的縮小殘差的輸出值有助於學習的穩定，即使進行了簡單的縮小，那么對最終結果也造成不了多大的損失，反而有助於穩定訓練。
- 在inception-resnet-v1與inception v3的對比中，inception-resnet-v1雖然訓練速度更快，不過最后結果有那么一丟丟的差於inception v3；
- 而在inception-resnet-v2與inception v4的對比中，inception-resnet-v2的訓練速度更塊，而且結果比inception v4也更好一點。所以最后勝出的就是inception-resnet-v2。
**圖3.3 不同模型的結果對比**
4. 代碼
4.1 Inception-V4
from keras.layers import Input
from keras.layers.merge import concatenate
from keras.layers import Dense, Dropout, Flatten, Activation, Conv2D
from keras.layers.convolutional import MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras import backend as K
from keras.models import Model
from keras.utils import plot_model
CONV_BLOCK_COUNT = 0 # 用來命名計數卷積編號
INCEPTION_A_COUNT = 0
INCEPTION_B_COUNT = 0
INCEPTION_C_COUNT = 0
def conv_block(x, nb_filters, nb_row, nb_col, strides=(1, 1), padding='same', use_bias=False):
global CONV_BLOCK_COUNT
CONV_BLOCK_COUNT += 1
with K.name_scope('conv_block_'+str(CONV_BLOCK_COUNT)):
x = Conv2D(filters=nb_filters,
kernel_size=(nb_row, nb_col),
strides=strides,
padding=padding,
use_bias=use_bias)(x)
x = BatchNormalization(axis=-1, momentum=0.9997, scale=False)(x)
x = Activation("relu")(x)
return x
def stem(x_input):
with K.name_scope('stem'):
x = conv_block(x_input, 32, 3, 3, strides=(2, 2), padding='valid')
x = conv_block(x, 32, 3, 3, padding='valid')
x = conv_block(x, 64, 3, 3)
x1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x)
x2 = conv_block(x, 96, 3, 3, strides=(2, 2), padding='valid')
x = concatenate([x1, x2], axis=-1)
x1 = conv_block(x, 64, 1, 1)
x1 = conv_block(x1, 96, 3, 3, padding='valid')
x2 = conv_block(x, 64, 1, 1)
x2 = conv_block(x2, 64, 7, 1)
x2 = conv_block(x2, 64, 1, 7)
x2 = conv_block(x2, 96, 3, 3, padding='valid')
x = concatenate([x1, x2], axis=-1)
x1 = conv_block(x, 192, 3, 3, strides=(2, 2), padding='valid')
x2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x)
merged_vector = concatenate([x1, x2], axis=-1)
return merged_vector
def inception_A(x_input):
"""35*35 卷積塊"""
global INCEPTION_A_COUNT
INCEPTION_A_COUNT += 1
with K.name_scope('inception_A' + str(INCEPTION_A_COUNT)):
averagepooling_conv1x1 = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x_input) # 35 * 35 * 192
averagepooling_conv1x1 = conv_block(averagepooling_conv1x1, 96, 1, 1) # 35 * 35 * 96
conv1x1 = conv_block(x_input, 96, 1, 1) # 35 * 35 * 96
conv1x1_3x3 = conv_block(x_input, 64, 1, 1) # 35 * 35 * 64
conv1x1_3x3 = conv_block(conv1x1_3x3, 96, 3, 3) # 35 * 35 * 96
conv3x3_3x3 = conv_block(x_input, 64, 1, 1) # 35 * 35 * 64
conv3x3_3x3 = conv_block(conv3x3_3x3, 96, 3, 3) # 35 * 35 * 96
conv3x3_3x3 = conv_block(conv3x3_3x3, 96, 3, 3) # 35 * 35 * 96
merged_vector = concatenate([averagepooling_conv1x1, conv1x1, conv1x1_3x3, conv3x3_3x3], axis=-1) # 35 * 35 * 384
return merged_vector
def inception_B(x_input):
"""17*17 卷積塊"""
global INCEPTION_B_COUNT
INCEPTION_B_COUNT += 1
with K.name_scope('inception_B' + str(INCEPTION_B_COUNT)):
averagepooling_conv1x1 = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x_input)
averagepooling_conv1x1 = conv_block(averagepooling_conv1x1, 128, 1, 1)
conv1x1 = conv_block(x_input, 384, 1, 1)
conv1x7_1x7 = conv_block(x_input, 192, 1, 1)
conv1x7_1x7 = conv_block(conv1x7_1x7, 224, 1, 7)
conv1x7_1x7 = conv_block(conv1x7_1x7, 256, 1, 7)
conv2_1x7_7x1 = conv_block(x_input, 192, 1, 1)
conv2_1x7_7x1 = conv_block(conv2_1x7_7x1, 192, 1, 7)
conv2_1x7_7x1 = conv_block(conv2_1x7_7x1, 224, 7, 1)
conv2_1x7_7x1 = conv_block(conv2_1x7_7x1, 224, 1, 7)
conv2_1x7_7x1 = conv_block(conv2_1x7_7x1, 256, 7, 1)
merged_vector = concatenate([averagepooling_conv1x1, conv1x1, conv1x7_1x7, conv2_1x7_7x1], axis=-1)
return merged_vector
def inception_C(x_input):
"""8*8 卷積塊"""
global INCEPTION_C_COUNT
INCEPTION_C_COUNT += 1
with K.name_scope('Inception_C' + str(INCEPTION_C_COUNT)):
averagepooling_conv1x1 = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x_input)
averagepooling_conv1x1 = conv_block(averagepooling_conv1x1, 256, 1, 1)
conv1x1 = conv_block(x_input, 256, 1, 1)
# 用 1x3 和 3x1 替代 3x3
conv3x3_1x1 = conv_block(x_input, 384, 1, 1)
conv3x3_1 = conv_block(conv3x3_1x1, 256, 1, 3)
conv3x3_2 = conv_block(conv3x3_1x1, 256, 3, 1)
conv2_3x3_1x1 = conv_block(x_input, 384, 1, 1)
conv2_3x3_1x1 = conv_block(conv2_3x3_1x1, 448, 1, 3)
conv2_3x3_1x1 = conv_block(conv2_3x3_1x1, 512, 3, 1)
conv2_3x3_1x1_1 = conv_block(conv2_3x3_1x1, 256, 3, 1)
conv2_3x3_1x1_2 = conv_block(conv2_3x3_1x1, 256, 1, 3)
merged_vector = concatenate([averagepooling_conv1x1, conv1x1, conv3x3_1, conv3x3_2, conv2_3x3_1x1_1, conv2_3x3_1x1_2], axis=-1)
return merged_vector
def reduction_A(x_input, k=192, l=224, m=256, n=384):
with K.name_scope('Reduction_A'):
"""Architecture of a 35 * 35 to 17 * 17 Reduction_A block."""
maxpool = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x_input)
conv3x3 = conv_block(x_input, n, 3, 3, strides=(2, 2), padding='valid')
conv2_3x3 = conv_block(x_input, k, 1, 1)
conv2_3x3 = conv_block(conv2_3x3, l, 3, 3)
conv2_3x3 = conv_block(conv2_3x3, m, 3, 3, strides=(2, 2), padding='valid')
merged_vector = concatenate([maxpool, conv3x3, conv2_3x3], axis=-1)
return merged_vector
def reduction_B(x_input):
"""Architecture of a 17 * 17 to 8 * 8 Reduction_B block."""
with K.name_scope('Reduction_B'):
maxpool = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x_input)
conv3x3 = conv_block(x_input, 192, 1, 1)
conv3x3 = conv_block(conv3x3, 192, 3, 3, strides=(2, 2), padding='valid')
conv1x7_7x1_3x3 = conv_block(x_input, 256, 1, 1)
conv1x7_7x1_3x3 = conv_block(conv1x7_7x1_3x3, 256, 1, 7)
conv1x7_7x1_3x3 = conv_block(conv1x7_7x1_3x3, 320, 7, 1)
conv1x7_7x1_3x3 = conv_block(conv1x7_7x1_3x3, 320, 3, 3, strides=(2, 2), padding='valid')
merged_vector = concatenate([maxpool, conv3x3, conv1x7_7x1_3x3], axis=-1)
return merged_vector
def inception_v4_backbone(nb_classes=1000, load_weights=True):
x_input = Input(shape=(299, 299, 3))
# Stem
x = stem(x_input) # 35 x 35 x 384
# 4 x Inception_A
for i in range(4):
x = inception_A(x) # 35 x 35 x 384
# Reduction_A
x = reduction_A(x, k=192, l=224, m=256, n=384) # 17 x 17 x 1024
# 7 x Inception_B
for i in range(7):
x = inception_B(x) # 17 x 17 x1024
# Reduction_B
x = reduction_B(x) # 8 x 8 x 1536
# Average Pooling
x = AveragePooling2D(pool_size=(8, 8))(x) # 1536
# dropout
x = Dropout(0.2)(x)
x = Flatten()(x) # 1536
# 全連接層
x = Dense(units=nb_classes, activation='softmax')(x)
model = Model(inputs=x_input, outputs=x, name='Inception-V4')
return model
if __name__ == '__main__':
inception_v4 = inception_v4_backbone()
plot_model(inception_v4, 'inception_v4.png', show_shapes=True)
4.2 inception_resnet_v1
from keras.layers import Input
from keras.layers.merge import concatenate, add
from keras.layers import Dense, Dropout, Lambda, Flatten, Activation, Conv2D
from keras.layers.convolutional import MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras import backend as K
from keras.utils import plot_model
RESNET_V1_A_COUNT = 0
RESNET_V1_B_COUNT = 0
RESNET_V1_C_COUNT = 0
def resnet_v1_stem(x_input):
with K.name_scope('Stem'):
x = Conv2D(filters=32, kernel_size=(3, 3), strides=(2, 2), activation='relu', padding='valid')(x_input)
x = Conv2D(32, (3, 3), activation='relu', padding='valid')(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D(pool_size=(3, 3), strides=2, padding='valid')(x)
x = Conv2D(80, (1, 1), activation='relu', padding='same')(x)
x = Conv2D(192, (3, 3), activation='relu', padding='valid')(x)
x = Conv2D(256, (3, 3), strides=(2, 2), activation='relu', padding='valid')(x)
x = BatchNormalization(axis=-1)(x)
x = Activation('relu')(x)
return x
def inception_resnet_v1_A(x_input, scale_residual=True):
""" 35x35 卷積核"""
global RESNET_V1_A_COUNT
RESNET_V1_A_COUNT += 1
with K.name_scope('resnet_v1_A' + str(RESNET_V1_A_COUNT)):
ar1 = Conv2D(32, (1, 1), activation='relu', padding='same')(x_input)
ar2 = Conv2D(32, (1, 1), activation='relu', padding='same')(x_input)
ar2 = Conv2D(32, (3, 3), activation='relu', padding='same')(ar2)
ar3 = Conv2D(32, (1, 1), activation='relu', padding='same')(x_input)
ar3 = Conv2D(32, (3, 3), activation='relu', padding='same')(ar3)
ar3 = Conv2D(32, (3, 3), activation='relu', padding='same')(ar3)
merged_vector = concatenate([ar1, ar2, ar3], axis=-1)
ar = Conv2D(256, (1, 1), activation='linear', padding='same')(merged_vector)
if scale_residual: # 是否縮小
ar = Lambda(lambda x: 0.1*x)(ar)
x = add([x_input, ar])
x = BatchNormalization(axis=-1)(x)
x = Activation('relu')(x)
return x
def inception_resnet_v1_B(x_input, scale_residual=True):
""" 17x17 卷積核"""
global RESNET_V1_B_COUNT
RESNET_V1_B_COUNT += 1
with K.name_scope('resnet_v1_B' + str(RESNET_V1_B_COUNT)):
br1 = Conv2D(128, (1, 1), activation='relu', padding='same')(x_input)
br2 = Conv2D(128, (1, 1), activation='relu', padding='same')(x_input)
br2 = Conv2D(128, (1, 7), activation='relu', padding='same')(br2)
br2 = Conv2D(128, (7, 1), activation='relu', padding='same')(br2)
merged_vector = concatenate([br1, br2], axis=-1)
br = Conv2D(896, (1, 1), activation='linear', padding='same')(merged_vector)
if scale_residual:
br = Lambda(lambda x: 0.1*x)(br)
x = add([x_input, br])
x = BatchNormalization(axis=-1)(x)
x = Activation('relu')(x)
return x
def inception_resnet_v1_C(x_input, scale_residual=True):
global RESNET_V1_C_COUNT
RESNET_V1_C_COUNT += 1
with K.name_scope('resnet_v1_C' + str(RESNET_V1_C_COUNT)):
cr1 = Conv2D(192, (1, 1), activation='relu', padding='same')(x_input)
cr2 = Conv2D(192, (1, 1), activation='relu', padding='same')(x_input)
cr2 = Conv2D(192, (1, 3), activation='relu', padding='same')(cr2)
cr2 = Conv2D(192, (3, 1), activation='relu', padding='same')(cr2)
merged_vector = concatenate([cr1, cr2], axis=-1)
cr = Conv2D(1792, (1, 1), activation='relu', padding='same')(merged_vector)
if scale_residual:
cr = Lambda(lambda x: 0.1*x)
x = add([x_input, cr])
x = BatchNormalization(axis=-1)(x)
x = Activation('relu')(x)
return x
def reduction_resnet_A(x_input, k=192, l=224, m=256, n=384):
with K.name_scope('reduction_resnet_A'):
ra1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid')(x_input)
ra2 = Conv2D(n, (3, 3), activation='relu', strides=(2, 2), padding='valid')(x_input)
ra3 = Conv2D(k, (1, 1), activation='relu', padding='same')(x_input)
ra3 = Conv2D(l, (3, 3), activation='relu', padding='same')(ra3)
ra3 = Conv2D(m, (3, 3), activation='relu', strides=(2, 2), padding='valid')(ra3)
merged_vector = concatenate([ra1, ra2, ra3], axis=-1)
x = BatchNormalization(axis=-1)(merged_vector)
x = Activation('relu')(x)
return x
def reduction_resnet_B(x_input):
with K.name_scope('reduction_resnet_B'):
rb1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2),padding='valid')(x_input)
rb2 = Conv2D(256, (1, 1), activation='relu', padding='same')(x_input)
rb2 = Conv2D(384, (3, 3), strides=(2, 2), activation='relu', padding='valid')(rb2)
rb3 = Conv2D(256, (1, 1),activation='relu', padding='same')(x_input)
rb3 = Conv2D(256, (3, 3), strides=(2, 2), activation='relu', padding='valid')(rb3)
rb4 = Conv2D(256, (1, 1), activation='relu', padding='same')(x_input)
rb4 = Conv2D(256, (3, 3), activation='relu', padding='same')(rb4)
rb4 = Conv2D(256, (3, 3), strides=(2, 2), activation='relu', padding='valid')(rb4)
merged_vector = concatenate([rb1, rb2, rb3, rb4], axis=-1)
x = BatchNormalization(axis=-1)(merged_vector)
x = Activation('relu')(x)
return x
def inception_resnet_v1_backbone(nb_classes=1000, scale=True):
x_input = Input(shape=(299, 299, 3))
# stem
x = resnet_v1_stem(x_input)
# 5 x inception_resnet_v1_A
for i in range(5):
x = inception_resnet_v1_A(x, scale_residual=False)
# reduction_resnet_A
x = reduction_resnet_A(x, k=192, l=192, m=256, n=384)
# 10 x inception_resnet_v1_B
for i in range(10):
x = inception_resnet_v1_B(x, scale_residual=True)
# Reduction B
x = reduction_resnet_B(x)
# 5 x Inception C
for i in range(5):
x = inception_resnet_v1_C(x, scale_residual=True)
# Average Pooling
x = AveragePooling2D(pool_size=(8, 8))(x)
# dropout
x = Dropout(0.2)(x)
x = Flatten()(x)
x = Dense(units=nb_classes, activation='softmax')(x)
return Model(inputs=x_input, outputs=x, name='Inception-Resnet-v1')
if __name__ == '__main__':
inception_resnet_v1_model = inception_resnet_v1_backbone()
plot_model(inception_resnet_v1_model, to_file='inception_resnet_v1.png', show_shapes=True)
4.3 inception_resnet_v2
from keras.layers import Input, add
from keras.layers.merge import concatenate
from keras.layers import Dense, Dropout, Lambda, Flatten, Activation, Conv2D
from keras.layers.convolutional import MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from inception_resnet_v1 import reduction_resnet_A
from keras.utils import plot_model
import keras.backend as K
RESNET_V2_A_COUNT = 0
RESNET_V2_B_COUNT = 0
RESNET_V2_C_COUNT = 0
def resnet_v2_stem(x_input):
'''The stem of the pure Inception-v4 and Inception-ResNet-v2 networks. This is input part of those networks.'''
# Input shape is 299 * 299 * 3 (Tensorflow dimension ordering)
with K.name_scope("stem"):
x = Conv2D(32, (3, 3), activation="relu", strides=(2, 2))(x_input) # 149 * 149 * 32
x = Conv2D(32, (3, 3), activation="relu")(x) # 147 * 147 * 32
x = Conv2D(64, (3, 3), activation="relu", padding="same")(x) # 147 * 147 * 64
x1 = MaxPooling2D((3, 3), strides=(2, 2))(x)
x2 = Conv2D(96, (3, 3), activation="relu", strides=(2, 2))(x)
x = concatenate([x1, x2], axis=-1) # 73 * 73 * 160
x1 = Conv2D(64, (1, 1), activation="relu", padding="same")(x)
x1 = Conv2D(96, (3, 3), activation="relu")(x1)
x2 = Conv2D(64, (1, 1), activation="relu", padding="same")(x)
x2 = Conv2D(64, (7, 1), activation="relu", padding="same")(x2)
x2 = Conv2D(64, (1, 7), activation="relu", padding="same")(x2)
x2 = Conv2D(96, (3, 3), activation="relu", padding="valid")(x2)
x = concatenate([x1, x2], axis=-1) # 71 * 71 * 192
x1 = Conv2D(192, (3, 3), activation="relu", strides=(2, 2))(x)
x2 = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = concatenate([x1, x2], axis=-1) # 35 * 35 * 384
x = BatchNormalization(axis=-1)(x)
x = Activation("relu")(x)
return x
def inception_resnet_v2_A(x_input, scale_residual=True):
'''Architecture of Inception_ResNet_A block which is a 35 * 35 grid module.'''
global RESNET_V2_A_COUNT
RESNET_V2_A_COUNT += 1
with K.name_scope('inception_resnet_v2_A' + str(RESNET_V2_A_COUNT)):
ar1 = Conv2D(32, (1, 1), activation="relu", padding="same")(x_input)
ar2 = Conv2D(32, (1, 1), activation="relu", padding="same")(x_input)
ar2 = Conv2D(32, (3, 3), activation="relu", padding="same")(ar2)
ar3 = Conv2D(32, (1, 1), activation="relu", padding="same")(x_input)
ar3 = Conv2D(48, (3, 3), activation="relu", padding="same")(ar3)
ar3 = Conv2D(64, (3, 3), activation="relu", padding="same")(ar3)
merged = concatenate([ar1, ar2, ar3], axis=-1)
ar = Conv2D(384, (1, 1), activation="linear", padding="same")(merged)
if scale_residual: ar = Lambda(lambda a: a * 0.1)(ar)
x = add([x_input, ar])
x = BatchNormalization(axis=-1)(x)
x = Activation("relu")(x)
return x
def inception_resnet_v2_B(x_input, scale_residual=True):
'''Architecture of Inception_ResNet_B block which is a 17 * 17 grid module.'''
global RESNET_V2_B_COUNT
RESNET_V2_B_COUNT += 1
with K.name_scope('inception_resnet_v2_B' + str(RESNET_V2_B_COUNT)):
br1 = Conv2D(192, (1, 1), activation="relu", padding="same")(x_input)
br2 = Conv2D(128, (1, 1), activation="relu", padding="same")(x_input)
br2 = Conv2D(160, (1, 7), activation="relu", padding="same")(br2)
br2 = Conv2D(192, (7, 1), activation="relu", padding="same")(br2)
merged = concatenate([br1, br2], axis=-1)
br = Conv2D(1152, (1, 1), activation="linear", padding="same")(merged)
if scale_residual: br = Lambda(lambda b: b * 0.1)(br)
x = add([x_input, br])
x = BatchNormalization(axis=-1)(x)
x = Activation("relu")(x)
return x
def inception_resnet_v2_C(x_input, scale_residual=True):
'''Architecture of Inception_ResNet_C block which is a 8 * 8 grid module.'''
global RESNET_V2_C_COUNT
RESNET_V2_C_COUNT += 1
with K.name_scope('inception_resnet_v2_C' + str(RESNET_V2_C_COUNT)):
cr1 = Conv2D(192, (1, 1), activation="relu", padding="same")(x_input)
cr2 = Conv2D(192, (1, 1), activation="relu", padding="same")(x_input)
cr2 = Conv2D(224, (1, 3), activation="relu", padding="same")(cr2)
cr2 = Conv2D(256, (3, 1), activation="relu", padding="same")(cr2)
merged = concatenate([cr1, cr2], axis=-1)
cr = Conv2D(2144, (1, 1), activation="linear", padding="same")(merged)
if scale_residual: cr = Lambda(lambda c: c * 0.1)(cr)
x = add([x_input, cr])
x = BatchNormalization(axis=-1)(x)
x = Activation("relu")(x)
return x
def reduction_resnet_v2_B(x_input):
'''Architecture of a 17 * 17 to 8 * 8 Reduction_ResNet_B block.'''
with K.name_scope('reduction_resnet_v2_B'):
rbr1 = MaxPooling2D((3, 3), strides=(2, 2), padding="valid")(x_input)
rbr2 = Conv2D(256, (1, 1), activation="relu", padding="same")(x_input)
rbr2 = Conv2D(384, (3, 3), activation="relu", strides=(2, 2))(rbr2)
rbr3 = Conv2D(256, (1, 1), activation="relu", padding="same")(x_input)
rbr3 = Conv2D(288, (3, 3), activation="relu", strides=(2, 2))(rbr3)
rbr4 = Conv2D(256, (1, 1), activation="relu", padding="same")(x_input)
rbr4 = Conv2D(288, (3, 3), activation="relu", padding="same")(rbr4)
rbr4 = Conv2D(320, (3, 3), activation="relu", strides=(2, 2))(rbr4)
merged = concatenate([rbr1, rbr2, rbr3, rbr4], axis=-1)
rbr = BatchNormalization(axis=-1)(merged)
rbr = Activation("relu")(rbr)
return rbr
def inception_resnet_v2(nb_classes=1001, scale=True):
'''Creates the Inception_ResNet_v1 network.'''
init = Input((299, 299, 3)) # Channels last, as using Tensorflow backend with Tensorflow image dimension ordering
# Input shape is 299 * 299 * 3
x = resnet_v2_stem(init) # Output: 35 * 35 * 256
# 5 x Inception A
for i in range(5):
x = inception_resnet_v2_A(x, scale_residual=scale)
# Output: 35 * 35 * 256
# Reduction A
x = reduction_resnet_A(x, k=256, l=256, m=384, n=384) # Output: 17 * 17 * 896
# 10 x Inception B
for i in range(10):
x = inception_resnet_v2_B(x, scale_residual=scale)
# Output: 17 * 17 * 896
# Reduction B
x = reduction_resnet_v2_B(x) # Output: 8 * 8 * 1792
# 5 x Inception C
for i in range(5):
x = inception_resnet_v2_C(x, scale_residual=scale)
# Output: 8 * 8 * 1792
# Average Pooling
x = AveragePooling2D((8, 8))(x) # Output: 1792
# Dropout
x = Dropout(0.2)(x) # Keep dropout 0.2 as mentioned in the paper
x = Flatten()(x) # Output: 1792
# Output layer
output = Dense(units=nb_classes, activation="softmax")(x) # Output: 10000
model = Model(init, output, name="Inception-ResNet-v2")
return model
if __name__ == "__main__":
inception_resnet_v2_model = inception_resnet_v2()
plot_model(inception_resnet_v2_model, to_file='inception_resnet_v2.png', show_shapes=True)