https://blog.csdn.net/weixin_39953502/article/details/80966046?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.edu_weight&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.edu_weight
当然还是这个讲解的最明白，给出了论文，不过很多人应该不会去看论文，，，还是看看别人的介绍把。不过在inceptionv4时候对图没有讲解，没看明白v4结构什么意思，我在研究研究。
https://blog.csdn.net/Biyoner/article/details/89848007?utm_medium=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.edu_weight&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.edu_weight
这个给出了inception以及resnetinception结构还是比较清晰的。
https://blog.csdn.net/weixin_40920290/article/details/84958133
给出来了代码，需要修改如下：参考代码，然后看结构对照着，比较好理解，其中densev1那个结构需要注释掉，（这句话是干吗的，大佬可以留言，加上后运行报错）运行结果就是，去本地文件夹目录下找inception_resnet_v1.png的图片，打开显示的是模型的总体图。
#if scale_residual:
# br = Lambda(lambda x: 0.1 * x)(br)
#https://blog.csdn.net/weixin_40920290/article/details/84958133
#代码链接文章
#结构图链接
#https://blog.csdn.net/Biyoner/article/details/89848007?utm_medium=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.edu_weight&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.edu_weight
"""""
# inception-v4结构图 可以跑通
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.vis_utils import plot_model
CONV_BLOCK_COUNT = 0 # 用来命名计数卷积编号
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):
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):
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):
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'):
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):
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
# 全连接层
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)
"""""
"""""
#inception_resnet_v1结构
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.vis_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):
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: # 是否缩小
# 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):
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)
"""""
#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 keras.utils.vis_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_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_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_v2_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)