python神经网络Inception ResnetV2模型复现详解

什么是Inception ResnetV2

Inception ResnetV2是Inception ResnetV1的一个加强版，两者的结构差距不大，如果大家想了解Inception ResnetV1可以看一下我的另一个blog。facenet的神经网络结构就是Inception ResnetV1。

神经网络学习——facenet详解及其keras实现

源码下载

Inception-ResNetV2的网络结构

Inception-ResNetV2和Inception-ResNetV1采用同一个主干网络。

它的结构很有意思！

如图所示为整个网络的主干结构：

python神经网络Inception ResnetV2模型复现详解

可以看到里面的结构分为几个重要的部分

1、stem

2、Inception-resnet-A

3、Inception-resnet-B

4、Inception-resnet-C

1、Stem的结构：

python神经网络Inception ResnetV2模型复现详解

在Inception-ResNetV2里，它的Input为299x299x3大小，输入后进行：三次卷积 -> 最大池化 -> 两次卷积 -> 最大池化 -> 四个分支 -> 堆叠python实现代码如下：

input_shape = [299,299,3]
img_input = Input(shape=input_shape)
# Stem block: 299,299,3 -> 35 x 35 x 192
x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid')
x = conv2d_bn(x, 32, 3, padding='valid')
x = conv2d_bn(x, 64, 3)
x = MaxPooling2D(3, strides=2)(x)
x = conv2d_bn(x, 80, 1, padding='valid')
x = conv2d_bn(x, 192, 3, padding='valid')
x = MaxPooling2D(3, strides=2)(x)
# Mixed 5b (Inception-A block):35 x 35 x 192 -> 35 x 35 x 320
branch_0 = conv2d_bn(x, 96, 1)
branch_1 = conv2d_bn(x, 48, 1)
branch_1 = conv2d_bn(branch_1, 64, 5)
branch_2 = conv2d_bn(x, 64, 1)
branch_2 = conv2d_bn(branch_2, 96, 3)
branch_2 = conv2d_bn(branch_2, 96, 3)
branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 64, 1)
branches = [branch_0, branch_1, branch_2, branch_pool]
x = Concatenate(name='mixed_5b')(branches)

2、Inception-resnet-A的结构：

python神经网络Inception ResnetV2模型复现详解

Inception-resnet-A的结构分为四个分支

1、未经处理直接输出

2、经过一次1×1的32通道的卷积处理

3、经过一次1×1的32通道的卷积处理和一次3×3的32通道的卷积处理

4、经过一次1×1的32通道的卷积处理、一次3×3的48通道和一次3×3的64通道卷积处理

234步的结果堆叠后进行一次卷积，并与第一步的结果相加，实质上这是一个残差网络结构。

实现代码如下：

branch_0 = conv2d_bn(x, 32, 1)
branch_1 = conv2d_bn(x, 32, 1)
branch_1 = conv2d_bn(branch_1, 32, 3)
branch_2 = conv2d_bn(x, 32, 1)
branch_2 = conv2d_bn(branch_2, 48, 3)
branch_2 = conv2d_bn(branch_2, 64, 3)
branches = [branch_0, branch_1, branch_2]
block_name = block_type + '_' + str(block_idx)
mixed = Concatenate(name=block_name + '_mixed')(branches)
up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,se_bias=True,name=block_name + '_conv')
x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
            output_shape=K.int_shape(x)[1:],
            arguments={'scale': scale},
            name=block_name)([x, up])
if activation is not None:
    x = Activation(activation, name=block_name + '_ac')(x)

3、Inception-resnet-B的结构：

python神经网络Inception ResnetV2模型复现详解

Inception-resnet-B的结构分为四个分支

1、未经处理直接输出

2、经过一次1×1的192通道的卷积处理

3、经过一次1×1的128通道的卷积处理、一次1×7的160通道的卷积处理和一次7×1的192通道的卷积处理

23步的结果堆叠后进行一次卷积，并与第一步的结果相加，实质上这是一个残差网络结构。

实现代码如下：

branch_0 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(x, 128, 1)
branch_1 = conv2d_bn(branch_1, 160, [1, 7])
branch_1 = conv2d_bn(branch_1, 192, [7, 1])
branches = [branch_0, branch_1]
block_name = block_type + '_' + str(block_idx)
mixed = Concatenate(name=block_name + '_mixed')(branches)
up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,se_bias=True,name=block_name + '_conv')
x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
            output_shape=K.int_shape(x)[1:],
            arguments={'scale': scale},
            name=block_name)([x, up])
if activation is not None:
    x = Activation(activation, name=block_name + '_ac')(x)

4、Inception-resnet-C的结构：

python神经网络Inception ResnetV2模型复现详解

Inception-resnet-B的结构分为四个分支

1、未经处理直接输出

2、经过一次1×1的192通道的卷积处理

3、经过一次1×1的192通道的卷积处理、一次1×3的224通道的卷积处理和一次3×1的256通道的卷积处理

23步的结果堆叠后进行一次卷积，并与第一步的结果相加，实质上这是一个残差网络结构。

实现代码如下：

branch_0 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(x, 192, 1)
branch_1 = conv2d_bn(branch_1, 224, [1, 3])
branch_1 = conv2d_bn(branch_1, 256, [3, 1])
branches = [branch_0, branch_1]
block_name = block_type + '_' + str(block_idx)
mixed = Concatenate(name=block_name + '_mixed')(branches)
up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,se_bias=True,name=block_name + '_conv')
x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
            output_shape=K.int_shape(x)[1:],
            arguments={'scale': scale},
            name=block_name)([x, up])
if activation is not None:
    x = Activation(activation, name=block_name + '_ac')(x)

全部代码

import warnings
import numpy as np
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Activation,AveragePooling2D,BatchNormalization,Concatenate
from keras.layers import Conv2D,Dense,GlobalAveragePooling2D,GlobalMaxPooling2D,Input,Lambda,MaxPooling2D
from keras.applications.imagenet_utils import decode_predictions
from keras.utils.data_utils import get_file
from keras import backend as K
BASE_WEIGHT_URL = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.7/'
def conv2d_bn(x,filters,kernel_size,strides=1,padding='same',activation='relu',use_bias=False,name=None):
    x = Conv2D(filters,
               kernel_size,
               strides=strides,
               padding=padding,
               use_bias=use_bias,
               name=name)(x)
    if not use_bias:
        bn_axis = 1 if K.image_data_format() == 'channels_first' else 3
        bn_name = None if name is None else name + '_bn'
        x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
    if activation is not None:
        ac_name = None if name is None else name + '_ac'
        x = Activation(activation, name=ac_name)(x)
    return x
def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):
    if block_type == 'block35':
        branch_0 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(branch_1, 32, 3)
        branch_2 = conv2d_bn(x, 32, 1)
        branch_2 = conv2d_bn(branch_2, 48, 3)
        branch_2 = conv2d_bn(branch_2, 64, 3)
        branches = [branch_0, branch_1, branch_2]
    elif block_type == 'block17':
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 128, 1)
        branch_1 = conv2d_bn(branch_1, 160, [1, 7])
        branch_1 = conv2d_bn(branch_1, 192, [7, 1])
        branches = [branch_0, branch_1]
    elif block_type == 'block8':
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(branch_1, 224, [1, 3])
        branch_1 = conv2d_bn(branch_1, 256, [3, 1])
        branches = [branch_0, branch_1]
    else:
        raise ValueError('Unknown Inception-ResNet block type. '
                         'Expects "block35", "block17" or "block8", '
                         'but got: ' + str(block_type))
    block_name = block_type + '_' + str(block_idx)
    mixed = Concatenate(name=block_name + '_mixed')(branches)
    up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,use_bias=True,name=block_name + '_conv')
    x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
               output_shape=K.int_shape(x)[1:],
               arguments={'scale': scale},
               name=block_name)([x, up])
    if activation is not None:
        x = Activation(activation, name=block_name + '_ac')(x)
    return x
def InceptionResNetV2(input_shape=[299,299,3],
                      classes=1000):
    input_shape = [299,299,3]
    img_input = Input(shape=input_shape)
    # Stem block: 299,299,3 -> 35 x 35 x 192
    x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid')
    x = conv2d_bn(x, 32, 3, padding='valid')
    x = conv2d_bn(x, 64, 3)
    x = MaxPooling2D(3, strides=2)(x)
    x = conv2d_bn(x, 80, 1, padding='valid')
    x = conv2d_bn(x, 192, 3, padding='valid')
    x = MaxPooling2D(3, strides=2)(x)
    # Mixed 5b (Inception-A block):35 x 35 x 192 -> 35 x 35 x 320
    branch_0 = conv2d_bn(x, 96, 1)
    branch_1 = conv2d_bn(x, 48, 1)
    branch_1 = conv2d_bn(branch_1, 64, 5)
    branch_2 = conv2d_bn(x, 64, 1)
    branch_2 = conv2d_bn(branch_2, 96, 3)
    branch_2 = conv2d_bn(branch_2, 96, 3)
    branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1)
    branches = [branch_0, branch_1, branch_2, branch_pool]
    x = Concatenate(name='mixed_5b')(branches)
    # 10次Inception-ResNet-A block:35 x 35 x 320 -> 35 x 35 x 320
    for block_idx in range(1, 11):
        x = inception_resnet_block(x,
                                   scale=0.17,
                                   block_type='block35',
                                   block_idx=block_idx)
    # Reduction-A block:35 x 35 x 320 -> 17 x 17 x 1088
    branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid')
    branch_1 = conv2d_bn(x, 256, 1)
    branch_1 = conv2d_bn(branch_1, 256, 3)
    branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid')
    branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
    branches = [branch_0, branch_1, branch_pool]
    x = Concatenate(name='mixed_6a')(branches)
    # 20次Inception-ResNet-B block: 17 x 17 x 1088 -> 17 x 17 x 1088 
    for block_idx in range(1, 21):
        x = inception_resnet_block(x,
                                   scale=0.1,
                                   block_type='block17',
                                   block_idx=block_idx)
    # Reduction-B block: 17 x 17 x 1088 -> 8 x 8 x 2080
    branch_0 = conv2d_bn(x, 256, 1)
    branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid')
    branch_1 = conv2d_bn(x, 256, 1)
    branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid')
    branch_2 = conv2d_bn(x, 256, 1)
    branch_2 = conv2d_bn(branch_2, 288, 3)
    branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid')
    branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
    branches = [branch_0, branch_1, branch_2, branch_pool]
    x = Concatenate(name='mixed_7a')(branches)
    # 10次Inception-ResNet-C block: 8 x 8 x 2080 -> 8 x 8 x 2080
    for block_idx in range(1, 10):
        x = inception_resnet_block(x,
                                   scale=0.2,
                                   block_type='block8',
                                   block_idx=block_idx)
    x = inception_resnet_block(x,
                               scale=1.,
                               activation=None,
                               block_type='block8',
                               block_idx=10)
    # 8 x 8 x 2080 -> 8 x 8 x 1536
    x = conv2d_bn(x, 1536, 1, name='conv_7b')
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)
    inputs = img_input
    # 创建模型
    model = Model(inputs, x, name='inception_resnet_v2')
    return model
def preprocess_input(x):
    x /= 255.
    x -= 0.5
    x *= 2.
    return x
if __name__ == '__main__':
    model = InceptionResNetV2()
    fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5'
    weights_path = get_file(fname,BASE_WEIGHT_URL + fname,cache_subdir='models',file_hash='e693bd0210a403b3192acc6073ad2e96')
    model.load_weights(fname)
    img_path = 'elephant.jpg'
    img = image.load_img(img_path, target_size=(299, 299))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    preds = model.predict(x)
    print('Predicted:', decode_predictions(preds))

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python神经网络Inception ResnetV2模型复现详解

目录

什么是Inception ResnetV2

Inception-ResNetV2的网络结构

1、Stem的结构：

2、Inception-resnet-A的结构：

3、Inception-resnet-B的结构：

4、Inception-resnet-C的结构：

全部代码

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python神经网络Inception ResnetV2模型复现详解

目录

什么是Inception ResnetV2

Inception-ResNetV2的网络结构

1、Stem的结构：

2、Inception-resnet-A的结构：

3、Inception-resnet-B的结构：

4、Inception-resnet-C的结构：

全部代码

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