mobilenet_v3

最新推荐文章于 2025-01-18 09:58:09 发布

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最新推荐文章于 2025-01-18 09:58:09 发布 · 194 阅读

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该博客详细介绍了如何使用Keras构建MobileNet V3模型，包括定义激活函数、卷积块、瓶颈层和注意力机制等关键组件。模型结构从160x160x3输入开始，通过一系列卷积和深度可分离卷积操作，最后进行全局平均池化、全连接层和Softmax激活，得到128维的特征向量。整个网络设计考虑了通道数的增减、步长的选择和注意力机制的应用，旨在提高模型的效率和准确性。

import math

import numpy as np
import tensorflow as tf
from keras import backend
from keras import backend as K
from keras.applications import imagenet_utils
from keras.applications.imagenet_utils import decode_predictions
from keras.layers import (Activation, Add, Conv2D, Dense, DepthwiseConv2D,
                          Dropout, GlobalAveragePooling2D, GlobalMaxPooling2D,
                          Input, Lambda, MaxPooling2D, ZeroPadding2D,Reshape,Multiply)
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.preprocessing import image
from keras.utils.data_utils import get_file

alpha = 1

def relu6(x):
#     定义relu6激活函数： relu6 = min(max(0, x), 6)
    return K.relu(x, max_value=6.0)

def hard_swish(x):
    return x*K.relu(x+3.0, max_value=6.0)/6.0

def return_activation(x, nl, block_id=0,conv_flag=True):
    if conv_flag:
        if nl == "HS":
            x = Activation(hard_swish,name="cba_%d_hs" % block_id)(x)
        if nl == "RE":
            x = Activation(relu6,name="cba_%d_re" % block_id)(x)   
        return x
    else:
        if nl == "HS":
            x = Activation(hard_swish,name="bneck%d_cba_hs" % block_id)(x)
        if nl == "RE":
            x = Activation(relu6,name="bneck%d_cba_re" % block_id)(x)   
        return x

def conv_block(inputs, filters, kernel, strides, nl, block_id=0,conv_flag=True):
    channel_axis = 1 if K.image_data_format()=='channels_first' else -1
    if conv_flag:
        x = Conv2D(filters, kernel, padding='same', strides=strides,kernel_initializer='random_uniform',
								name="cba_%d_c" % block_id)(inputs)
        x = BatchNormalization(axis=channel_axis,name=

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