使用 Kears 实现ResNet-34 CNN

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于 2022-04-17 12:59:21 发布

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分类专栏：机器学习文章标签：机器学习深度学习 python

本文链接：https://blog.youkuaiyun.com/qq_51153436/article/details/124227878

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让我们开始实现ResNet-34.
一、创建一个ResidualUnit层。

from functools import partial
import tensorflow as tf
from tensorflow import keras



DefaultConv2D = partial(keras.layers.Conv2D, kernel_size=3, strides=1,
                        padding="SAME", use_bias=False)

class ResidualUnit(keras.layers.Layer):
    def __init__(self, filters, strides=1, activation="relu", **kwargs):
        super().__init__(**kwargs)
        self.activation = keras.activations.get(activation)
        self.main_layers = [
            DefaultConv2D(filters, strides=strides),
            keras.layers.BatchNormalization(),
            self.activation,
            DefaultConv2D(filters),
            keras.layers.BatchNormalization()]
        self.skip_layers = []
        if strides > 1:
            self.skip_layers = [
                DefaultConv2D(filters, kernel_size=1, strides=strides),
                keras.layers.BatchNormalization()]

    def call(self, inputs):
        Z = inputs
        for layer in self.main_layers:
            Z = layer(Z)
        skip_Z = inputs
        for layer in self.skip_layers:
            skip_Z = layer(skip_Z)
        return self.activation(Z