TensorFlow2.0之CIFAR10与ResNet18实战

最新推荐文章于 2025-07-30 12:11:54 发布

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最新推荐文章于 2025-07-30 12:11:54 发布 · 742 阅读

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#深度学习 #卷积 #tensorflow #神经网络

博客聚焦于TensorFlow2.0，开展了CIFAR10与ResNet18的实战。涉及深度学习、卷积和神经网络等信息技术领域知识，借助TensorFlow工具进行实践操作。

TensorFlow2.0之CIFAR10与ResNet18实战

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, Sequential
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession

config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)


class BasicBlock(layers.Layer):
    # 残差模块
    def __init__(self, filter_num, stride=1):
        super(BasicBlock, self).__init__()
        # 第一个卷积单元
        self.conv1 = layers.Conv2D(filter_num, (3, 3), strides=stride, padding='same')
        self.bn1 = layers.BatchNormalization()
        self.relu = layers.Activation('relu')
        # 第二个卷积单元
        self.conv2 = layers.Conv2D(filter_num, (3, 3), strides=1, padding='same')
        self.bn2 = layers.BatchNormalization()
        # 通过1*1卷积完成shape匹配
        if stride != 1:
            self.downsample = Sequential()
            self.downsample.add(layers.Conv2D(filter_num, (1, 1), strides=stride))
        # shape匹配，直接短接
        else:
            self.downsample = lambda x: x

    def call(self, inputs, training=None):
        # 前向计算函数
        # [b, h, w, c]通过第一个卷积单元
        out = self.conv1(inputs)