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CaffeNet训练教程

最新推荐文章于 2024-07-23 20:33:03 发布

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最新推荐文章于 2024-07-23 20:33:03 发布 · 880 阅读

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本文详细介绍如何使用Caffe框架搭建并训练CaffeNet深度学习模型，包括数据预处理、网络结构定义、损失函数与优化器设置等关键步骤。

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import sys
caffe_root = 'E:/code/caffe/caffe-windows/Build/x64/Release/pycaffe/'
sys.path.insert(0, caffe_root)
import caffe
from caffe import layers as L, params as P
from caffe.proto import caffe_pb2

weight_param = dict(lr_mult=1, decay_mult=1)
bias_param   = dict(lr_mult=2, decay_mult=0)
learned_param = [weight_param, bias_param]

frozen_param = [dict(lr_mult=0)] * 2

def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,
              param=learned_param,
              weight_filler=dict(type='gaussian', std=0.01),
              bias_filler=dict(type='constant', value=1)):
    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
                         num_output=nout, pad=pad, group=group,
                         param=param, weight_filler=weight_filler,
                         bias_filler=bias_filler)
    return conv, L.ReLU(conv, in_place=True)

def fc_relu(bottom, nout, param=learned_param,
            weight_filler=dict(type='gaussian', std=0.01),
            bias_filler=dict(type=<