1. 全栈程序员必看首页

BN层代码实现_无代码

全栈程序员-用户IM • • 未分类

BN层代码实现_无代码BatchNormalization开发环境项目代码结构生成虚拟数据程序神经网络构建带初始化模型的神经网络构建带BN的FC网络和不带BN的FC网络对比不同初始化方式带BN的网络模型对比开发环境python–3.7torch–1.8+cu101torchsummarytorchvision–0.6.1+cu101PILnumpyopencv-pythonpillow项目代码结构src文件夹存储了带有BN的FC训练文件、基于初始化的带BN的FC训练文件以及BN在训练阶段的操作。t

大家好，又见面了，我是你们的朋友全栈君。如果您正在找激活码,请点击查看最新教程,关注关注公众号 “全栈程序员社区” 获取激活教程,可能之前旧版本教程已经失效.最新Idea2022.1教程亲测有效,一键激活。

Jetbrains全系列IDE稳定放心使用

Batch Normalization

开发环境

  • python–3.7
  • torch–1.8+cu101
  • torchsummary
  • torchvision–0.6.1+cu101
  • PIL
  • numpy
  • opencv-python
  • pillow

项目代码结构

在这里插入图片描述
src文件夹存储了带有BN的FC训练文件、基于初始化的带BN的FC训练文件以及BN在训练阶段的操作。
tools存储了通用数据集：生成虚拟数据集和FC模型。

生成虚拟数据程序

import numpy as np
import torch

def generate_data(num_samples):
	# training data
	x = np.linspace(-7, 10, num_samples)[:, np.newaxis]  # 在-7到10区间内等距离划分num_samples个数据，并添加一维，生成二维数据。
	noise = np.random.normal(0, 2, x.shape)  # 添加噪声
	y = np.square(x) - 5 + noise    # y与x的关系
	
	# testing data
	test_x = np.linspace(-7, 10, 200)[:, np.newaxis]
    noise = np.random.normal(0, 2, test_x.shape)
    test_y = np.square(test_x) - 5 + noise

	# to tensor
    train_x = torch.from_numpy(x).float()
    train_y = torch.from_numpy(y).float()
    test_x = torch.from_numpy(test_x).float()
    test_y = torch.from_numpy(test_y).float()

    return train_x, train_y, test_x, test_y

神经网络构建

class Net(nn.Module):
    def __init__(self, n_hidden, act_func, batch_normalization=False):
        super(Net, self).__init__()
        self.do_bn = batch_normalization
        self.fcs = []
        self.bns = []
        self.bn_input = nn.BatchNorm1d(1, momentum=0.5)
        self.act_func = act_func
        self.n_hidden = n_hidden

        for i in range(n_hidden):
            input_size = 1 if i == 0 else 10
            fc = nn.Linear(input_size, 10)
            setattr(self, 'fc%i' % i, fc)

            self._set_init(fc)
            self.fcs.append(fc)

            if self.do_bn:
                bn = nn.BatchNorm1d(10, momentum=0.5)
                setattr(self, 'bn%i' % i, bn)
                self.bns.append(bn)
        self.predict = nn.Linear(10, 1)
        self._set_init(self.predict)

    def _set_init(self, layer):
        nn.init.normal_(layer.weight, mean=0., std=1.)
        nn.init.constant_(layer.bias, -0.2)

    def forward(self, x):
        pre_activation = [x]
        if self.do_bn:
            x = self.bn_input(x)

        layer_input = [x]

        for i in range(self.n_hidden):
            x = self.fcs[i](x)
            pre_activation.append(x)

            if self.do_bn:
                x = self.bns[i](x)

            x = self.act_func(x)

            layer_input.append(x)

        out = self.predict(x)
        return out, layer_input, pre_activation

带初始化模型的神经网络构建

class MLP(nn.Module):
    def __init__(self, neural_num, layers=100, do_bn=False):
        super(MLP, self).__init__()
        self.linears = nn.ModuleList([nn.Linear(neural_num, neural_num, bias=False) for i in range(layers)])
        self.bns = nn.ModuleList([nn.BatchNorm1d(neural_num) for i in range(layers)])
        self.neural_num = neural_num
        self.do_bn = do_bn

    def forward(self, x):
        for (i, linear), bn in zip(enumerate(self.linears), self.bns):
            x = linear(x)

            if self.do_bn:
                x = bn(x)
            x = torch.relu(x)

            if torch.isnan(x.std()):
                print("output is nan in {} layers".format(i))
                break
            print("layers:{}, std:{}".format(i, x.std().item()))

        return x

    def initialize(self, mode, std_init=1):
        for m in self.modules():
            if isinstance(m, nn.Linear):
                if mode == "normal":
                    nn.init.normal_(m.weight.data, std=std_init)
                elif mode == "kaiming":
                    nn.init.kaiming_normal_(m.weight.data)
                else:
                    print("不支持{}输入".format(mode))

带BN的FC网络和不带BN的FC网络对比

import torch
import torch.utils.data as Data
import matplotlib.pyplot as plt
import numpy as np
from tools.common_tools import generate_data, Net


torch.manual_seed(1)
np.random.seed(1)


def plot_histogram(l_in, l_in_bn, pre_ac, pre_ac_bn):
    for i, (ax_pa, ax_pa_bn, ax, ax_bn) in enumerate(zip(axs[0, :], axs[1, :], axs[2, :], axs[3, :])):
        [a.clear() for a in [ax_pa, ax_pa_bn, ax, ax_bn]]
        if i == 0:
            p_range = (-7, 10)
            the_range = (-7, 10)

        else:
            p_range = (-4, 4)
            the_range = (-1, 1)

        ax_pa.set_title('L' + str(i))
        ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5)
        ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5)
        ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359')
        ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF')

        for a in [ax_pa, ax, ax_pa_bn, ax_bn]:
            a.set_yticks(())
            a.set_xticks(())

        ax_pa_bn.set_xticks(p_range)
        ax_bn.set_xticks(the_range)

        axs[0, 0].set_ylabel('PreAct')
        axs[1, 0].set_ylabel('BN PreAct')
        axs[2, 0].set_ylabel('Act')
        axs[3, 0].set_ylabel('BN Act')

    plt.suptitle("Activation:{} epoch:{}/{}".format(act_name, epoch, EPOCH))
    plt.pause(0.05)


if __name__ == "__main__":
    act_name = "ReLU"
    # act_name = "Tanh"
    # act_name = "Sigmoid"

    activations = { 
   "ReLU": torch.relu, "Tanh": torch.tanh, "Sigmoid": torch.sigmoid}
    ACTIVATION = activations[act_name]

    # config
    EPOCH = 12
    LR = 0.03
    N_HIDDEN = 8
    N_SAMPLES = 2000
    BATCH_SIZE = 64
    B_INIT = -0.2  # use a bad bias constant initializer

    # 1. 生成虚假数据
    train_x, train_y, test_x, test_y = generate_data(N_SAMPLES)
    train_dataset = Data.TensorDataset(train_x, train_y)
    train_loader = Data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)

    # show data
    plt.scatter(train_x.numpy(), train_y.numpy(), c='#FF9359', s=50, alpha=0.2, label='train')
    plt.legend(loc='upper left')

    # 2. 创建网络/loss/优化器
    nets = [Net(N_HIDDEN, ACTIVATION, batch_normalization=False), Net(N_HIDDEN, ACTIVATION, batch_normalization=True)]
    loss_func = torch.nn.MSELoss()
    opts = [torch.optim.Adam(net.parameters(), lr=LR) for net in nets]

    # 3. 训练，绘图
    f, axs = plt.subplots(4, N_HIDDEN + 1, figsize=(10, 5))
    plt.ion()  # something about plotting
    plt.show()

    losses = [[], []]  # recode loss for two networks
    for epoch in range(EPOCH):
        print('Epoch: {}/{}'.format(epoch, EPOCH))

        # 记录数据
        layer_inputs, pre_acts = [], []
        for net, l in zip(nets, losses):
            net.eval()  # set eval mode to fix moving_mean and moving_var
            pred, layer_input, pre_act = net(test_x)
            l.append(loss_func(pred, test_y).data.item())
            layer_inputs.append(layer_input)
            pre_acts.append(pre_act)
            net.train()  # free moving_mean and moving_var
        plot_histogram(*layer_inputs, *pre_acts)  # plot histogram

        # 训练更新模型
        for step, (b_x, b_y) in enumerate(train_loader):
            for net, opt in zip(nets, opts):  # train for each network
                pred, _, _ = net(b_x)
                loss = loss_func(pred, b_y)
                opt.zero_grad()
                loss.backward()
                opt.step()  # it will also learns the parameters in Batch Normalization

    plt.ioff()

    # plot training loss
    plt.figure(2)
    plt.plot(losses[0], c='#FF9359', lw=3, label='Original')
    plt.plot(losses[1], c='#74BCFF', lw=3, label='Batch Normalization')
    plt.xlabel('step')
    plt.ylabel('test loss')
    plt.ylim((0, 2000))
    plt.legend(loc='best')

    # evaluation
    # set net to eval mode to freeze the parameters in batch normalization layers
    [net.eval() for net in nets]  # set eval mode to fix moving_mean and moving_var
    preds = [net(test_x)[0] for net in nets]
    plt.figure(3)
    plt.plot(test_x.data.numpy(), preds[0].data.numpy(), c='#FF9359', lw=4, label='Original')
    plt.plot(test_x.data.numpy(), preds[1].data.numpy(), c='#74BCFF', lw=4, label='Batch Normalization')
    plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='r', s=50, alpha=0.2, label='train')
    plt.legend(loc='best')
    plt.show()

不同初始化方式带BN的网络模型对比

import torch
import numpy as np
import torch.nn as nn


torch.manual_seed(1)
np.random.seed(1)


class MLP(nn.Module):
    def __init__(self, neural_num, layers=100, do_bn=False):
        super(MLP, self).__init__()
        self.linears = nn.ModuleList([nn.Linear(neural_num, neural_num, bias=False) for i in range(layers)])
        self.bns = nn.ModuleList([nn.BatchNorm1d(neural_num) for i in range(layers)])
        self.neural_num = neural_num
        self.do_bn = do_bn

    def forward(self, x):
        for (i, linear), bn in zip(enumerate(self.linears), self.bns):
            x = linear(x)

            if self.do_bn:
                x = bn(x)
            x = torch.relu(x)

            if torch.isnan(x.std()):
                print("output is nan in {} layers".format(i))
                break
            print("layers:{}, std:{}".format(i, x.std().item()))

        return x

    def initialize(self, mode, std_init=1):
        for m in self.modules():
            if isinstance(m, nn.Linear):
                if mode == "normal":
                    nn.init.normal_(m.weight.data, std=std_init)
                elif mode == "kaiming":
                    nn.init.kaiming_normal_(m.weight.data)
                else:
                    print("不支持{}输入".format(mode))


if __name__ == "__main__":
    neural_nums = 256
    layer_nums = 100
    batch_size = 16

    net = MLP(neural_nums, layer_nums, do_bn=False)   # 1. 无初始化； # 2. normal_初始化； # 3。 kaiming初始化
    # net = MLP(neural_nums, layer_nums, do_bn=True) # 4. BN+无初始化； 5. BN + normal; 6. BN + kaiming, 7. BN+1000
    # net.initialize("normal", std_init=1)
    # net.initialize("normal", std_init=1000)
    net.initialize("kaiming")

    inputs = torch.randn((batch_size, neural_nums))  # normal: mean=0, std=1

    output = net(inputs)
    print(output)

BN层原理及代码

在这里插入图片描述

  • 训练阶段:均值和标准差通过指数滑动平均统计得来的, γ \gamma γ和 β \beta β通过梯度反向传播不断更新
  • 测试阶段:均值和标准差是固定的, γ \gamma γ和 β \beta β也是固定的.
  • 指数滑动平均计算公式
    m v t = d e c a y ∗ m v t − 1 + ( 1 − d e c a y ) ∗ a t mv_t=decay*mv_{t-1}+(1-decay)*a_t mvt​=decay∗mvt−1​+(1−decay)∗at​
  • pytorch中的指数滑动平均计算公式
    m v t = ( 1 − m o m e n t u m ) ∗ m v t − 1 + m o m e n t u m ∗ a t mv_t=(1-momentum)*mv_{t-1}+momentum*a_t mvt​=(1−momentum)∗mvt−1​+momentum∗at​
    pytorch中的BN层
import torch.nn as nn

# 1dBN
nn.BatchNorm1d
# 2d
nn.BatchNorm2d
# 3d
nn.BatchNorm3d

# 参数
_init_(self, num_features,
		eps = 1e-5,
		momentum = 0.1, 
		affine = True,
		track_running_stats = True)

""" num_features: 一个样本特征数量 eps: 分母修正项 momentum: 指数加权平均估计当前mean/var affine:是否需要affine transform track_running_stats:是否需要统计mean/var """
版权声明：本文内容由互联网用户自发贡献，该文观点仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 举报，一经查实，本站将立刻删除。

发布者：全栈程序员-用户IM，转载请注明出处：https://javaforall.cn/182025.html原文链接：https://javaforall.cn

【正版授权，激活自己账号】： Jetbrains全家桶Ide使用，1年售后保障，每天仅需1毛

【官方授权 正版激活】： 官方授权 正版激活 支持Jetbrains家族下所有IDE 使用个人JB账号...

赞 (0)
全栈程序员-用户IM全栈程序员-用户IM
0 0


相关推荐

发表回复

您的电子邮箱地址不会被公开。

关注全栈程序员社区公众号