paddle深度学习基础之训练调试与优化

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paddle深度学习基础之训练调试与优化

前言

上一节咱们讨论了四种不同的优化算法，这一节，咱们讨论训练过程中的优化问题。本次代码修改模型全是在卷积神经网络

文章目录
paddle深度学习基础之训练调试与优化
前言
网络结构
优化思路
一、计算模型的分类准确率
二、检查模型训练过程
三、加入校验或者测试，更好的评价模型
四、加入正则化项，避免模型过拟合
过拟合现象
导致过拟合原因
正则化项
代码实现

五、可视化库
- **Matplotlib** 显示
- tb-paddle

网络结构

优化思路

计算分类准确率，观测模型训练效果
检查模型训练过程，通过输出训练过程中的某些参数或者中间结果，识别潜在问题
加入校验或测试，更好评价模型效果
加入正则化项，避免模型过拟合
可视化分析

一、计算模型的分类准确率

通过计算训练的准确度，能够比较直接的反应模型的精准程度。

在paddle框架中，我们可以使用自带的准确率计算方法：

代码语言：javascript复制

fluit.layers.accuracy(prediction,lable)

第一个参数是预测值，第二个参数是实际标签值。下面是代码中需要修改的地方：

代码语言：javascript复制

    def forward(self, inputs,label):
conv1 = self.conv1(inputs)
pool1 = self.pool1(conv1)
conv2 = self.conb2(pool1)
pool2 = self.pool2(conv2)
pool2 = fluid.layers.reshape(pool2, [pool2.shape[0], -1])
outputs = self.linear(pool2)
if label is not None:#添加
acc = fluid.layers.accuracy(input=outputs,label=label)#添加
return outputs,acc
else:
return outputs

输出结果：

代码语言：javascript复制

epoch: 0, batch: 0, loss is: [2.796657], acc is [0.04]
epoch: 0, batch: 200, loss is: [0.50403804], acc is [0.88]
epoch: 0, batch: 400, loss is: [0.2659506], acc is [0.92]
epoch: 1, batch: 0, loss is: [0.22079289], acc is [0.92]
epoch: 1, batch: 200, loss is: [0.23240374], acc is [0.92]
epoch: 1, batch: 400, loss is: [0.16370663], acc is [0.95]
epoch: 2, batch: 0, loss is: [0.37291032], acc is [0.92]
epoch: 2, batch: 200, loss is: [0.23772442], acc is [0.92]
epoch: 2, batch: 400, loss is: [0.18071894], acc is [0.95]
epoch: 3, batch: 0, loss is: [0.15938215], acc is [0.95]
epoch: 3, batch: 200, loss is: [0.21112804], acc is [0.92]
epoch: 3, batch: 400, loss is: [0.05794979], acc is [0.99]
epoch: 4, batch: 0, loss is: [0.24466723], acc is [0.93]
epoch: 4, batch: 200, loss is: [0.14045799], acc is [0.96]
epoch: 4, batch: 400, loss is: [0.12366832], acc is [0.94]

二、检查模型训练过程

在我们训练模型时，时常会出现结果和我们预期有很大差距。此时，我们就想了解训练过程中数据的变化过程。恰巧，paddle深度学习框架支持这些功能，我们一起去看看如何做的：

代码语言：javascript复制

class MNIST(fluid.dygraph.Layer):
def __init__(self):
super(MNIST, self).__init__()
# self.linear1 = Linear(input_dim=28*28,output_dim=10,act=None)
# self.linear2 = Linear(input_dim=10,output_dim=10,act='sigmoid')
# self.linear3 = Linear(input_dim=10,output_dim=1,act='sigmoid')
self.conv1 = Conv2D(num_channels=1, num_filters=20, filter_size=5, stride=1, padding=2, act='relu')
self.pool1 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')
self.conb2 = Conv2D(num_channels=20, num_filters=20, filter_size=5, stride=1, padding=2, act='relu')
self.pool2 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')
self.linear = Linear(input_dim=980, output_dim=10, act='softmax')
def forward(self, inputs,label,check_shape=False,check_content=False):
conv1 = self.conv1(inputs)
pool1 = self.pool1(conv1)
conv2 = self.conb2(pool1)
pool2 = self.pool2(conv2)
pool21 = fluid.layers.reshape(pool2, [pool2.shape[0], -1])
outputs = self.linear(pool21)
# hidden1 = self.linear1(inputs)
# hidden2 = self.linear2(hidden1)
# outputs = self.linear3(hidden2)
if(check_shape):
print("n------------打印各个层设置的网络超参数的尺寸 -------------")
print("conv1-- kernel_size:{}, padding:{}, stride:{}".format(self.conv1.weight.shape, self.conv1._padding, self.conv1._stride))
print("conv2-- kernel_size:{}, padding:{}, stride:{}".format(self.conv2.weight.shape, self.conv2._padding, self.conv2._stride))
print("pool1-- pool_type:{}, pool_size:{}, pool_stride:{}".format(self.pool1._pool_type, self.pool1._pool_size, self.pool1._pool_stride))
print("pool2-- pool_type:{}, poo2_size:{}, pool_stride:{}".format(self.pool2._pool_type, self.pool2._pool_size, self.pool2._pool_stride))
print("liner-- weight_size:{}, bias_size_{}, activation:{}".format(self.fc.weight.shape, self.fc.bias.shape, self.fc._act))
print("n------------打印各个层的形状 -------------")
print("inputs_shape: {}".format(inputs.shape))
print("outputs1_shape: {}".format(conv1.shape))
print("outputs2_shape: {}".format(pool1.shape))
print("outputs3_shape: {}".format(conv2.shape))
print("outputs4_shape: {}".format(pool2.shape))
print("outputs5_shape: {}".format(outputs.shape))
if check_content:
# 打印卷积层的参数-卷积核权重，权重参数较多，此处只打印部分参数
print("n########## print convolution layer's kernel ###############")
print("conv1 params -- kernel weights:", self.conv1.weight[0][0])
print("conv2 params -- kernel weights:", self.conv2.weight[0][0])
# 创建随机数，随机打印某一个通道的输出值
idx1 = np.random.randint(0, conv1.shape[1])
idx2 = np.random.randint(0, conv1.shape[1])
# 打印卷积-池化后的结果，仅打印batch中第一个图像对应的特征
print("nThe {}th channel of conv1 layer: ".format(idx1), conv1[0][idx1])
print("The {}th channel of conv2 layer: ".format(idx2), conv1[0][idx2])
print("The output of last layer:", conv1[0], 'n')
if label is not None:
acc = fluid.layers.accuracy(input=outputs,label=label)
return outputs,acc
else:
return outputs

输出结果：

代码语言：javascript复制

------------打印各个层设置的网络超参数的尺寸 -------------
conv1-- kernel_size:[20, 1, 5, 5], padding:[2, 2], stride:[1, 1]
conv2-- kernel_size:[20, 20, 5, 5], padding:[2, 2], stride:[1, 1]
pool1-- pool_type:max, pool_size:[2, 2], pool_stride:[2, 2]
pool2-- pool_type:max, poo2_size:[2, 2], pool_stride:[2, 2]
liner-- weight_size:[980, 10], bias_size_[10], activation:softmax
------------打印各个层的形状 -------------
inputs_shape: [20, 1, 28, 28]
outputs1_shape: [20, 20, 28, 28]
outputs2_shape: [20, 20, 14, 14]
outputs3_shape: [20, 20, 14, 14]
outputs4_shape: [20, 20, 7, 7]
outputs5_shape: [20, 10]

三、加入校验或者测试，更好的评价模型

通常在我们获取数据集时，通常将数据集分成三个部分，分别是训练集，校验集，测试集。

训练集 ：用于训练模型的参数，即训练过程中主要完成的工作。
校验集 ：用于对模型超参数的选择，比如网络结构的调整、正则化项权重的选择等。
测试集 ：用于模拟模型在应用后的真实效果。因为测试集没有参与任何模型优化或参数训练的工作，所以它对模型来说是完全未知的样本。在不以校验数据优化网络结构或模型超参数时，校验数据和测试数据的效果是类似的，均更真实的反映模型效果。

四、加入正则化项，避免模型过拟合

过拟合现象

对于样本量有限、但需要使用强大模型的复杂任务，模型很容易出现过拟合的表现，即在训练集上的损失小，在验证集或测试集上的损失较大，如图2 所示。

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图2：过拟合现象，训练误差不断降低，但测试误差先降后增

反之，如果模型在训练集和测试集上均损失较大，则称为欠拟合。过拟合表示模型过于敏感，学习到了训练数据中的一些误差，而这些误差并不是真实的泛化规律（可推广到测试集上的规律）。欠拟合表示模型还不够强大，还没有很好的拟合已知的训练样本，更别提测试样本了。因为欠拟合情况容易观察和解决，只要训练loss不够好，就不断使用更强大的模型即可，因此实际中我们更需要处理好过拟合的问题。

导致过拟合原因

造成过拟合的原因是模型过于敏感，而训练数据量太少或其中的噪音太多。

如图3 所示，理想的回归模型是一条坡度较缓的抛物线，欠拟合的模型只拟合出一条直线，显然没有捕捉到真实的规律，但过拟合的模型拟合出存在很多拐点的抛物线，显然是过于敏感，也没有正确表达真实规律。

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图3：回归模型的过拟合，理想和欠拟合状态的表现

如图4 所示，理想的分类模型是一条半圆形的曲线，欠拟合用直线作为分类边界，显然没有捕捉到真实的边界，但过拟合的模型拟合出很扭曲的分类边界，虽然对所有的训练数据正确分类，但对一些较为个例的样本所做出的妥协，高概率不是真实的规律。

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图4：分类模型的欠拟合，理想和过拟合状态的表现

正则化项

前面咱们提到，过拟合现象就是因为模型过于复杂，对数据太敏感。所以，我们在无法获取更多数据时，只能降低模型的复杂度。

具体来说，在模型的优化目标（损失）中人为加入对参数规模的惩罚项。当参数越多或取值越大时，该惩罚项就越大。通过调整惩罚项的权重系数，可以使模型在“尽量减少训练损失”和“保持模型的泛化能力”之间取得平衡。泛化能力表示模型在没有见过的样本上依然有效。正则化项的存在，增加了模型在训练集上的损失。

代码实现

代码语言：javascript复制

 optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.01, regularization=fluid.regularizer.L2Decay(regularization_coeff=0.1),
parameter_list=model.parameters())

主要是通过这一句：

代码语言：javascript复制

regularization=fluid.regularizer.L2Decay(regularization_coeff=0.1)

我们可以调节正则化项的权重，也就是regularization_coeff的值。

五、可视化库

训练模型时，经常需要观察模型的评价指标，分析模型的优化过程，以确保训练是有效的。可视化分析有两种工具：Matplotlib库和tb-paddle。

Matplotlib库：Matplotlib库是Python中使用的最多的2D图形绘图库，它有一套完全仿照MATLAB的函数形式的绘图接口，使用轻量级的PLT库（Matplotlib）作图是非常简单的。
tb-paddle：如果期望使用更加专业的作图工具，可以尝试tb-paddle。tb-paddle能够有效地展示飞桨框架在运行过程中的计算图、各种指标随着时间的变化趋势以及训练中使用到的数据信息。

Matplotlib 显示

通常，我们会把模型训练的的损失值和训练次数存进列表中，再通过matplotlib 相关函数进行可视化显示。

代码语言：javascript复制

 if batch_id % 100 == 0:
print("epoch: {}, batch: {}, loss is: {}, acc is {}".format(epoch_id, batch_id, avg_loss.numpy(), acc.numpy()))
iters.append(iter)
losses.append(avg_loss.numpy())
iter = iter   100
#画出训练过程中Loss的变化曲线
plt.figure()
plt.title("train loss", fontsize=24)
plt.xlabel("iter", fontsize=14)
plt.ylabel("loss", fontsize=14)
plt.plot(iters, losses,color='red',label='train loss') 
plt.grid()
plt.show()

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tb-paddle

步骤1：引入tb_paddle库，定义作图数据存储位置（供第3步使用），本案例的路径是“log/data”。

代码语言：javascript复制

from tb_paddle import SummaryWriter
data_writer = SummaryWriter(logdir="log/data")

步骤2：在训练过程中插入作图语句。当每100个batch训练完成后，将当前损失作为一个新增的数据点(scalar_x和loss的映射对)存储到第一步设置的文件中。使用变量scalar_x记录下已经训练的批次数，作为作图的X轴坐标。

代码语言：javascript复制

data_writer.add_scalar("train/loss", avg_loss.numpy(), scalar_x)
data_writer.add_scalar("train/accuracy", avg_acc.numpy(), scalar_x)
scalar_x = scalar_x   100

步骤3：命令行启动 tensorboard。

使用“tensorboard –logdir [数据文件所在文件夹路径] 的命令启动Tensor board。在Tensor board启动后，命令行会打印出可用浏览器查阅图形结果的网址。

代码语言：javascript复制

$ tensorboard --logdir log/data

步骤4：打开浏览器，查看作图结果，如图6 所示。

查阅的网址在第三步的启动命令后会打印出来（如TensorBoard 2.0.0 at http://localhost:6006/），将该网址输入浏览器地址栏刷新页面的效果如下图所示。除了右侧对数据点的作图外，左侧还有一个控制板，可以调整诸多作图的细节。

注意：这里需要安装tensorboard，要不然无法打开这个这个。

令行会打印出可用浏览器查阅图形结果的网址。

代码语言：javascript复制

$ tensorboard --logdir log/data

步骤4：打开浏览器，查看作图结果，如图6 所示。

注意：这里需要安装tensorboard，要不然无法打开这个这个。

发布者：全栈程序员栈长，转载请注明出处：https://javaforall.cn/196350.html原文链接：https://javaforall.cn

神经网络深度学习人工智能图像处理正则表达式

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