paddlepaddle模型训练和预测之基础API

Posted 2022-09-29 修炼之路

导读

在上篇文章paddlepaddle模型训练和预测之高级API我们已经介绍过了如何通过高级API来搭建网络模型进行模型的训练和预测，这篇文章我们主要来介绍如何通过paddlepaddle的基础API来实现模型的训练，其实高级的API接口也是基于基础的API接口进行封装的。

模型训练

这里我们将上篇文章中的高级API拆分成基础的API进行介绍

• 模型构建

在高级API中，我们是采用Sequential来构建网络模型的，它能够直接将卷积、激活函数、池化等操作直接进行组合。当，我们需要设计比较复杂的结构时，就需要用到Layer来实现了

import paddle
from paddle import nn,vision
import numpy as np

class LeNet(nn.Layer):
    def __init__(self,num_classes=10):
        super(LeNet, self).__init__()
        self.num_classes = num_classes
        self.features = nn.Sequential(
            nn.Conv2D(1,6,3,stride=1,padding=1),
            nn.ReLU(),
            nn.MaxPool2D(2,2),
            nn.Conv2D(6,16,5,stride=1,padding=0),
            nn.ReLU(),
            nn.MaxPool2D(2,2)
        )
        if self.num_classes > 0:
            self.fc = nn.Sequential(
                nn.Linear(400,120),
                nn.Linear(120,84),
                nn.Linear(84,self.num_classes)
            )
    def forward(self,inputs):
        x = self.features(inputs)
        if self.num_classes > 0:
            x = paddle.flatten(x,1)
            x = self.fc(x)
        return x

• 超参设置

#模型参数设置
epochs = 10
batch_size = 64
learning_rate = 0.001
num_classes = 10
#设置输出日志的频率
freq_batch = 900

• 数据加载

在高级API中，我们是直接使用datasets将它添加到fit或evaluate接口中。而，在这里我们还需要一个DataLoader在模型进行训练和评估的时候，能够通过它从里面取数据。

#输入图片的预处理
transform = vision.Normalize(mean=[127.5],std=[127.5],data_format="CHW")
#加载数据集
train_datasets = vision.datasets.FashionMNIST(mode="train",transform=transform)
test_datasets = vision.datasets.FashionMNIST(mode="test",transform=transform)
#数据加载器
train_dataloader = paddle.io.DataLoader(train_datasets,batch_size=batch_size,shuffle=True)
test_dataloader = paddle.io.DataLoader(test_datasets,batch_size=batch_size,shuffle=False)

• 模型训练和评估

在模型训练一个epoch之后，我们会在测试集上评估模型的准确率，并且只保存best模型

#构建模型
model = LeNet(num_classes)
#设置梯度下降的优化算法
optim = paddle.optimizer.Adam(learning_rate=learning_rate,parameters=model.parameters())
#设置损失函数
loss_fn = paddle.nn.CrossEntropyLoss()
#用来保存最好的准确率的模型
best_acc = 0
#训练模型
for epoch in range(epochs):
    # 将模型切换到训练模式
    model.train()
    #使用训练集进行模型训练
    for batch_id,batch_data in enumerate(train_dataloader):
        batch_x,batch_y = batch_data
        #获取模型的预测结果
        batch_pred_y = model(batch_x)
        #计算损失
        loss = loss_fn(batch_pred_y,batch_y)
        #计算准确率
        batch_acc = paddle.metric.accuracy(batch_pred_y,batch_y)
        #反向传播
        loss.backward()
        #更新网络参数
        optim.step()
        optim.clear_grad()

        #打印日志
        if batch_id % freq_batch == 0:
            print("epoch:,batch id:,loss::.2f,acc::.4f".format(epoch,batch_id+1,
                                                               loss.numpy()[0],batch_acc.numpy()[0]))
    #将模型切换到预测模型
    model.eval()
    #用来保存loss和准确率
    loss_list = []
    acc_list = []
    for batch_id,batch_data in enumerate(test_dataloader):
        batch_x,batch_y = batch_data
        pred_batch_y = model(batch_x)
        #计算损失值
        batch_loss = loss_fn(pred_batch_y,batch_y)
        #计算准确率
        batch_acc = paddle.metric.accuracy(pred_batch_y,batch_y)
        loss_list.append(batch_loss.numpy()[0])
        acc_list.append(batch_acc.numpy()[0])
    val_loss = np.mean(loss_list)
    val_acc = np.mean(acc_list)
    print("validate data,loss::.2f,acc::.4f".format(val_loss,val_acc))
    if val_acc > best_acc:
        best_acc = val_acc
        #保存模型参数
        paddle.save(model.state_dict(),"save/best.pdparams")
        #保存模型训练中设置的参数
        paddle.save(optim.state_dict(),"save/best.pdopt")

epoch:0,batch id:1,loss:2.85,acc:0.0156
epoch:0,batch id:901,loss:0.28,acc:0.9375
validate data,loss:0.40,acc:0.8550
epoch:1,batch id:1,loss:0.30,acc:0.8438
epoch:1,batch id:901,loss:0.40,acc:0.8594
validate data,loss:0.36,acc:0.8690
epoch:2,batch id:1,loss:0.41,acc:0.8125
epoch:2,batch id:901,loss:0.34,acc:0.8438
validate data,loss:0.35,acc:0.8761
epoch:3,batch id:1,loss:0.47,acc:0.7969
epoch:3,batch id:901,loss:0.19,acc:0.9531
validate data,loss:0.33,acc:0.8825
epoch:4,batch id:1,loss:0.18,acc:0.9688
epoch:4,batch id:901,loss:0.17,acc:0.9531
validate data,loss:0.32,acc:0.8835
epoch:5,batch id:1,loss:0.41,acc:0.8750
epoch:5,batch id:901,loss:0.19,acc:0.9375
validate data,loss:0.31,acc:0.8885
epoch:6,batch id:1,loss:0.20,acc:0.9375
epoch:6,batch id:901,loss:0.24,acc:0.9062
validate data,loss:0.32,acc:0.8849
epoch:7,batch id:1,loss:0.23,acc:0.8594
epoch:7,batch id:901,loss:0.25,acc:0.8750
validate data,loss:0.32,acc:0.8882
epoch:8,batch id:1,loss:0.23,acc:0.8906
epoch:8,batch id:901,loss:0.35,acc:0.8906
validate data,loss:0.32,acc:0.8886
epoch:9,batch id:1,loss:0.17,acc:0.9375
epoch:9,batch id:901,loss:0.24,acc:0.8750
validate data,loss:0.31,acc:0.8883

• 模型加载和预测

from matplotlib import pyplot as plt

#加载模型参数
model_params = paddle.load("save/best.pdparams")
#加载模型参数
model.set_state_dict(model_params)

#显示预测的结果
plt.figure(figsize=(8,8))
#设置行和列的数量
row_num = 4
col_num = 4
for i in range(16):
    img,true_label = test_datasets[i]
    img_tensor = paddle.to_tensor([img])
    output = model(img_tensor)
    pred_label = output.argmax().numpy()
    plt.subplot(row_num,col_num,i+1)
    img = img[0]
    plt.imshow(img)
    plt.title("ture:\\n predict:".format(true_label[0],pred_label[0]))
    #隐藏x轴和y轴
    plt.xticks([])
    plt.yticks([])

plt.show()