paddle一个由三个卷积层组成的网络完成cifar10数据集的图像分类任务

paddle一个由三个卷积层组成的网络完成cifar10数据集的图像分类任务

文章内容 来源 paddle 官网，代码并不十分完整，部分有修改，保证完整的运行代码和效果图

摘要: 本示例教程将会演示如何使用飞桨的卷积神经网络来完成图像分类任务。这是一个较为简单的示例，将会使用一个由三个卷积层组成的网络完成cifar10数据集的图像分类任务。 

一、环境配置

 

import paddle
import paddle.nn.functional as F
from paddle.vision.transforms import ToTensor
import numpy as np
import matplotlib.pyplot as plt

print(paddle.__version__)

paddle的安装和配置方法  这里略了

二、加载数据集

本案例将会使用飞桨提供的API完成数据集的下载并为后续的训练任务准备好数据迭代器。cifar10数据集由60000张大小为32 * 32的彩色图片组成，其中有50000张图片组成了训练集，另外10000张图片组成了测试集。这些图片分为10个类别，将训练一个模型能够把图片进行正确的分类。 

 

transform = ToTensor()
cifar10_train = paddle.vision.datasets.Cifar10(mode='train',
                                               transform=transform)
cifar10_test = paddle.vision.datasets.Cifar10(mode='test',
                                              transform=transform)

三、组建网络

接下来使用飞桨定义一个使用了三个二维卷积（ Conv2D ) 且每次卷积之后使用 relu 激活函数，两个二维池化层（ MaxPool2D ），和两个线性变换层组成的分类网络，来把一个(32, 32, 3)形状的图片通过卷积神经网络映射为10个输出，这对应着10个分类的类别。

 

class MyNet(paddle.nn.Layer):
    def __init__(self, num_classes=1):
        super(MyNet, self).__init__()

        self.conv1 = paddle.nn.Conv2D(in_channels=3, out_channels=32, kernel_size=(3, 3))
        self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)

        self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=(3,3))
        self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)

        self.conv3 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=(3,3))

        self.flatten = paddle.nn.Flatten()

        self.linear1 = paddle.nn.Linear(in_features=1024, out_features=64)
        self.linear2 = paddle.nn.Linear(in_features=64, out_features=num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = F.relu(x)
        x = self.pool2(x)

        x = self.conv3(x)
        x = F.relu(x)

        x = self.flatten(x)
        x = self.linear1(x)
        x = F.relu(x)
        x = self.linear2(x)
        return x

四、模型训练&预测¶

接下来，用一个循环来进行模型的训练，将会:

• 使用 paddle.optimizer.Adam 优化器来进行优化。

• 使用 F.cross_entropy 来计算损失值。

• 使用 paddle.io.DataLoader 来加载数据并组建batch。

epoch_num = 10
batch_size = 32
learning_rate = 0.001

val_acc_history = []
val_loss_history = []

def train(model):
    print('start training ... ')
    # turn into training mode
    model.train()

    opt = paddle.optimizer.Adam(learning_rate=learning_rate,
                                parameters=model.parameters())

    train_loader = paddle.io.DataLoader(cifar10_train,
                                        shuffle=True,
                                        batch_size=batch_size)

    valid_loader = paddle.io.DataLoader(cifar10_test, batch_size=batch_size)
    
    for epoch in range(epoch_num):
        for batch_id, data in enumerate(train_loader()):
            x_data = data[0]
            y_data = paddle.to_tensor(data[1])
            y_data = paddle.unsqueeze(y_data, 1)

            logits = model(x_data)
            loss = F.cross_entropy(logits, y_data)

            if batch_id % 1000 == 0:
                print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, loss.numpy()))
            loss.backward()
            opt.step()
            opt.clear_grad()

        # evaluate model after one epoch
        model.eval()
        accuracies = []
        losses = []
        for batch_id, data in enumerate(valid_loader()):
            x_data = data[0]
            y_data = paddle.to_tensor(data[1])
            y_data = paddle.unsqueeze(y_data, 1)

            logits = model(x_data)
            loss = F.cross_entropy(logits, y_data)
            acc = paddle.metric.accuracy(logits, y_data)
            accuracies.append(acc.numpy())
            losses.append(loss.numpy())

        avg_acc, avg_loss = np.mean(accuracies), np.mean(losses)
        print("[validation] accuracy/loss: {}/{}".format(avg_acc, avg_loss))
        val_acc_history.append(avg_acc)
        val_loss_history.append(avg_loss)
        model.train()

model = MyNet(num_classes=10)
train(model)

运行结果

start training ... 
epoch: 0, batch_id: 0, loss is: [2.7433677]
epoch: 0, batch_id: 1000, loss is: [1.5053985]
[validation] accuracy/loss: 0.5752795338630676/1.1952502727508545
epoch: 1, batch_id: 0, loss is: [1.2686675]
epoch: 1, batch_id: 1000, loss is: [0.6766195]
[validation] accuracy/loss: 0.6521565318107605/0.9908956289291382
epoch: 2, batch_id: 0, loss is: [0.97449476]
epoch: 2, batch_id: 1000, loss is: [0.7748282]
[validation] accuracy/loss: 0.680111825466156/0.9200474619865417
epoch: 3, batch_id: 0, loss is: [0.7913307]
epoch: 3, batch_id: 1000, loss is: [1.0034081]
[validation] accuracy/loss: 0.6979832053184509/0.8721970915794373
epoch: 4, batch_id: 0, loss is: [0.6251695]
epoch: 4, batch_id: 1000, loss is: [0.6004331]
[validation] accuracy/loss: 0.6930910348892212/0.8982931971549988
epoch: 5, batch_id: 0, loss is: [0.6123275]
epoch: 5, batch_id: 1000, loss is: [0.8438066]
[validation] accuracy/loss: 0.710463285446167/0.8458449840545654
epoch: 6, batch_id: 0, loss is: [0.47533002]
epoch: 6, batch_id: 1000, loss is: [0.41863057]
[validation] accuracy/loss: 0.7125598788261414/0.8965839147567749
epoch: 7, batch_id: 0, loss is: [0.64983004]
epoch: 7, batch_id: 1000, loss is: [0.61536294]
[validation] accuracy/loss: 0.7009784579277039/0.9212258458137512
epoch: 8, batch_id: 0, loss is: [0.79953825]
epoch: 8, batch_id: 1000, loss is: [0.6168741]
[validation] accuracy/loss: 0.7134584784507751/0.8829751014709473
epoch: 9, batch_id: 0, loss is: [0.33510458]
epoch: 9, batch_id: 1000, loss is: [0.3573485]
[validation] accuracy/loss: 0.6938897967338562/0.9611227512359619

显示曲线图的的代码

plt.plot(val_acc_history, label = 'validation accuracy')

plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 0.8])
plt.legend(loc='lower right')

显示如下