使用卷积神经网络进行图像分类

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一、环境配置

本教程基于Paddle 2.0 编写,如果你的环境不是本版本,请先参考官网安装 Paddle 2.0 。

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__)
2.0.1

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)
Cache file /home/aistudio/.cache/paddle/dataset/cifar/cifar-10-python.tar.gz not found, downloading https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz
Begin to download

Download finished

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)
start training ...
epoch: 0, batch_id: 0, loss is: [2.402275]
epoch: 0, batch_id: 1000, loss is: [1.4317133]
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')
<matplotlib.legend.Legend at 0x12c3686d0>

png

The End

从上面的示例可以看到,在cifar10数据集上,使用简单的卷积神经网络,用飞桨可以达到70%以上的准确率。你也可以通过调整网络结构和参数,达到更好的效果。


标题:使用卷积神经网络进行图像分类
作者:给我丶鼓励
地址:https://blog.doiduoyi.com/articles/1615987087521.html

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