[人工智能]卷积神经网络图像分类

# 查看当前挂载的数据集目录, 该目录下的变更重启环境后会自动还原
# View dataset directory. 
# This directory will be recovered automatically after resetting environment. 
!ls /home/aistudio/data

# 查看工作区文件, 该目录下的变更将会持久保存. 请及时清理不必要的文件, 避免加载过慢.
# View personal work directory. 
# All changes under this directory will be kept even after reset. 
# Please clean unnecessary files in time to speed up environment loading. 
!ls /home/aistudio/work

# 如果需要进行持久化安装, 需要使用持久化路径, 如下方代码示例:
# If a persistence installation is required, 
# you need to use the persistence path as the following: 
!mkdir /home/aistudio/external-libraries
!pip install beautifulsoup4 -t /home/aistudio/external-libraries

# 同时添加如下代码, 这样每次环境(kernel)启动的时候只要运行下方代码即可: 
# Also add the following code, 
# so that every time the environment (kernel) starts, 
# just run the following code: 
import sys 
sys.path.append('/home/aistudio/external-libraries')

请点击此处查看本环境基本用法.

Please click here for more detailed instructions.

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__)

2.1.2

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

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=(2, 2))
        self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)

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

        self.conv3 = paddle.nn.Conv2D(in_channels=64, out_channels=128, kernel_size=(2,2))
        self.pool3 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        
        self.conv4 = paddle.nn.Conv2D(in_channels=128, out_channels=128, kernel_size=(2,2))

        self.flatten = paddle.nn.Flatten()

        self.linear1 = paddle.nn.Linear(in_features=512, 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.pool3(x)

        x = self.conv4(x)
        x = F.relu(x)

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

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: [2.6684964]

plt.plot(val_acc_history, label = 'validation accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 0.8])
_history, label = 'validation accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 0.8])
plt.legend(loc='lower right')

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  if isinstance(obj, collections.Iterator):
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  return list(data) if isinstance(data, collections.MappingView) else data





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[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-LxxLVrCe-1634309325209)(output_10_2.png)]