paddlepaddle！！！作业

import paddle
from paddle.nn import Linear
import paddle.nn.functional as F
import os
import numpy as np
import matplotlib.pyplot as plt

train_dataset = paddle.vision.datasets.MNIST(mode='train')
train_data0 = np.array(train_dataset[0][0])
train_label_0 = np.array(train_dataset[0][1])


plt.figure("Image") 
plt.figure(figsize=(2,2))
plt.imshow(train_data0, cmap=plt.cm.binary)
plt.axis('on')
plt.title('image') 
plt.show()

print("图像数据形状和对应数据为:", train_data0.shape)
print("图像标签形状和对应数据为:", train_label_0.shape, train_label_0)
print("n打印第一个batch的第一个图像，对应标签数字为{}".format(train_label_0))

实现截图：

import paddle
from paddle.nn import Linear
import paddle.nn.functional as F
import os
import numpy as np
import matplotlib.pyplot as plt

class MNIST(paddle.nn.Layer):
    def __init__(self):
        super(MNIST, self).__init__()
        
        # 定义一层全连接层，输出维度是1
        self.fc = paddle.nn.Linear(in_features=784, out_features=1)
        
    # 定义网络结构的前向计算过程
    def forward(self, inputs):
        outputs = self.fc(inputs)
        return outputs

model = MNIST()

def train(model):
    # 启动训练模式
    model.train()
    # 加载训练集 batch_size 设为 16
    train_loader = paddle.io.DataLoader(paddle.vision.datasets.MNIST(mode='train'), 
                                        batch_size=16, 
                                        shuffle=True)
    # 定义优化器，使用随机梯度下降SGD优化器，学习率设置为0.001
    opt = paddle.optimizer.SGD(learning_rate=0.001, parameters=model.parameters())

def norm_img(img):
    # 验证传入数据格式是否正确，img的shape为[batch_size, 28, 28]
    assert len(img.shape) == 3
    batch_size, img_h, img_w = img.shape[0], img.shape[1], img.shape[2]
    # 归一化图像数据
    img = img / 255
    # 将图像形式reshape为[batch_size, 784]
    img = paddle.reshape(img, [batch_size, img_h*img_w])
    
    return img
    
paddle.vision.set_image_backend('cv2')

# 声明网络结构
model = MNIST()

def train(model):
    # 启动训练模式
    model.train()
    # 加载训练集 batch_size 设为 16
    train_loader = paddle.io.DataLoader(paddle.vision.datasets.MNIST(mode='train'), 
                                        batch_size=16, 
                                        shuffle=True)
    # 定义优化器，使用随机梯度下降SGD优化器，学习率设置为0.001
    opt = paddle.optimizer.SGD(learning_rate=0.001, parameters=model.parameters())
    EPOCH_NUM = 10
    for epoch in range(EPOCH_NUM):
        for batch_id, data in enumerate(train_loader()):
            images = norm_img(data[0]).astype('float32')
            labels = data[1].astype('float32')
            
            #前向计算的过程
            predicts = model(images)
            
            # 计算损失
            loss = F.square_error_cost(predicts, labels)
            avg_loss = paddle.mean(loss)
            
            #每训练了1000批次的数据，打印下当前Loss的情况
            if batch_id % 1000 == 0:
                print("epoch_id: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, avg_loss.numpy()))
            
            #后向传播，更新参数的过程
            avg_loss.backward()
            opt.step()
            opt.clear_grad()
            
train(model)
paddle.save(model.state_dict(), './mnist.pdparams')

实现截图：