手写体识别。（待续）

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])

import matplotlib.pyplot as plt
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("tuxiang:",train_data0.shape)
print("tuxiang:",train_label_0.shape,train_label_0)
print("ndayintuxiangshuzi{}".format(train_label_0))

class MNIST(paddle.nn.Layer):
    def __init__(self):
        super(MNIST,self).__init__()

        self.fc = paddle.nn.Linear(in_features=784, out_features=1)

        def forward(self, inputs):
            outputs = self.fc(inputs)
            return outputs
        model = MNIST()

        def train(model):

            model.train()

            train_loader = paddle.io.DataLoader(paddle.vision.datasets.MNIST(mode='train'),batch_size=16,shuffle=True)

            opt = paddle.optimizer.SGD(learning_rate=0.001,parameters=model.parameters())

            def norm_img(img):

                assert len(img.shape) == 3
                batch_size,img_h,img_w = img.shape[0],img.shape[1],img.shape[2]

                img = img / 255

                img = paddle.reshape(img,[batch_size,img_h*img_w])

                return img
            import paddle

            paddle.vision.set_image_backend('cv2')

            model = MNIST()

            def train(model):

                model.train()

                train_loader = paddle.io.DataLoader(paddle.vision.datasets.MNIST(mode='train'),batch_size=16,shuffle=True)

                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')

                        predicts = model(images)

                        loss = F.square_error_cost(predicts,labels)
                        avg_loss = paddle.mean(loss)

                        if batch_id % 1000 == 0:
                            print("epoch_id:{},loss is: {}".format(epoch,batch_id,avg_loss.numpy()))

                            avg_loss.backward()
                            opt.step()
                            opt.clear_grad()

            train(model)
            paddle.save(model.state_dict(),'.mnist.pdparams')