Introduction to paddle - using lenet to realize image classification method II in MNIST

Use LeNet stay MNIST Implement image classification method II

Compared with method 1 , Method 2 A little bit of a problem , Suitable for advanced

5、 ... and 、 The way 2： Based on the foundation API, Complete the training and prediction of the model

 

5.1 model training ¶

After networking , Start training the model , Build first train_loader, Load training data , Then define train function , After setting the loss function , Press batch Load data , Complete the training of the model .

import paddle.nn.functional as F
train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)
#  Load training set  batch_size  Set to  64
def train(model):
    model.train()
    epochs = 2
    optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
    #  use Adam As an optimization function 
    for epoch in range(epochs):
        for batch_id, data in enumerate(train_loader()):
            x_data = data[0]
            y_data = data[1]
            predicts = model(x_data)
            loss = F.cross_entropy(predicts, y_data)
            #  Calculate the loss 
            acc = paddle.metric.accuracy(predicts, y_data)
            loss.backward()
            if batch_id % 300 == 0:
                print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy()))
            optim.step()
            optim.clear_grad()
model = LeNet()
train(model)

  Running results

epoch: 0, batch_id: 0, loss is: [3.2611141], acc is: [0.078125]
epoch: 0, batch_id: 300, loss is: [0.24404016], acc is: [0.921875]
epoch: 0, batch_id: 600, loss is: [0.03953885], acc is: [1.]
epoch: 0, batch_id: 900, loss is: [0.03700985], acc is: [0.984375]
epoch: 1, batch_id: 0, loss is: [0.05806625], acc is: [0.96875]
epoch: 1, batch_id: 300, loss is: [0.06538856], acc is: [0.953125]
epoch: 1, batch_id: 600, loss is: [0.03884572], acc is: [0.984375]
epoch: 1, batch_id: 900, loss is: [0.01922364], acc is: [0.984375]

5.2 Model validation ¶

After training , The effect of the model needs to be verified , here , Load test data set , Then use the trained model to predict the test set , Calculation loss and accuracy .

test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64)
#  Load test data set 
def test(model):
    model.eval()
    batch_size = 64
    for batch_id, data in enumerate(test_loader()):
        x_data = data[0]
        y_data = data[1]
        predicts = model(x_data)
        #  Get forecast results 
        loss = F.cross_entropy(predicts, y_data)
        acc = paddle.metric.accuracy(predicts, y_data)
        if batch_id % 20 == 0:
            print("batch_id: {}, loss is: {}, acc is: {}".format(batch_id, loss.numpy(), acc.numpy()))
test(model)

  Running results

batch_id: 0, loss is: [0.01972857], acc is: [0.984375]
batch_id: 20, loss is: [0.19958115], acc is: [0.9375]
batch_id: 40, loss is: [0.23575728], acc is: [0.953125]
batch_id: 60, loss is: [0.07018849], acc is: [0.984375]
batch_id: 80, loss is: [0.02309197], acc is: [0.984375]
batch_id: 100, loss is: [0.00239462], acc is: [1.]
batch_id: 120, loss is: [0.01583934], acc is: [1.]
batch_id: 140, loss is: [0.00399609], acc is: [1.]

End of mode 2 ¶

The above is mode 2 , Through the ground floor API, You can clearly see every step of the training and testing process . however , This way is more complicated . therefore , We offer a training method , Use high-level API To complete the training and prediction of the model . Compare the bottom API, high-level API Can be faster 、 Efficiently complete the training and testing of the model .

6、 ... and 、 summary ¶

The above is the use of LeNet For handwritten digital data and MNIST To classify . This example provides two ways to train the model , One can quickly complete the establishment and prediction of the model , It is very suitable for novice users . The other requires multiple steps to complete the training of the model , Suitable for advanced users .

The complete code of method 2 is as follows ：

import os
import cv2
import numpy as np
from paddle.io import Dataset
import paddle.vision.transforms as T

import matplotlib.pyplot as plt


import paddle.nn.functional as F
import paddle
from paddle.metric import Accuracy

from paddle.vision.transforms import Compose, Normalize

transform = Compose([Normalize(mean=[127.5],
                               std=[127.5],
                               data_format='CHW')])
#  Use transform Normalize the data set 
print('download training data and load training data')
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
print('load finished')
class LeNet(paddle.nn.Layer):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
        self.max_pool1 = paddle.nn.MaxPool2D(kernel_size=2,  stride=2)
        self.conv2 = paddle.nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1)
        self.max_pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)
        self.linear2 = paddle.nn.Linear(in_features=120, out_features=84)
        self.linear3 = paddle.nn.Linear(in_features=84, out_features=10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.max_pool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.max_pool2(x)
        x = paddle.flatten(x, start_axis=1,stop_axis=-1)
        x = self.linear1(x)
        x = F.relu(x)
        x = self.linear2(x)
        x = F.relu(x)
        x = self.linear3(x)
        return x

# After networking , Start training the model , Build first train_loader, Load training data , Then define train function , After setting the loss function , Press batch Load data , Complete the training of the model .

train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)
#  Load training set  batch_size  Set to  64
def train(model):
    model.train()
    epochs = 2
    optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
    #  use Adam As an optimization function 
    for epoch in range(epochs):
        for batch_id, data in enumerate(train_loader()):
            x_data = data[0]
            y_data = data[1]
            predicts = model(x_data)
            loss = F.cross_entropy(predicts, y_data)
            #  Calculate the loss 
            acc = paddle.metric.accuracy(predicts, y_data)
            loss.backward()
            if batch_id % 300 == 0:
                print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy()))
            optim.step()
            optim.clear_grad()
model = LeNet()
train(model)