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# Author: Acer Zhang
# Datetime: 2021/3/19
# Copyright belongs to the author.
# Please indicate the source for reprinting.
# Author: Acer Zhang
# Datetime: 2021/2/25
# Copyright belongs to the author.
# Please indicate the source for reprinting.
import paddle
import paddle.nn as nn
from paddle.vision.transforms import Compose, ToTensor
from paddle.vision.datasets import MNIST
# 导入RIFLE模块
from paddle_rifle.rifle import RIFLECallback
class Net(nn.Layer):
def __init__(self, num_classes=10):
super(Net, self).__init__()
self.conv1 = nn.Conv2D(1, 3, 3)
self.mp = nn.MaxPool2D(2)
self.conv2 = nn.Conv2D(3, 16, 3)
self.mp2 = nn.MaxPool2D(2)
self.fc1 = nn.Linear(400, 100)
self.fc2 = nn.Linear(100, num_classes)
def forward(self, inputs):
x = self.conv1(inputs)
x = self.mp(x)
x = self.conv2(x)
x = self.mp2(x)
x = paddle.flatten(x, 1)
x = self.fc1(x)
x = self.fc2(x)
return x
def main(use_init: bool = False):
transform = Compose([ToTensor()])
train_data = MNIST(transform=transform)
test_data = MNIST(mode="test", transform=transform)
net = Net(num_classes=10)
fc_layer = net.fc2
model = paddle.Model(network=net,
inputs=paddle.static.InputSpec([1, 28, 28], name="ipt"),
labels=paddle.static.InputSpec([1], dtype="int64", name="lab"))
rifle_cb = RIFLECallback(fc_layer,
re_init_epoch=1,
max_re_num=3,
weight_initializer=paddle.nn.initializer.XavierNormal() if use_init else None)
sgd = paddle.optimizer.SGD(parameters=model.parameters())
loss = paddle.nn.loss.CrossEntropyLoss()
acc = paddle.metric.Accuracy((1, 5))
model.prepare(sgd, loss, acc)
# 开始训练并传入RIFLE Callback
model.fit(train_data,
test_data,
batch_size=256,
epochs=2,
log_freq=10,
callbacks=[rifle_cb])
if __name__ == '__main__':
main()
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