from paddle.optimizer.lr import LinearWarmup
from paddle.optimizer.lr import CosineAnnealingDecayclass Cosine(CosineAnnealingDecay):    """    Cosine learning rate decay    lr = 0.05 * (math.cos(epoch * (math.pi / epochs)) + 1)
    Args:        lr(float): initial learning rate        step_each_epoch(int): steps each epoch        epochs(int): total training epochs    """
    def __init__(self, lr, step_each_epoch, epochs, **kwargs):        super(Cosine, self).__init__(            learning_rate=lr,            T_max=step_each_epoch * epochs, )
        self.update_specified = False
class CosineWarmup(LinearWarmup):    """    Cosine learning rate decay with warmup    [0, warmup_epoch): linear warmup    [warmup_epoch, epochs): cosine decay
    Args:        lr(float): initial learning rate        step_each_epoch(int): steps each epoch        epochs(int): total training epochs        warmup_epoch(int): epoch num of warmup    """
    def __init__(self, lr, step_each_epoch, epochs, warmup_epoch=5, **kwargs):        assert epochs > warmup_epoch, "total epoch({}) should be larger than warmup_epoch({}) in CosineWarmup.".format(            epochs, warmup_epoch)        warmup_step = warmup_epoch * step_each_epoch        start_lr = 0.0        end_lr = lr        lr_sch = Cosine(lr, step_each_epoch, epochs - warmup_epoch)
        super(CosineWarmup, self).__init__(            learning_rate=lr_sch,            warmup_steps=warmup_step,            start_lr=start_lr,            end_lr=end_lr)
        self.update_specified = False

import paddleimport paddle.nn.functional as Ffrom paddle.vision.transforms import ToTensorfrom paddle import fluidimport paddle.nn as nnprint(paddle.__version__)

2.0.2

transform = ToTensor()cifar10_train = paddle.vision.datasets.Cifar10(mode='train',                                               transform=transform)cifar10_test = paddle.vision.datasets.Cifar10(mode='test',                                              transform=transform)
# 构建训练集数据加载器train_loader = paddle.io.DataLoader(cifar10_train, batch_size=64, shuffle=True)
# 构建测试集数据加载器test_loader = paddle.io.DataLoader(cifar10_test, batch_size=64, shuffle=True)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=10):        super(MyNet, self).__init__()
        self.conv1 = paddle.nn.Conv2D(in_channels=3, out_channels=32, kernel_size=(3, 3), stride=1, padding = 1)        # self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)        self.conv2 = paddle.nn.Conv2D(in_channels=32, out_channels=64, kernel_size=(3,3),  stride=2, padding = 0)        # self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        self.conv3 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=(3,3), stride=2, padding = 0)        # self.DropBlock =  DropBlock(block_size=5, keep_prob=0.9, name='le')        self.conv4 = paddle.nn.Conv2D(in_channels=64, out_channels=64, kernel_size=(3,3), stride=2, padding = 1)
        self.flatten = paddle.nn.Flatten()
        self.linear1 = paddle.nn.Linear(in_features=1024, 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)        # print(x.shape)        x = self.conv2(x)        x = F.relu(x)        # x = self.pool2(x)        # print(x.shape)
        x = self.conv3(x)        x = F.relu(x)        # print(x.shape)                # x = self.DropBlock(x)        x = self.conv4(x)        x = F.relu(x)        # print(x.shape)
        x = self.flatten(x)        x = self.linear1(x)        x = F.relu(x)        x = self.linear2(x)        return x

# 可视化模型
cnn2 = MyNet()
model2 = paddle.Model(cnn2)
model2.summary((64, 3, 32, 32))

--------------------------------------------------------------------------- Layer (type)       Input Shape          Output Shape         Param #    ===========================================================================   Conv2D-1      [[64, 3, 32, 32]]     [64, 32, 32, 32]         896         Conv2D-2      [[64, 32, 32, 32]]    [64, 64, 15, 15]       18,496        Conv2D-3      [[64, 64, 15, 15]]     [64, 64, 7, 7]        36,928        Conv2D-4       [[64, 64, 7, 7]]      [64, 64, 4, 4]        36,928        Flatten-1      [[64, 64, 4, 4]]        [64, 1024]             0          Linear-1         [[64, 1024]]           [64, 64]           65,600        Linear-2          [[64, 64]]            [64, 10]             650      ===========================================================================Total params: 159,498Trainable params: 159,498Non-trainable params: 0---------------------------------------------------------------------------Input size (MB): 0.75Forward/backward pass size (MB): 25.60Params size (MB): 0.61Estimated Total Size (MB): 26.96---------------------------------------------------------------------------{'total_params': 159498, 'trainable_params': 159498}

# 配置模型
from paddle.metric import Accuracyscheduler = CosineWarmup(        lr=0.5, step_each_epoch=100, epochs=8, warmup_steps=20, start_lr=0, end_lr=0.5, verbose=True)optim = paddle.optimizer.SGD(learning_rate=scheduler, parameters=model2.parameters())
model2.prepare(    optim,    paddle.nn.CrossEntropyLoss(),    Accuracy()    )
# 模型训练与评估model2.fit(train_loader,        test_loader,        epochs=10,        verbose=1,        )

The loss value printed in the log is the current step, and the metric is the average value of previous step.Epoch 1/3/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  return (isinstance(seq, collections.Sequence) andstep 782/782 [==============================] - loss: 1.9828 - acc: 0.2280 - 106ms/step         Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 157/157 [==============================] - loss: 1.5398 - acc: 0.3646 - 35ms/step        Eval samples: 10000Epoch 2/3step 782/782 [==============================] - loss: 1.7682 - acc: 0.3633 - 106ms/step         Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 157/157 [==============================] - loss: 1.7934 - acc: 0.3867 - 34ms/step        Eval samples: 10000Epoch 3/3step 782/782 [==============================] - loss: 1.3394 - acc: 0.4226 - 105ms/step         Eval begin...The loss value printed in the log is the current batch, and the metric is the average value of previous step.step 157/157 [==============================] - loss: 1.4539 - acc: 0.3438 - 35ms/step        Eval samples: 10000