MNISTඅත්හදා බැලීම

11import torch.nn as nn
12import torch.utils.data
13from labml_helpers.module import Module
14
15from labml import tracker
16from labml.configs import option
17from labml_helpers.datasets.mnist import MNISTConfigs as MNISTDatasetConfigs
18from labml_helpers.device import DeviceConfigs
19from labml_helpers.metrics.accuracy import Accuracy
20from labml_helpers.train_valid import TrainValidConfigs, BatchIndex, hook_model_outputs
21from labml_nn.optimizers.configs import OptimizerConfigs

24class MNISTConfigs(MNISTDatasetConfigs, TrainValidConfigs):

32    optimizer: torch.optim.Adam

34    device: torch.device = DeviceConfigs()

37    model: Module

39    epochs: int = 10

42    inner_iterations = 10

45    accuracy = Accuracy()

47    loss_func = nn.CrossEntropyLoss()

49    def init(self):

54        tracker.set_scalar("loss.*", True)
55        tracker.set_scalar("accuracy.*", True)

57        hook_model_outputs(self.mode, self.model, 'model')

62        self.state_modules = [self.accuracy]

64    def step(self, batch: any, batch_idx: BatchIndex):

70        self.model.train(self.mode.is_train)

73        data, target = batch[0].to(self.device), batch[1].to(self.device)

76        if self.mode.is_train:
77            tracker.add_global_step(len(data))

80        with self.mode.update(is_log_activations=batch_idx.is_last):

82            output = self.model(data)

85        loss = self.loss_func(output, target)
86        tracker.add("loss.", loss)

89        self.accuracy(output, target)
90        self.accuracy.track()

93        if self.mode.is_train:

95            loss.backward()

97            self.optimizer.step()

99            if batch_idx.is_last:
100                tracker.add('model', self.model)

102            self.optimizer.zero_grad()

105        tracker.save()

108@option(MNISTConfigs.optimizer)
109def _optimizer(c: MNISTConfigs):

113    opt_conf = OptimizerConfigs()
114    opt_conf.parameters = c.model.parameters()
115    opt_conf.optimizer = 'Adam'
116    return opt_conf