This is a repository where I personally save machine learning models, most of which are based on pytorch.
These models are not all fully tested. It is very likely the codes contain bugs. Be careful when you use the code.
Typically, models have the following methods.
These methods are necessary to use the modules in my_utils such as Trainer and Evaluator.
def forward(self, inputs):
'''
Perform forward computation.
'''
return output
def fit(self, inputs, labels, optimizer):
'''
Caliculate loss and update parameters.
'''
return loss_item
def predict(self, inputs):
'''
Outputs predicted labels (or values) from inputs.
'''
return predicted>>> train, test = get_dataset()
>>> from my_utils import get_device
>>> device = get_device()
===== Device =====
cpu
>>> from my_utils import get_device, DataLoader, torch_stack
>>> train_loader = DataLoader(train, batch_size=64, trans_func=torch_stack)
>>> test_loader = DataLoader(test, batch_size=64, trans_func=torch_stack)
>>> from torch_models import MLP
>>> model = MLP([784, 50, 10])
>>> print(model)
MLP(
(fc_0): Linear(in_features=784, out_features=50, bias=True)
(fc_out): Linear(in_features=50, out_features=10, bias=True)
(criterion): CrossEntropyLoss()
(activation): Tanh()
)
>>> from torch.optim import SGD
>>> from my_utils import Trainer, EvaluatorC
>>> optimizer = SGD(model.parameters(), lr=0.1)
>>> trainer = Trainer(model, train_loader)
>>> evaluator = EvaluatorC(model, test_loader)
>>> trainer.train_epoch(optimizer, max_epoch=10, evaluator=evaluator, show_log=True)
epoch 0 loss: 0.1835 accuracy: 0.9491
epoch 1 loss: 0.1595 accuracy: 0.9544
epoch 2 loss: 0.1428 accuracy: 0.956
epoch 3 loss: 0.1298 accuracy: 0.9598
epoch 4 loss: 0.1188 accuracy: 0.9629
epoch 5 loss: 0.1094 accuracy: 0.964
epoch 6 loss: 0.1022 accuracy: 0.9663
epoch 7 loss: 0.09551 accuracy: 0.9665
epoch 8 loss: 0.08942 accuracy: 0.968
epoch 9 loss: 0.08407 accuracy: 0.9706