PyTorch Section 11

* 강사님의 자료는 저작권이 있기 때문에 배운 내용을 최대한 간단하게 정리하는 수준으로 작성하였습니다.

training, validating, testing 코드 템플릿

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Subset
from torhvision import datasets, transforms
from sklearn.model_selection import train_test_split
import numpy as np

# -- train, val split --
validation_rate = 
train_indexes, val_indexes, _, _ = train_test_split(
	# x indexes,
    # y (targets),
    stratify = ,
    test_size = 
    )
    
# -- train_set, val_set --
train_set = Subset( , train_indexes)
val_set = Subset(, val_indexes)

# -- minibatch --
batch_size = 

train_batches = DataLoader(train_set, batch_size = batch_size, shuffle = True)
val_batches = DataLoader(val_set, batch_size = batch_size = shuffle = True)
test_batches = DataLoader(test_set, batch_size = batch_size, shuffle = False)

# -- example model --
class ExampleModel(nn.Module):
	def __init__(self, input_dim, output_dim):
    super().__init__()
    
    	self.linear_layers = nn.Sequential(
    		...
    	)
     
     def forward(self, x):
     y = self.linear_layers(x)
     return y
     
 minibatch_size = 
 input_dim = 
 output_dim = 
 model = ExampleModel(input_dim, output_dim)
 
 loss_func = nn.NLLLoss()
 optimizer = torch.optim.Adam(model.parameters())
 
 # -- training model --
 from copy import deepcopy
 
 def train(model, early_stop, n_epochs, progress_interval):
 
 	train_losses, valid_losses, lowest_loss = [], [], np.inf
    
    for epoch in range(n_epochs):
    	...
    
    return ...
  
 nb_epochs = 
 progress_interval = 
 early_stop = 
 
 model, lowest_lowss, train_losses, valid_losses = train_model(...)
 
 # -- evaluation with test dataset --
 test_loss = 0
 correct = 0
 model.eval()
 
 ...

* written on July 4th