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mnist_classifier_l1loss.py
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mnist_classifier_l1loss.py
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import os
import numpy as np
import torch
import torchvision
from torch import nn, optim
from torch.utils.data.sampler import SequentialSampler, SubsetRandomSampler
from common import train, test, save_state, save_data, draw_line_graph, draw_multi_lines_graph
# model
class MnistClassifierL1Loss(nn.Module):
def __init__(self):
super().__init__()
self.fc = nn.Linear(in_features=(28 * 28), out_features=10) # 784 -> 10
def forward(self, x):
x = x.view(-1, (28 * 28)) # flatten
x = self.fc(x)
return x
# experiment
def mnist_classifier_l1loss():
# paths
path = dict()
path['project'] = os.path.dirname(os.path.abspath(__file__))
path['state'] = os.path.join(path['project'], 'epoch')
path['dataset'] = os.path.join(path['project'], 'dataset')
path['graph'] = os.path.join(path['project'], 'graph')
path['array'] = os.path.join(path['project'], 'array')
for key, value in path.items():
if not os.path.exists(path[key]):
os.mkdir(path[key])
# parameters
batch_size = 1000
number_of_epochs = 20
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
mean = 0.1307
std = 0.3081
loss = nn.L1Loss()
info_per_batch = 6
validation_ratio = 0.1
# transform
transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=(mean,), std=(std,))
])
# dataset
train_dataset = torchvision.datasets.MNIST(root=path['dataset'], train=True, transform=transform, download=True)
test_dataset = torchvision.datasets.MNIST(root=path['dataset'], train=False, transform=transform, download=True)
# validation dataset
validation_limit = int((1 - validation_ratio) * len(train_dataset))
index_list = list(range(len(train_dataset)))
train_indexes, validation_indexes = index_list[:validation_limit], index_list[validation_limit:]
train_sampler = SubsetRandomSampler(train_indexes)
validation_sampler = SequentialSampler(validation_indexes)
# dataset loaders
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, sampler=train_sampler)
validation_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size,
sampler=validation_sampler)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size)
# model
model = MnistClassifierL1Loss().to(device)
# optimizer
optimizer = optim.SGD(params=model.parameters(), lr=learning_rate)
epochs = np.arange(start=1, stop=(number_of_epochs + 1), step=1, dtype=int)
print('Mnist Classifier L1Loss')
train_losses = []
train_accuracies = []
validation_losses = []
validation_accuracies = []
test_losses = []
test_accuracies = []
for epoch in epochs:
info = 'Epoch {epoch_index}/{number_of_epochs}'
print(info.format(epoch_index=epoch, number_of_epochs=number_of_epochs))
# train
train_loss, train_accuracy = train(model=model,
device=device,
loader=train_loader,
optimizer=optimizer,
loss=loss,
info_per_batch=info_per_batch,
one_hot_encoded=True)
info = 'Train: Average Loss: {train_loss:.5f}, Accuracy: % {train_accuracy:.2f}'
print(info.format(train_loss=train_loss, train_accuracy=(100 * train_accuracy)))
train_losses.append(train_loss)
train_accuracies.append(train_accuracy)
# validation
validation_loss, validation_accuracy = test(model=model,
loader=validation_loader,
device=device,
loss=loss,
info_per_batch=info_per_batch,
info_name='Validation',
one_hot_encoded=True)
info = 'Validation: Average Loss: {validation_loss:.5f}, Accuracy: % {validation_accuracy:.2f}'
print(info.format(validation_loss=validation_loss, validation_accuracy=(100 * validation_accuracy)))
validation_losses.append(validation_loss)
validation_accuracies.append(validation_accuracy)
# test
test_loss, test_accuracy = test(model=model,
loader=test_loader,
device=device,
loss=loss,
info_per_batch=info_per_batch,
info_name='Test',
one_hot_encoded=True)
info = 'Test: Average Loss: {test_loss:.5f}, Accuracy: % {test_accuracy:.2f}'
print(info.format(test_loss=test_loss, test_accuracy=(100 * test_accuracy)))
test_losses.append(test_loss)
test_accuracies.append(test_accuracy)
# epoch state
state_file_name = 'mnist_classifier_l1loss_epoch_{epoch_index}.pkl'.format(epoch_index=epoch)
save_state(model=model, directory=path['state'], file_name=state_file_name)
# train loss
save_data(array=train_losses, directory=path['array'],
file_name='mnist_classifier_l1loss_train_loss.npy')
draw_line_graph(x=epochs, y=train_losses,
x_label='Epoch', y_label='Loss',
title='Mnist Classifier L1Loss Train Loss',
directory=path['graph'],
file_name='mnist_classifier_l1loss_train_loss.png')
# train accuracy
save_data(array=train_accuracies, directory=path['array'],
file_name='mnist_classifier_l1loss_train_accuracy.npy')
draw_line_graph(x=epochs, y=train_accuracies,
x_label='Epoch', y_label='Accuracy',
title='Mnist Classifier L1Loss Train Accuracy',
directory=path['graph'],
file_name='mnist_classifier_l1loss_train_accuracy.png')
# validation loss
save_data(array=validation_losses, directory=path['array'],
file_name='mnist_classifier_l1loss_validation_loss.npy')
draw_line_graph(x=epochs, y=validation_losses,
x_label='Epoch', y_label='Loss',
title='Mnist Classifier L1Loss Validation Loss',
directory=path['graph'],
file_name='mnist_classifier_l1loss_validation_loss.png')
# validation accuracy
save_data(array=validation_accuracies, directory=path['array'],
file_name='mnist_classifier_l1loss_validation_accuracy.npy')
draw_line_graph(x=epochs, y=validation_accuracies,
x_label='Epoch', y_label='Accuracy',
title='Mnist Classifier L1Loss Validation Accuracy',
directory=path['graph'],
file_name='mnist_classifier_l1loss_validation_accuracy.png')
# test loss
save_data(array=test_losses, directory=path['array'],
file_name='mnist_classifier_l1loss_test_loss.npy')
draw_line_graph(x=epochs, y=test_losses,
x_label='Epoch', y_label='Loss',
title='Mnist Classifier L1Loss Test Loss',
directory=path['graph'],
file_name='mnist_classifier_l1loss_test_loss.png')
# test accuracy
save_data(array=test_accuracies, directory=path['array'],
file_name='mnist_classifier_l1loss_test_accuracy.npy')
draw_line_graph(x=epochs, y=test_accuracies,
x_label='Epoch', y_label='Accuracy',
title='Mnist Classifier L1Loss Test Accuracy',
directory=path['graph'],
file_name='mnist_classifier_l1loss_test_accuracy.png')
# loss
draw_multi_lines_graph(
lines=[
dict(label='Train', data=dict(x=epochs, y=train_losses)),
dict(label='Validation', data=dict(x=epochs, y=validation_losses)),
dict(label='Test', data=dict(x=epochs, y=test_losses))
],
x_label='Epoch', y_label='Loss',
title='Mnist Classifier L1Loss Loss',
directory=path['graph'],
file_name='mnist_classifier_l1loss_loss.png')
# accuracy
draw_multi_lines_graph(
lines=[
dict(label='Train', data=dict(x=epochs, y=train_accuracies)),
dict(label='Validation', data=dict(x=epochs, y=validation_accuracies)),
dict(label='Test', data=dict(x=epochs, y=test_accuracies))
],
x_label='Epoch', y_label='Accuracy',
title='Mnist Classifier L1Loss Accuracy',
directory=path['graph'],
file_name='mnist_classifier_l1loss_accuracy.png')
# main
if __name__ == '__main__':
mnist_classifier_l1loss()