分类： Python/Ruby
2022-07-27 17:35:09
import numpy as np
from layers import FCLayer
from dataloader import build_dataloader
from network import Network
from optimizer import SGD
from loss import SoftmaxCrossEntropyLoss
from visualize import plot_loss_and_acc
class Solver(object):
def __init__(self, cfg):
self.cfg = cfg
# build dataloader
train_loader, val_loader, test_loader = self.build_loader(cfg)
self.train_loader = train_loader
self.val_loader = val_loader
self.test_loader = test_loader
# build model
self.model = self.build_model(cfg)
# build optimizer
self.optimizer = self.build_optimizer(self.model, cfg)
# build evaluation criterion
self.criterion = SoftmaxCrossEntropyLoss()
@staticmethod
def build_loader(cfg):
train_loader = build_dataloader(
cfg['data_root'], cfg['max_epoch'], cfg['batch_size'], shuffle=True, mode='train')
val_loader = build_dataloader(
cfg['data_root'], 1, cfg['batch_size'], shuffle=False, mode='val')
test_loader = build_dataloader(
cfg['data_root'], 1, cfg['batch_size'], shuffle=False, mode='test')
return train_loader, val_loader, test_loader
@staticmethod
def build_model(cfg):
model = Network()
model.add(FCLayer(784, 10))
return model
@staticmethod
def build_optimizer(model, cfg):
return SGD(model, cfg['learning_rate'], cfg['momentum'])
def train(self):
max_epoch = self.cfg['max_epoch']
epoch_train_loss, epoch_train_acc = [], []
for epoch in range(max_epoch):
iteration_train_loss, iteration_train_acc = [], []
for iteration, (images, labels) in enumerate(self.train_loader):
# forward pass
logits = self.model.forward(images)
loss, acc = self.criterion.forward(logits, labels)
# backward_pass
delta = self.criterion.backward()
self.model.backward(delta)
# updata the model weights
self.optimizer.step()
# restore loss and accuracy
iteration_train_loss.append(loss)
iteration_train_acc.append(acc)
# display iteration training info
if iteration % self.cfg['display_freq'] == 0:
print("Epoch [{}][{}]\t Batch [{}][{}]\t Training Loss {:.4f}\t Accuracy {:.4f}".format(
epoch, max_epoch, iteration, len(self.train_loader), loss, acc))
avg_train_loss, avg_train_acc = np.mean(iteration_train_loss), np.mean(iteration_train_acc)
epoch_train_loss.append(avg_train_loss)
epoch_train_acc.append(avg_train_acc)
# validate
avg_val_loss, avg_val_acc = self.validate()
# display epoch training info
print('\nEpoch [{}]\t Average training loss {:.4f}\t Average training accuracy {:.4f}'.format(
epoch, avg_train_loss, avg_train_acc))
# display epoch valiation info
print('Epoch [{}]\t Average validation loss {:.4f}\t Average validation accuracy {:.4f}\n'.format(
epoch, avg_val_loss, avg_val_acc))
return epoch_train_loss, epoch_train_acc
def validate(self):
logits_set, labels_set = [], []
for images, labels in self.val_loader:
logits = self.model.forward(images)
logits_set.append(logits)
labels_set.append(labels)
logits = np.concatenate(logits_set)
labels = np.concatenate(labels_set)
loss, acc = self.criterion.forward(logits, labels)
return loss, acc
def test(self):
logits_set, labels_set = [], []
for images, labels in self.test_loader:
logits = self.model.forward(images)
logits_set.append(logits)
labels_set.append(labels)
logits =跟单网gendan5.com np.concatenate(logits_set)
labels = np.concatenate(labels_set)
loss, acc = self.criterion.forward(logits, labels)
return loss, acc
if __name__ == '__main__':
# You can modify the hyerparameters by yourself.
relu_cfg = {
'data_root': 'data',
'max_epoch': 10,
'batch_size': 100,
'learning_rate': 0.1,
'momentum': 0.9,
'display_freq': 50,
'activation_function': 'relu',
}
runner = Solver(relu_cfg)
relu_loss, relu_acc = runner.train()
test_loss, test_acc = runner.test()
print('Final test accuracy {:.4f}\n'.format(test_acc))
# You can modify the hyerparameters by yourself.
sigmoid_cfg = {
'data_root': 'data',
'max_epoch': 10,
'batch_size': 100,
'learning_rate': 0.1,
'momentum': 0.9,
'display_freq': 50,
'activation_function': 'sigmoid',
}
runner = Solver(sigmoid_cfg)
sigmoid_loss, sigmoid_acc = runner.train()
test_loss, test_acc = runner.test()
print('Final test accuracy {:.4f}\n'.format(test_acc))
plot_loss_and_acc({
"relu": [relu_loss, relu_acc],
"sigmoid": [sigmoid_loss, sigmoid_acc],
})
