|
代码：
在控制台输入命令下载数据集
gdown --id '1HPkcmQmFGu-3OknddKIa5dNDsR05lIQR' --output data.zip
unzip data.zip
ls
开始真正运行代码：
import numpy as np
print('Loading data ...')
data_root='./timit_11/'
train = np.load(data_root + 'train_11.npy')
train_label = np.load(data_root + 'train_label_11.npy')
test = np.load(data_root + 'test_11.npy')
print('Size of training data: {}'.format(train.shape))
print('Size of testing data: {}'.format(test.shape))
import torch
from torch.utils.data import Dataset
class TIMITDataset(Dataset):
def __init__(self, X, y=None):
self.data = torch.from_numpy(X).float()
if y is not None:
y = y.astype(np.int)
self.label = torch.LongTensor(y)
else:
self.label = None
def __getitem__(self, idx):
if self.label is not None:
return self.data[idx], self.label[idx]
else:
return self.data[idx]
def __len__(self):
return len(self.data)
VAL_RATIO = 0.2
percent = int(train.shape[0] * (1 - VAL_RATIO))
train_x, train_y, val_x, val_y = train[:percent], train_label[:percent], train[percent:], train_label[percent:]
print('Size of training set: {}'.format(train_x.shape))
print('Size of validation set: {}'.format(val_x.shape))
BATCH_SIZE = 64
from torch.utils.data import DataLoader
train_set = TIMITDataset(train_x, train_y)
val_set = TIMITDataset(val_x, val_y)
train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) #only shuffle the training data
val_loader = DataLoader(val_set, batch_size=BATCH_SIZE, shuffle=False)
import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self):
super(Classifier, self).__init__()
self.layer1 = nn.Linear(429, 1024)
self.layer2 = nn.Linear(1024, 512)
self.layer3 = nn.Linear(512, 128)
self.out = nn.Linear(128, 39)
# self.act_fn = nn.Sigmoid()
self.act_fn = nn.ReLU()
self.batch_norm1 = nn.BatchNorm1d(num_features=1024)
self.batch_norm2 = nn.BatchNorm1d(num_features=512)
self.batch_norm3 = nn.BatchNorm1d(num_features=128)
def forward(self, x):
x = self.layer1(x)
x = self.act_fn(x)
x = self.batch_norm1(x)
x = self.layer2(x)
x = self.act_fn(x)
x = self.batch_norm2(x)
x = self.layer3(x)
x = self.act_fn(x)
x = self.batch_norm3(x)
x = self.out(x)
return x
#check device
def get_device():
return 'cuda' if torch.cuda.is_available() else 'cpu'
# fix random seed
def same_seeds(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# fix random seed for reproducibility
same_seeds(0)
# get device
device = get_device()
print(f'DEVICE: {device}')
# training parameters
num_epoch = 20 # number of training epoch
learning_rate = 0.0005 # learning rate
# the path where checkpoint saved
model_path = './model.ckpt'
# create model, define a loss function, and optimizer
model = Classifier().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate, alpha=0.9)
best_acc = 0.0
for epoch in range(num_epoch):
train_acc = 0.0
train_loss = 0.0
val_acc = 0.0
val_loss = 0.0
# training
model.train() # set the model to training mode
for i, data in enumerate(train_loader):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
batch_loss = criterion(outputs, labels)
_, train_pred = torch.max(outputs, 1) # get the index of the class with the highest probability
batch_loss.backward()
optimizer.step()
train_acc += (train_pred.cpu() == labels.cpu()).sum().item()
train_loss += batch_loss.item()
# validation
if len(val_set) > 0:
model.eval() # set the model to evaluation mode
with torch.no_grad():
for i, data in enumerate(val_loader):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
batch_loss = criterion(outputs, labels)
_, val_pred = torch.max(outputs, 1)
val_acc += (val_pred.cpu() == labels.cpu()).sum().item() # get the index of the class with the highest probability
val_loss += batch_loss.item()
print('[{:03d}/{:03d}] Train Acc: {:3.6f} Loss: {:3.6f} | Val Acc: {:3.6f} loss: {:3.6f}'.format(
epoch + 1, num_epoch, train_acc/len(train_set), train_loss/len(train_loader), val_acc/len(val_set), val_loss/len(val_loader)
))
# if the model improves, save a checkpoint at this epoch
if val_acc > best_acc:
best_acc = val_acc
torch.save(model.state_dict(), model_path)
print('saving model with acc {:.3f}'.format(best_acc/len(val_set)))
else:
print('[{:03d}/{:03d}] Train Acc: {:3.6f} Loss: {:3.6f}'.format(
epoch + 1, num_epoch, train_acc/len(train_set), train_loss/len(train_loader)
))
# if not validating, save the last epoch
if len(val_set) == 0:
torch.save(model.state_dict(), model_path)
print('saving model at last epoch')
# create testing dataset
test_set = TIMITDataset(test, None)
test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False)
# create model and load weights from checkpoint
model = Classifier().to(device)
model.load_state_dict(torch.load(model_path))
predict = []
model.eval() # set the model to evaluation mode
with torch.no_grad():
for i, data in enumerate(test_loader):
inputs = data
inputs = inputs.to(device)
outputs = model(inputs)
_, test_pred = torch.max(outputs, 1) # get the index of the class with the highest probability
for y in test_pred.cpu().numpy():
predict.append(y)
with open('prediction.csv', 'w') as f:
f.write('Id,Class\n')
for i, y in enumerate(predict):
f.write('{},{}\n'.format(i, y))
与原始代码相比，modle的每一层layer增加了batch?normal层，激活函数从sigmoid换成ReLU，learning?rate为0.0005，优化器换成RMSprop，准确率提高至0.722
|