def main():
data = pd.read_csv('wine.csv')
y = data['class'].values
X = data.drop('class', axis=1).values
X = normalize(X) #数据标准化
label = ['best', 'better', 'good']
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4) #将数划分为训练集和测试集，标签也做同样的划分
clf = NaiveBayes() #引用朴素贝叶斯分类器
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
while accuracy < 0.98:
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4)
clf = NaiveBayes()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# 使用PCA将维数降为2并绘制结果
Plot().plot_in_2d(X_test,
y_pred,
title="Naive Bayes",
accuracy=accuracy,
legend_labels=label)
def evaluate():
use_cuda = torch.cuda.is_available()
path = os.path.expanduser('/home/yxk/data/')
val_data = voc_loader.VOC2012ClassSeg(root=path,
split='val',
transform=True)
val_loader = torch.utils.data.DataLoader(val_data,
batch_size=1,
shuffle=False,
num_workers=5)
print('load model .....')
vgg_model = models.VGGNet(requires_grad=True)
fcn_model = models.FCN8s(pretrained_net=vgg_model, n_class=n_class)
fcn_model.load_state_dict(torch.load('params.pth'))
if use_cuda:
fcn_model.cuda()
fcn_model.eval()
label_trues, label_preds = [], []
# for idx, (img, label) in enumerate(val_loader):
for idx in range(len(val_data)):
img, label = val_data[idx]
img = img.unsqueeze(0)
if use_cuda:
img = img.cuda()
img = Variable(img)
out = fcn_model(img) # 1, 21, 320, 320
pred = out.data.max(1)[1].squeeze_(1).squeeze_(0) # 320, 320
if use_cuda:
pred = pred.cpu()
label_trues.append(label.numpy())
label_preds.append(pred.numpy())
if idx % 30 == 0:
print('evaluate [%d/%d]' % (idx, len(val_loader)))
metrics = tools.accuracy_score(label_trues, label_preds)
metrics = np.array(metrics)
metrics *= 100
print('''\
Accuracy: {0}
Accuracy Class: {1}
Mean IU: {2}
FWAV Accuracy: {3}'''.format(*metrics))
def oneFit(X,
y,
activation="relu",
hidden_layers=(20, 20),
test_size=0.2,
loss=False):
'''
process of one fit
loss: return loss during iteration information if True
return accuracy or array of loss
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=test_size)
y_label = array2Label(y_test) # transform 2d array into 1d labels
# 激活函数使用relu
nn = NeuralNetworkClassifier(hidden_layer_sizes=hidden_layers,
activation=activation)
nn.fit(X_train, y_train)
y_pred = nn.predict(X_test)
# random guess 0.006
if not loss:
return accuracy_score(y_label, y_pred)
return nn.getIterLoss()
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
avg_loss += loss.data[0]
# Compute stats
net.eval()
avg_loss /= len(trainloader)
val_loss = compute_loss(criterion, validationloder, net)
VAL_LOSS.append(val_loss)
if epoch % display_step == 0:
test_acc = accuracy_score(net, testloader)
val_acc = accuracy_score(net, validationloder)
test_loss = compute_loss(criterion, testloader, net)
train_acc = accuracy_score(net, trainloader)
print(
"Epoch: {}, Time: {:.0f}, Train loss: {:.3f}, Validation accuracy: {:.3f}, Validation loss: {:.3f}"
.format(epoch,
time() - start_time, avg_loss, val_acc, val_loss))
TEST_ACC.append(test_acc)
VAL_ACC.append(val_acc)
TRAIN_ACC.append(train_acc)
TEST_LOSS.append(test_loss)
TRAIN_LOSS.append(avg_loss)
if len(VAL_LOSS) > early_stopping: