for i in range(nb_classes):
时间: 2024-05-26 15:15:23 浏览: 20
This is a Python code snippet using a for loop to iterate over a range of values. The range() function generates a sequence of numbers starting from 0 (by default) up to the specified number of nb_classes. The loop variable i takes on the values in this sequence one by one, and the code within the loop body is executed for each value of i. The purpose of this code snippet depends on the context in which it is used, but it is often used in machine learning for iterating over the number of classes in a classification problem.
相关问题
TypeError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_10804\3545198718.py in <module> 9 best_knn.fit(har_train_ftrs,har_train_cls) 10 # 进行预测 ---> 11 knn_eva=evaluate_classification(har_train_ftrs, har_test_ftrs, har_train_cls, har_test_cls,best_knn) ~\AppData\Local\Temp\ipykernel_10804\127123804.py in evaluate_classification(har_train_ftrs, har_test_ftrs, har_train_cls, har_test_cls, classification_model) 44 for i in range(n_classes): 45 fpr[i], tpr[i], _ = roc_curve(har_test_one_hot[:, i], har_pred_prob[:, i]) ---> 46 roc_auc[i] = auc(fpr[i], tpr[i]) 47 48 plt.figure() TypeError: 'numpy.float64' object is not callable请问这是什么错误
这个错误通常发生在你命名了一个名为`auc`的变量,然后尝试将其当作函数来调用。由于`auc`是一个numpy数组中的浮点数,因此会引发`TypeError`。
为了解决这个问题,你可以更改变量名,以避免与auc函数重名。例如,将变量名`auc`更改为`auc_score`或其他不会引起冲突的名称。
修改后的代码示例:
```python
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
# 模型名称
models = ['knn', 'forest', 'xgboost', 'svm', 'decision', 'nb']
# 模型评估结果
evaluation_results = {
'knn': knn_eva,
'forest': forest_eva,
'xgboost': xgboost_eva,
'svm': svm_eva,
'decision': decision_eva,
'nb': nb_eva
}
# 评估指标
metrics = ['Accuracy', 'Precision', 'Recall', 'F1-score', 'AUC']
# 绘制条形图
for metric in metrics:
values = [evaluation_results[model][metric] for model in models]
plt.bar(models, values)
plt.xlabel('Models')
plt.ylabel(metric)
plt.title(f'{metric} for each model')
plt.show()
```
请确保在修改后的代码中没有重复定义`auc`变量。如果你仍然遇到问题,请检查其他部分的代码是否存在类似的命名冲突。
上述代码pytorch实现
以下是使用PyTorch实现上述TCN模型的代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, dilation_rate, nb_filters, kernel_size, padding, dropout_rate=0.0):
super(ResidualBlock, self).__init__()
# 第一层卷积
self.conv1 = nn.Conv1d(in_channels=nb_filters, out_channels=nb_filters, kernel_size=kernel_size,
dilation=dilation_rate, padding=padding)
self.bn1 = nn.BatchNorm1d(num_features=nb_filters)
# 第二层卷积
self.conv2 = nn.Conv1d(in_channels=nb_filters, out_channels=nb_filters, kernel_size=kernel_size,
dilation=dilation_rate, padding=padding)
self.bn2 = nn.BatchNorm1d(num_features=nb_filters)
# 添加dropout
self.dropout = nn.Dropout(p=dropout_rate)
def forward(self, x):
# 第一层卷积
res = self.bn1(self.conv1(x))
res = F.relu(res)
# 第二层卷积
res = self.bn2(self.conv2(res))
res = F.relu(res)
# 添加残差连接
res += x
# 添加dropout
res = self.dropout(res)
return res
class ResidualPooling(nn.Module):
def __init__(self, nb_filters, kernel_size, padding='valid'):
super(ResidualPooling, self).__init__()
# 第一层卷积
self.conv1 = nn.Conv1d(in_channels=nb_filters, out_channels=nb_filters, kernel_size=kernel_size, padding=padding)
self.bn1 = nn.BatchNorm1d(num_features=nb_filters)
# 第二层卷积
self.conv2 = nn.Conv1d(in_channels=nb_filters, out_channels=nb_filters, kernel_size=kernel_size, padding=padding)
self.bn2 = nn.BatchNorm1d(num_features=nb_filters)
# 最大池化
self.pooling = nn.MaxPool1d(kernel_size=2, stride=2)
def forward(self, x):
# 第一层卷积
res = self.bn1(self.conv1(x))
res = F.relu(res)
# 第二层卷积
res = self.bn2(self.conv2(res))
res = F.relu(res)
# 最大池化
res = self.pooling(res)
return res
class TCN(nn.Module):
def __init__(self, input_shape, nb_filters, kernel_size, nb_stacks, nb_classes, padding='causal', dropout_rate=0.0):
super(TCN, self).__init__()
self.input_shape = input_shape
self.nb_filters = nb_filters
self.kernel_size = kernel_size
self.nb_stacks = nb_stacks
self.nb_classes = nb_classes
self.padding = padding
self.dropout_rate = dropout_rate
# 添加卷积层
self.conv1 = nn.Conv1d(in_channels=input_shape[1], out_channels=nb_filters, kernel_size=kernel_size, padding=padding)
self.bn1 = nn.BatchNorm1d(num_features=nb_filters)
# 添加残差块和池化层
self.res_blocks = nn.ModuleList()
self.res_poolings = nn.ModuleList()
for s in range(nb_stacks):
res_blocks = nn.ModuleList()
for r in [2 ** i for i in range(7)]:
res_blocks.append(ResidualBlock(dilation_rate=r, nb_filters=nb_filters, kernel_size=kernel_size, padding=padding, dropout_rate=dropout_rate))
self.res_blocks.append(res_blocks)
self.res_poolings.append(ResidualPooling(nb_filters=nb_filters, kernel_size=kernel_size, padding=padding))
# 添加全局平均池化层和输出层
self.global_pooling = nn.AvgPool1d(kernel_size=input_shape[0])
self.fc = nn.Linear(in_features=nb_filters, out_features=nb_classes)
def forward(self, x):
# 添加卷积层
x = self.bn1(self.conv1(x))
x = F.relu(x)
# 添加残差块和池化层
for s in range(self.nb_stacks):
for r in self.res_blocks[s]:
x = r(x)
x = self.res_poolings[s](x)
# 添加全局平均池化层和输出层
x = self.global_pooling(x)
x = torch.flatten(x, start_dim=1)
x = self.fc(x)
return x
# 设置超参数
input_shape = (1, 2000)
nb_filters = 64
kernel_size = 3
nb_stacks = 3
nb_classes = 7
padding = 'causal'
dropout_rate = 0.2
# 获取模型
model = TCN(input_shape, nb_filters, kernel_size, nb_stacks, nb_classes, padding, dropout_rate)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 生成随机数据进行训练
X_train = torch.rand(100, 1, 2000)
Y_train = torch.randint(low=0, high=nb_classes, size=(100, 2000))
# 训练模型
for epoch in range(10):
optimizer.zero_grad()
# 前向传播
Y_pred = model(X_train)
# 计算损失
loss = criterion(Y_pred, Y_train)
# 反向传播
loss.backward()
# 更新权重
optimizer.step()
# 打印日志
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, loss.item()))
# 使用模型进行预测
X_test = torch.rand(10, 1, 2000)
Y_pred = model(X_test)
```
上述代码中,使用PyTorch实现了上述TCN模型,并使用随机数据进行训练和预测。在训练模型时,使用了交叉熵损失函数和Adam优化器,并使用了反向传播算法来更新权重。最后,使用模型进行预测并得到预测结果。
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