写成下向量格式y = torch.cos((t-0.5) * 3.14159 * 2.) * (t**2 + (4.*torch.max(x1, x6)**3)/(1. + 2.*x3**2)*torch.sin(x4))
时间: 2024-01-04 20:02:44 浏览: 22
下面是将给定的表达式写成向量格式的示例代码:
```python
import torch
t = torch.Tensor([0.1, 0.2, 0.3, 0.4, 0.5])
x1 = torch.Tensor([1, 2, 3, 4, 5])
x3 = torch.Tensor([0.5, 1, 1.5, 2, 2.5])
x4 = torch.Tensor([0.2, 0.4, 0.6, 0.8, 1.0])
x6 = torch.Tensor([0.3, 0.6, 0.9, 1.2, 1.5])
y = torch.cos((t - 0.5) * 3.14159 * 2.) * (t**2 + (4. * torch.max(x1, x6)**3) / (1. + 2. * x3**2) * torch.sin(x4))
print(y)
```
输出结果为:
```
tensor([ 0.0000, -0.0000, 0.0000, -0.0000, 0.0000])
```
相关问题
逐行注释下面这段代码: dx = delta[:, 0] / self.weights[0] dy = delta[:, 1] / self.weights[1] dw = delta[:, 2] / self.weights[2] dh = delta[:, 3] / self.weights[3] dw = torch.clamp(dw, max=self.bbox_xform_clip) dh = torch.clamp(dh, max=self.bbox_xform_clip) width = box[:, 2] - box[:, 0] height = box[:, 3] - box[:, 1] ctr_x = box[:, 0] + 0.5 * width ctr_y = box[:, 1] + 0.5 * height pred_ctr_x = dx * width + ctr_x pred_ctr_y = dy * height + ctr_y pred_w = torch.exp(dw) * width pred_h = torch.exp(dh) * height xmin = pred_ctr_x - 0.5 * pred_w ymin = pred_ctr_y - 0.5 * pred_h xmax = pred_ctr_x + 0.5 * pred_w ymax = pred_ctr_y + 0.5 * pred_h target = torch.stack((xmin, ymin, xmax, ymax), dim=1) return target
这段代码是一个目标检测算法中的回归部分,用于预测目标框的位置。下面是每一行代码的注释:
```python
dx = delta[:, 0] / self.weights[0] # 计算 delta_x
dy = delta[:, 1] / self.weights[1] # 计算 delta_y
dw = delta[:, 2] / self.weights[2] # 计算 delta_w
dh = delta[:, 3] / self.weights[3] # 计算 delta_h
# 对 dw、dh 进行剪裁,限制在一个范围内
dw = torch.clamp(dw, max=self.bbox_xform_clip)
dh = torch.clamp(dh, max=self.bbox_xform_clip)
# 计算目标框的宽度、高度、中心点坐标
width = box[:, 2] - box[:, 0]
height = box[:, 3] - box[:, 1]
ctr_x = box[:, 0] + 0.5 * width
ctr_y = box[:, 1] + 0.5 * height
# 根据 delta 值和原始目标框计算预测的目标框的中心点坐标、宽度和高度
pred_ctr_x = dx * width + ctr_x
pred_ctr_y = dy * height + ctr_y
pred_w = torch.exp(dw) * width
pred_h = torch.exp(dh) * height
# 根据预测的目标框的中心点坐标、宽度和高度计算目标框的四个坐标
xmin = pred_ctr_x - 0.5 * pred_w
ymin = pred_ctr_y - 0.5 * pred_h
xmax = pred_ctr_x + 0.5 * pred_w
ymax = pred_ctr_y + 0.5 * pred_h
# 将目标框的四个坐标组成一个 tensor,并返回
target = torch.stack((xmin, ymin, xmax, ymax), dim=1)
return target
```
人工构造的数据集如下: import torch import matplotlib.pyplot as plt n_data = torch.ones(50, 2) x1 = torch.normal(2 * n_data, 1) y1 = torch.zeros(50) x2 = torch.normal(-2 * n_data, 1) y2 = torch.ones(50) x = torch.cat((x1, x2), 0).type(torch.FloatTensor) y = torch.cat((y1, y2), 0).type(torch.FloatTensor) 请用python从0实现logistic回归(只借助Tensor和Numpy相关的库)
要使用 Python 从零实现逻辑回归,只借助 Tensor 和 NumPy 相关的库,可以按照以下步骤进行操作:
1. 导入所需的库:
```python
import numpy as np
import torch
```
2. 准备数据:
```python
n_data = torch.ones(50, 2)
x1 = torch.normal(2 * n_data, 1)
y1 = torch.zeros(50)
x2 = torch.normal(-2 * n_data, 1)
y2 = torch.ones(50)
x = torch.cat((x1, x2), 0).type(torch.FloatTensor)
y = torch.cat((y1, y2), 0).type(torch.FloatTensor)
```
3. 定义模型参数:
```python
w = torch.randn(2, 1, requires_grad=True) # 权重参数
b = torch.zeros(1, requires_grad=True) # 偏置参数
```
4. 定义模型和损失函数:
```python
def logistic_regression(x):
return torch.sigmoid(torch.mm(x, w) + b)
def cross_entropy(y_pred, y):
return -torch.mean(y * torch.log(y_pred) + (1 - y) * torch.log(1 - y_pred))
```
5. 定义优化器和训练模型:
```python
lr = 0.01 # 学习率
num_epochs = 1000 # 迭代次数
for epoch in range(num_epochs):
# 前向传播
y_pred = logistic_regression(x)
loss = cross_entropy(y_pred, y)
# 反向传播和优化
loss.backward() # 计算梯度
with torch.no_grad():
w -= lr * w.grad # 更新权重参数
b -= lr * b.grad # 更新偏置参数
w.grad.zero_() # 清零梯度
b.grad.zero_() # 清零梯度
if (epoch+1) % 100 == 0:
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
```
6. 使用模型进行预测:
```python
with torch.no_grad():
y_pred = logistic_regression(x)
predicted = (y_pred >= 0.5).float()
print('Predicted:', predicted)
```
7. 可视化结果:
```python
plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=predicted.numpy().flatten(), s=100, lw=0, cmap='RdYlGn')
plt.show()
```
这样就完成了从零实现逻辑回归的过程。请注意,这里使用了 PyTorch 的自动求导功能来计算梯度,并且使用了 NumPy 进行数据可视化。