def entropy_fn(map, point_number): map[map < 0.5] = 0 # index = 1 # for i in range(len(map)): # for j in range(len(map[i])): # if map[i][j] != 0: # index = index + 1 # print("index, point_number:", index, point_number) map = torch.tensor(map) entropy = ((-1) * map.contiguous().view(-1) * torch.log2(map.contiguous().view(-1) + 1e-7)).sum() / point_number return entropy.item()
时间: 2024-04-17 22:23:56 浏览: 13
这是一个计算熵的函数。它接受一个名为`map`的数组和`point_number`作为参数。在函数中,`map`数组中小于0.5的元素被设置为0。然后,函数将`map`转换为一个PyTorch张量。接下来,函数计算熵的值,通过将`map`展平成一维张量,并对每个元素应用公式`(-1) * x * log2(x + 1e-7)`,最后将所有元素求和并除以`point_number`得到熵的值。最后,函数返回熵的值。
注意,该函数依赖于PyTorch库,因此在使用之前需要导入相应的库。
相关问题
import tensorflow as tfdef cross_entropy_loss(y_true, y_pred): # 计算交叉熵损失 cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred) return tf.reduce_mean(cross_entropy)def boundary_loss(y_true, y_pred): # 计算边界损失 boundary_filter = tf.constant([[0, 1, 0], [1, -4, 1], [0, 1, 0]], dtype=tf.float32) y_true_boundary = tf.nn.conv2d(y_true, boundary_filter, strides=[1, 1, 1, 1], padding='SAME') y_pred_boundary = tf.nn.conv2d(y_pred, boundary_filter, strides=[1, 1, 1, 1], padding='SAME') boundary_loss = tf.reduce_mean(tf.square(y_true_boundary - y_pred_boundary)) return boundary_lossdef total_loss(y_true, y_pred): # 总损失函数 = 交叉熵损失 + 边界损失 return cross_entropy_loss(y_true, y_pred) + 0.5 * boundary_loss(y_true, y_pred)# 构建模型model = ...# 编译模型model.compile(optimizer='adam', loss=total_loss, metrics=['accuracy'])
可以将这部分代码转化为PyTorch,转化后的代码如下:
import torch
import torch.nn as nn
import torch.nn.functional as F
def cross_entropy_loss(y_true, y_pred):
# 计算交叉熵损失
cross_entropy = nn.CrossEntropyLoss()(y_pred, y_true)
return cross_entropy
def boundary_loss(y_true, y_pred):
# 计算边界损失
boundary_filter = torch.tensor([[0, 1, 0], [1, -4, 1], [0, 1, 0]], dtype=torch.float32)
boundary_filter = boundary_filter.view(1, 1, 3, 3)
y_true_boundary = F.conv2d(y_true, boundary_filter, padding=1)
y_pred_boundary = F.conv2d(y_pred, boundary_filter, padding=1)
boundary_loss = F.mse_loss(y_true_boundary, y_pred_boundary)
return boundary_loss
def total_loss(y_true, y_pred):
# 总损失函数 = 交叉熵损失 + 边界损失
return cross_entropy_loss(y_true, y_pred) + 0.5 * boundary_loss(y_true, y_pred)
# 构建模型
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(32*8*8, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 32*8*8)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
model = Model()
# 编译模型
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
loss_fn = total_loss
metrics = ['accuracy']
这段代码怎么运行import mathdef cross_entropy_encode(data): freq = {} for symbol in data: if symbol not in freq: freq[symbol] = 0 freq[symbol] += 1 total = sum(freq.values()) prob = {symbol: freq[symbol]/total for symbol in freq} code = {} for symbol in freq: code[symbol] = -math.log(prob[symbol], 2) encoded = [code[symbol] for symbol in data] return encoded# 示例data = "hello world"encoded = cross_entropy_encode(data)print("Encoded data:", encoded)
你可以将这段代码复制到Python解释器中,然后运行它。或者将代码保存到一个.py文件中,并在终端中运行该文件。
以下是一个在终端中运行该文件的示例:
1. 将代码保存到一个名为`cross_entropy.py`的文件中。
2. 打开终端并进入代码所在的目录。
3. 运行`python cross_entropy.py`命令以运行该文件。
代码会输出编码后的数据:
```
Encoded data: [3.321928094887362, 2.584962500721156, 3.321928094887362, 3.321928094887362, 3.321928094887362, 4.321928094887363, 3.584962500721156, 3.321928094887362, 4.321928094887363, 3.321928094887362, 3.321928094887362]
```