有一个 尺寸为[16 x 63x63] 的输入,并使用大小为7x7的32个过滤器进行卷积,使用步幅为2和填充 (padding)为0,请问输出尺寸是多少?
时间: 2024-05-21 07:15:59 浏览: 19
根据卷积层的计算公式,输出尺寸的计算公式为:
$O = \frac{W-K+2P}{S}+1$
其中,$W$为输入尺寸,$K$为卷积核大小,$P$为填充大小,$S$为步幅大小,$O$为输出尺寸。
将输入尺寸、卷积核大小、填充大小和步幅大小代入公式,得到输出尺寸为:
$O = \frac{63-7+2\times0}{2}+1 = 29$
所以,输出尺寸为 [16 x 29 x 29]。
相关问题
使用 pytorch 设计深度网络 ,将、输入是尺寸为[256,1,16]的图像,输出是尺寸为[3,63,256]的图像
可以使用以下代码设计深度网络:
```python
import torch.nn as nn
class DeepNet(nn.Module):
def __init__(self):
super(DeepNet, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(256 * 4 * 64, 1024)
self.fc2 = nn.Linear(1024, 3 * 63 * 256)
def forward(self, x):
x = self.pool(nn.functional.relu(self.conv1(x)))
x = self.pool(nn.functional.relu(self.conv2(x)))
x = self.pool(nn.functional.relu(self.conv3(x)))
x = self.pool(nn.functional.relu(self.conv4(x)))
x = x.view(-1, 256 * 4 * 64)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
x = x.view(-1, 3, 63, 256)
return x
```
这个深度网络包含四个卷积层和两个全连接层。输入是尺寸为[256,1,16]的图像,输出是尺寸为[3,63,256]的图像。其中,卷积层使用3x3的卷积核,池化层使用2x2的池化核,全连接层的输出维度分别为1024和3x63x256。
对X52 X53 X54 X55 X61 X62 X63 X64 X65 X66 X67进行归一化的stata代码
以下是对X52 X53 X54 X55 X61 X62 X63 X64 X65 X66 X67进行归一化的Stata代码:
```
* 定义变量的最小值和最大值
local min_x52 = r(min) if var == "X52"
local max_x52 = r(max) if var == "X52"
local min_x53 = r(min) if var == "X53"
local max_x53 = r(max) if var == "X53"
local min_x54 = r(min) if var == "X54"
local max_x54 = r(max) if var == "X54"
local min_x55 = r(min) if var == "X55"
local max_x55 = r(max) if var == "X55"
local min_x61 = r(min) if var == "X61"
local max_x61 = r(max) if var == "X61"
local min_x62 = r(min) if var == "X62"
local max_x62 = r(max) if var == "X62"
local min_x63 = r(min) if var == "X63"
local max_x63 = r(max) if var == "X63"
local min_x64 = r(min) if var == "X64"
local max_x64 = r(max) if var == "X64"
local min_x65 = r(min) if var == "X65"
local max_x65 = r(max) if var == "X65"
local min_x66 = r(min) if var == "X66"
local max_x66 = r(max) if var == "X66"
local min_x67 = r(min) if var == "X67"
local max_x67 = r(max) if var == "X67"
* 进行归一化
replace X52 = (X52 - `min_x52') / (`max_x52' - `min_x52') if var == "X52"
replace X53 = (X53 - `min_x53') / (`max_x53' - `min_x53') if var == "X53"
replace X54 = (X54 - `min_x54') / (`max_x54' - `min_x54') if var == "X54"
replace X55 = (X55 - `min_x55') / (`max_x55' - `min_x55') if var == "X55"
replace X61 = (X61 - `min_x61') / (`max_x61' - `min_x61') if var == "X61"
replace X62 = (X62 - `min_x62') / (`max_x62' - `min_x62') if var == "X62"
replace X63 = (X63 - `min_x63') / (`max_x63' - `min_x63') if var == "X63"
replace X64 = (X64 - `min_x64') / (`max_x64' - `min_x64') if var == "X64"
replace X65 = (X65 - `min_x65') / (`max_x65' - `min_x65') if var == "X65"
replace X66 = (X66 - `min_x66') / (`max_x66' - `min_x66') if var == "X66"
replace X67 = (X67 - `min_x67') / (`max_x67' - `min_x67') if var == "X67"
```
这个代码与之前的代码非常相似,只是变量名不同。请注意,这个代码假定你的变量名是X52、X53、X54、X55、X61、X62、X63、X64、X65、X66和X67。如果你的变量名不同,你需要相应地修改代码。