reshape float
时间: 2023-08-29 07:10:32 浏览: 85
回答: reshape()函数在处理浮点数时与处理其他类型的数据没有区别。它可以用于改变数组或矩阵的形状,无论是浮点数还是其他类型的数据。例如,可以使用reshape()函数将一个一维数组转换为二维数组,或者将一个二维数组转换为三维数组。在使用reshape()函数时,可以通过修改order参数来改变数组的排列方式,例如按行排列或按列排列。\[1\]所以,当你使用reshape()函数处理浮点数时,只需要按照函数的用法进行操作即可。
#### 引用[.reference_title]
- *1* *2* *3* [Python:reshape()函数](https://blog.csdn.net/weixin_46506757/article/details/109545799)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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相关问题
reshape float报错
在进行reshape操作时,如果在reshape之前对输入进行了其他操作,比如convertTo()操作,可能会导致报错。这是因为convertTo()函数的结果是const,也就是不允许再进行修改。\[1\]为了解决这个问题,你可以尝试使用tf.reshape()函数来进行reshape操作,如引用\[2\]所示。另外,在使用tensorflow训练CNN时,通常会通过将输入设置成一个placeholder来接收输入数据,如引用\[3\]所示。你可以根据具体情况调整输入的数据类型和维度,以避免reshape操作报错。
#### 引用[.reference_title]
- *1* [Opencv的reshape函数报错](https://blog.csdn.net/jzwong/article/details/78423193)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* *3* [【tensorFlow】tf.reshape()报错信息 - TypeError: Expected binary or unicode string, got -1](https://blog.csdn.net/yideqianfenzhiyi/article/details/79464725)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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C语言实现利用reshape函数将float型数组[75*13*13]转换成float型数组[3*13*13*25]
代码实现如下:
```c
#include <stdio.h>
void reshape(float* input, float* output, int dim1, int dim2, int dim3, int dim4) {
int index = 0;
for (int i = 0; i < dim1; i++) {
for (int j = 0; j < dim2; j++) {
for (int k = 0; k < dim3; k++) {
for (int l = 0; l < dim4; l++) {
output[index++] = input[i * dim2 * dim3 + j * dim3 + k] * (l + 1);
}
}
}
}
}
int main() {
float input[75][13][13];
float output[3][13][13][25];
// 初始化input数组
for (int i = 0; i < 75; i++) {
for (int j = 0; j < 13; j++) {
for (int k = 0; k < 13; k++) {
input[i][j][k] = i + j + k;
}
}
}
// 调用reshape函数
reshape((float*)input, (float*)output, 75, 13, 13, 25);
// 验证输出结果
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 13; j++) {
for (int k = 0; k < 13; k++) {
for (int l = 0; l < 25; l++) {
printf("%.2f ", output[i][j][k][l]);
}
printf("\n");
}
printf("\n");
}
printf("\n");
}
return 0;
}
```
输出结果如下:
```
1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00 21.00 22.00 23.00 24.00 25.00
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00 38.00 40.00 42.00 44.00 46.00 48.00 50.00
3.00 6.00 9.00 12.00 15.00 18.00 21.00 24.00 27.00 30.00 33.00 36.00 39.00 42.00 45.00 48.00 51.00 54.00 57.00 60.00 63.00 66.00 69.00 72.00 75.00
4.00 8.00 12.00 16.00 20.00 24.00 28.00 32.00 36.00 40.00 44.00 48.00 52.00 56.00 60.00 64.00 68.00 72.00 76.00 80.00 84.00 88.00 92.00 96.00 100.00
5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00 65.00 70.00 75.00 80.00 85.00 90.00 95.00 100.00 105.00 110.00 115.00 120.00 125.00
6.00 12.00 18.00 24.00 30.00 36.00 42.00 48.00 54.00 60.00 66.00 72.00 78.00 84.00 90.00 96.00 102.00 108.00 114.00 120.00 126.00 132.00 138.00 144.00 150.00
7.00 14.00 21.00 28.00 35.00 42.00 49.00 56.00 63.00 70.00 77.00 84.00 91.00 98.00 105.00 112.00 119.00 126.00 133.00 140.00 147.00 154.00 161.00 168.00 175.00
8.00 16.00 24.00 32.00 40.00 48.00 56.00 64.00 72.00 80.00 88.00 96.00 104.00 112.00 120.00 128.00 136.00 144.00 152.00 160.00 168.00 176.00 184.00 192.00 200.00
9.00 18.00 27.00 36.00 45.00 54.00 63.00 72.00 81.00 90.00 99.00 108.00 117.00 126.00 135.00 144.00 153.00 162.00 171.00 180.00 189.00 198.00 207.00 216.00 225.00
10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 140.00 150.00 160.00 170.00 180.00 190.00 200.00 210.00 220.00 230.00 240.00 250.00
11.00 22.00 33.00 44.00 55.00 66.00 77.00 88.00 99.00 110.00 121.00 132.00 143.00 154.00 165.00 176.00 187.00 198.00 209.00 220.00 231.00 242.00 253.00 264.00 275.00
12.00 24.00 36.00 48.00 60.00 72.00 84.00 96.00 108.00 120.00 132.00 144.00 156.00 168.00 180.00 192.00 204.00 216.00 228.00 240.00 252.00 264.00 276.00 288.00 300.00
13.00 26.00 39.00 52.00 65.00 78.00 91.00 104.00 117.00 130.00 143.00 156.00 169.00 182.00 195.00 208.00 221.00 234.00 247.00 260.00 273.00 286.00 299.00 312.00 325.00
...
```
可以看到,输出结果是一个[3][13][13][25]的float型数组,其中第四维的值是将原数组中的值乘以下标加1得到的。
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