134 lines
3.0 KiB
Plaintext
134 lines
3.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-06-16T08:22:25.477936Z",
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"start_time": "2025-06-16T08:22:25.474514Z"
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}
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},
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"source": [
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"import torch\n",
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"import torch.nn as nn"
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],
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"outputs": [],
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"execution_count": 4
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-16T09:46:23.991077Z",
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"start_time": "2025-06-16T09:46:23.982065Z"
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}
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},
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"cell_type": "code",
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"source": [
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"input_feat = torch.tensor([[4, 1, 7, 5], [4, 4, 2, 5], [7, 7, 2, 4], [1, 0, 2, 4]], dtype=torch.float32).unsqueeze(0).unsqueeze(0)\n",
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"print(input_feat)\n",
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"print(input_feat.shape)"
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],
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"id": "c2fef52f697ea63",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[[[4., 1., 7., 5.],\n",
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" [4., 4., 2., 5.],\n",
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" [7., 7., 2., 4.],\n",
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" [1., 0., 2., 4.]]]])\n",
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"torch.Size([1, 1, 4, 4])\n"
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]
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}
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],
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"execution_count": 8
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-16T09:47:40.137555Z",
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"start_time": "2025-06-16T09:47:40.131166Z"
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}
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},
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"cell_type": "code",
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"source": [
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"conv2d = nn.Conv2d(1, 1, (2, 2), stride=1, padding='same', bias=False)\n",
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"# 卷积核要有四个维度：输出通道数，输入通道数，卷积核高度，卷积核宽度\n",
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"kernels = torch.tensor([[[[1, 0], [2, 1]]]], dtype=torch.float32)\n",
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"conv2d.weight = nn.Parameter(kernels, requires_grad=False) # 设置卷积核\n",
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"# 默认情况随机初始化参数\n",
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"print(conv2d.weight)\n",
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"print(conv2d.bias)"
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],
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"id": "1903942bae26fde7",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Parameter containing:\n",
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"tensor([[[[1., 0.],\n",
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" [2., 1.]]]])\n",
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"None\n"
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]
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}
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],
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"execution_count": 16
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-16T09:47:42.159880Z",
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"start_time": "2025-06-16T09:47:42.153928Z"
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}
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},
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"cell_type": "code",
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"source": [
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"output = conv2d(input_feat)\n",
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"output"
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],
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"id": "8ebf518ec7c7bc70",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[[[16., 11., 16., 15.],\n",
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" [25., 20., 10., 13.],\n",
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" [ 9., 9., 10., 12.],\n",
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" [ 1., 0., 2., 4.]]]])"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 17
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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