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feat: refactor Jupyter notebook for convolution layer demonstration with updated input tensor and fixed kernel parameters

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fada
2025-06-16 17:49:41 +08:00
parent a1eb1c7f5c
commit c8b00eea2d
1 changed files with 49 additions and 17 deletions
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09.ipynb
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@ -20,13 +20,13 @@
{ {
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
"end_time": "2025-06-16T08:20:55.729969Z", "end_time": "2025-06-16T09:46:23.991077Z",
"start_time": "2025-06-16T08:20:55.664951Z" "start_time": "2025-06-16T09:46:23.982065Z"
} }
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"input_feat = torch.tensor([[4, 1, 7, 5], [4, 4, 2, 5], [7, 7, 2, 4], [1, 0, 2, 4]], dtype=torch.float32)\n", "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",
"print(input_feat)\n", "print(input_feat)\n",
"print(input_feat.shape)" "print(input_feat.shape)"
], ],
@ -36,26 +36,29 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"tensor([[4., 1., 7., 5.],\n", "tensor([[[[4., 1., 7., 5.],\n",
" [4., 4., 2., 5.],\n", " [4., 4., 2., 5.],\n",
" [7., 7., 2., 4.],\n", " [7., 7., 2., 4.],\n",
" [1., 0., 2., 4.]])\n", " [1., 0., 2., 4.]]]])\n",
"torch.Size([4, 4])\n" "torch.Size([1, 1, 4, 4])\n"
] ]
} }
], ],
"execution_count": 2 "execution_count": 8
}, },
{ {
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
"end_time": "2025-06-16T08:22:26.834622Z", "end_time": "2025-06-16T09:47:40.137555Z",
"start_time": "2025-06-16T08:22:26.825132Z" "start_time": "2025-06-16T09:47:40.131166Z"
} }
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"conv2d = nn.Conv2d(1, 1, (2, 2), stride=1, padding='same', bias=True)\n", "conv2d = nn.Conv2d(1, 1, (2, 2), stride=1, padding='same', bias=False)\n",
"# 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",
"kernels = torch.tensor([[[[1, 0], [2, 1]]]], dtype=torch.float32)\n",
"conv2d.weight = nn.Parameter(kernels, requires_grad=False) # 设置卷积核\n",
"# 默认情况随机初始化参数\n", "# 默认情况随机初始化参数\n",
"print(conv2d.weight)\n", "print(conv2d.weight)\n",
"print(conv2d.bias)" "print(conv2d.bias)"
@ -67,14 +70,43 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Parameter containing:\n", "Parameter containing:\n",
"tensor([[[[ 0.4068, -0.3036],\n", "tensor([[[[1., 0.],\n",
" [ 0.4212, 0.4779]]]], requires_grad=True)\n", " [2., 1.]]]])\n",
"Parameter containing:\n", "None\n"
"tensor([0.0521], requires_grad=True)\n"
] ]
} }
], ],
"execution_count": 5 "execution_count": 16
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-16T09:47:42.159880Z",
"start_time": "2025-06-16T09:47:42.153928Z"
}
},
"cell_type": "code",
"source": [
"output = conv2d(input_feat)\n",
"output"
],
"id": "8ebf518ec7c7bc70",
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[[16., 11., 16., 15.],\n",
" [25., 20., 10., 13.],\n",
" [ 9., 9., 10., 12.],\n",
" [ 1., 0., 2., 4.]]]])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 17
} }
], ],
"metadata": { "metadata": {