feat: add Jupyter notebook and main script for tensor operations demonstration
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fada
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5.ipynb
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236
5.ipynb
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{
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"cells": [
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "import torch",
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"id": "d3cf166c4c1b5e28"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# 连接操作\n",
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"A = torch.ones(3, 3)\n",
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"B = 2 * torch.ones(3, 3)\n",
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"\n",
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"C = torch.cat((A, B), 1)\n",
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"C"
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],
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"id": "8995ad1fc7997846"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# 堆叠操作\n",
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"A = torch.arange(0, 4)\n",
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"B = torch.arange(4, 8)\n",
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"\n",
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"C = torch.stack((A, B), 1)\n",
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"C"
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],
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"id": "3bc82f18d81a5906"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# 堆叠操作\n",
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"A = torch.arange(6).reshape(2, 3)\n",
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"B = torch.arange(7, 13).reshape(2, 3)\n",
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"C = torch.stack((A, B), 2)\n",
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"print(C.shape)\n",
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"C"
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],
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"id": "e33ad7cbb371544b"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"A = torch.tensor([[1, 2], [3, 4]]) # shape = (2, 2)\n",
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"B = torch.tensor([[5, 6], [7, 8]]) # shape = (2, 2)\n",
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"\n",
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"S = torch.stack((A, B), dim=0) # 在最外面添加一个新维度 → shape = (2, 2, 2)\n",
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"print(S)\n",
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"S = torch.stack((A, B), dim=1) # 插入第1维 → shape = (2, 2, 2)\n",
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"print(S)\n",
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"S = torch.stack((A, B), dim=2) # 插入第2维 → shape = (2, 2, 2)\n",
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"print(S)"
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],
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"id": "3ae1ac55280d699"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# 切分操作\n",
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"A = torch.arange(10) + 1\n",
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"B = torch.chunk(A, 3, 0)\n",
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"B"
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],
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"id": "d05ece3cb5c8b356"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# 切分操作\n",
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"A = torch.arange(10) + 1\n",
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"B = torch.split(A, 3, 0)\n",
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"B"
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],
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"id": "e6b4083b682fa47f"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T11:53:25.664652Z",
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"start_time": "2025-06-12T11:53:25.653976Z"
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}
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},
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"cell_type": "code",
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"source": [
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"x = torch.arange(10)\n",
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"torch.chunk(x, 3)\n",
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"# 输出：3个 tensor，形状为 [4], [3], [3]"
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],
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"id": "b59b47ca78552e94",
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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([0, 1, 2, 3]), tensor([4, 5, 6, 7]), tensor([8, 9]))"
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]
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},
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"execution_count": 36,
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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": 36
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T11:58:17.374940Z",
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"start_time": "2025-06-12T11:58:17.367564Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 索引操作\n",
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"A = torch.arange(16).view(4, 4)\n",
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"torch.index_select(A, 1, torch.tensor([1, 3]))"
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],
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"id": "66be63810446009e",
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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([[ 1, 3],\n",
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" [ 5, 7],\n",
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" [ 9, 11],\n",
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" [13, 15]])"
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]
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},
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"execution_count": 53,
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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": 53
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T12:01:09.454904Z",
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"start_time": "2025-06-12T12:01:09.448698Z"
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}
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},
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"cell_type": "code",
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"source": [
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"A = torch.rand(5)\n",
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"torch.masked_select(A, A > 0.3)"
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],
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"id": "75ba3061d9102d3f",
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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([0.5685, 0.4049, 0.4695])"
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]
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},
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"execution_count": 61,
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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": 61
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T12:08:23.692196Z",
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"start_time": "2025-06-12T12:08:23.683368Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 提取出其中第一行的第一个，第二行的第一、第二个，第三行的最后一个\n",
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"A = torch.tensor([[4, 5, 7], [3, 9, 8], [2, 3, 4]])\n",
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"torch.diagonal(A)"
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],
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"id": "ae81a2f0efa79d83",
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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([4, 9, 4])"
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]
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},
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"execution_count": 62,
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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": 62
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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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17
main.py
17
main.py
@ -2,3 +2,20 @@ import torch
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import numpy as np
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print(torch.__version__)
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A = torch.tensor([[1, 2, 3], [4, 5, 6]]) # shape = (2, 3)
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B0 = torch.stack([A, A], dim=0)
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print("dim=0:\n", B0)
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print("shape:", B0.shape)
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B1 = torch.stack([A, A], dim=1)
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print("dim=1:\n", B1)
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print("shape:", B1.shape)
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B2 = torch.stack([A, A], dim=2)
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print("dim=2:\n", B2)
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print("shape:", B2.shape)
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np.max()
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