343 lines
7.0 KiB
Plaintext
343 lines
7.0 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": "2025-06-12T02:34:22.530839Z",
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"start_time": "2025-06-12T02:34:20.159404Z"
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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 numpy as np\n",
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"\n",
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"torch.__version__"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'2.2.1'"
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]
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},
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"execution_count": 1,
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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": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T02:34:24.139479Z",
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"start_time": "2025-06-12T02:34:24.116052Z"
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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.tensor(1)\n",
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"b = a.item()\n",
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"print(a)\n",
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"print(b)"
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],
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"id": "ec73dc2f6feeece4",
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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(1)\n",
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"1\n"
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]
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}
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],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T02:34:26.619937Z",
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"start_time": "2025-06-12T02:34:26.608636Z"
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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 = [1, 2, 3]\n",
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"b = torch.tensor(a)\n",
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"c = b.numpy().tolist()\n",
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"print(c)"
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],
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"id": "6c3e0063d8fcc299",
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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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"[1, 2, 3]\n"
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]
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}
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],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T02:34:28.340829Z",
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"start_time": "2025-06-12T02:34:28.333276Z"
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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.zeros(2, 3, 5)\n",
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"print(a.shape)\n",
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"\n",
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"print(a.size())\n",
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"\n",
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"print(a.numel())\n"
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],
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"id": "d04c60d3f01351c2",
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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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"torch.Size([2, 3, 5])\n",
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"torch.Size([2, 3, 5])\n",
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"30\n"
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]
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}
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],
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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": "2025-06-12T02:34:37.031549Z",
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"start_time": "2025-06-12T02:34:36.991394Z"
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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.rand(2, 3, 5)\n",
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"print(x.shape)\n",
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"print(x)"
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],
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"id": "774add1439f9aa94",
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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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"torch.Size([2, 3, 5])\n",
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"tensor([[[0.1437, 0.3582, 0.4219, 0.4514, 0.6537],\n",
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" [0.0089, 0.5737, 0.0201, 0.7728, 0.1827],\n",
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" [0.6573, 0.1262, 0.0877, 0.2302, 0.0151]],\n",
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"\n",
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" [[0.0757, 0.7126, 0.4238, 0.0535, 0.0578],\n",
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" [0.4909, 0.5616, 0.7342, 0.7925, 0.8879],\n",
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" [0.3011, 0.1606, 0.2856, 0.8165, 0.4100]]])\n"
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]
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}
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],
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"execution_count": 6
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T02:34:48.128975Z",
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"start_time": "2025-06-12T02:34:48.116080Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 矩阵转秩\n",
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"x = x.permute(2, 1, 0)\n",
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"print(x.shape)\n",
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"print(x)"
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],
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"id": "ceb1debce1c62ffd",
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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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"torch.Size([5, 3, 2])\n",
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"tensor([[[0.1437, 0.0757],\n",
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" [0.0089, 0.4909],\n",
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" [0.6573, 0.3011]],\n",
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"\n",
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" [[0.3582, 0.7126],\n",
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" [0.5737, 0.5616],\n",
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" [0.1262, 0.1606]],\n",
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"\n",
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" [[0.4219, 0.4238],\n",
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" [0.0201, 0.7342],\n",
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" [0.0877, 0.2856]],\n",
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"\n",
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" [[0.4514, 0.0535],\n",
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" [0.7728, 0.7925],\n",
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" [0.2302, 0.8165]],\n",
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"\n",
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" [[0.6537, 0.0578],\n",
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" [0.1827, 0.8879],\n",
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" [0.0151, 0.4100]]])\n"
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]
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}
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],
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"execution_count": 7
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T02:36:03.393949Z",
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"start_time": "2025-06-12T02:36:03.381874Z"
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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.rand(2, 3, 4)\n",
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"x = x.transpose(1, 0)\n",
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"print(x.shape)"
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],
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"id": "e56528fc1753cf04",
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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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"torch.Size([3, 2, 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": "2025-06-12T02:49:57.093978Z",
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"start_time": "2025-06-12T02:49:57.088700Z"
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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.rand(4, 4)\n",
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"x = x.view(2, 8)\n",
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"x = x.permute(1, 0)\n",
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"# x.view(4,4) # 不能直接用view，因为view需要连续的内存\n",
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"x.reshape(4, 4)"
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],
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"id": "74df80d1396ec1d3",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([2, 8])"
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]
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},
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"execution_count": 13,
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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": 13
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T03:34:30.576671Z",
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"start_time": "2025-06-12T03:34:30.569216Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 增减维度\n",
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"x = torch.rand(2, 1, 3)\n",
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"print(x)\n",
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"x = x.squeeze(1) # 去掉维度为1的维度\n",
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"\n",
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"print(x.shape)\n",
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"print(x)\n",
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"\n"
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],
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"id": "f5009aa3b8b1335c",
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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([[[0.0287, 0.7995, 0.4072]],\n",
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"\n",
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" [[0.4378, 0.6384, 0.2777]]])\n",
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"torch.Size([2, 3])\n",
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"tensor([[0.0287, 0.7995, 0.4072],\n",
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" [0.4378, 0.6384, 0.2777]])\n"
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]
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}
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],
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"execution_count": 29
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-12T03:42:20.284801Z",
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"start_time": "2025-06-12T03:42:20.271042Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 增减维度\n",
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"x = torch.rand(2, 1, 3)\n",
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"print(x)\n",
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"x = x.unsqueeze() # 去掉维度为1的维度\n",
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"\n",
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"print(x.shape)\n",
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"print(x)\n",
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"\n"
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],
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"id": "dc138eb85bed2f3e",
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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([[[0.4243, 0.1581, 0.4620]],\n",
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"\n",
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" [[0.8510, 0.5490, 0.7694]]])\n",
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"torch.Size([2, 1, 1, 3])\n",
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"tensor([[[[0.4243, 0.1581, 0.4620]]],\n",
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"\n",
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"\n",
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" [[[0.8510, 0.5490, 0.7694]]]])\n"
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]
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}
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],
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"execution_count": 30
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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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