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mohyeah
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25/2/10 learning~
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‎AI Learn/Stage 7 DeepLearning/pytorch_intro.ipynb‎

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{
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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": "2025-02-10T04:18:05.355158Z",
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"start_time": "2025-02-10T04:18:05.351633Z"
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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"
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],
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"outputs": [],
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"execution_count": 11
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-10T04:22:35.185879Z",
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"start_time": "2025-02-10T04:22:35.166676Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 据已有数据创建张量\n",
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"t1 = torch.tensor([1,2,3])\n",
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"print(t1, t1.dtype)\n",
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"# 创建一个空的张量\n",
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"data = np.random.randn(2, 3)\n",
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"t2 = torch.Tensor(data)\n",
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"# 创建2x3的浮点型张量\n",
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"t3 = torch.DoubleTensor(2, 3)\n",
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"t4 = torch.empty(1, 1)\n",
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"print(t2, t2.dtype)\n",
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"print(t3)\n",
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"print(t4)"
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],
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"id": "40a326c76f34101e",
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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, 2, 3]) torch.int64\n",
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"tensor([[ 0.4412, -0.8755, -0.6073],\n",
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" [ 0.2609, -0.6145, -0.2360]]) torch.float32\n",
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"tensor([[1.0964e-311, 1.5184e+00, 6.3766e-01],\n",
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" [ 5.5828e-01, 5.9672e-01, 1.1690e+00]], dtype=torch.float64)\n",
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"tensor([[8.4078e-45]])\n"
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]
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}
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],
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"execution_count": 18
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-10T07:27:01.366871Z",
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"start_time": "2025-02-10T07:27:01.351960Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 创建线性张量\n",
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"\n",
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"t5 = torch.arange(0, 10, 2)\n",
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"t6 = torch.linspace(0, 1, steps=5)\n",
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"print(t5)\n",
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"print(t6)"
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],
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"id": "4743a694ba8502e5",
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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, 2, 4, 6, 8])\n",
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"tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])\n"
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]
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}
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],
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"execution_count": 28
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-10T07:31:31.078292Z",
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"start_time": "2025-02-10T07:31:31.063271Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 创建随机张量\n",
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"print(torch.random.initial_seed())\n",
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"# 设置随机种子\n",
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"torch.random.manual_seed(100)\n",
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"print(torch.random.initial_seed())\n",
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"t7 = torch.randn(2, 3)\n",
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"print(t7)\n",
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"t8 = torch.randn(2, 3)\n",
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"print(t8)"
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],
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"id": "182724d722624ae",
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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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"100\n",
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"100\n",
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"tensor([[-2.3652, -0.8047, 0.6587],\n",
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" [-0.2586, -0.2510, 0.4770]])\n",
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"tensor([[-0.5883, -0.6131, 0.4322],\n",
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" [ 0.4612, -0.9014, -0.2675]])\n"
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]
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}
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],
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"execution_count": 40
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-10T07:40:55.740948Z",
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"start_time": "2025-02-10T07:40:55.732437Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 0, 1张量\n",
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"t9 = torch.zeros(2, 3)\n",
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"t10 = torch.ones(2, 3)\n",
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"# 创建指定形状的张量\n",
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"t11 = torch.zeros_like(t10)\n",
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"# 创建指定形状的常数张量\n",
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"t12 = torch.full_like(t10, 200)\n",
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"print(t9, t9.dtype)\n",
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"print(t10, t10.dtype)\n",
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"print(t11, t11.dtype)\n",
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"print(t12, t12.dtype)"
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],
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"id": "f9c1d37a292fb540",
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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., 0., 0.],\n",
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" [0., 0., 0.]]) torch.float32\n",
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"tensor([[1., 1., 1.],\n",
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" [1., 1., 1.]]) torch.float32\n",
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"tensor([[0., 0., 0.],\n",
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" [0., 0., 0.]]) torch.float32\n",
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"tensor([[200., 200., 200.],\n",
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" [200., 200., 200.]]) torch.float32\n"
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]
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}
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],
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"execution_count": 45
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-10T07:46:34.725310Z",
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"start_time": "2025-02-10T07:46:34.704484Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 张量类型转换\n",
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"t13 = torch.full([2, 3], 10)\n",
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"print(t13, t13.dtype)\n",
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"t14 = t13.type(torch.DoubleTensor) # 转换为浮点型张量,返回新的张量\n",
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"print(t14)\n",
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"t15 = t13.double() # 转换为浮点型张量,返回新的张量\n",
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"print(t15)"
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],
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"id": "665774dfdf5dc460",
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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([[10, 10, 10],\n",
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" [10, 10, 10]]) torch.int64\n",
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"tensor([[10., 10., 10.],\n",
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" [10., 10., 10.]], dtype=torch.float64)\n",
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"tensor([[10., 10., 10.],\n",
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" [10., 10., 10.]], dtype=torch.float64)\n"
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]
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}
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],
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"execution_count": 54
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-10T08:11:55.944142Z",
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"start_time": "2025-02-10T08:11:55.936161Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 张量基本计算\n",
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"# 1. 基本计算\n",
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"t1 = torch.randint(0, 10, [2, 3]).cuda() # 随机生成一个2x3, 在0-10区间的矩阵\n",
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"t1.add_(10) # 原地+10\n",
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"t1.mul(0) # 返回*0的新张量\n"
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],
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"id": "ca1f49526fdb3b78",
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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, 0, 0],\n",
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" [0, 0, 0]], device='cuda:0') torch.int64\n"
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]
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}
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],
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"execution_count": 66
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-10T08:36:06.259580Z",
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"start_time": "2025-02-10T08:36:06.244032Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 2. 阿达玛乘\n",
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"# mul 函数 or *\n",
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"t1 = torch.tensor([[1, 2], [3, 4]]) # 指定在GPU上创建张量\n",
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"t2 = torch.tensor([[5, 6], [7, 8]])\n",
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"#t3 = t1.mul(t2)\n",
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"t3 = t1 * t2\n",
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"t3\n"
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],
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"id": "893bab24914baedf",
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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([[ 5, 12],\n",
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" [21, 32]])"
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]
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},
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"execution_count": 85,
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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": 85
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-10T08:25:05.044458Z",
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"start_time": "2025-02-10T08:25:05.026760Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 3. 点积运算\n",
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"# @ or matmul\n",
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"# mm 对二维\n",
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"# bmm 对三维\n",
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"t1 = torch.tensor([[1, 2], [3, 4], [5, 6]])\n",
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"t2 = torch.tensor([[5, 6], [7, 8]])\n",
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"t3 = t1 @ t2 # cuda 不支持整型运算\n",
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"t3\n",
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"\n"
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],
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"id": "44a32009e5f6755e",
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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([[19, 22],\n",
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" [43, 50],\n",
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" [67, 78]])"
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]
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},
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"execution_count": 77,
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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": 77
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-10T08:37:53.411553Z",
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"start_time": "2025-02-10T08:37:53.397941Z"
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}
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},
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"cell_type": "code",
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"source": [
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"t4 = torch.randn(3, 4, 5).cuda(device=0) # 3批, 5行, 6列的张量\n",
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"t5 = torch.randn( 5, 6).cuda(0)\n",
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"t6 = t4 @ t5\n",
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"t6.shape"
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],
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"id": "949e9ed96952c716",
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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([3, 4, 6])"
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]
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},
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"execution_count": 88,
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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": 88
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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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"id": "ecb76577b372b4"
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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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