Skip to content

Instantly share code, notes, and snippets.

@MrMarvel

MrMarvel/hw1.ipynb

Created December 4, 2022 17:24
Show Gist options
  • Save MrMarvel/3affb6aa298d2371b790b714211b5d75 to your computer and use it in GitHub Desktop.
Save MrMarvel/3affb6aa298d2371b790b714211b5d75 to your computer and use it in GitHub Desktop.
Download ZIP
HW1.ipynb
Display the source blob
Display the rendered blob
Raw
hw1.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"gpuClass": "standard",
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/MrMarvel/3affb6aa298d2371b790b714211b5d75/hw1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Получение информации об оборудовании"
],
"metadata": {
"id": "SE_-SpH-mtFc"
}
},
{
"cell_type": "markdown",
"source": [
"**Windows** - GUI\\\n",
"**Linux** (Google colab) - terminal"
],
"metadata": {
"id": "lnGz2Eopm3ag"
}
},
{
"cell_type": "code",
"source": [
"# версия Linux\n",
"!cat /proc/version"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vPDZJB92omAL",
"outputId": "bf9f92de-eb1a-4b2d-ab6f-c069041efbfa"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Linux version 5.10.133+ (builder@b1018906abc3) (Chromium OS 12.0_pre422132_p20210405-r10 clang version 13.0.0 (/var/tmp/portage/sys-devel/llvm-12.0_pre422132_p20210405-r10/work/llvm-12.0_pre422132_p20210405/clang cd442157cff4aad209ae532cbf031abbe10bc1df), LLD 13.0.0 (/var/tmp/portage/sys-devel/llvm-12.0_pre422132_p20210405-r10/work/llvm-12.0_pre422132_p20210405/lld cd442157cff4aad209ae532cbf031abbe10bc1df)) #1 SMP Fri Aug 26 08:44:51 UTC 2022\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# версия дистрибутива Linux\n",
"!lsb_release -a"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ePuVTgneo0Rs",
"outputId": "24b71eb0-8339-4e78-9a5d-5dc490a588a8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"No LSB modules are available.\n",
"Distributor ID:\tUbuntu\n",
"Description:\tUbuntu 18.04.6 LTS\n",
"Release:\t18.04\n",
"Codename:\tbionic\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eXAC5VIPiJl7",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "31b60351-1635-4f57-8659-4f307f02c939"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sun Dec 4 04:15:36 2022 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 76C P0 35W / 70W | 8288MiB / 15109MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
"# видеокарта\n",
"!nvidia-smi"
]
},
{
"cell_type": "markdown",
"source": [
"CUDA Version в nvidia-smi - это не ваша текущая версия CUDA, а [максимально поддерживаемая версия](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html) для текущего драйвера видеокарты."
],
"metadata": {
"id": "CPIo9i8Tx0aX"
}
},
{
"cell_type": "code",
"source": [
"# cuda compiler\n",
"!nvcc --version"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TF2LKwK9wsiH",
"outputId": "9fddc05d-4659-4368-abc9-d1f8e5925229"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"nvcc: NVIDIA (R) Cuda compiler driver\n",
"Copyright (c) 2005-2021 NVIDIA Corporation\n",
"Built on Sun_Feb_14_21:12:58_PST_2021\n",
"Cuda compilation tools, release 11.2, V11.2.152\n",
"Build cuda_11.2.r11.2/compiler.29618528_0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# процессор\n",
"!cat /proc/cpuinfo"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "W1vqoHiFnzCk",
"outputId": "77e08d28-0e3f-45c9-b202-16a12384368e"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"processor\t: 0\n",
"vendor_id\t: GenuineIntel\n",
"cpu family\t: 6\n",
"model\t\t: 85\n",
"model name\t: Intel(R) Xeon(R) CPU @ 2.00GHz\n",
"stepping\t: 3\n",
"microcode\t: 0x1\n",
"cpu MHz\t\t: 2000.162\n",
"cache size\t: 39424 KB\n",
"physical id\t: 0\n",
"siblings\t: 2\n",
"core id\t\t: 0\n",
"cpu cores\t: 1\n",
"apicid\t\t: 0\n",
"initial apicid\t: 0\n",
"fpu\t\t: yes\n",
"fpu_exception\t: yes\n",
"cpuid level\t: 13\n",
"wp\t\t: yes\n",
"flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities\n",
"bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa mmio_stale_data retbleed\n",
"bogomips\t: 4000.32\n",
"clflush size\t: 64\n",
"cache_alignment\t: 64\n",
"address sizes\t: 46 bits physical, 48 bits virtual\n",
"power management:\n",
"\n",
"processor\t: 1\n",
"vendor_id\t: GenuineIntel\n",
"cpu family\t: 6\n",
"model\t\t: 85\n",
"model name\t: Intel(R) Xeon(R) CPU @ 2.00GHz\n",
"stepping\t: 3\n",
"microcode\t: 0x1\n",
"cpu MHz\t\t: 2000.162\n",
"cache size\t: 39424 KB\n",
"physical id\t: 0\n",
"siblings\t: 2\n",
"core id\t\t: 0\n",
"cpu cores\t: 1\n",
"apicid\t\t: 1\n",
"initial apicid\t: 1\n",
"fpu\t\t: yes\n",
"fpu_exception\t: yes\n",
"cpuid level\t: 13\n",
"wp\t\t: yes\n",
"flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities\n",
"bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa mmio_stale_data retbleed\n",
"bogomips\t: 4000.32\n",
"clflush size\t: 64\n",
"cache_alignment\t: 64\n",
"address sizes\t: 46 bits physical, 48 bits virtual\n",
"power management:\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# процессор\n",
"!lscpu"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_q3ACTnJoMJU",
"outputId": "f4474860-0303-4c3d-8979-9b0806063b57"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Architecture: x86_64\n",
"CPU op-mode(s): 32-bit, 64-bit\n",
"Byte Order: Little Endian\n",
"CPU(s): 2\n",
"On-line CPU(s) list: 0,1\n",
"Thread(s) per core: 2\n",
"Core(s) per socket: 1\n",
"Socket(s): 1\n",
"NUMA node(s): 1\n",
"Vendor ID: GenuineIntel\n",
"CPU family: 6\n",
"Model: 85\n",
"Model name: Intel(R) Xeon(R) CPU @ 2.00GHz\n",
"Stepping: 3\n",
"CPU MHz: 2000.162\n",
"BogoMIPS: 4000.32\n",
"Hypervisor vendor: KVM\n",
"Virtualization type: full\n",
"L1d cache: 32K\n",
"L1i cache: 32K\n",
"L2 cache: 1024K\n",
"L3 cache: 39424K\n",
"NUMA node0 CPU(s): 0,1\n",
"Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# оперативная память\n",
"!grep MemTotal /proc/meminfo"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e_AQVQQaoVFR",
"outputId": "8e998edb-71cd-498e-a8c9-1170f6ab200c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"MemTotal: 13297228 kB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# диск\n",
"!df -h"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "58uYqrEBqrqj",
"outputId": "bbff7a82-94b7-45a3-d780-ee83d5b4d0a1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Filesystem Size Used Avail Use% Mounted on\n",
"overlay 79G 23G 56G 29% /\n",
"tmpfs 64M 0 64M 0% /dev\n",
"shm 5.7G 0 5.7G 0% /dev/shm\n",
"/dev/root 2.0G 1.1G 910M 54% /sbin/docker-init\n",
"tmpfs 6.4G 40K 6.4G 1% /var/colab\n",
"/dev/sda1 75G 41G 35G 55% /opt/bin/.nvidia\n",
"tmpfs 6.4G 0 6.4G 0% /proc/acpi\n",
"tmpfs 6.4G 0 6.4G 0% /proc/scsi\n",
"tmpfs 6.4G 0 6.4G 0% /sys/firmware\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!du -h | sort -h"
],
"metadata": {
"id": "6AZaA5fOwXXC",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e054bc0a-691d-4443-f365-bac6410e1133"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"8.0K\t./.config/configurations\n",
"72K\t./.config/logs/2022.12.01\n",
"76K\t./.config/logs\n",
"108K\t./.config\n",
"55M\t.\n",
"55M\t./sample_data\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# права в Google Colab\n",
"!whoami"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gBYRJ67qqMJ2",
"outputId": "270e2a9c-4cd1-4d10-ff75-d0c03bea9a9c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"root\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Библиотека Pytorch"
],
"metadata": {
"id": "xJef-8rXrMR2"
}
},
{
"cell_type": "markdown",
"source": [
"## Установка"
],
"metadata": {
"id": "mV8n8faxvOQO"
}
},
{
"cell_type": "markdown",
"source": [
"Ссылки для установки под ваше оборудование можно найти [тут](https://pytorch.org/get-started/locally/)."
],
"metadata": {
"id": "ZOyzr4Vp2vgZ"
}
},
{
"cell_type": "code",
"source": [
"import torch"
],
"metadata": {
"id": "b_dqa87wrRMu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"torch.cuda.is_available()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2vcNoQZ4zVJG",
"outputId": "a5ff9f0b-7803-45a0-f4f6-bfdf89764cba"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 90
}
]
},
{
"cell_type": "code",
"source": [
"!python -m pip list | grep torch"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Fs_NS9SKzZeH",
"outputId": "46abd90e-2ca7-4dcc-a208-0cba42c66d51"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"torch 1.12.1+cu113\n",
"torchaudio 0.12.1+cu113\n",
"torchsummary 1.5.1\n",
"torchtext 0.13.1\n",
"torchvision 0.13.1+cu113\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Домашнее задание"
],
"metadata": {
"id": "DJ9Uxo7DEns4"
}
},
{
"cell_type": "markdown",
"source": [
"Дедлайн домашнего задания - текущее воскресенье 23:59. "
],
"metadata": {
"id": "3hbaklz_96EN"
}
},
{
"cell_type": "markdown",
"source": [
"**Домашнее задание 0:** Создайте репозиторий на Github. Вся дальнейшая домашка загружается туда. Чуть позже в телеграм чате будет выложена форма для сдачи."
],
"metadata": {
"id": "E7VzrzlHyyci"
}
},
{
"cell_type": "code",
"source": [
"#https://github.com/MrMarvel/PyTorchDeep2022"
],
"metadata": {
"id": "e5A8OELNGdpr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Домашнее задание 1:** реализуйте XOR с помощью 3 нейронов. Запишите ответ в виде выражения, состоящего из объектов neuron() – моделей нейрона с пороговой функцией активации, внутри скобок может быть что угодно. Входы верхнего уровня называются x1 и x2. Пример фрагмента записи: neuron(1*x1 + 5*x2 - 0.1) + neuron(x1) (ответ будет выглядеть чуть сложнее, но других символов вроде && не потребуется)."
],
"metadata": {
"id": "me6riR-YEuYg"
}
},
{
"cell_type": "code",
"source": [
"# TODO"
],
"metadata": {
"id": "KZnZmqMlWzYO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Домашнее задание 2:** нарисуйте backward граф для выражения `a*b+c*d`. [Теория и пример оформления](https://www.youtube.com/watch?v=MswxJw-8PvE). Сравните полученные теоретические значения с аттрибутами grad у исходных тензоров."
],
"metadata": {
"id": "UQmVNc2kEyQP"
}
},
{
"cell_type": "code",
"source": [
"# TODO"
],
"metadata": {
"id": "IlwVlc-_WvJv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"a = torch.tensor([2.0], requires_grad=True)\n",
"b = torch.tensor([4.0], requires_grad=True)\n",
"c = torch.tensor([1.0], requires_grad=True)\n",
"d = torch.tensor([5.0], requires_grad=False)"
],
"metadata": {
"id": "rHAT-rNxnh-X"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(a.grad)"
],
"metadata": {
"id": "yQUF6a8rnjyF",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6ed2bd81-89b7-4d30-924c-3b045889da5f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"loss = a*b + c*d\n",
"loss.backward()"
],
"metadata": {
"id": "WQewq5DCnnPF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(a.grad)"
],
"metadata": {
"id": "tDxWZCwQnojN",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2be6561a-0dfe-49b7-c654-1fd7ff49e4af"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"tensor([4.])\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**Домашнее задание 3:** Поэксперементируйте с размером тензоров, которые влезут на видеоркарту в Colab. Найдите максимальный размер тензора для типа данных float32, float64, float16, int32, int64. На сколько они отличаются."
],
"metadata": {
"id": "NxKEOwWDwv3Y"
}
},
{
"cell_type": "code",
"source": [
"# TODO"
],
"metadata": {
"id": "cCrGQpIdWoj3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Домашнее задание 4:** Напишите хороший пример неэффективного кода для занятия памяти видеокарты, который вызовет ошибку out of memory"
],
"metadata": {
"id": "CBR5pWOlzOtl"
}
},
{
"cell_type": "code",
"source": [
"\n",
"torch.cuda.empty_cache()\n",
"\n",
"def allocate_empty_tensor(dim_size):\n",
" a=torch.zeros(1024*1024*128,dim_size,dtype=torch.float32,device='cuda')\n",
" return a\n",
"\n",
"print(torch.cuda.memory_allocated()/1024/1024, \"MB\")\n",
"print(torch.cuda.memory_reserved()/1024/1024/1024, \"GB\")\n",
"print()\n",
"print(\"FILLING\")\n",
"try:\n",
" for i in range(13): # Trying to work with tensors from 1 GB to 13GB. GPU MAX 16GB\n",
" temp_working_tensor = allocate_empty_tensor(i)\n",
" print(torch.cuda.memory_allocated()/1024/1024, \"MB\")\n",
" print(torch.cuda.memory_reserved()/1024/1024/1024, \"GB\")\n",
" print()\n",
"except Exception as e:\n",
" print(e)\n",
" print()\n",
"\n",
"print(\"CLEARING\")\n",
"torch.cuda.empty_cache()\n",
"print(torch.cuda.memory_allocated()/1024/1024, \"MB\")\n",
"print(torch.cuda.memory_reserved()/1024/1024/1024, \"GB\")\n",
"print()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MpP1UciHPaj9",
"outputId": "1998c8a7-69e0-489f-d5f1-efcf15610bd5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"4608.0 MB\n",
"4.5 GB\n",
"\n",
"FILLING\n",
"4608.0 MB\n",
"4.5 GB\n",
"\n",
"5120.0 MB\n",
"5.0 GB\n",
"\n",
"5632.0 MB\n",
"6.0 GB\n",
"\n",
"2560.0 MB\n",
"7.5 GB\n",
"\n",
"3072.0 MB\n",
"7.5 GB\n",
"\n",
"3584.0 MB\n",
"7.5 GB\n",
"\n",
"4096.0 MB\n",
"10.5 GB\n",
"\n",
"4608.0 MB\n",
"10.5 GB\n",
"\n",
"7680.0 MB\n",
"9.5 GB\n",
"\n",
"8192.0 MB\n",
"14.0 GB\n",
"\n",
"9728.0 MB\n",
"14.0 GB\n",
"\n",
"CUDA out of memory. Tried to allocate 5.50 GiB (GPU 0; 14.76 GiB total capacity; 9.50 GiB already allocated; 4.66 GiB free; 9.50 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n",
"\n",
"CLEARING\n",
"9728.0 MB\n",
"9.5 GB\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"torch.cuda.empty_cache()\n",
"torch.cuda.memory_reserved()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6TR7m9TLRCVE",
"outputId": "3657ac3c-7365-4ef1-c2e2-2aef24903c68"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"11274289152"
]
},
"metadata": {},
"execution_count": 98
}
]
},
{
"cell_type": "markdown",
"source": [
"**Домашнее задание 5:** Используя один линейный слой `nn.Linear` и один входной тензор `x` подберите подберите размерности так, чтобы занимать всю видеопамять.\n",
"Попробуйте применить линейный слой к тензору `x`. Что произойдет? Кратко опишите ваши эксперименты. Что вы поняли?"
],
"metadata": {
"id": "0_hOtvvR89jq"
}
},
{
"cell_type": "code",
"source": [
"# TODO"
],
"metadata": {
"id": "oKECp39TW1Lv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Рекомендуемые ссылки"
],
"metadata": {
"id": "_Z2Ot37UODKM"
}
},
{
"cell_type": "markdown",
"source": [
"- [Нейронные сети и компьютерное зрение, Samsung AI Center, часть 1](https://stepik.org/course/50352/syllabus)\n",
"- [Cимулятор](https://playground.tensorflow.org/) нейронов и нейронных сетей"
],
"metadata": {
"id": "SnFY95T7kNji"
}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment