320 lines
6.4 KiB
Plaintext
320 lines
6.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torchvision"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 3,
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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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"source": [
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"torch.cuda.is_available()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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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([[0.2504, 0.6128, 0.9066, 0.9701],\n",
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" [0.9145, 0.5638, 0.2492, 0.8657],\n",
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" [0.9521, 0.5454, 0.6647, 0.9666],\n",
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" [0.9705, 0.8375, 0.9598, 0.4804]])\n"
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]
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}
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],
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"source": [
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"r = torch.rand(4,4)\n",
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"print(r)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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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([[-0.6323, -0.4558, -0.9853, 1.5795],\n",
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" [-1.5415, 0.3864, -0.0094, 0.4048],\n",
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" [ 1.2190, 0.7174, 0.0796, 0.0580],\n",
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" [-0.8419, 1.5195, 0.9428, 0.5261]])\n",
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"torch.float32\n"
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]
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}
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],
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"source": [
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"r2 = torch.randn(4,4)\n",
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"print(r2)\n",
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"print(r2.dtype)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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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([7, 9, 9, 8, 8])\n",
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"torch.int64\n"
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]
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}
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],
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"source": [
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"in_array = torch.randint(6,10,(5,))\n",
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"print(in_array)\n",
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"print(in_array.dtype)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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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([[6, 6, 7],\n",
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" [9, 7, 6],\n",
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" [9, 8, 6]])\n"
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]
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}
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],
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"source": [
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"in_array_2 = torch.randint(6,10,(3,3))\n",
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"print(in_array_2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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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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"5\n",
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"9\n"
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]
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}
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],
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"source": [
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"print(torch.numel(in_array))\n",
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"print(torch.numel(in_array_2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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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],\n",
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" [0, 0, 0]])\n",
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"torch.int64\n",
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"tensor([[1., 1., 1.],\n",
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" [1., 1., 1.],\n",
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" [1., 1., 1.]])\n",
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"torch.float32\n"
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]
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}
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],
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"source": [
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"z = torch.zeros(3,3, dtype=torch.long)\n",
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"print(z)\n",
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"print(z.dtype)\n",
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"o = torch.ones(3,3)\n",
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"print(o)\n",
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"print(o.dtype)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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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([[ 0.0830, -0.5093, -0.0874, 0.4360],\n",
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" [-0.4503, 1.4857, -0.9082, 0.9302],\n",
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" [-1.1958, 0.2207, 1.0333, 0.1410],\n",
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" [-0.0071, -2.7461, 0.8460, 0.8057]], dtype=torch.float64)\n"
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]
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}
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],
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"source": [
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"r2_like = torch.randn_like(r2, dtype=torch.double)\n",
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"print(r2_like)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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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([[-0.3819, 0.1570, -0.0787, 2.5496],\n",
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" [-0.6270, 0.9503, 0.2398, 1.2705],\n",
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" [ 2.1711, 1.2629, 0.7444, 1.0246],\n",
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" [ 0.1286, 2.3570, 1.9026, 1.0065]])\n"
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]
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}
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],
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"source": [
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"add_result = torch.add(r,r2)\n",
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"print(add_result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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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([4, 4])"
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]
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},
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"execution_count": 14,
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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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"source": [
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"add_result.size()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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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([[-0.3819, 0.1570, -0.0787, 2.5496],\n",
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" [-0.6270, 0.9503, 0.2398, 1.2705],\n",
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" [ 2.1711, 1.2629, 0.7444, 1.0246],\n",
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" [ 0.1286, 2.3570, 1.9026, 1.0065]])\n"
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]
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}
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],
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"source": [
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"# this function reassings the value to r2\n",
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"r2.add_(r)\n",
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"print(r2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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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([0.1570, 0.9503, 1.2629, 2.3570])\n",
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"tensor([[-0.3819, 0.1570],\n",
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" [-0.6270, 0.9503],\n",
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" [ 2.1711, 1.2629],\n",
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" [ 0.1286, 2.3570]])\n",
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"tensor([[-0.3819, 0.1570, -0.0787, 2.5496],\n",
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" [-0.6270, 0.9503, 0.2398, 1.2705],\n",
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" [ 2.1711, 1.2629, 0.7444, 1.0246]])\n",
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"tensor(1.0246)\n",
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"1.0245939493179321\n",
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"tensor([2.1711, 1.2629, 0.7444, 1.0246])\n"
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]
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}
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],
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"source": [
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"print(r2[:,1])\n",
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"print(r2[:,:2])\n",
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"print(r2[:3,:])\n",
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"num_ten = r2[2,3]\n",
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"print(num_ten)\n",
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"print(num_ten.item())\n",
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"print(r2[2,:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": 3
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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": "ipython3",
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"version": "3.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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