357 lines
7.0 KiB
Plaintext
357 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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"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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"False"
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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": "markdown",
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"metadata": {},
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"source": [
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"# Torch tensors"
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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([2, 2, 1])\n"
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]
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}
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],
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"source": [
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"# This is a 1D tensor\n",
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"a = torch.tensor([2,2,1])\n",
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"print(a)"
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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([[ 2, 3, 4],\n",
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" [ 1, 2, 3],\n",
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" [ 0, 8, 7],\n",
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" [10, 11, 12]])\n"
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]
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}
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],
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"source": [
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"# This is a 2D tensor\n",
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"b = torch.tensor([[2,3,4],[1,2,3],[0,8,7],[10,11,12]])\n",
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"print(b)"
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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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"torch.Size([3])\n",
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"torch.Size([3])\n",
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"torch.Size([4, 3])\n",
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"torch.Size([4, 3])\n"
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]
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}
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],
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"source": [
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"# the size of a tensor\n",
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"print(a.shape)\n",
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"print(a.size())\n",
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"print(b.shape)\n",
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"print(b.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": 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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"4\n"
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]
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}
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],
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"source": [
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"# get the number of rows\n",
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"print(b.shape[0])"
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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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"source": [
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"c = torch.FloatTensor([[2,3,4],[1,2,3],[0,8,7],[10,11,12]])"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"d = torch.DoubleTensor([[2,3,4],[1,2,3],[0,8,7],[10,11,12]])"
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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([[ 2., 3., 4.],\n",
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" [ 1., 2., 3.],\n",
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" [ 0., 8., 7.],\n",
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" [10., 11., 12.]])\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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"print(c)\n",
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"print(c.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": 11,
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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([[ 2., 3., 4.],\n",
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" [ 1., 2., 3.],\n",
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" [ 0., 8., 7.],\n",
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" [10., 11., 12.]], dtype=torch.float64)\n",
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"torch.float64\n"
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]
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}
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],
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"source": [
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"print(d)\n",
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"print(d.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(5.2500)\n"
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]
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}
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],
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"source": [
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"print(c.mean())"
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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(5.2500, 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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"print(d.mean())"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor(4.1588)\n"
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]
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}
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],
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"source": [
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"print(c.std())"
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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": 15,
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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(4.1588, 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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"print(d.std())"
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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": 19,
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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([[ 2],\n",
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" [ 3],\n",
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" [ 4],\n",
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" [ 1],\n",
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" [ 2],\n",
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" [ 3],\n",
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" [ 0],\n",
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" [ 8],\n",
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" [ 7],\n",
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" [10],\n",
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" [11],\n",
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" [12]])\n",
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"tensor([ 2, 3, 4, 1, 2, 3, 0, 8, 7, 10, 11, 12])\n",
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"tensor([[ 2, 3, 4, 1],\n",
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" [ 2, 3, 0, 8],\n",
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" [ 7, 10, 11, 12]])\n",
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"tensor([[ 2, 3, 4, 1, 2, 3, 0, 8, 7, 10, 11, 12]])\n",
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"torch.Size([1, 12])\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"tensor([[[ 2.3594, 0.3172, -2.0944, 0.5019],\n",
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" [ 0.4300, 0.4082, -0.1532, 0.6677],\n",
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" [-1.0891, 1.3434, -0.2586, -1.2845]],\n",
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"\n",
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" [[-2.2463, -0.6665, 0.5781, 1.1152],\n",
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" [-1.9292, -0.5129, -0.2301, -0.1059],\n",
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" [-0.5839, 2.9470, -0.6610, 1.1021]]])\n",
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"tensor([[ 2.3594, 0.3172, -2.0944, 0.5019, 0.4300, 0.4082, -0.1532, 0.6677,\n",
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" -1.0891, 1.3434, -0.2586, -1.2845],\n",
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" [-2.2463, -0.6665, 0.5781, 1.1152, -1.9292, -0.5129, -0.2301, -0.1059,\n",
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" -0.5839, 2.9470, -0.6610, 1.1021]])\n",
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"tensor([[ 2.3594, 0.3172, -2.0944, 0.5019, 0.4300, 0.4082, -0.1532, 0.6677,\n",
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" -1.0891, 1.3434, -0.2586, -1.2845],\n",
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" [-2.2463, -0.6665, 0.5781, 1.1152, -1.9292, -0.5129, -0.2301, -0.1059,\n",
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" -0.5839, 2.9470, -0.6610, 1.1021]])\n"
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]
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}
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],
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"source": [
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"print(b.view(-1,1))\n",
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"print(b.view(12))\n",
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"print(b.view(-1,4))\n",
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"\n",
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"b = b.view(1,-1)\n",
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"print(b)\n",
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"print(b.shape)\n",
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"print(\"\\n\")\n",
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"#this is the format of a Tensor (channels,rows,columns)\n",
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"three_dim = torch.randn(2,3,4)\n",
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"print(\"\\n\")\n",
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"print(three_dim)\n",
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"print(three_dim.view(2,12))\n",
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"print(three_dim.view(2,-1))"
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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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