462 lines
8.8 KiB
Plaintext
462 lines
8.8 KiB
Plaintext
{
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"cells": [
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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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"## 为什么要学习pytorch\n",
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"\n",
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"tensorflow的学习曲线陡峭\n",
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"\n",
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"pytorch出自facebook\n",
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"\n",
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"PyTorch 可以当做 NumPy 用\n",
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"\n",
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"静态图 vs 动态图\n"
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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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"最新版本全部使用tensor就可以\n",
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"\n",
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"numpy能实现的东西，在pytorch基本可以实现\n",
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"\n",
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"torch.randn()=numpy.random.randn()\n",
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"\n",
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"torch.max()=np.max()\n",
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"\n",
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"torch.zeros()=np.zeros()\n",
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"\n",
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"\n",
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"如果不能实现,Tensor与numpy之间的可以相互转化\n",
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"\n",
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"tensor.numpy()\n",
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"\n"
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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": 1,
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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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"1.1.0\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"print(torch.__version__)"
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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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{
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"data": {
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"text/plain": [
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"device(type='cpu')"
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]
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},
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"execution_count": 2,
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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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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"device"
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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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"tensor([0., 1., 2., 3., 4., 5., 6.])"
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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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"# python struct to torch tensor\n",
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"temp=[0, 1, 2, 3, 4, 5, 6]\n",
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"x = torch.tensor(temp, dtype=torch.float, device=device)\n",
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"x"
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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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"#gpu/cpu\n",
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"x = torch.tensor(temp).cuda()\n",
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"x = x.cpu()"
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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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"x=x.long()\n",
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"x=x.float()"
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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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"## numpy reshape squeeze expand_dims"
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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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"scrolled": true
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},
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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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"[0 1 2 3 4 5 6 7 8 9]\n",
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"(10,)\n"
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]
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}
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],
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"source": [
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"a = np.arange(10)\n",
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"print(a)\n",
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"print(a.shape)"
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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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"scrolled": false
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},
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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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"[[[0]\n",
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" [1]]\n",
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"\n",
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" [[2]\n",
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" [3]]\n",
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"\n",
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" [[4]\n",
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" [5]]\n",
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"\n",
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" [[6]\n",
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" [7]]\n",
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"\n",
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" [[8]\n",
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" [9]]]\n",
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"(5, 2, 1)\n"
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]
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}
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],
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"source": [
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"#a=a.reshape(1,-1)\n",
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"#a.reshape(1,10)\n",
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"a=a.reshape(5,2,1)\n",
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"print(a)\n",
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"print(a.shape)"
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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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"scrolled": true
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},
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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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"[[0 1]\n",
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" [2 3]\n",
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" [4 5]\n",
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" [6 7]\n",
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" [8 9]]\n",
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"(5, 2)\n"
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]
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}
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],
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"source": [
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"b = np.squeeze(a)\n",
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"print(b)\n",
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"print(b.shape)"
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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, 1, 2)\n"
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]
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}
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],
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"source": [
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"a = np.arange(10)\n",
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"a=a.reshape(5,1,2)\n",
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"print(a.shape)"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0 1]\n",
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" [2 3]\n",
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" [4 5]\n",
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" [6 7]\n",
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" [8 9]]\n",
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"(5, 2)\n"
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]
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}
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],
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"source": [
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"b = np.squeeze(a)#b=a.reshape(5,2)\n",
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"print(b)\n",
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"print(b.shape)\n",
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"#np.squeeze(e,axis = 0,1,2)"
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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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"a (10,)\n",
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"b axis=0 (1, 10)\n",
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"c axis=1 (10, 1)\n"
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]
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}
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],
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"source": [
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"#expand_dims\n",
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"a = np.arange(10)\n",
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"print(\"a\",a.shape)\n",
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"b = np.expand_dims(a, axis=0)\n",
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"print(\"b axis=0\",b.shape)\n",
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"c = np.expand_dims(a, axis=1)\n",
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"print(\"c axis=1\",c.shape)"
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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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"## pytorch reshape squeeze unsqueeze"
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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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"x tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
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"x torch.Size([10])\n",
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"b axis=0 torch.Size([1, 10])\n",
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"c axis=1 torch.Size([10, 1])\n"
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]
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}
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],
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"source": [
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"x = torch.arange(0,10)\n",
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"print(\"x\",x)\n",
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"print(\"x\",x.shape)\n",
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"b = x.unsqueeze(0)\n",
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"print(\"b axis=0\",b.shape)\n",
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"c = torch.unsqueeze(x,1)\n",
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"print(\"c axis=1\",c.shape)\n"
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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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"d torch.Size([10])\n",
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"d torch.Size([10])\n"
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]
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}
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],
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"source": [
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"d=c.squeeze(1)\n",
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"print(\"d\",d.shape)\n",
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"d=c.squeeze()\n",
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"print(\"d\",d.shape)\n"
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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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"d torch.Size([10])\n"
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]
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}
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],
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"source": [
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"d=b.squeeze()\n",
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"print(\"d\",d.shape)"
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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": 48,
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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([8])\n",
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"torch.Size([8])\n",
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"tensor(0.4091, grad_fn=<BinaryCrossEntropyWithLogitsBackward>)\n"
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]
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}
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],
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"source": [
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"import torch.nn.functional as F\n",
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"m = nn.Sigmoid()\n",
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"\n",
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"loss = nn.BCEWithLogitsLoss()\n",
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"temp=[0, 1, 2,0, 1, 2,0, 1]\n",
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"input =torch.tensor(temp, dtype=torch.float, requires_grad=True)\n",
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"#input = torch.randn(3, requires_grad=True)\n",
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"target = torch.tensor([0,1,1,0,1,1,0,1], dtype=torch.float)\n",
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"lossinput = input\n",
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"output = loss(lossinput, target)\n",
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"\n",
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"\n",
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"print(lossinput.shape)\n",
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"\n",
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"print(target.shape)\n",
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"\n",
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"print(output)\n",
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"\n"
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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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"## logist regression\n",
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"\n",
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"$$y(z) = \\frac{1}{1 + exp(-\\theta^T z)}$$\n",
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"\n",
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"nn.BCELoss\n",
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"$$\n",
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" \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad\n",
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" l_n = - w_n \\left[ y_n \\cdot \\log x_n + (1 - y_n) \\cdot \\log (1 - x_n) \\right],\n",
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" $$ \n",
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" "
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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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"import torch.nn.functional as F # 激励函数库\n",
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"\n",
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"feature_num=100\n",
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"\n",
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"class LogistRegression(torch.nn.Module):\n",
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" def __init__(self):\n",
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" super(LogistRegression, self).__init__()\n",
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" self.linear = torch.nn.Linear(feature_num, 1)\n",
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" def forward(self, x):\n",
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" z=self.linear(x)\n",
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" y_pred = F.sigmoid(z)\n",
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" return y_pred\n",
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" def loss(y_pred,label):#Binary Cross Entropy\n",
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" criterion = torch.nn.BCELoss(size_average=True)\n",
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" return criterion(y_pred,label)"
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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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"nn.CrossEntropyLoss() 自带sigmod，不需要加"
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]
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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": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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