From d4605960cb65d371d22b255a3f7b80c2fd972763 Mon Sep 17 00:00:00 2001 From: "[wrh]" <[wangruiheng0018@163.com]> Date: Fri, 14 Jul 2023 16:40:32 +0800 Subject: [PATCH] =?UTF-8?q?=E7=AC=AC=E4=BA=8C=E7=BB=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 2、幸福感数据分析/第2组--王瑞恒/fnn.ipynb | 316 ++++++++++++++++++++++ 1 file changed, 316 insertions(+) create mode 100644 2、幸福感数据分析/第2组--王瑞恒/fnn.ipynb diff --git a/2、幸福感数据分析/第2组--王瑞恒/fnn.ipynb b/2、幸福感数据分析/第2组--王瑞恒/fnn.ipynb new file mode 100644 index 0000000..cfb5def --- /dev/null +++ b/2、幸福感数据分析/第2组--王瑞恒/fnn.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4., 1., 1., ..., 0., 0., 0.],\n", + " [4., 1., 1., ..., 0., 0., 0.],\n", + " [4., 0., 3., ..., 0., 0., 0.],\n", + " ...,\n", + " [4., 1., 1., ..., 0., 0., 0.],\n", + " [3., 1., 1., ..., 0., 0., 0.],\n", + " [4., 1., 1., ..., 0., 0., 0.]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import Dataset, DataLoader, random_split\n", + "from torchvision import transforms, datasets, models\n", + "import pandas as pd\n", + "import sklearn\n", + "df=pd.read_csv(\"Analysis.csv\").drop(\"Unnamed: 0\",axis=1)\n", + "df=df.values\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on the CPU\n" + ] + } + ], + "source": [ + "torch.manual_seed(0) # 设置随机种子, 用于复现\n", + "torch.cuda.is_available()\n", + "# 超参数\n", + "EPOCH = 100 # 前向后向传播迭代次数\n", + "LR = 0.001 # 学习率 learning rate\n", + "BATCH_SIZE = 50 # 批量训练时候一次送入数据的size\n", + "DOWNLOAD_MNIST = True\n", + "if torch.cuda.is_available():\n", + " device = torch.device(\"cuda:0\") # you can continue going on here, like cuda:1 cuda:2....etc.\n", + " print(\"Running on the GPU\")\n", + "else:\n", + " device = torch.device(\"cpu\")\n", + " print(\"Running on the CPU\")\n", + "train_size = int(len(df) * 0.8) # 这里按照8：2进行训练和测试\n", + "test_size = len(df) - train_size\n", + "train_dataset, test_dataset = random_split(df, [train_size, test_size])\n", + "train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)\n", + "test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.nn import functional as F\n", + "\n", + "\n", + "class focal_loss_multi(nn.Module):\n", + " def __init__(self, alpha=[0.3, 0.3, 0.15,0.05,0.15], gamma=2, num_classes=5, size_average=True):\n", + " super(focal_loss_multi, self).__init__()\n", + " self.size_average = size_average\n", + " if isinstance(alpha, (float, int)): # 只设置第一类别的权重\n", + " self.alpha = torch.zeros(num_classes)\n", + " self.alpha[0] += alpha\n", + " self.alpha[1:] += (1 - alpha) # self.alpha = [0.25,0.75,0.75,0.75,0.75]\n", + " if isinstance(alpha, list): # 全部权重自己设置\n", + " self.alpha = torch.Tensor(alpha)\n", + " self.gamma = gamma\n", + "\n", + " def forward(self, inputs, targets):\n", + " alpha = torch.tensor(self.alpha).cuda()\n", + " N = inputs.size(0)\n", + " C = inputs.size(1)\n", + " # 下面这些只是为了获取四个样本的概率probs\n", + " # 如模型中有softmax，则不需要下一行代码\n", + " P = F.softmax(inputs, dim=1)\n", + "\n", + " class_mask = inputs.data.new(N, C).fill_(0) # 生成和input一样shape的tensor\n", + " class_mask = class_mask.requires_grad_() # 加入梯度计算\n", + " ids = targets.view(-1, 1) # 获取目标的索引\n", + " alpha = alpha.gather(0, ids.view(-1))\n", + " # one hot\n", + " class_mask.data.scatter_(1, ids.data, 1.) # 利用scatter将索引丢给mask\n", + " probs = (P * class_mask).sum(1).view(-1, 1)\n", + " # focal loss公式\n", + " log_p = probs.log()\n", + " loss = torch.pow((1 - probs), self.gamma) * log_p\n", + " batch_loss = (-alpha * loss).t()\n", + "\n", + " # batch loss求平均\n", + " if self.size_average:\n", + " loss = batch_loss.mean()\n", + " else:\n", + " loss = batch_loss.sum()\n", + " return loss" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "Torch not compiled with CUDA enabled", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[18], line 21\u001b[0m\n\u001b[0;32m 19\u001b[0m device \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mdevice(\u001b[39m\"\u001b[39m\u001b[39mcuda:0\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 20\u001b[0m fnn \u001b[39m=\u001b[39m FNN()\n\u001b[1;32m---> 21\u001b[0m fnn \u001b[39m=\u001b[39m fnn\u001b[39m.\u001b[39;49mto(device)\n\u001b[0;32m 22\u001b[0m optimizer \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39moptim\u001b[39m.\u001b[39mAdam(fnn\u001b[39m.\u001b[39mparameters(), lr\u001b[39m=\u001b[39mLR) \u001b[39m# 定义优化器\u001b[39;00m\n\u001b[0;32m 23\u001b[0m loss_func \u001b[39m=\u001b[39m focal_loss_multi() \u001b[39m# 定义损失函数\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:927\u001b[0m, in \u001b[0;36mModule.to\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 923\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mto(device, dtype \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mis_floating_point() \u001b[39mor\u001b[39;00m t\u001b[39m.\u001b[39mis_complex() \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 924\u001b[0m non_blocking, memory_format\u001b[39m=\u001b[39mconvert_to_format)\n\u001b[0;32m 925\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mto(device, dtype \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mis_floating_point() \u001b[39mor\u001b[39;00m t\u001b[39m.\u001b[39mis_complex() \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m, non_blocking)\n\u001b[1;32m--> 927\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_apply(convert)\n", + "File \u001b[1;32mc:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:579\u001b[0m, in \u001b[0;36mModule._apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m 577\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_apply\u001b[39m(\u001b[39mself\u001b[39m, fn):\n\u001b[0;32m 578\u001b[0m \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mchildren():\n\u001b[1;32m--> 579\u001b[0m module\u001b[39m.\u001b[39;49m_apply(fn)\n\u001b[0;32m 581\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[0;32m 582\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[0;32m 583\u001b[0m \u001b[39m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[0;32m 584\u001b[0m \u001b[39m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 589\u001b[0m \u001b[39m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[0;32m 590\u001b[0m \u001b[39m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:602\u001b[0m, in \u001b[0;36mModule._apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m 598\u001b[0m \u001b[39m# Tensors stored in modules are graph leaves, and we don't want to\u001b[39;00m\n\u001b[0;32m 599\u001b[0m \u001b[39m# track autograd history of `param_applied`, so we have to use\u001b[39;00m\n\u001b[0;32m 600\u001b[0m \u001b[39m# `with torch.no_grad():`\u001b[39;00m\n\u001b[0;32m 601\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[1;32m--> 602\u001b[0m param_applied \u001b[39m=\u001b[39m fn(param)\n\u001b[0;32m 603\u001b[0m should_use_set_data \u001b[39m=\u001b[39m compute_should_use_set_data(param, param_applied)\n\u001b[0;32m 604\u001b[0m \u001b[39mif\u001b[39;00m should_use_set_data:\n", + "File \u001b[1;32mc:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:925\u001b[0m, in \u001b[0;36mModule.to..convert\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m 922\u001b[0m \u001b[39mif\u001b[39;00m convert_to_format \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m t\u001b[39m.\u001b[39mdim() \u001b[39min\u001b[39;00m (\u001b[39m4\u001b[39m, \u001b[39m5\u001b[39m):\n\u001b[0;32m 923\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mto(device, dtype \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mis_floating_point() \u001b[39mor\u001b[39;00m t\u001b[39m.\u001b[39mis_complex() \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 924\u001b[0m non_blocking, memory_format\u001b[39m=\u001b[39mconvert_to_format)\n\u001b[1;32m--> 925\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39;49mto(device, dtype \u001b[39mif\u001b[39;49;00m t\u001b[39m.\u001b[39;49mis_floating_point() \u001b[39mor\u001b[39;49;00m t\u001b[39m.\u001b[39;49mis_complex() \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m, non_blocking)\n", + "File \u001b[1;32mc:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\cuda\\__init__.py:211\u001b[0m, in \u001b[0;36m_lazy_init\u001b[1;34m()\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[0;32m 208\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mCannot re-initialize CUDA in forked subprocess. To use CUDA with \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 209\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mmultiprocessing, you must use the \u001b[39m\u001b[39m'\u001b[39m\u001b[39mspawn\u001b[39m\u001b[39m'\u001b[39m\u001b[39m start method\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 210\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(torch\u001b[39m.\u001b[39m_C, \u001b[39m'\u001b[39m\u001b[39m_cuda_getDeviceCount\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m--> 211\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mTorch not compiled with CUDA enabled\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 212\u001b[0m \u001b[39mif\u001b[39;00m _cudart \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 213\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\n\u001b[0;32m 214\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mlibcudart functions unavailable. It looks like you have a broken build?\u001b[39m\u001b[39m\"\u001b[39m)\n", + "\u001b[1;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled" + ] + } + ], + "source": [ + "class FNN(nn.Module):\n", + " def __init__(self):\n", + " super(FNN, self).__init__()\n", + " self.len = 256\n", + " channel_list = [35, 20, 10, 5]\n", + " self.fc1 = nn.Linear(35, 20)\n", + " self.fc2 = nn.Linear(20, 10)\n", + " self.fc3 = nn.Linear(10, 5)\n", + "\n", + " def forward(self, x):\n", + " x = x.to(torch.float32)\n", + " x = F.relu(self.fc1(x))\n", + " x = F.relu(self.fc2(x))\n", + " x = self.fc3(x)\n", + "\n", + " return x\n", + "\n", + "\n", + "device = torch.device(\"cuda:0\")\n", + "fnn = FNN()\n", + "fnn = fnn.to(device)\n", + "optimizer = torch.optim.Adam(fnn.parameters(), lr=LR) # 定义优化器\n", + "loss_func = focal_loss_multi() # 定义损失函数" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "mat1 and mat2 shapes cannot be multiplied (128x319 and 35x20)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[14], line 19\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[39m# 清空梯度缓存\u001b[39;00m\n\u001b[0;32m 17\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[1;32m---> 19\u001b[0m outputs \u001b[39m=\u001b[39m fnn(inputs)\n\u001b[0;32m 21\u001b[0m loss \u001b[39m=\u001b[39m loss_func(outputs, labels\u001b[39m.\u001b[39mlong())\n\u001b[0;32m 22\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n", + "File \u001b[1;32mc:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1129\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "Cell \u001b[1;32mIn[12], line 12\u001b[0m, in \u001b[0;36mFNN.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[0;32m 11\u001b[0m x \u001b[39m=\u001b[39m x\u001b[39m.\u001b[39mto(torch\u001b[39m.\u001b[39mfloat32)\n\u001b[1;32m---> 12\u001b[0m x \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfc1(x))\n\u001b[0;32m 13\u001b[0m x \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc2(x))\n\u001b[0;32m 14\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc3(x)\n", + "File \u001b[1;32mc:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1129\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "File \u001b[1;32mc:\\Users\\王瑞恒\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\linear.py:114\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 114\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mlinear(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n", + "\u001b[1;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (128x319 and 35x20)" + ] + } + ], + "source": [ + "import time\n", + "import matplotlib.pyplot as plt\n", + "\n", + "start = time.time()\n", + "running_loss_list = []\n", + "test_loss_list = []\n", + "\n", + "for epoch in range(50):\n", + "\n", + " running_loss = 0.0\n", + " test_loss = 0.0\n", + " for data in train_loader:\n", + " # 获取输入数据\n", + " inputs = data[:, 1:].to(device)\n", + " labels = (data[:, 0]-1).to(device)\n", + " # 清空梯度缓存\n", + " optimizer.zero_grad()\n", + "\n", + " outputs = fnn(inputs)\n", + "\n", + " loss = loss_func(outputs, labels.long())\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # 打印统计信息\n", + " running_loss += loss.item()\n", + "\n", + " running_loss_list.append(running_loss)\n", + " test_loss_list.append(test_loss)\n", + " for i, data in enumerate(test_loader, 0):\n", + " # 获取输入数据\n", + " inputs = data[:, 1:].to(device)\n", + " labels = (data[:, 0]-1).to(device)\n", + "\n", + " # 清空梯度缓存\n", + " outputs = fnn(inputs)\n", + " loss = loss_func(outputs, labels.long())\n", + " # 打印统计信息\n", + " test_loss += loss.item()\n", + " running_loss_list.append(running_loss)\n", + " test_loss_list.append(test_loss)\n", + " print(f\"epoch:{epoch},loss: {running_loss}\")\n", + "plt.style.use(\"ggplot\") # matplotlib的美化样式\n", + "plt.figure()\n", + "plt.title(\"loss and accuracy\")\n", + "plt.xlabel(\"epoch\")\n", + "plt.ylabel(\"loss\")\n", + "plt.plot(test_loss_list,label='train')\n", + "plt.plot(running_loss_list,label='train')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy of the network on the test images: 59 %\n" + ] + } + ], + "source": [ + "correct = 0\n", + "total = 0\n", + "with torch.no_grad():\n", + " for data in test_loader:\n", + " inputs = data[:, 1:].to(device)\n", + " labels = (data[:, 0]-1).to(device)\n", + " outputs = fnn(inputs)\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " total += labels.size(0)\n", + " correct += (predicted == labels).sum().item()\n", + "\n", + "print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))" + ] + }, + { + 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