new

Author: plenari

pytorch/.ipynb_checkpoints/datasets-checkpoint.ipynb

@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class file(Dataset):\n",
+    "    def __init__(self,files):\n",
+    "        super(file,self).__init__()\n",
+    "        with open(files) as f:            \n",
+    "            self.file=f.readlines()\n",
+    "    def __len__(self):\n",
+    "        return len(self.file)\n",
+    "    def __getitem__(self,idx):\n",
+    "        return self.file[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f=file('backward.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['# -*- coding: utf-8 -*-\\n',\n",
+       " '\"\"\"\\n',\n",
+       " 'Created on Sun Apr 29 16:23:43 2018\\n',\n",
+       " '\\n',\n",
+       " '@author: omf\\n',\n",
+       " '\"\"\"\\n',\n",
+       " 'import torch as t\\n',\n",
+       " 'from torch.autograd import Variable as v\\n',\n",
+       " '# compute jacobian matrix\\n',\n",
+       " 'x = t.FloatTensor([2, 1]).view(1, 2)\\n',\n",
+       " 'x = v(x, requires_grad=True)\\n',\n",
+       " 'y = v(t.FloatTensor([[1, 2], [3, 4]]))t\\n',\n",
+       " '\\n',\n",
+       " 'z = t.mm(x, y)\\n',\n",
+       " 'jacobian = t.zeros((2, 2))\\n',\n",
+       " 'z.backward(t.FloatTensor([[1, 0]]), retain_graph=True)  # dz1/dx1, dz1/dx2\\n',\n",
+       " 'jacobian[:, 0] = x.grad.data\\n',\n",
+       " 'x.grad.data.zero_()\\n',\n",
+       " 'z.backward(t.FloatTensor([[0, 1]]))  # dz2/dx1, dz2/dx2\\n',\n",
+       " 'jacobian[:, 1] = x.grad.data\\n',\n",
+       " \"print('=========jacobian========')\\n\",\n",
+       " \"print('x')\\n\",\n",
+       " 'print(x.data)\\n',\n",
+       " \"print('y')\\n\",\n",
+       " 'print(y.data)\\n',\n",
+       " \"print('compute result')\\n\",\n",
+       " 'print(z.data)\\n',\n",
+       " \"print('jacobian matrix is')\\n\",\n",
+       " 'print(jacobian)']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f.file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'from torch.autograd import Variable as v\\n'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f[7]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataloader = DataLoader(f, batch_size=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "4\n",
+      "4\n",
+      "4\n",
+      "4\n",
+      "4\n",
+      "4\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i,j in enumerate(dataloader):\n",
+    "    print(len(j))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}