add 4.5

code/chapter04_DL_computation/4.5_read-write.ipynb

@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4.5 读取和存储"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from torch import nn\n",
+    "\n",
+    "print(torch.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.5.1 读写`Tensor`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = torch.ones(3)\n",
+    "torch.save(x, 'x.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([1., 1., 1.])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2 = torch.load('x.pt')\n",
+    "x2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[tensor([1., 1., 1.]), tensor([0., 0., 0., 0.])]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = torch.zeros(4)\n",
+    "torch.save([x, y], 'xy.pt')\n",
+    "xy_list = torch.load('xy.pt')\n",
+    "xy_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'x': tensor([1., 1., 1.]), 'y': tensor([0., 0., 0., 0.])}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.save({'x': x, 'y': y}, 'xy_dict.pt')\n",
+    "xy = torch.load('xy_dict.pt')\n",
+    "xy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.5.2 读写模型\n",
+    "### 4.5.2.1 `state_dict`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('hidden.weight', tensor([[ 0.1836, -0.1812, -0.1681],\n",
+       "                      [ 0.0406,  0.3061,  0.4599]])),\n",
+       "             ('hidden.bias', tensor([-0.3384,  0.1910])),\n",
+       "             ('output.weight', tensor([[0.0380, 0.4919]])),\n",
+       "             ('output.bias', tensor([0.1451]))])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class MLP(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(MLP, self).__init__()\n",
+    "        self.hidden = nn.Linear(3, 2)\n",
+    "        self.act = nn.ReLU()\n",
+    "        self.output = nn.Linear(2, 1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        a = self.act(self.hidden(x))\n",
+    "        return self.output(a)\n",
+    "\n",
+    "net = MLP()\n",
+    "net.state_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'param_groups': [{'dampening': 0,\n",
+       "   'lr': 0.001,\n",
+       "   'momentum': 0.9,\n",
+       "   'nesterov': False,\n",
+       "   'params': [4624483024, 4624484608, 4624484680, 4624484752],\n",
+       "   'weight_decay': 0}],\n",
+       " 'state': {}}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
+    "optimizer.state_dict()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.5.2.2 保存和加载模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[1],\n",
+       "        [1]], dtype=torch.uint8)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = torch.randn(2, 3)\n",
+    "Y = net(X)\n",
+    "\n",
+    "PATH = \"./net.pt\"\n",
+    "torch.save(net.state_dict(), PATH)\n",
+    "\n",
+    "net2 = MLP()\n",
+    "net2.load_state_dict(torch.load(PATH))\n",
+    "Y2 = net2(X)\n",
+    "Y2 == Y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "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": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}