Add notebooks

tutorial-contents-notebooks/303_build_nn_quickly.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 303 Build NN Quickly\n",
+    "\n",
+    "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n",
+    "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n",
+    "\n",
+    "Dependencies:\n",
+    "* torch: 0.1.11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn.functional as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# replace following class code with an easy sequential network\n",
+    "class Net(torch.nn.Module):\n",
+    "    def __init__(self, n_feature, n_hidden, n_output):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\n",
+    "        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.relu(self.hidden(x))      # activation function for hidden layer\n",
+    "        x = self.predict(x)             # linear output\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "net1 = Net(1, 10, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# easy and fast way to build your network\n",
+    "net2 = torch.nn.Sequential(\n",
+    "    torch.nn.Linear(1, 10),\n",
+    "    torch.nn.ReLU(),\n",
+    "    torch.nn.Linear(10, 1)\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Net (\n",
+      "  (hidden): Linear (1 -> 10)\n",
+      "  (predict): Linear (10 -> 1)\n",
+      ")\n",
+      "Sequential (\n",
+      "  (0): Linear (1 -> 10)\n",
+      "  (1): ReLU ()\n",
+      "  (2): Linear (10 -> 1)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(net1)     # net1 architecture\n",
+    "print(net2)     # net2 architecture"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}