coursera_deep_neural_network/4.4.training_multiple_output_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DL0110EN-SkillsNetwork/Template/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Linear Regression Multiple Outputs</h1> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Objective</h2><ul><li> How to create a complicated models using pytorch build in functions.</li></ul> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "In this lab, you will create a model the Pytroch way. This will help you as models get more complicated.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "<li><a href=\"#ref0\">Make Some Data</a></li>\n",
    "<li><a href=\"#ref1\">Create the Model and Cost Function the Pytorch way</a></li>\n",
    "<li><a href=\"#ref2\">Train the Model: Batch Gradient Descent</a></li>\n",
    "<li><a href=\"#ref3\">Practice Questions </a></li>\n",
    "<br>\n",
    "<p></p>\n",
    "Estimated Time Needed: <strong>20 min</strong>\n",
    "</div>\n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the following libraries:  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:01:59.017247Z",
     "start_time": "2021-07-12T10:01:58.402841Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from torch import nn,optim\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the random seed:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:00.324514Z",
     "start_time": "2021-07-12T10:02:00.319666Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f4eec0c1770>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref0\"></a>\n",
    "\n",
    "<h2 align=center>Make Some Data </h2>\n",
    "Create a dataset class with two-dimensional features and two targets: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:06.001822Z",
     "start_time": "2021-07-12T10:02:05.997376Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "class Data(Dataset):\n",
    "    def __init__(self):\n",
    "            self.x=torch.zeros(20,2)\n",
    "            self.x[:,0]=torch.arange(-1,1,0.1)\n",
    "            self.x[:,1]=torch.arange(-1,1,0.1)\n",
    "            self.w=torch.tensor([ [1.0,-1.0],[1.0,3.0]])\n",
    "            self.b=torch.tensor([[1.0,-1.0]])\n",
    "            self.f=torch.mm(self.x,self.w)+self.b\n",
    "            \n",
    "            self.y=self.f+0.001*torch.randn((self.x.shape[0],1))\n",
    "            self.len=self.x.shape[0]\n",
    "\n",
    "    def __getitem__(self,index):\n",
    "\n",
    "        return self.x[index],self.y[index]\n",
    "    \n",
    "    def __len__(self):\n",
    "        return self.len"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create a dataset object \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:07.935679Z",
     "start_time": "2021-07-12T10:02:07.933249Z"
    }
   },
   "outputs": [],
   "source": [
    "data_set=Data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref1\"></a>\n",
    "\n",
    "<h2 align=center>Create the Model, Optimizer, and Total Loss Function (cost)</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a custom module:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:10.231996Z",
     "start_time": "2021-07-12T10:02:10.229469Z"
    }
   },
   "outputs": [],
   "source": [
    "class linear_regression(nn.Module):\n",
    "    def __init__(self,input_size,output_size):\n",
    "        super(linear_regression,self).__init__()\n",
    "        self.linear=nn.Linear(input_size,output_size)\n",
    "    def forward(self,x):\n",
    "        yhat=self.linear(x)\n",
    "        return yhat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an optimizer object and set the learning rate to 0.1. **Don't forget to enter the model parameters in the constructor.**  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:13.775210Z",
     "start_time": "2021-07-12T10:02:13.773215Z"
    }
   },
   "outputs": [],
   "source": [
    "model=linear_regression(2,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an optimizer object and set the learning rate to 0.1. **Don't forget to enter the model parameters in the constructor.**  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"https://ibm.box.com/shared/static/f8hskuwrnctjg21agud69ddla0jkbef5.png\" width = 100, align = \"center\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:16.759829Z",
     "start_time": "2021-07-12T10:02:16.757936Z"
    }
   },
   "outputs": [],
   "source": [
    "optimizer = optim.SGD(model.parameters(), lr = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the criterion function that calculates the total loss or cost:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:18.551063Z",
     "start_time": "2021-07-12T10:02:18.548919Z"
    }
   },
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a data loader object and set the batch_size to 5:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:19.695208Z",
     "start_time": "2021-07-12T10:02:19.693332Z"
    }
   },
   "outputs": [],
   "source": [
    "train_loader=DataLoader(dataset=data_set,batch_size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref2\"></a>\n",
    "\n",
    "<h2 align=center>Train the Model via Mini-Batch Gradient Descent </h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run 100 epochs of Mini-Batch Gradient Descent and store the total loss or cost for every iteration. Remember that this is an approximation of the true total loss or cost.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:21.891997Z",
     "start_time": "2021-07-12T10:02:21.756935Z"
    }
   },
   "outputs": [],
   "source": [
    "LOSS=[]\n",
    " \n",
    "epochs=100\n",
    "   \n",
    "for epoch in range(epochs):\n",
    "    for x,y in train_loader:\n",
    "        #make a prediction \n",
    "        yhat=model(x)\n",
    "        #calculate the loss\n",
    "        loss=criterion(yhat,y)\n",
    "        #store loss/cost \n",
    "        LOSS.append(loss.item())\n",
    "        #clear gradient \n",
    "        optimizer.zero_grad()\n",
    "        #Backward pass: compute gradient of the loss with respect to all the learnable parameters\n",
    "        loss.backward()\n",
    "        #the step function on an Optimizer makes an update to its parameters\n",
    "        optimizer.step()\n",
    "     \n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the cost:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T10:02:23.914033Z",
     "start_time": "2021-07-12T10:02:23.805065Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(LOSS)\n",
    "plt.xlabel(\"iterations \")\n",
    "plt.ylabel(\"Cost/total loss \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://dataplatform.cloud.ibm.com/registration/stepone?context=cpdaas&apps=data_science_experience,watson_machine_learning\"><img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DL0110EN-SkillsNetwork/Template/module%201/images/Watson_Studio.png\"/></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About the Authors:\n",
    "\n",
    " [Joseph Santarcangelo](https://www.linkedin.com/in/joseph-s-50398b136/?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0110EN-SkillsNetwork-20647811) has a PhD in Electrical Engineering. His research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. \n",
    "\n",
    "Other contributors: [Michelle Carey](https://www.linkedin.com/in/michelleccarey/?utm_email=Email&utm_source=Nurture&utm_content=000026UJ&utm_term=10006555&utm_campaign=PLACEHOLDER&utm_id=SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0110EN-SkillsNetwork-20647811) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                                          |\n",
    "| ----------------- | ------- | ---------- | ----------------------------------------------------------- |\n",
    "| 2020-09-23        | 2.0     | Shubham    | Migrated Lab to Markdown and added to course repo in GitLab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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