coursera_deep_neural_network/8.2.1dropoutPredictin_v2.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>Using Dropout for Classification </h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Objective for this Notebook<h3>    \n",
    "<h5> 1. Create the Model and Cost Function the PyTorch way.</h5>\n",
    "<h5> 2. Batch Gradient Descent </h5>    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<p>In this lab, you will see how adding dropout to your model will decrease overfitting.</p>\n",
    "\n",
    "<ul>\n",
    "<li><a href=\"#Makeup_Data\">Make Some Data</a></li>\n",
    "<li><a href=\"#Model_Cost\">Create the Model and Cost Function the PyTorch way</a></li>\n",
    "<li><a href=\"#BGD\">Batch Gradient Descent</a></li>\n",
    "</ul>\n",
    "<p>Estimated Time Needed: <strong>20 min</strong></p>\n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Preparation</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll need the following libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:18:37.162199Z",
     "start_time": "2021-07-15T13:18:36.619960Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import the libraries we need for this lab\n",
    "\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "from matplotlib.colors import ListedColormap\n",
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this function only for plotting:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:18:37.790060Z",
     "start_time": "2021-07-15T13:18:37.780258Z"
    }
   },
   "outputs": [],
   "source": [
    "# The function for plotting the diagram\n",
    "\n",
    "def plot_decision_regions_3class(data_set, model=None):\n",
    "    cmap_light = ListedColormap([ '#0000FF','#FF0000'])\n",
    "    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#00AAFF'])\n",
    "    X = data_set.x.numpy()\n",
    "    y = data_set.y.numpy()\n",
    "    h = .02\n",
    "    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1 \n",
    "    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1 \n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "    newdata = np.c_[xx.ravel(), yy.ravel()]\n",
    "    \n",
    "    Z = data_set.multi_dim_poly(newdata).flatten()\n",
    "    f = np.zeros(Z.shape)\n",
    "    f[Z > 0] = 1\n",
    "    f = f.reshape(xx.shape)\n",
    "    if model != None:\n",
    "        model.eval()\n",
    "        XX = torch.Tensor(newdata)\n",
    "        _, yhat = torch.max(model(XX), 1)\n",
    "        yhat = yhat.numpy().reshape(xx.shape)\n",
    "        plt.pcolormesh(xx, yy, yhat, cmap=cmap_light)\n",
    "        plt.contour(xx, yy, f, cmap=plt.cm.Paired)\n",
    "    else:\n",
    "        plt.contour(xx, yy, f, cmap=plt.cm.Paired)\n",
    "        plt.pcolormesh(xx, yy, f, cmap=cmap_light) \n",
    "\n",
    "    plt.title(\"decision region vs True decision boundary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this function to calculate accuracy: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:18:40.752958Z",
     "start_time": "2021-07-15T13:18:40.741309Z"
    }
   },
   "outputs": [],
   "source": [
    "# The function for calculating accuracy\n",
    "\n",
    "def accuracy(model, data_set):\n",
    "    _, yhat = torch.max(model(data_set.x), 1)\n",
    "    return (yhat == data_set.y).numpy().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Makeup_Data\">Make Some Data</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a nonlinearly separable dataset:    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:18:42.282708Z",
     "start_time": "2021-07-15T13:18:42.251577Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create data class for creating dataset object\n",
    "\n",
    "class Data(Dataset):\n",
    "    \n",
    "    # Constructor\n",
    "    def __init__(self, N_SAMPLES=1000, noise_std=0.15, train=True):\n",
    "        a = np.matrix([-1, 1, 2, 1, 1, -3, 1]).T\n",
    "        self.x = np.matrix(np.random.rand(N_SAMPLES, 2))\n",
    "        self.f = np.array(a[0] + (self.x) * a[1:3] + np.multiply(self.x[:, 0], self.x[:, 1]) * a[4] + np.multiply(self.x, self.x) * a[5:7]).flatten()\n",
    "        self.a = a\n",
    "       \n",
    "        self.y = np.zeros(N_SAMPLES)\n",
    "        self.y[self.f > 0] = 1\n",
    "        self.y = torch.from_numpy(self.y).type(torch.LongTensor)\n",
    "        self.x = torch.from_numpy(self.x).type(torch.FloatTensor)\n",
    "        self.x = self.x + noise_std * torch.randn(self.x.size())\n",
    "        self.f = torch.from_numpy(self.f)\n",
    "        self.a = a\n",
    "        if train == True:\n",
    "            torch.manual_seed(1)\n",
    "            self.x = self.x + noise_std * torch.randn(self.x.size())\n",
    "            torch.manual_seed(0)\n",
    "        \n",
    "    # Getter        \n",
    "    def __getitem__(self, index):    \n",
    "        return self.x[index], self.y[index]\n",
    "    \n",
    "    # Get Length\n",
    "    def __len__(self):\n",
    "        return self.len\n",
    "    \n",
    "    # Plot the diagram\n",
    "    def plot(self):\n",
    "        X = data_set.x.numpy()\n",
    "        y = data_set.y.numpy()\n",
    "        h = .02\n",
    "        x_min, x_max = X[:, 0].min(), X[:, 0].max()\n",
    "        y_min, y_max = X[:, 1].min(), X[:, 1].max() \n",
    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "        Z = data_set.multi_dim_poly(np.c_[xx.ravel(), yy.ravel()]).flatten()\n",
    "        f = np.zeros(Z.shape)\n",
    "        f[Z > 0] = 1\n",
    "        f = f.reshape(xx.shape)\n",
    "        \n",
    "        plt.title('True decision boundary  and sample points with noise ')\n",
    "        plt.plot(self.x[self.y == 0, 0].numpy(), self.x[self.y == 0,1].numpy(), 'bo', label='y=0') \n",
    "        plt.plot(self.x[self.y == 1, 0].numpy(), self.x[self.y == 1,1].numpy(), 'ro', label='y=1')\n",
    "        plt.contour(xx, yy, f,cmap=plt.cm.Paired)\n",
    "        plt.xlim(0,1)\n",
    "        plt.ylim(0,1)\n",
    "        plt.legend()\n",
    "    \n",
    "    # Make a multidimension ploynomial function\n",
    "    def multi_dim_poly(self, x):\n",
    "        x = np.matrix(x)\n",
    "        out = np.array(self.a[0] + (x) * self.a[1:3] + np.multiply(x[:, 0], x[:, 1]) * self.a[4] + np.multiply(x, x) * self.a[5:7])\n",
    "        out = np.array(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataset object:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:18:45.025854Z",
     "start_time": "2021-07-15T13:18:44.882846Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dataset object\n",
    "\n",
    "data_set = Data(noise_std=0.2)\n",
    "data_set.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Validation data: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:18:47.515359Z",
     "start_time": "2021-07-15T13:18:47.512776Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get some validation data\n",
    "\n",
    "torch.manual_seed(0) \n",
    "validation_set = Data(train=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Model_Cost\">Create the Model, Optimizer, and Total Loss Function (Cost)</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a custom module with three layers. <code>in_size</code> is the size of the input features, <code>n_hidden</code> is the size of the layers, and <code>out_size</code> is the size. <code>p</code> is the dropout probability. The default is 0, that is, no dropout.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:18:51.996733Z",
     "start_time": "2021-07-15T13:18:51.993150Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create Net Class\n",
    "\n",
    "class Net(nn.Module):\n",
    "    \n",
    "    # Constructor\n",
    "    def __init__(self, in_size, n_hidden, out_size, p=0):\n",
    "        super(Net, self).__init__()\n",
    "        self.drop = nn.Dropout(p=p)\n",
    "        self.linear1 = nn.Linear(in_size, n_hidden)\n",
    "        self.linear2 = nn.Linear(n_hidden, n_hidden)\n",
    "        self.linear3 = nn.Linear(n_hidden, out_size)\n",
    "    \n",
    "    # Prediction function\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.drop(self.linear1(x)))\n",
    "        x = F.relu(self.drop(self.linear2(x)))\n",
    "        x = self.linear3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create two model objects: <code>model</code> had no dropout and <code>model_drop</code> has a dropout probability of 0.5:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:18:54.468719Z",
     "start_time": "2021-07-15T13:18:54.465174Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create two model objects: model without dropout and model with dropout\n",
    "\n",
    "model = Net(2, 300, 2)\n",
    "model_drop = Net(2, 300, 2, p=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"BGD\">Train the Model via Mini-Batch Gradient Descent</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the model using dropout to training mode; this is the default mode, but it's  good practice to write this in your code : \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:18:57.067497Z",
     "start_time": "2021-07-15T13:18:57.065030Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (drop): Dropout(p=0.5, inplace=False)\n",
       "  (linear1): Linear(in_features=2, out_features=300, bias=True)\n",
       "  (linear2): Linear(in_features=300, out_features=300, bias=True)\n",
       "  (linear3): Linear(in_features=300, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the model to training mode\n",
    "\n",
    "model_drop.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model by using the Adam optimizer. See the unit on other optimizers. Use the Cross Entropy Loss:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:02.416871Z",
     "start_time": "2021-07-15T13:19:02.411057Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set optimizer functions and criterion functions\n",
    "\n",
    "optimizer_ofit = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "optimizer_drop = torch.optim.Adam(model_drop.parameters(), lr=0.01)\n",
    "criterion = torch.nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize a dictionary that stores the training and validation loss for each model:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:04.496592Z",
     "start_time": "2021-07-15T13:19:04.491130Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize the LOSS dictionary to store the loss\n",
    "\n",
    "LOSS = {}\n",
    "LOSS['training data no dropout'] = []\n",
    "LOSS['validation data no dropout'] = []\n",
    "LOSS['training data dropout'] = []\n",
    "LOSS['validation data dropout'] = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run 500 iterations of batch gradient gradient descent: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:28.031196Z",
     "start_time": "2021-07-15T13:19:23.411774Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:04<00:00, 108.41it/s]\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "from tqdm import tqdm\n",
    "\n",
    "epochs = 500\n",
    "\n",
    "def train_model(epochs):\n",
    "    \n",
    "    for epoch in tqdm(range(epochs)):\n",
    "        #all the samples are used for training \n",
    "        yhat = model(data_set.x)\n",
    "        yhat_drop = model_drop(data_set.x)\n",
    "        loss = criterion(yhat, data_set.y)\n",
    "        loss_drop = criterion(yhat_drop, data_set.y)\n",
    "\n",
    "        #store the loss for both the training and validation data for both models \n",
    "        LOSS['training data no dropout'].append(loss.item())\n",
    "        LOSS['validation data no dropout'].append(criterion(model(validation_set.x), validation_set.y).item())\n",
    "        LOSS['training data dropout'].append(loss_drop.item())\n",
    "        model_drop.eval()\n",
    "        LOSS['validation data dropout'].append(criterion(model_drop(validation_set.x), validation_set.y).item())\n",
    "        model_drop.train()\n",
    "\n",
    "        optimizer_ofit.zero_grad()\n",
    "        optimizer_drop.zero_grad()\n",
    "        loss.backward()\n",
    "        loss_drop.backward()\n",
    "        optimizer_ofit.step()\n",
    "        optimizer_drop.step()\n",
    "        \n",
    "train_model(epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the model with dropout to evaluation mode: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:29.427222Z",
     "start_time": "2021-07-15T13:19:29.424746Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (drop): Dropout(p=0.5, inplace=False)\n",
       "  (linear1): Linear(in_features=2, out_features=300, bias=True)\n",
       "  (linear2): Linear(in_features=300, out_features=300, bias=True)\n",
       "  (linear3): Linear(in_features=300, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the model to evaluation model\n",
    "\n",
    "model_drop.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the model without dropout on the validation data: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:32.342157Z",
     "start_time": "2021-07-15T13:19:32.331359Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model without dropout:  0.824\n"
     ]
    }
   ],
   "source": [
    "# Print out the accuracy of the model without dropout\n",
    "\n",
    "print(\"The accuracy of the model without dropout: \", accuracy(model, validation_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the model with dropout on the validation data: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:33.861348Z",
     "start_time": "2021-07-15T13:19:33.851140Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model with dropout:  0.851\n"
     ]
    }
   ],
   "source": [
    "# Print out the accuracy of the model with dropout\n",
    "\n",
    "print(\"The accuracy of the model with dropout: \", accuracy(model_drop, validation_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You see that the model with dropout performs better on the validation data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>True Function</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the decision boundary and the prediction of the networks in different colors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:38.101923Z",
     "start_time": "2021-07-15T13:19:37.955213Z"
    }
   },
   "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": [
    "# Plot the decision boundary and the prediction\n",
    "\n",
    "plot_decision_regions_3class(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model without Dropout:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:40.532436Z",
     "start_time": "2021-07-15T13:19:40.371067Z"
    }
   },
   "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": [
    "# The model without dropout\n",
    "\n",
    "plot_decision_regions_3class(data_set, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model with Dropout:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:42.316793Z",
     "start_time": "2021-07-15T13:19:42.171552Z"
    }
   },
   "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": [
    "# The model with dropout\n",
    "\n",
    "plot_decision_regions_3class(data_set, model_drop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the model using dropout does better at tracking the function that generated the data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot out the loss for the training and validation data on both models, we use the log to make the difference more apparent\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T13:19:47.828131Z",
     "start_time": "2021-07-15T13:19:47.656731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 439.2x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the LOSS\n",
    "\n",
    "plt.figure(figsize=(6.1, 10))\n",
    "def plot_LOSS():\n",
    "    for key, value in LOSS.items():\n",
    "        plt.plot(np.log(np.array(value)), label=key)\n",
    "        plt.legend()\n",
    "        plt.xlabel(\"iterations\")\n",
    "        plt.ylabel(\"Log of cost or total loss\")\n",
    "\n",
    "plot_LOSS()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You see that the model without dropout performs better on the training data, but it performs worse on the validation data. This suggests overfitting.  However, the model using dropout performed better on the validation data, but worse on the training data. \n"
   ]
  },
  {
   "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": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
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    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> 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. Joseph has been working for IBM since he completed his PhD. \n"
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   "source": [
    "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/\">Michelle Carey</a>, <a href=\"www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a>\n"
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   "source": [
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                                          |\n",
    "| ----------------- | ------- | ---------- | ----------------------------------------------------------- |\n",
    "| 2020-09-23        | 2.0     | Srishti    | Migrated Lab to Markdown and added to course repo in GitLab |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
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