01-a-single-neuron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a818403",
   "metadata": {
    "papermill": {
     "duration": 0.008025,
     "end_time": "2023-12-29T13:22:50.189574",
     "exception": false,
     "start_time": "2023-12-29T13:22:50.181549",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**This notebook is an exercise in the [Intro to Deep Learning](https://www.kaggle.com/learn/intro-to-deep-learning) course.  You can reference the tutorial at [this link](https://www.kaggle.com/ryanholbrook/a-single-neuron).**\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37757d4",
   "metadata": {
    "papermill": {
     "duration": 0.006423,
     "end_time": "2023-12-29T13:22:50.203222",
     "exception": false,
     "start_time": "2023-12-29T13:22:50.196799",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Introduction #\n",
    "\n",
    "In the tutorial we learned about the building blocks of neural networks: *linear units*. We saw that a model of just one linear unit will fit a linear function to a dataset (equivalent to linear regression). In this exercise, you'll build a linear model and get some practice working with models in Keras.\n",
    "\n",
    "Before you get started, run the code cell below to set everything up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30f4bbff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:22:50.218821Z",
     "iopub.status.busy": "2023-12-29T13:22:50.218366Z",
     "iopub.status.idle": "2023-12-29T13:23:06.940768Z",
     "shell.execute_reply": "2023-12-29T13:23:06.939419Z"
    },
    "papermill": {
     "duration": 16.734165,
     "end_time": "2023-12-29T13:23:06.944074",
     "exception": false,
     "start_time": "2023-12-29T13:22:50.209909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19/3895727876.py:4: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use('seaborn-whitegrid')\n"
     ]
    }
   ],
   "source": [
    "# Setup plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "# Set Matplotlib defaults\n",
    "plt.rc('figure', autolayout=True)\n",
    "plt.rc('axes', labelweight='bold', labelsize='large',\n",
    "       titleweight='bold', titlesize=18, titlepad=10)\n",
    "\n",
    "# Setup feedback system\n",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.deep_learning_intro.ex1 import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ed727b",
   "metadata": {
    "papermill": {
     "duration": 0.006432,
     "end_time": "2023-12-29T13:23:06.958127",
     "exception": false,
     "start_time": "2023-12-29T13:23:06.951695",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The *Red Wine Quality* dataset consists of physiochemical measurements from about 1600 Portuguese red wines.  Also included is a quality rating for each wine from blind taste-tests. \n",
    "\n",
    "First, run the next cell to display the first few rows of this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7189e3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:23:06.975667Z",
     "iopub.status.busy": "2023-12-29T13:23:06.974445Z",
     "iopub.status.idle": "2023-12-29T13:23:07.039625Z",
     "shell.execute_reply": "2023-12-29T13:23:07.038475Z"
    },
    "papermill": {
     "duration": 0.076845,
     "end_time": "2023-12-29T13:23:07.042561",
     "exception": false,
     "start_time": "2023-12-29T13:23:06.965716",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "red_wine = pd.read_csv('../input/dl-course-data/red-wine.csv')\n",
    "red_wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70575c01",
   "metadata": {
    "papermill": {
     "duration": 0.00678,
     "end_time": "2023-12-29T13:23:07.056564",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.049784",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can get the number of rows and columns of a dataframe (or a Numpy array) with the `shape` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0de57770",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:23:07.075451Z",
     "iopub.status.busy": "2023-12-29T13:23:07.074513Z",
     "iopub.status.idle": "2023-12-29T13:23:07.082254Z",
     "shell.execute_reply": "2023-12-29T13:23:07.081150Z"
    },
    "papermill": {
     "duration": 0.020554,
     "end_time": "2023-12-29T13:23:07.084766",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.064212",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_wine.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70150931",
   "metadata": {
    "papermill": {
     "duration": 0.006948,
     "end_time": "2023-12-29T13:23:07.099408",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.092460",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1) Input shape #\n",
    "\n",
    "How well can we predict a wine's perceived quality from the physiochemical measurements?  \n",
    "\n",
    "The target is `'quality'`, and the remaining columns are the features.  How would you set the `input_shape` parameter for a Keras model on this task?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b5bf53f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:23:07.116831Z",
     "iopub.status.busy": "2023-12-29T13:23:07.115976Z",
     "iopub.status.idle": "2023-12-29T13:23:07.127022Z",
     "shell.execute_reply": "2023-12-29T13:23:07.125572Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.023167,
     "end_time": "2023-12-29T13:23:07.129710",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.106543",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"1_Q1\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "input_shape = [11]\n",
    "\n",
    "# Check your answer\n",
    "q_1.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e467ed0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:23:07.148441Z",
     "iopub.status.busy": "2023-12-29T13:23:07.147931Z",
     "iopub.status.idle": "2023-12-29T13:23:07.153528Z",
     "shell.execute_reply": "2023-12-29T13:23:07.152050Z"
    },
    "papermill": {
     "duration": 0.01804,
     "end_time": "2023-12-29T13:23:07.156085",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.138045",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_1.hint()\n",
    "#q_1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937e3f2d",
   "metadata": {
    "papermill": {
     "duration": 0.007633,
     "end_time": "2023-12-29T13:23:07.171531",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.163898",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 2) Define a linear model\n",
    "\n",
    "Now define a linear model appropriate for this task. Pay attention to how many inputs and outputs the model should have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b66ee2c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:23:07.190049Z",
     "iopub.status.busy": "2023-12-29T13:23:07.189555Z",
     "iopub.status.idle": "2023-12-29T13:23:07.561648Z",
     "shell.execute_reply": "2023-12-29T13:23:07.560409Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.385372,
     "end_time": "2023-12-29T13:23:07.565061",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.179689",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"2_Q2\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# YOUR CODE HERE\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(units=1, input_shape=input_shape)\n",
    "])\n",
    "\n",
    "# Check your answer\n",
    "q_2.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b49bbc4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:23:07.583692Z",
     "iopub.status.busy": "2023-12-29T13:23:07.582949Z",
     "iopub.status.idle": "2023-12-29T13:23:07.588098Z",
     "shell.execute_reply": "2023-12-29T13:23:07.586786Z"
    },
    "papermill": {
     "duration": 0.018489,
     "end_time": "2023-12-29T13:23:07.591489",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.573000",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_2.hint()\n",
    "#q_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91363335",
   "metadata": {
    "papermill": {
     "duration": 0.00781,
     "end_time": "2023-12-29T13:23:07.607731",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.599921",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3) Look at the weights\n",
    "\n",
    "Internally, Keras represents the weights of a neural network with **tensors**. Tensors are basically TensorFlow's version of a Numpy array with a few differences that make them better suited to deep learning. One of the most important is that tensors are compatible with [GPU](https://www.kaggle.com/docs/efficient-gpu-usage) and [TPU](https://www.kaggle.com/docs/tpu)) accelerators. TPUs, in fact, are designed specifically for tensor computations.\n",
    "\n",
    "A model's weights are kept in its `weights` attribute as a list of tensors. Get the weights of the model you defined above. (If you want, you could display the weights with something like: `print(\"Weights\\n{}\\n\\nBias\\n{}\".format(w, b))`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b23eb68e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:23:07.626463Z",
     "iopub.status.busy": "2023-12-29T13:23:07.625908Z",
     "iopub.status.idle": "2023-12-29T13:23:07.636880Z",
     "shell.execute_reply": "2023-12-29T13:23:07.635567Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.023496,
     "end_time": "2023-12-29T13:23:07.639421",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.615925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> Do you see how there's one weight for each input (and a bias)? Notice though that there doesn't seem to be any pattern to the values the weights have. Before the model is trained, the weights are set to random numbers (and the bias to 0.0). A neural network learns by finding better values for its weights.\n"
      ],
      "text/plain": [
       "Correct: Do you see how there's one weight for each input (and a bias)? Notice though that there doesn't seem to be any pattern to the values the weights have. Before the model is trained, the weights are set to random numbers (and the bias to 0.0). A neural network learns by finding better values for its weights."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE \n",
    "w,b=model.weights\n",
    "\n",
    "\n",
    "\n",
    "# Check your answer\n",
    "q_3.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a94e9847",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:23:07.659341Z",
     "iopub.status.busy": "2023-12-29T13:23:07.658488Z",
     "iopub.status.idle": "2023-12-29T13:23:07.663377Z",
     "shell.execute_reply": "2023-12-29T13:23:07.661832Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.017812,
     "end_time": "2023-12-29T13:23:07.666040",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.648228",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_3.hint()\n",
    "#q_3.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd29b47",
   "metadata": {
    "papermill": {
     "duration": 0.008262,
     "end_time": "2023-12-29T13:23:07.683035",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.674773",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "(By the way, Keras represents weights as tensors, but also uses tensors to represent data. When you set the `input_shape` argument, you are telling Keras the dimensions of the array it should expect for each example in the training data. Setting `input_shape=[3]` would create a network accepting vectors of length 3, like `[0.2, 0.4, 0.6]`.)\n",
    " \n",
    "\n",
    "# Optional: Plot the output of an untrained linear model\n",
    " \n",
    "The kinds of problems we'll work on through Lesson 5 will be *regression* problems, where the goal is to predict some numeric target. Regression problems are like \"curve-fitting\" problems: we're trying to find a curve that best fits the data. Let's take a look at the \"curve\" produced by a linear model. (You've probably guessed that it's a line!)\n",
    " \n",
    "We mentioned that before training a model's weights are set randomly. Run the cell below a few times to see the different lines produced with a random initialization. (There's no coding for this exercise -- it's just a demonstration.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63ed3d6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-29T13:23:07.702384Z",
     "iopub.status.busy": "2023-12-29T13:23:07.701563Z",
     "iopub.status.idle": "2023-12-29T13:23:08.630079Z",
     "shell.execute_reply": "2023-12-29T13:23:08.628608Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.941572,
     "end_time": "2023-12-29T13:23:08.633045",
     "exception": false,
     "start_time": "2023-12-29T13:23:07.691473",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 4ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(1, input_shape=[1]),\n",
    "])\n",
    "\n",
    "x = tf.linspace(-1.0, 1.0, 100)\n",
    "y = model.predict(x)\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "plt.plot(x, y, 'k')\n",
    "plt.xlim(-1, 1)\n",
    "plt.ylim(-1, 1)\n",
    "plt.xlabel(\"Input: x\")\n",
    "plt.ylabel(\"Target y\")\n",
    "w, b = model.weights # you could also use model.get_weights() here\n",
    "plt.title(\"Weight: {:0.2f}\\nBias: {:0.2f}\".format(w[0][0], b[0]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b11966",
   "metadata": {
    "papermill": {
     "duration": 0.008681,
     "end_time": "2023-12-29T13:23:08.650898",
     "exception": false,
     "start_time": "2023-12-29T13:23:08.642217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Keep Going #\n",
    "\n",
    "Add hidden layers and [**make your models deep**](https://www.kaggle.com/ryanholbrook/deep-neural-networks) in Lesson 2."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2ec87f",
   "metadata": {
    "papermill": {
     "duration": 0.009246,
     "end_time": "2023-12-29T13:23:08.669278",
     "exception": false,
     "start_time": "2023-12-29T13:23:08.660032",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/intro-to-deep-learning/discussion) to chat with other learners.*"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "formats": "ipynb"
  },
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 829369,
     "sourceId": 1480608,
     "sourceType": "datasetVersion"
    }
   ],
   "isGpuEnabled": false,
   "isInternetEnabled": false,
   "language": "python",
   "sourceType": "notebook"
  },
  "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": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 25.50242,
   "end_time": "2023-12-29T13:23:10.609385",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-12-29T13:22:45.106965",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}