exercise-a-single-neuron.ipynb

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   "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"
   ]
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    "# 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."
   ]
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    "# 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 *"
   ]
  },
  {
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   "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."
   ]
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       "<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",
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       "   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  "
      ]
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     "execution_count": 2,
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "red_wine = pd.read_csv('../input/dl-course-data/red-wine.csv')\n",
    "red_wine.head()"
   ]
  },
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   "source": [
    "You can get the number of rows and columns of a dataframe (or a Numpy array) with the `shape` attribute."
   ]
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    {
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      "text/plain": [
       "(1599, 12)"
      ]
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   "source": [
    "red_wine.shape # (rows, columns)"
   ]
  },
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   "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?"
   ]
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       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
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   "source": [
    "# YOUR CODE HERE\n",
    "input_shape = [11]\n",
    "\n",
    "# Check your answer\n",
    "q_1.check()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "bc507c67",
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   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_1.hint()\n",
    "#q_1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a471e1",
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   },
   "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",
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     "text": [
      "2023-01-04 05:12:01.251816: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
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       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
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   ],
   "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=[11])\n",
    "])\n",
    "\n",
    "# Check your answer\n",
    "q_2.check()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "d8feaf94",
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   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_2.hint()\n",
    "#q_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4d5f5fc",
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   "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))`)."
   ]
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     "text": [
      "Weights\n",
      "<tf.Variable 'dense/kernel:0' shape=(11, 1) dtype=float32, numpy=\n",
      "array([[ 0.15738916],\n",
      "       [ 0.475712  ],\n",
      "       [ 0.42854863],\n",
      "       [ 0.13776875],\n",
      "       [-0.42820707],\n",
      "       [ 0.500217  ],\n",
      "       [-0.38158795],\n",
      "       [ 0.5809439 ],\n",
      "       [-0.5275067 ],\n",
      "       [-0.14120865],\n",
      "       [-0.324847  ]], dtype=float32)>\n",
      "\n",
      "Bias\n",
      "<tf.Variable 'dense/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>\n"
     ]
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      "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",
    "print(\"Weights\\n{}\\n\\nBias\\n{}\".format(w, b))\n",
    "\n",
    "# Check your answer\n",
    "q_3.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d590504",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-04T05:12:01.385113Z",
     "iopub.status.busy": "2023-01-04T05:12:01.384418Z",
     "iopub.status.idle": "2023-01-04T05:12:01.387642Z",
     "shell.execute_reply": "2023-01-04T05:12:01.387024Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.010887,
     "end_time": "2023-01-04T05:12:01.389158",
     "exception": false,
     "start_time": "2023-01-04T05:12:01.378271",
     "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": "d16323e4",
   "metadata": {
    "papermill": {
     "duration": 0.004812,
     "end_time": "2023-01-04T05:12:01.398888",
     "exception": false,
     "start_time": "2023-01-04T05:12:01.394076",
     "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": "67ac1ace",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-04T05:12:01.410072Z",
     "iopub.status.busy": "2023-01-04T05:12:01.409690Z",
     "iopub.status.idle": "2023-01-04T05:12:02.065260Z",
     "shell.execute_reply": "2023-01-04T05:12:02.064390Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.663372,
     "end_time": "2023-01-04T05:12:02.067199",
     "exception": false,
     "start_time": "2023-01-04T05:12:01.403827",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-04 05:12:01.691740: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 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": "4eff9265",
   "metadata": {
    "papermill": {
     "duration": 0.005164,
     "end_time": "2023-01-04T05:12:02.078102",
     "exception": false,
     "start_time": "2023-01-04T05:12:02.072938",
     "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": "2260173e",
   "metadata": {
    "papermill": {
     "duration": 0.005164,
     "end_time": "2023-01-04T05:12:02.088619",
     "exception": false,
     "start_time": "2023-01-04T05:12:02.083455",
     "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": {
  "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 16.91398,
   "end_time": "2023-01-04T05:12:04.794570",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-01-04T05:11:47.880590",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}