exercise-deep-neural-networks.ipynb

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    "**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/deep-neural-networks).**\n",
    "\n",
    "---\n"
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   "source": [
    "# Introduction #\n",
    "\n",
    "In the tutorial, we saw how to build deep neural networks by stacking layers inside a `Sequential` model. By adding an *activation function* after the hidden layers, we gave the network the ability to learn more complex (non-linear) relationships in the data.\n",
    "\n",
    "In these exercises, you'll build a neural network with several hidden layers and then explore some activation functions beyond ReLU. Run this next cell to set everything up!"
   ]
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    "import tensorflow as tf\n",
    "\n",
    "# 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.ex2 import *"
   ]
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   "source": [
    "In the *Concrete* dataset, your task is to predict the compressive strength of concrete manufactured according to various recipes.\n",
    "\n",
    "Run the next code cell without changes to load the dataset."
   ]
  },
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    {
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     "text": [
      "(1030, 9)\n"
     ]
    },
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cement</th>\n",
       "      <th>BlastFurnaceSlag</th>\n",
       "      <th>FlyAsh</th>\n",
       "      <th>Water</th>\n",
       "      <th>Superplasticizer</th>\n",
       "      <th>CoarseAggregate</th>\n",
       "      <th>FineAggregate</th>\n",
       "      <th>Age</th>\n",
       "      <th>CompressiveStrength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.99</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>270</td>\n",
       "      <td>40.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>365</td>\n",
       "      <td>41.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.6</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.4</td>\n",
       "      <td>825.5</td>\n",
       "      <td>360</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "   Cement  BlastFurnaceSlag  FlyAsh  Water  Superplasticizer  CoarseAggregate  \\\n",
       "0   540.0               0.0     0.0  162.0               2.5           1040.0   \n",
       "1   540.0               0.0     0.0  162.0               2.5           1055.0   \n",
       "2   332.5             142.5     0.0  228.0               0.0            932.0   \n",
       "3   332.5             142.5     0.0  228.0               0.0            932.0   \n",
       "4   198.6             132.4     0.0  192.0               0.0            978.4   \n",
       "\n",
       "   FineAggregate  Age  CompressiveStrength  \n",
       "0          676.0   28                79.99  \n",
       "1          676.0   28                61.89  \n",
       "2          594.0  270                40.27  \n",
       "3          594.0  365                41.05  \n",
       "4          825.5  360                44.30  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "concrete = pd.read_csv('../input/dl-course-data/concrete.csv')\n",
    "print(concrete.shape)\n",
    "concrete.head()"
   ]
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   "source": [
    "# 1) Input Shape #\n",
    "\n",
    "The target for this task is the column `'CompressiveStrength'`. The remaining columns are the features we'll use as inputs.\n",
    "\n",
    "What would be the input shape for this dataset?"
   ]
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       "Correct"
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    "# YOUR CODE HERE\n",
    "input_shape = [8]\n",
    "\n",
    "# Check your answer\n",
    "q_1.check()"
   ]
  },
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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": "7d37fc19",
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   "source": [
    "# 2) Define a Model with Hidden Layers #\n",
    "\n",
    "Now create a model with three hidden layers, each having 512 units and the ReLU activation.  Be sure to include an output layer of one unit and no activation, and also `input_shape` as an argument to the first layer."
   ]
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     "text": [
      "2023-01-04 05:11:54.985932: 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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       "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",
    "    # the hidden ReLU layers\n",
    "    layers.Dense(units=512, activation='relu', input_shape=[8]),\n",
    "    layers.Dense(units=512, activation='relu'),\n",
    "    layers.Dense(units=512, activation='relu'),\n",
    "    # the linear output layer \n",
    "    layers.Dense(units=1),\n",
    "])\n",
    "\n",
    "# Check your answer\n",
    "q_2.check()"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_2.hint()\n",
    "#q_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72317678",
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   "source": [
    "# 3) Activation Layers #\n",
    "\n",
    "Let's explore activations functions some.\n",
    "\n",
    "The usual way of attaching an activation function to a `Dense` layer is to include it as part of the definition with the `activation` argument. Sometimes though you'll want to put some other layer between the `Dense` layer and its activation function. (We'll see an example of this in Lesson 5 with *batch normalization*.) In this case, we can define the activation in its own `Activation` layer, like so:\n",
    "\n",
    "```\n",
    "layers.Dense(units=8),\n",
    "layers.Activation('relu')\n",
    "```\n",
    "\n",
    "This is completely equivalent to the ordinary way: `layers.Dense(units=8, activation='relu')`.\n",
    "\n",
    "Rewrite the following model so that each activation is in its own `Activation` layer."
   ]
  },
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   "source": [
    "### YOUR CODE HERE: rewrite this to use activation layers\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(units=32, input_shape=[8]),\n",
    "    layers.Activation('relu'),\n",
    "    layers.Dense(units=32),\n",
    "    layers.Activation('relu'),\n",
    "    layers.Dense(1),\n",
    "])\n",
    "\n",
    "# Check your answer\n",
    "q_3.check()"
   ]
  },
  {
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   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_3.hint()\n",
    "#q_3.solution()"
   ]
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  {
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   "source": [
    "# Optional: Alternatives to ReLU #\n",
    "\n",
    "There is a whole family of variants of the `'relu'` activation -- `'elu'`, `'selu'`, and `'swish'`, among others -- all of which you can use in Keras. Sometimes one activation will perform better than another on a given task, so you could consider experimenting with activations as you develop a model. The ReLU activation tends to do well on most problems, so it's a good one to start with.\n",
    "\n",
    "Let's look at the graphs of some of these. Change the activation from `'relu'` to one of the others named above. Then run the cell to see the graph. (Check out the [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/activations) for more ideas.)"
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     "start_time": "2023-01-04T05:11:55.381924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE: Change 'relu' to 'elu', 'selu', 'swish'... or something else\n",
    "for act in ['relu', 'elu', 'selu', 'swish']:\n",
    "    activation_layer = layers.Activation(act)\n",
    "\n",
    "    x = tf.linspace(-3.0, 3.0, 100)\n",
    "    y = activation_layer(x) # once created, a layer is callable just like a function\n",
    "\n",
    "    plt.figure(dpi=100)\n",
    "    plt.plot(x, y)\n",
    "    plt.title(act)\n",
    "    plt.xlim(-3, 3)\n",
    "    plt.xlabel(\"Input\")\n",
    "    plt.ylabel(\"Output\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88826f7",
   "metadata": {
    "papermill": {
     "duration": 0.006966,
     "end_time": "2023-01-04T05:11:56.594007",
     "exception": false,
     "start_time": "2023-01-04T05:11:56.587041",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Keep Going #\n",
    "\n",
    "Now move on to Lesson 3 and [**learn how to train neural networks**](https://www.kaggle.com/ryanholbrook/stochastic-gradient-descent) with stochastic gradient descent."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e882ac",
   "metadata": {
    "papermill": {
     "duration": 0.006731,
     "end_time": "2023-01-04T05:11:56.607904",
     "exception": false,
     "start_time": "2023-01-04T05:11:56.601173",
     "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": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
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  "papermill": {
   "default_parameters": {},
   "duration": 21.243765,
   "end_time": "2023-01-04T05:11:59.935243",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-01-04T05:11:38.691478",
   "version": "2.3.4"
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