Tutorial#3 Tensorflow Regression and Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Advantages of TensorFlow:\n",
    "\n",
    "- Mature Ecosystem: TensorFlow has been around longer and has a more mature ecosystem. It has a wide range of libraries, tools, and resources, making it suitable for various applications.\n",
    "\n",
    "- Deployment and Production: TensorFlow offers production-ready tools like TensorFlow Serving and TensorFlow Lite for deploying models to production environments and mobile devices.\n",
    "\n",
    "- Scalability: TensorFlow provides distributed computing capabilities, including TensorFlow Distributed, TensorFlow Dataflow, and TensorFlow on Spark, making it well-suited for large-scale projects.\n",
    "\n",
    "- High-Level APIs: TensorFlow provides high-level APIs like Keras (which is integrated into TensorFlow) that make it easy to build and experiment with neural network architectures.\n",
    "\n",
    "- Graph Compilation: TensorFlow's static computation graph can lead to optimization opportunities and better performance for certain scenarios.\n",
    "\n",
    "Advantages of PyTorch:\n",
    "\n",
    "- Dynamic Computational Graph: PyTorch uses a dynamic computation graph, which is more intuitive for debugging and dynamic architectures. It also allows for more flexible and dynamic model building.\n",
    "\n",
    "- Ease of Use: PyTorch's dynamic nature and Pythonic syntax make it easier to learn, experiment with, and debug. It's often preferred by researchers and practitioners for this reason.\n",
    "\n",
    "- Tight Integration with Python: PyTorch's tight integration with Python allows for seamless integration with other scientific libraries and tools.\n",
    "\n",
    "- Strong Community: PyTorch has gained rapid adoption and has a strong and active community that contributes to its development and provides resources and tutorials.\n",
    "\n",
    "- Research-Oriented: PyTorch is popular among researchers due to its ease of use for rapid prototyping and experimentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's do a regression example!\n",
    " \n",
    " Let's predict fuel efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The imports\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "tf.compat.v1.enable_eager_execution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's download the data! MPG (miles per Gallon, feul efficiency)\n",
    "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n",
    "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "\n",
    "raw_dataset = pd.read_csv(url, names=column_names,\n",
    "                          na_values='?', comment='\\t',\n",
    "                          sep=' ', skipinitialspace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Origin  \n",
       "393          82       1  \n",
       "394          82       2  \n",
       "395          82       1  \n",
       "396          82       1  \n",
       "397          82       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data cleaning\n",
    "dataset = raw_dataset.copy()\n",
    "dataset = dataset.dropna()\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's convert origin to numeric value\n",
    "dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "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>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Japan</th>\n",
       "      <th>USA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "0  18.0          8         307.0       130.0  3504.0          12.0   \n",
       "1  15.0          8         350.0       165.0  3693.0          11.5   \n",
       "2  18.0          8         318.0       150.0  3436.0          11.0   \n",
       "3  16.0          8         304.0       150.0  3433.0          12.0   \n",
       "4  17.0          8         302.0       140.0  3449.0          10.5   \n",
       "\n",
       "   Model Year  Europe  Japan  USA  \n",
       "0          70       0      0    1  \n",
       "1          70       0      0    1  \n",
       "2          70       0      0    1  \n",
       "3          70       0      0    1  \n",
       "4          70       0      0    1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pd.get_dummies is a function used for one-hot encoding categorical data. \n",
    "dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- One-Hot Encoding:\n",
    "\n",
    "    - Distinct Representation: One-hot encoding creates a unique binary feature (0 or 1) for each category in a categorical variable. This distinct representation ensures that no category is considered more important than another, preventing the model from inferring an ordinal relationship between categories when there isn't one.\n",
    "\n",
    "    - Interpretability: One-hot encoding is straightforward to interpret because each binary feature corresponds directly to a specific category. It makes it easy to understand which category is associated with a particular observation.\n",
    "\n",
    "    - Non-linearity: One-hot encoding introduces non-linearity into the model, which can be beneficial when dealing with categorical variables that do not have a natural order or when interactions between categories are significant.\n",
    "\n",
    "    - Suitable for Most Algorithms: One-hot encoding is compatible with a wide range of machine learning algorithms, including linear models, decision trees, random forests, and neural networks. It's a safe choice in many scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's split the data into training and testing!\n",
    "train_dataset = dataset.sample(frac=0.8, random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2518bd20400>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's look at the data\n",
    "sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's split the features (x) from the labels (y)\n",
    "train_features = train_dataset.copy()\n",
    "test_features = test_dataset.copy()\n",
    "\n",
    "train_labels = train_features.pop('MPG')\n",
    "test_labels = test_features.pop('MPG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Japan</th>\n",
       "      <th>USA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>4</td>\n",
       "      <td>90.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2890.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>4456.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>4</td>\n",
       "      <td>105.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2145.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2990.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>4220.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2391.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>4</td>\n",
       "      <td>83.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2464.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>314 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cylinders  Displacement  Horsepower  Weight  Acceleration  Model Year  \\\n",
       "146          4          90.0        75.0  2125.0          14.5          74   \n",
       "282          4         140.0        88.0  2890.0          17.3          79   \n",
       "69           8         350.0       160.0  4456.0          13.5          72   \n",
       "378          4         105.0        63.0  2125.0          14.7          82   \n",
       "331          4          97.0        67.0  2145.0          18.0          80   \n",
       "..         ...           ...         ...     ...           ...         ...   \n",
       "281          6         200.0        85.0  2990.0          18.2          79   \n",
       "229          8         400.0       180.0  4220.0          11.1          77   \n",
       "150          4         108.0        93.0  2391.0          15.5          74   \n",
       "145          4          83.0        61.0  2003.0          19.0          74   \n",
       "182          4         107.0        86.0  2464.0          15.5          76   \n",
       "\n",
       "     Europe  Japan  USA  \n",
       "146       0      0    1  \n",
       "282       0      0    1  \n",
       "69        0      0    1  \n",
       "378       0      0    1  \n",
       "331       0      1    0  \n",
       "..      ...    ...  ...  \n",
       "281       0      0    1  \n",
       "229       0      0    1  \n",
       "150       0      1    0  \n",
       "145       0      1    0  \n",
       "182       1      0    0  \n",
       "\n",
       "[314 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146    28.0\n",
       "282    22.3\n",
       "69     12.0\n",
       "378    38.0\n",
       "331    33.8\n",
       "       ... \n",
       "281    19.8\n",
       "229    16.0\n",
       "150    26.0\n",
       "145    32.0\n",
       "182    28.0\n",
       "Name: MPG, Length: 314, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Faster Convergence: Normalized data typically leads to faster convergence during the training process. Neural networks are optimized using gradient-based methods, and normalizing the data can help ensure that the optimization landscape is smoother. This often results in the model converging to a good solution more quickly.\n",
    "\n",
    "- Numerical Stability: Normalizing data can prevent numerical instability issues during training. Large input values can cause gradients to become very small (vanishing gradients) or very large (exploding gradients), making training difficult or unstable. Normalizing inputs to a smaller range helps mitigate these problems.\n",
    "\n",
    "- Improved Generalization: Normalization can lead to better generalization performance. When features have widely varying scales, the model may assign disproportionately more importance to features with larger values. This can lead to suboptimal generalization, as some features may dominate the learning process. Normalization ensures that all features contribute more equally to the learning process.\n",
    "\n",
    "- Model Robustness: Normalized data makes the model more robust to changes in the scale of input features. If the scale of a feature changes (e.g., due to different units or measurement scales), a properly normalized model is less likely to be affected by such changes.\n",
    "\n",
    "- Regularization: Normalization can act as a form of regularization. By constraining the input data to a certain range (e.g., between 0 and 1 or with a mean of 0 and standard deviation of 1), it can help prevent overfitting, especially when the training dataset is small.\n",
    "\n",
    "- Efficient Optimization: Many optimization algorithms assume that the input data is centered around zero and has a similar scale across dimensions. Normalization helps fulfill these assumptions and can lead to more efficient optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.4777069e+00 1.9531847e+02 1.0486943e+02 2.9902517e+03 1.5559236e+01\n",
      " 7.5898087e+01 1.7834395e-01 1.9745223e-01 6.2420380e-01]\n"
     ]
    }
   ],
   "source": [
    "#scale and normalize features or variables within a dataset. it helps make different features or variables more comparable\n",
    "normalizer = preprocessing.Normalization(axis=-1)\n",
    "normalizer.adapt(np.array(train_features))\n",
    "print(normalizer.mean.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.70795125],\n",
       "       [ 0.38576168],\n",
       "       [ 0.80108184],\n",
       "       [-0.846263  ],\n",
       "       [-1.1580343 ],\n",
       "       [ 0.41178077],\n",
       "       [-1.1251799 ],\n",
       "       [ 1.8009801 ],\n",
       "       [ 0.30437303],\n",
       "       [-0.59053963]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's do a linear model first!\n",
    "linear_model = tf.keras.Sequential([\n",
    "    normalizer,\n",
    "    layers.Dense(units=1)\n",
    "])\n",
    "linear_model.predict(train_features[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model.compile(\n",
    "    optimizer=tf.optimizers.Adam(learning_rate=0.1),\n",
    "    loss='mean_absolute_error')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- verbose=0: This setting means \"silent\" mode. When you set verbose to 0, the training process will run without providing any output or progress updates to the console or the user interface. This mode is useful when you want to run training in the background without generating any unnecessary output.\n",
    "\n",
    "- verbose=1: This is the default setting in many libraries. When you set verbose to 1, the training process will provide regular updates and progress information, such as the epoch number, training loss, and, in some cases, metrics like accuracy. This mode is useful for monitoring the training process and getting a sense of how the model is performing as it trains.\n",
    "\n",
    "- verbose=2: This setting provides more detailed output during training. It typically includes the same information as verbose=1 but with additional details, such as the time taken for each epoch, batch-wise progress, and possibly more detailed debugging information. It's useful when you want a more detailed log of the training process for debugging or optimization purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 11.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = linear_model.fit(\n",
    "    train_features, train_labels, \n",
    "    epochs=100,\n",
    "    # suppress logging\n",
    "    verbose=0,\n",
    "    # Calculate validation results on 20% of the training data\n",
    "    validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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": [
    "def plot_loss(history):\n",
    "    plt.plot(history.history['loss'], label='loss')\n",
    "    plt.plot(history.history['val_loss'], label='val_loss')\n",
    "    plt.ylim([0, 10])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Error [MPG]')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.513322591781616\n"
     ]
    }
   ],
   "source": [
    "test_results = {}\n",
    "test_results['linear_model'] = linear_model.evaluate(\n",
    "    test_features, test_labels, verbose=0)\n",
    "print(test_results['linear_model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "normalization (Normalization (None, 9)                 19        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,884\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 19\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Let's now try a DNN\n",
    "def build_and_compile_model(norm):\n",
    "    model = keras.Sequential([\n",
    "        norm,\n",
    "        layers.Dense(64, activation='relu'),\n",
    "        layers.Dense(64, activation='relu'),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "\n",
    "    model.compile(loss='mean_absolute_error',\n",
    "                optimizer=tf.keras.optimizers.Adam(0.001))\n",
    "    return model\n",
    "dnn_model = build_and_compile_model(normalizer)\n",
    "dnn_model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 13.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = dnn_model.fit(\n",
    "    train_features, train_labels,\n",
    "    validation_split=0.2,\n",
    "    verbose=0, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results['dnn_model'] = dnn_model.evaluate(test_features, test_labels, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>Mean absolute error [MPG]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>linear_model</th>\n",
       "      <td>2.510665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dnn_model</th>\n",
       "      <td>1.791294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mean absolute error [MPG]\n",
       "linear_model                   2.510665\n",
       "dnn_model                      1.791294"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's do a classification example!\n",
    " \n",
    " Let's classify images of clothing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the dataset\n",
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's plot a random image\n",
    "plt.figure()\n",
    "plt.imshow(train_images[11])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's normalize our data RGB channel with value range 0-255\n",
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's look at the data\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now let's build the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.4975 - accuracy: 0.8269\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.3744 - accuracy: 0.8650\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.3360 - accuracy: 0.8770\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.3121 - accuracy: 0.8864\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2959 - accuracy: 0.8919\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2798 - accuracy: 0.8958\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2667 - accuracy: 0.9007\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2597 - accuracy: 0.9022\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.2474 - accuracy: 0.9089\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2399 - accuracy: 0.9100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2810363bcd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train\n",
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.3396 - accuracy: 0.8819\n",
      "\n",
      "Test accuracy: 0.8819000124931335\n"
     ]
    }
   ],
   "source": [
    "#Let's test its accuracy!\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make predictions\n",
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- tf.keras.layers.Softmax(): This is a Softmax activation layer, which is typically used as the final layer in a classification neural network. The Softmax function takes a vector of numerical scores (logits) and converts them into probabilities. Each score is transformed into a probability value between 0 and 1, and all the probabilities sum up to 1. This is crucial for multi-class classification tasks, as it helps in assigning class probabilities to each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.70652492e-06, 5.83204762e-10, 1.68496996e-07, 5.47623336e-11,\n",
       "       2.32136770e-07, 7.39320815e-02, 8.75699186e-07, 1.29138725e-02,\n",
       "       1.11439988e-06, 9.13145959e-01], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ax functions\n",
    "def plot_image(i, predictions_array, true_label, img):\n",
    "    true_label, img = true_label[i], img[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    if predicted_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "\n",
    "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "    true_label = true_label[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks(range(10))\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "    plt.ylim([0, 1])\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "    thisplot[predicted_label].set_color('red')\n",
    "    thisplot[true_label].set_color('blue')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADCCAYAAAB3whgdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAASw0lEQVR4nO3de7BdVX3A8e+PBEhIAoTwfkjUhiIiAiKjoowKtYIOSqujaDtFqu0M1lcHgXZaoLUzFW2pdhxLVfBRAWtjHNF2CFAFQSFAEEh4V0mQNwFJeEPg1z/2jtzcs/a9++QmWST5fmbu5J7fWWvvtXeS39l7PfaJzESStP5tVrsBkrSpMgFLUiUmYEmqxAQsSZWYgCWpEhOwJFUyuXYDpNq23377nD17du1maCO1cOHCZZm5Q+k9E7A2ebNnz+aaa66p3QxtpCJiadd7dkFIUiUmYEmqxAQsSZUM1QfsYIXWpSVLlrBs2bKo3Q5pfRkqATtYoXXpoIMOqt0Eab2yC0KSKjEBS1IlJmBJqsQELEmVmIAlqRITsCRVYgKWpEpMwJJUsPPOENH/Z+edh9+HCViSCu6/f92WBxOwJFVjApakSkzAklSJCViSKjEBS1IlJmBJqsQELEmVmIAlqRITsCRVYgKWpEpMwJJUiQlYkioxAUtSJSZgSarEBCxJlZiAJakSE7AkVWIClqRKTMCSVIkJWJIqMQFLUiUmYEmqxAQsSZWYgCWpEhOwJFViApakSibXboDWnueee24gttlm5c/YiOi93aeffnogtuWWWxbL3n777QOxOXPm9N6XtCnxCliSKjEBS1IlJmBJqsQELEmVmIAlqRJnQawlmdkrBuWZCXfffXex7BVXXDEQO+KII4plp02bNlYT11jXjIeSefPmDcROOumktdkcaaPhFbAkVWIClqRKTMCSVIkJWJIqcRBuHepaBlxy2WWXFeMLFiwYiN1zzz3Fsh//+Md7728YDzzwwEBs/vz5xbIzZsxYJ22QNkZeAUtSJSZgSarEBCxJlZiAJakSE7AkVeIsiLWk9DD0yZPLp/fqq68eiN18883FsjvttNNArPTQc4Cjjz56IDZz5sxi2aeeemogtueeexbLPvTQQwOxFStWFMvutttuxbikQV4BS1IlJmBJqsQELEmVmIAlqRIH4dbA888/PxArDbg9/vjjxfpz584diHU9c7c0WPboo48Wyw7zTOJS/MYbbyyW3X333QdiXYN7pcFISWVeAUtSJSZgSarEBCxJlZiAJakSE7AkVbLBzYIojd5HRLFsabZCV9lSvGtEf9KkSWM18bfOPPPMYry0vHjKlCnFskuXLh2IlWZGdG135cqVxbKl4+36VuXSDI3ly5cXyz799NMDsa7ZIOvqW5ylDYVXwJJUiQlYkioxAUtSJSZgSarkRTEIN8zAWle8ZJhvJS4NuPUdbAM477zzBmL33XdfsewBBxwwEOsaLHvkkUcGYtttt12x7KxZswZiy5YtK5Z97LHHerehpGuJ8xNPPDEQ63p+8f777997f9LGyCtgSarEBCxJlZiAJakSE7AkVfKiGIQbZmCttLqtFIPyIFrXvoYZcDv77LMHYrfddttAbI899ijWL33JZdeg1pNPPjkQ6/riy9JzgruOd6utthqIda2wG2aQtGT+/PnFuINw2tR5BSxJlZiAJakSE7AkVWIClqRKTMCSVMk6mwXRNTOhpDSi3jUroLS8eJglx13uueeegdi8efOKZUszE+bMmTMQKy33hfIzc0szIwA233zzgVjXDITSMuAupXPW9c3MpbJdz/Itte1nP/tZ73ZJmxKvgCWpEhOwJFViApakSkzAklTJ0INwo5+b27WEd6IDY8MsdX3wwQeL8SVLlgzEbr311mLZe++9dyC2xRZbFMtuvfXWA7HSc3tXrFhRrP/ss88OxEoDc1A+v6XjgvLzfLfddtti2dKxdX0JaWlAdOrUqcWypW1Mnz69WHbx4sWrvS4NbkobM6+AJakSE7AkVWIClqRKTMCSVIkJWJIqGXoWRN8Hl99///0DsaVLlxbLPv74471iUB4pv+OOO4plS0tzJ08uH/KMGTMGYl3LqZcvX96rXV37KrWra1ZBaXnwM888Uyy7yy67DMS6ZmKU2jBz5sxi2dKS6ocffrhYtjTjoevboUdvo2sWhrSx8gpYkioxAUtSJSZgSarEBCxJlUz4ecAXX3xxMV56vm7XoFRpKXHXgExpEHCYgbWuZ/SWBoq6nklcWjZcGsDqGsQrtaHreEvP3e1a2ltadty1THsYpWPrWmpeGozsGjTs+nuTNhVeAUtSJSZgSarEBCxJlZiAJakSE7AkVTLUMPSKFSu48MILV4udddZZxbJ77733QKy0VBaGWwY80QeJl/YF5ZH6rpH+Rx99tNe+uh4wXnrYfNcxlGZnlJZ5A9x0000Dsa4ZCMMs+y3NuuhaKj5lypRe9QF23HHH1V6XvgFa2ph5BSxJlZiAJakSE7AkVWIClqRKhhqEmzZtGgcffPBqsSuvvLJYdtGiRQOxyy+/vPe+ugZkSoNo2223XbFsKb7NNtsUy5YGq7qWIj/00EMDsdK3LZeeuQvlZ/R2fQv09ddfPxDbb7/9imVnz549ELvooouKZUvLqYf5JuuuZcS77rrrQKz0LdIwOJjp84C1qfEKWJIqMQFLUiUmYEmqxAQsSZWYgCWpkqFmQUyaNGngod+nnHJK7/pdD0NfsGDBQKw0qwDg5z//+UBsyZIlxbI33HDDQKxrCW1pxkPXzITSbIHSjItXvepVxfqHH374QOzII48sli0t7R3GUUcdVYzfeeedA7FZs2YVy5ZmMXQt6S7Njih9szPAXnvttdrriR6rtKHxCliSKjEBS1IlJmBJqsQELEmVrNevpe16Luxhhx3WKwZw/PHHr9U2bezOP//82k3obZil0NLGwH/xklSJCViSKjEBS1IlJmBJqsQELEmVmIAlqRITsCRVYgKWpEpMwJJUiQlYkioxAUtSJSZgSarEBCxJlZiAJakSE7AkVWIClqRKTMCSVIkJWJIqMQFLUiUmYEmqxAQsSZWYgCWpEhOwJFViApakSkzAklSJCViSKplcuwGSNkwnn3xy77Kf/exn12FLNlxeAUtSJSZgSarEBCxJlZiAJakSE7AkVTLULIiFCxcui4il66ox2uTtWbsB0vo0VALOzB3WVUMkaVNjF4QkVWIClqRKXAknaYOwMa68WytXwBEcHUFGsHfP8ksi2L4Qf2zI/Q5VfoztHBvBrh3vvTqCKyJYFMEPI9i6jc+K4CcRPBbBl0aU3zKCCyJYHMHxI+JfieCAMdrw7ghOGRW7PoLzhjiGLxXip0VwQp9trEn5MbYzO4IPjHj9qgi+MdHtShuTtXUFfAxwOfB+4LS1tM316VhgMXBP4b2vASdkcmkExwGfBv4WeKr9c9/2Z5XfBxYCRwLXAl+O4NXAZpn8Yow2nAgctepFBK+g+YA8NIJpmTy+hsdWy2zgA8C5AJksimD3CF6SyZ1VWzbKGs7u2R5Ytga72yTrnX766et1n+t7f6tEFMt3z+7JzAn9QE6HvBtyL8hbRsTfDHkJ5FzIWyDPgYz2vSWQ20NOhbwA8iNt/LER9T8NeTXkDZB/17HvxyD/GfJayP+F3KGN7w95ZVv3+5Azu+KQ72m3cyvkdZBTR+1jxYh27wF506j3j4X80ojXR0CeAbkF5HVt7HzIXcc4h3tB/mRU7DOQJ0J+HfKYEfFLIE+HvAryNsg3jW4H5Dsgr2jP8WmQJ7Txl7fneyHkZZB7F9pyGuR/QP4Y8vYRfzcB+XnIxZCLIN83TvxKyOXtOf1UG/sE5IkT/Tf3YvgBrrHe2qu3IbV1Isc4+mdtdEG8G7ggk9uAhyM4cMR7BwCfBPYBXgYcMuK96cAPgXMz+erIDUbwNmAOcDCwP/CaCA4t7HsacG0mBwKXAqe28W8BJ2WyH7BorHgmc4FrgA9msn8mT47ax2JeuDJ9L7DHOOfjImBnYAHwuQiOAhZmFq+uVzmE5mp5pPcB/wmcR3OHMdLkTA6mObenjnwjgqOBk4EjMwc+pb8CfCyT1wAnAF/uaM9+wDuA1wOntN0zf0Dzd/Fq4HDg8xHsMkb8ZOCy9pz+S7vda4A3jXEepE3K2uiCOAb4Qvv7d9rXq5LJVZncBRDBdTS3pZe37/0A+Fwm5xS2+bb2Z9Ut+3SahPzTUeWep0lSAN8G5kWwDbBtJpe28W8C/9UV73F8xwH/2vbPng88M1bhTFbS3HoTwebAfOCoCM4AXgJ8K5PzR1XbBXhw1YsIXgs8mMnSCO4Czo5gZia/aYvMa/9cSHNOV3kLcBDwtkxWjNxBBNOBN9Cci1W27DiMH7QfRE9G8BOaD8I3Audl8hxwfwSXAq8dI76isN0HoNzXLm2KJpSAI5gFvBXYN4IEJgEZwYltkadHFH9u1P5+BhwRwbmZ5OhNA/+Yyb8P2aTR25mwTG6h+TAggr1orgz7Op4m0b+eJnG/D7gCBhLwk8A2I14fA+wdwZL29dbAH9L0R8ML53X0Of0VzZ3GXjRXmyNtBjySyf492j36PCbN30lJV7xkCgzcYWyovmK9tVqvxj5rHONqJtoF8R6aK7o9M5mdyR7AHTRXReM5BXiI8m3wfOC49qqNCHaLYMdCuc3aNkBz1Xl5JsuB30T89lb3j4FLu+Lt748CM0qNXLXfCDYD/gY4s8exEcFM4J003R5b0VytJ00SGu1m4HdG7Oe9wH7tOZ0NvIvBboiSpTRdAt+K4JUj32iviO+I4L3tfqIdHCx5VwRT2g/YNwNX09x9vC+CSRHsABwKXDVGvHRO96Lp0tngZeYa/Se03otnnzWOcbSJJuBjgO+Pin0PXph+NI5PAlMi+NzIYCYX0oyeXxHBImAu5QT5OPDKCBbSXIn/fRv/E5q+yBto+ifHi38DODOC6yKYOvoYI7gNuIVmlsTXV73RXqGeARwbwV0R7DOi3inAP7RX9/NpugYWwer93a2fAgdEEDQJ7O5M7h71/j5t3+qYMrkV+CBNV8PLR739QeBPI7geuJEmsZdcBfw3cCXwmbb/+vvADcD1wI+BEzO5b4z4DcDKdirdp9rtvqXdriSgHd1XbRF8EfhhJhfXbsu6EMGWNHccb2z7yTdIEfF24Is03W1fy8xeM/4j4myaO6IHMnPf8cqPqLcHzV3UzjR3UV/JzC/2qDeF5oN7S5puqrmZeerYtVarP4mmG+vuzHxnzzpLaO58ngNWZuZBPettS9O9ti/NXeJxmXnFOHV+lxfGf6DpejslM7/QUWVk3U8BH273tQj4UGY+1aPeJ4CP0HS7fbXPvsa1tqZT+DOxH8idII+q3Y51eHxzIN9cux0TOwYmAb+k+c++Bc1V/z496x4KHAgsHnKfuwAHtr/PAG7rs882SUxvf9+cZlbO64bY71/S3IX+aIg6S4Dt1+C8fhP4cPv7FsC2a/D3ch+wZ4+yu9F0k05tX38XOLZHvX1pus+2ovlAuxiYM9F/Uz4L4kUik/tzcHbERiOT2zO5pHY7Juhg4P8y81eZ+QzNrJ+ubpzVZOZPgYeH3WFm3puZ17a/P0ozXrBbj3qZmatWim7e/vS63Y2I3WkGm782XtmJioitaT6czgLIzGcy85EhN3MY8MvM7LuYZjIwNSIm0yTUsaaIrvIK4MrMfCIzV9LczR09ZDsHmICl/nYDfj3i9V30SIZrS0TMpplbv6Bn+UkRcR3N9L+LMrNXPZpppSfSdHkMI4ELI2JhRPxZzzovo5mC+fWI+EVEfC0ipg253/dDvyX7mXk38E/AncC9wPLMvLBH1cXAoRExKyK2olnpOt6agHGZgKX+SlPu1ssgSkRMpxng/mRmluZYD8jM5zJzf2B34OCIGLfvOSJW9VMvXINmHpKZBwJHAB+NiNLiqdEm03TN/FtmHkAzsN77qTsRsQXNQqk+c/qJiJk0dy0vpZmTPi0i/mi8epl5M3A6zUKrC2i6nyY8lmEClvq7i9Wvenan3+3rhETE5jTJ95zMnDde+dHaW/pLgLf3KH4IcFQ7oPYd4K0R8e2e+7mn/fMBmtkxB/eodhdw14ir87mw2mra8RwBXJuZ9/csfzhwR2Y+mJnP0ixqekOfipl5VmYemJmH0nQn3T5EO4tMwFJ/VwNzIuKl7ZXX+xlcVLNWRUTQ9I/enJlnDFFvh3Z2ARExlSbx3DJevcz8q8zcPTNn0xzfjzNz3CvEiJgWETNW/U6zeGncOd+ZeR/w63ZWAzT9uTeNV2+EY+jZ/dC6E3hdRGzVntvDaPrVxxUR7ZqAeAnNfPth9lvk84ClnjJzZUT8Bc287knA2Zl5Y5+6EXEezaKW7SPiLuDUzDyrR9VDaBYNLWr7cwH+OjP/Z5x6uwDfbKeTbQZ8NzN/1Keta2gn4PtNTmMycG5mXtCz7seAc9oPtV8BH+pTqe2L/T3gz/s2MjMXRMRcmsclrKR53EHfhRXfi4hZwLPARzPzN+NVGI/zgCWpErsgJKkSE7AkVWIClqRKTMCSVIkJWJIqMQFLUiUmYEmqxAQsSZX8Py0nF2TiZlbiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 100\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "    plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "    plot_image(i, predictions[i], test_labels, test_images)\n",
    "    plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "    plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's navigate some of the options that we used!\n",
    "\n",
    "### Layers\n",
    "We used Dense above, which is just your regular densely-connected NN layer.\n",
    "\n",
    "    ''' \n",
    "    tf.keras.layers.Dense(\n",
    "    units, activation=None, use_bias=True,\n",
    "    kernel_initializer='glorot_uniform',\n",
    "    bias_initializer='zeros', kernel_regularizer=None,\n",
    "    bias_regularizer=None, activity_regularizer=None, kernel_constraint=None,\n",
    "    bias_constraint=None, **kwargs\n",
    "    )\n",
    "    '''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, 32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a `Sequential` model and add a Dense layer as the first layer.\n",
    "modell = tf.keras.models.Sequential()\n",
    "modell.add(tf.keras.Input(shape=(16,)))\n",
    "modell.add(tf.keras.layers.Dense(32, activation='relu'))\n",
    "# Now the model will take as input arrays of shape (None, 16)\n",
    "# and output arrays of shape (None, 32).\n",
    "# Note that after the first layer, you don't need to specify\n",
    "# the size of the input anymore:\n",
    "modell.add(tf.keras.layers.Dense(32))\n",
    "\n",
    "modell.output_shape\n",
    "##The None dimension means that the model can handle varying batch sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 32)                1056      \n",
      "=================================================================\n",
      "Total params: 1,600\n",
      "Trainable params: 1,600\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "modell.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More than 30 types of layers in tensorflow, check them here! https://www.tensorflow.org/api_docs/python/tf/keras/layers\n",
    "\n",
    "### activation functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 4. 4.]\n"
     ]
    }
   ],
   "source": [
    "#Relu\n",
    "foo = tf.constant([-10, -5, 0.0, 5, 10], dtype = tf.float32)\n",
    "m=tf.keras.activations.relu(foo,alpha=0.0, max_value=4, threshold=0).numpy()\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-9.999546 -9.932621  0.        5.       10.      ]\n"
     ]
    }
   ],
   "source": [
    "#elu\n",
    "# x if x > 0 \n",
    "# if x<0 alpha * (exp(x) - 1) \n",
    "foo = tf.constant([-10, -5, 0.0, 5, 10], dtype = tf.float32)\n",
    "m=tf.keras.activations.elu(foo,alpha=10).numpy()\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.         -0.99990916  0.          0.99990916  1.        ]\n"
     ]
    }
   ],
   "source": [
    "#tanh\n",
    "# tanh(x) = sinh(x)/cosh(x) = ((exp(x) - exp(-x))/(exp(x) + exp(-x))).\n",
    "foo = tf.constant([-1000, -5, 0.0, 5, 1000], dtype = tf.float32)\n",
    "m=tf.keras.activations.tanh(foo).numpy()\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.00669286 0.5        0.9933071  1.        ]\n"
     ]
    }
   ],
   "source": [
    "#Sigmoid\n",
    "# sigmoid(x) = 1 / (1 + exp(-x))\n",
    "foo = tf.constant([-10000, -5, 0.0, 5, 10000], dtype = tf.float32)\n",
    "m=tf.keras.activations.sigmoid(foo).numpy()\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more on activation functions: https://www.tensorflow.org/api_docs/python/tf/keras/activations\n",
    "\n",
    "### Optimizers \n",
    "Let's now go over some "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2011 - accuracy: 0.9263\n",
      "Epoch 2/2\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1943 - accuracy: 0.9294\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x28103410160>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Keras basic optimizer\n",
    "model.compile(optimizer='SGD',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "#train\n",
    "model.fit(train_images, train_labels, epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2327 - accuracy: 0.9143\n",
      "Epoch 2/2\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.2323 - accuracy: 0.9151\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x28103199880>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RMSprop\n",
    "model.compile(optimizer='RMSprop',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "#train\n",
    "model.fit(train_images, train_labels, epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2261 - accuracy: 0.9161\n",
      "Epoch 2/2\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2154 - accuracy: 0.9197\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x281035807c0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adam\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "#train\n",
    "model.fit(train_images, train_labels, epochs=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Learning rate scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.1882 - accuracy: 0.9311\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 2s 981us/step - loss: 0.1878 - accuracy: 0.9313\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1878 - accuracy: 0.9313\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2817bcc6310>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_learning_rate = 0.1\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "    initial_learning_rate,\n",
    "    decay_steps=1,\n",
    "    decay_rate=0.9,\n",
    "    staircase=True)\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=lr_schedule),\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_labels, epochs=3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more on optimizers: https://www.tensorflow.org/api_docs/python/tf/keras/optimizers\n",
    "\n",
    "### Loss functions \n",
    "Loss functions for regression are different from that for classification\n",
    "\n",
    "These functions greatly impact the model. Some are harder to optimiz but has a physical meaning to the problem!\n",
    "\n",
    "Some functions on tensorflow:\n",
    "class BinaryCrossentropy: Computes the cross-entropy loss between true labels and predicted labels.\n",
    "\n",
    "<code> class CategoricalCrossentropy </code>: Computes the crossentropy loss between the labels and predictions.\n",
    "\n",
    "<code>class CategoricalHinge</code>: Computes the categorical hinge loss between y_true and y_pred.\n",
    "\n",
    "<code>class CosineSimilarity</code>: Computes the cosine similarity between labels and predictions.\n",
    "\n",
    "<code>class Hinge</code>: Computes the hinge loss between y_true and y_pred.\n",
    "\n",
    "<code>class KLDivergence</code>: Computes Kullback-Leibler divergence loss between y_true and y_pred.\n",
    "\n",
    "\n",
    "<code>class MeanAbsoluteError</code>: Computes the mean of absolute difference between labels and predictions.\n",
    "\n",
    "<code>class MeanAbsolutePercentageError</code>: Computes the mean absolute percentage error between y_true and y_pred.\n",
    "\n",
    "<code>class MeanSquaredError</code>: Computes the mean of squares of errors between labels and predictions.\n",
    "\n",
    "<code>class MeanSquaredLogarithmicError</code>: Computes the mean squared logarithmic error between y_true and y_pred.\n",
    "\n",
    "\n",
    "<code>class SparseCategoricalCrossentropy</code>: Computes the crossentropy loss between the labels and predictions.\n",
    "\n",
    "<code>class SquaredHinge</code>: Computes the squared hinge loss between y_true and y_pred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2136 - accuracy: 0.9206\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2018 - accuracy: 0.9243\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1985 - accuracy: 0.9269\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2817e5c8610>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SparseCategoricalCrossentropy\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "#train\n",
    "model.fit(train_images, train_labels, epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.9383 - accuracy: 0.1325\n",
      "Epoch 2/2\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1011 - accuracy: 0.0966\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2817e5c8340>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SparseCategoricalCrossentropy\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.Hinge(),\n",
    "              metrics=['accuracy'])\n",
    "#train\n",
    "model.fit(train_images, train_labels, epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: -53.3942 - accuracy: 0.1284\n",
      "Epoch 2/2\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: -53.4957 - accuracy: 0.1533\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2810360e970>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#BinaryCrossentropy (not applicable)\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "              metrics=['accuracy'])\n",
    "#train\n",
    "model.fit(train_images, train_labels, epochs=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more on loss functions: https://www.tensorflow.org/api_docs/python/tf/keras/losses\n",
    "\n",
    "### Metrics \n",
    "Metrics do not impact the performance of the model or the optimization process. They are there to help you evaluate the performance of the model, but they are different from regression to calssification. \n",
    "\n",
    "https://www.tensorflow.org/api_docs/python/tf/keras/metrics\n",
    "\n",
    "for example \n",
    "<code> class CategoricalHinge </code>: Computes the categorical hinge metric between y_true and y_pred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 2s 1ms/step - loss: 2.1538 - categorical_hinge: 984.1354\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2810569b400>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hinge(not applicable)\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "              metrics=['CategoricalHinge'])\n",
    "#train\n",
    "model.fit(train_images, train_labels, epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 2s 1ms/step - loss: 2.2437 - mean_squared_error: 1723.5112\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x281032b5be0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(not useful)\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "              metrics=['MeanSquaredError'])\n",
    "#train\n",
    "model.fit(train_images, train_labels, epochs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "References:\n",
    "    - François Chollet\n",
    "    - Tensorflow.org"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}