02_neural_network_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "02_neural_network_classification.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOTMWo9pkcXJ628ybGT/xpY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prachuryanath/TF-Learning/blob/main/02_neural_network_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUL_p5T7wPpH"
      },
      "source": [
        "# Introduction to neural network classification with TensorFlow\n",
        "\n",
        "In this notebook we're going to learn how to write neural networks for classification for problems.\n",
        "\n",
        "* Binary classification\n",
        "* Multiclass classification\n",
        "* Multilabel classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NNpIj1UT5XJ"
      },
      "source": [
        "## Creating data to view and fit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9D8OBKKsT95O"
      },
      "source": [
        "from sklearn.datasets import make_circles\n",
        "\n",
        "# Make 1000 examples\n",
        "n_samples = 1000\n",
        "\n",
        "# Create circles \n",
        "X, y = make_circles(n_samples,\n",
        "                    noise=0.03,\n",
        "                    random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "buhSFAcQUadG",
        "outputId": "c6045796-0db0-46ed-8c9c-95e7e8b209d2"
      },
      "source": [
        "# Check out features\n",
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.75424625,  0.23148074],\n",
              "       [-0.75615888,  0.15325888],\n",
              "       [-0.81539193,  0.17328203],\n",
              "       ...,\n",
              "       [-0.13690036, -0.81001183],\n",
              "       [ 0.67036156, -0.76750154],\n",
              "       [ 0.28105665,  0.96382443]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSuLnnx_VEIp",
        "outputId": "645eb26c-8d8a-44f4-b883-9e43d167fcf7"
      },
      "source": [
        "# Check the labels\n",
        "y[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 0, 1, 1, 1, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbqkiN5GVG8g"
      },
      "source": [
        "Our data is a little hard to understand right now ... let's visualize it !"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "pYSEmhTgeekF",
        "outputId": "28c519fa-b806-4f53-fa69-56aa1b7e3c38"
      },
      "source": [
        "import pandas as pd\n",
        "circles = pd.DataFrame({\"X0\":X[:,0],\"X1\":X[:,1], \"label\":y})\n",
        "circles"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>X0</th>\n",
              "      <th>X1</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.754246</td>\n",
              "      <td>0.231481</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.756159</td>\n",
              "      <td>0.153259</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.815392</td>\n",
              "      <td>0.173282</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.393731</td>\n",
              "      <td>0.692883</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.442208</td>\n",
              "      <td>-0.896723</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>0.244054</td>\n",
              "      <td>0.944125</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>-0.978655</td>\n",
              "      <td>-0.272373</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>-0.136900</td>\n",
              "      <td>-0.810012</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>0.670362</td>\n",
              "      <td>-0.767502</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>0.281057</td>\n",
              "      <td>0.963824</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           X0        X1  label\n",
              "0    0.754246  0.231481      1\n",
              "1   -0.756159  0.153259      1\n",
              "2   -0.815392  0.173282      1\n",
              "3   -0.393731  0.692883      1\n",
              "4    0.442208 -0.896723      0\n",
              "..        ...       ...    ...\n",
              "995  0.244054  0.944125      0\n",
              "996 -0.978655 -0.272373      0\n",
              "997 -0.136900 -0.810012      1\n",
              "998  0.670362 -0.767502      0\n",
              "999  0.281057  0.963824      0\n",
              "\n",
              "[1000 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "eIAVx16qe4gK",
        "outputId": "b9596a0a-d648-4e3e-a68b-72aec8db6195"
      },
      "source": [
        "# Visualize with a plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X[:,0], X[:,1], c=y, cmap= plt.cm.RdYlBu);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEdS8yxlfc_V"
      },
      "source": [
        "### Input and Output shapes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UoBGL1GEgZbM",
        "outputId": "92dc3567-5cd6-4e02-ea69-2f957cfd1724"
      },
      "source": [
        "# Check the shapes of our features and labels\n",
        "X.shape, y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1000, 2), (1000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R13GYRf1ghW-",
        "outputId": "0c64143d-9def-48a1-b845-95ec4cd402de"
      },
      "source": [
        "# View the first example of features and labels\n",
        "X[0], y[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.75424625, 0.23148074]), 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4KPpwjZg35v"
      },
      "source": [
        "## Steps in modelling\n",
        "\n",
        "The steps in modelling with TensorFlow are typically :\n",
        "1. Create or import a model\n",
        "2. Compile the model\n",
        "3. Fit the model\n",
        "4. Evaluate the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hx4mzcfdh5lb"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4Bv7e_vhMrY",
        "outputId": "d2fa163c-2c99-4dd8-fb70-35c013bb3c49"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_1 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_1.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.SGD(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_1.fit(X,y, epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "32/32 [==============================] - 3s 2ms/step - loss: 4.1198 - accuracy: 0.4378\n",
            "Epoch 2/5\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 0.7207 - accuracy: 0.5530\n",
            "Epoch 3/5\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 0.7033 - accuracy: 0.4836\n",
            "Epoch 4/5\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 0.6925 - accuracy: 0.5180\n",
            "Epoch 5/5\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 0.6937 - accuracy: 0.5157\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7faea0697410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaCrywfhh3iJ",
        "outputId": "10439c4a-4201-454e-971e-6e020390881c"
      },
      "source": [
        "# Let's try and improve our model by fitting it for longer\n",
        "model_1.fit(X,y, epochs=200, verbose=0)\n",
        "model_1.evaluate(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.6935 - accuracy: 0.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6934829950332642, 0.5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXJWIVXdjLEq",
        "outputId": "3ff4e8d1-5225-4bc4-fc48-b49007593fe3"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_2 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(10),\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_2.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.SGD(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_2.fit(X,y, epochs=100, verbose=0)\n",
        "\n",
        "# 4. Evaluate the model\n",
        "model_2.evaluate(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.6943 - accuracy: 0.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6942555904388428, 0.5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fvUI7Dwjr4d"
      },
      "source": [
        "## Improving our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SdKUcQkkMP4",
        "outputId": "ca8d0c50-87f4-4e0f-c55c-5c3f46bf3b23"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_3 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(300, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(200, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(100, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_3.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = model_3.fit(X,y, epochs=100, verbose=0)\n",
        "model_3.summary()\n",
        "\n",
        "# 4. Evaluate the model\n",
        "model_3.evaluate(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 300)               900       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 200)               60200     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 100)               20100     \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 81,301\n",
            "Trainable params: 81,301\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 2.9866e-04 - accuracy: 0.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.00029866292607039213, 0.5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t5SVDdcLkb1s"
      },
      "source": [
        "To visualize our model's predictions, let's create a function `plot_decision_boundary()`, this function will :\n",
        "\n",
        "* Take in a trained model, features (X) and labels (y)\n",
        "* Create a meshgrid of the different X values\n",
        "* Make predictions across the meshgrid\n",
        "* Plot the preditions as well as a line between zones (where each unique class falls)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zK6osyAkncd5"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mltbagfZl1q-"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def plot_decision_boundary(model, X, y):\n",
        "  \"\"\"\n",
        "  Plots the decision boundary created by a model predicting on X.\n",
        "  \"\"\"\n",
        "  # Define the axis boundaries of the plot and create a meshgrid\n",
        "  x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "  y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "  xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
        "                       np.linspace(y_min, y_max, 100))\n",
        "  \n",
        "  # Create X values (we're going to predict on all of these)\n",
        "  x_in = np.c_[xx.ravel(), yy.ravel()] # stack 2D arrays together: https://numpy.org/devdocs/reference/generated/numpy.c_.html\n",
        "  \n",
        "  # Make predictions using the trained model\n",
        "  y_pred = model.predict(x_in)\n",
        "\n",
        "  # Check for multi-class\n",
        "  if len(y_pred[0]) > 1:\n",
        "    print(\"doing multiclass classification...\")\n",
        "    # We have to reshape our predictions to get them ready for plotting\n",
        "    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n",
        "  else:\n",
        "    print(\"doing binary classifcation...\")\n",
        "    y_pred = np.round(y_pred).reshape(xx.shape)\n",
        "  \n",
        "  # Plot decision boundary\n",
        "  plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)\n",
        "  plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "  plt.xlim(xx.min(), xx.max())\n",
        "  plt.ylim(yy.min(), yy.max())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "St6bFjsCqDMC",
        "outputId": "5996426a-cd4e-4cbd-fc7e-635da463e02b"
      },
      "source": [
        "# Check out the predictions our model is making\n",
        "plot_decision_boundary(model=model_1,\n",
        "                       X=X,\n",
        "                       y=y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "doing binary classifcation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 958
        },
        "id": "gGNEkprRqSWv",
        "outputId": "a2e31810-d038-4316-b371-b67f4ce6af7a"
      },
      "source": [
        "# Let's see if our model can be used for a regression problem...\n",
        "\n",
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create some regression data\n",
        "X_regression = np.arange(0, 1000, 5)\n",
        "y_regression = np.arange(100, 1100, 5)\n",
        "\n",
        "# Split it into training and test sets\n",
        "X_reg_train = X_regression[:150]\n",
        "X_reg_test = X_regression[150:]\n",
        "y_reg_train = y_regression[:150]\n",
        "y_reg_test = y_regression[150:]\n",
        "\n",
        "# Fit our model to the data\n",
        "model_3.fit(X_reg_train, y_reg_train, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-757d1513a67e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# Fit our model to the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mmodel_3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_reg_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_reg_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1098\u001b[0m                 _r=1):\n\u001b[1;32m   1099\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    853\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    856\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2939\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2940\u001b[0m       (graph_function,\n\u001b[0;32m-> 2941\u001b[0;31m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0m\u001b[1;32m   2942\u001b[0m     return graph_function._call_flat(\n\u001b[1;32m   2943\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   3356\u001b[0m               call_context_key in self._function_cache.missed):\n\u001b[1;32m   3357\u001b[0m             return self._define_function_with_shape_relaxation(\n\u001b[0;32m-> 3358\u001b[0;31m                 args, kwargs, flat_args, filtered_flat_args, cache_key_context)\n\u001b[0m\u001b[1;32m   3359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3360\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_define_function_with_shape_relaxation\u001b[0;34m(self, args, kwargs, flat_args, filtered_flat_args, cache_key_context)\u001b[0m\n\u001b[1;32m   3278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3279\u001b[0m     graph_function = self._create_graph_function(\n\u001b[0;32m-> 3280\u001b[0;31m         args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)\n\u001b[0m\u001b[1;32m   3281\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marg_relaxed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrank_only_cache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   3204\u001b[0m             \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3205\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3206\u001b[0;31m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m   3207\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3208\u001b[0m         \u001b[0mfunction_spec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_spec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m    988\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m       \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    632\u001b[0m             \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m           \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    635\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    975\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 977\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    978\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *\n        return step_function(self, iterator)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica\n        return fn(*args, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step  **\n        outputs = model.train_step(data)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:754 train_step\n        y_pred = self(x, training=True)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:998 __call__\n        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py:259 assert_input_compatibility\n        ' but received input with shape ' + display_shape(x.shape))\n\n    ValueError: Input 0 of layer sequential_2 is incompatible with the layer: expected axis -1 of input shape to have value 2 but received input with shape (None, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECEKL5DpsaKz"
      },
      "source": [
        "Oh wait... we compiled our model for a binary classification problem.\n",
        "\n",
        "But... we're now working on a regression problem, let's change the model to suit the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFhMd5cPs3nd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e040213-b844-449f-cdb1-c8c341a5092c"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_3 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(300, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(100, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_3.compile(loss=tf.keras.losses.mae,\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_3.fit(X_reg_train,y_reg_train, epochs=100, verbose=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7faea044ca50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71zTJ_wetEwW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "f506a1b8-a73f-4094-9b9f-91d90a3b13e4"
      },
      "source": [
        "# Make predictions with our trained model\n",
        "y_reg_preds = model_3.predict(y_reg_test)\n",
        "\n",
        "# Plot the model's predictions against our regression data\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.scatter(X_reg_train, y_reg_train, c='b', label='Training data')\n",
        "plt.scatter(X_reg_test, y_reg_test, c='g', label='Testing data')\n",
        "plt.scatter(X_reg_test, y_reg_preds.squeeze(), c='r', label='Predictions')\n",
        "plt.legend();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZlYzpRCuuyS"
      },
      "source": [
        "## The missing piece: Non-linearity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLZ8gRpdxO3N"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_4 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.linear)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_4.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = model_4.fit(X,y, epochs=100, verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "-1ftg5lRyaAR",
        "outputId": "a1454f79-86f7-49d6-f5ce-1d3bd3c5d2d4"
      },
      "source": [
        "# Check out our data\n",
        "plt.scatter(X[:,0],X[:,1],c=y,cmap=plt.cm.RdYlBu)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7faea00cfbd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "xbUjONO2yOyI",
        "outputId": "ce2cb9c3-c916-4e0c-9997-3bb79d1b1c7c"
      },
      "source": [
        "# Check the decision boundary for our latest model\n",
        "plot_decision_boundary(model=model_4,\n",
        "                       X=X,\n",
        "                       y=y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "doing binary classifcation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cC61-JEgy1YL"
      },
      "source": [
        "Let's try build our first neural network with a non-linear actvation function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQVppc6KzZ5d"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_5 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.relu)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_5.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = model_5.fit(X,y, epochs=100, verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kIHI-bbzhS6"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_6 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_6.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = model_6.fit(X,y, epochs=200, verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQqnW1Uq09rr",
        "outputId": "267e20ef-f8a2-45b4-8813-720687991280"
      },
      "source": [
        "model_6.evaluate(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32/32 [==============================] - 0s 1ms/step - loss: 0.4710 - accuracy: 0.5290\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4709894061088562, 0.5289999842643738]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "cnK3aDcJ0izX",
        "outputId": "80e12227-5cd4-48fd-fdf1-bc392c1ff0fd"
      },
      "source": [
        "# Check the decision boundary for our latest model\n",
        "plot_decision_boundary(model=model_6,\n",
        "                       X=X,\n",
        "                       y=y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "doing multiclass classification...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9eM3UbU0rdI"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_7 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_7.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = model_7.fit(X,y, epochs=200, verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7BgHZ_Ccbvi",
        "outputId": "cd174400-9d71-41e6-b5a8-7f0c81eaebc9"
      },
      "source": [
        "model_7.evaluate(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32/32 [==============================] - 0s 2ms/step - loss: 0.0690 - accuracy: 0.9930\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.06901050359010696, 0.9929999709129333]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "3fb46wYIcOuR",
        "outputId": "73f980ca-2e5b-42cb-e3e8-53972739b834"
      },
      "source": [
        "# Check the decision boundary for our latest model\n",
        "plot_decision_boundary(model=model_7,\n",
        "                       X=X,\n",
        "                       y=y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "doing binary classifcation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRB_M2EXcU6E"
      },
      "source": [
        "# Create a toy A tensor\n",
        "A = tf.cast(range(-10,10), tf.float32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "EddWq-t7fGRH",
        "outputId": "bc50deba-9d48-42a1-ed2c-7ee3d29a2809"
      },
      "source": [
        "plt.plot(A)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fae4d6f6690>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMwWmcXUfMS7",
        "outputId": "af7266e9-7634-48fd-8d71-7689fc155bf4"
      },
      "source": [
        "# Let's start by replicating sigmoid\n",
        "def sigmoid(x):\n",
        "  return 1/(1+tf.exp(-x))\n",
        "\n",
        "# Use the sigmoid function on our toy tensor\n",
        "sigmoid(A)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([4.5397868e-05, 1.2339458e-04, 3.3535014e-04, 9.1105117e-04,\n",
              "       2.4726230e-03, 6.6928510e-03, 1.7986210e-02, 4.7425874e-02,\n",
              "       1.1920292e-01, 2.6894143e-01, 5.0000000e-01, 7.3105854e-01,\n",
              "       8.8079703e-01, 9.5257413e-01, 9.8201376e-01, 9.9330717e-01,\n",
              "       9.9752742e-01, 9.9908900e-01, 9.9966466e-01, 9.9987662e-01],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "KgxtMXEOfflJ",
        "outputId": "a9d14218-301c-4662-b82b-5386bc9c4bd4"
      },
      "source": [
        "# Plot our toy tensor transformed by sigmoid\n",
        "plt.plot(sigmoid(A))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fae4d2d3290>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ERSSvg8f0lC",
        "outputId": "4caa0d69-ad24-4d3b-98f0-54e7d4023d1d"
      },
      "source": [
        "# Let's recreate the relu function\n",
        "def relu(x):\n",
        "  return tf.maximum(0, x)\n",
        "\n",
        "# Pass our toy tensor to our custom relu function\n",
        "relu(A)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3., 4., 5., 6.,\n",
              "       7., 8., 9.], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Ly-KhrZcgjCB",
        "outputId": "4537103a-cc1d-4b17-d882-ffb74b2b2efb"
      },
      "source": [
        "# Plot our toy tensor transformed by relu\n",
        "plt.plot(relu(A))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fae4d076750>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AG_z9cMYgr3A",
        "outputId": "565d6cff-5499-4ede-db3a-2b25e8f3f4f1"
      },
      "source": [
        "# Let's try the linear activaton function\n",
        "tf.keras.activations.linear(A)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
              "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gg2RAe71g71W",
        "outputId": "ebbeedac-d920-44ad-90f4-e91952d77c83"
      },
      "source": [
        "# Does A even change ?\n",
        "A == tf.keras.activations.linear(A)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=bool, numpy=\n",
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True])>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hLfaX61hJCh"
      },
      "source": [
        "## Evaluating and improving our classification\n",
        "\n",
        "So far we've been training and testing on the test. So, let's create a training and test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NiDApW6wiUws",
        "outputId": "916f8675-7cc1-4133-be17-dbba40613e0c"
      },
      "source": [
        "# Check the number of examples\n",
        "len(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JM6Zfm-IhfkQ"
      },
      "source": [
        "# Split into train and test sets\n",
        "X_train, y_train = X[:800], y[:800]\n",
        "X_test, y_test = X[800:], y[800:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0hViIFl1iYTc",
        "outputId": "a72d42a2-6945-41cd-997f-8f5d2e7967c1"
      },
      "source": [
        "# Let'r recreate a model to fit on the training data and evaluate on the test data\n",
        "\n",
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_8 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_8.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.01),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = model_8.fit(X_train,y_train, epochs=25, verbose=0)\n",
        "\n",
        "# 4. Evaluate the model\n",
        "model_8.evaluate(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.1247 - accuracy: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.12468842417001724, 1.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "YlCEw2Lsi5Tw",
        "outputId": "e6c22db5-b685-4d67-e6ff-d19466841cd7"
      },
      "source": [
        "# Check the decision boundary for our latest model\n",
        "plot_decision_boundary(model=model_8,\n",
        "                       X=X_train,\n",
        "                       y=y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "doing binary classifcation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "IjlJfan2jxaa",
        "outputId": "96274d40-dced-41bf-c04e-372673197b34"
      },
      "source": [
        "plot_decision_boundary(model=model_8,\n",
        "                       X=X_test,\n",
        "                       y=y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "doing binary classifcation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zNAIxH-6kCOO"
      },
      "source": [
        "## Plot the loss(or training) curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 787
        },
        "id": "-5qEwYtSsPnj",
        "outputId": "be5fa15a-11c0-4eb3-949c-3eda8d76d175"
      },
      "source": [
        "pd.DataFrame(history.history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>loss</th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.684651</td>\n",
              "      <td>0.54250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.677721</td>\n",
              "      <td>0.55250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.673594</td>\n",
              "      <td>0.55125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.668149</td>\n",
              "      <td>0.57750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.663269</td>\n",
              "      <td>0.58500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.654567</td>\n",
              "      <td>0.58375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.641257</td>\n",
              "      <td>0.67500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.626428</td>\n",
              "      <td>0.70125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.603831</td>\n",
              "      <td>0.74875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.571403</td>\n",
              "      <td>0.77375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.540443</td>\n",
              "      <td>0.76500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.501504</td>\n",
              "      <td>0.78375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.468332</td>\n",
              "      <td>0.79750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.411301</td>\n",
              "      <td>0.84500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.362506</td>\n",
              "      <td>0.91250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.320904</td>\n",
              "      <td>0.93125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.284708</td>\n",
              "      <td>0.94875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.259720</td>\n",
              "      <td>0.95250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.237469</td>\n",
              "      <td>0.95625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.213520</td>\n",
              "      <td>0.96625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.193820</td>\n",
              "      <td>0.97750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.175244</td>\n",
              "      <td>0.97375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.161893</td>\n",
              "      <td>0.97875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.154989</td>\n",
              "      <td>0.97750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.148973</td>\n",
              "      <td>0.97625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        loss  accuracy\n",
              "0   0.684651   0.54250\n",
              "1   0.677721   0.55250\n",
              "2   0.673594   0.55125\n",
              "3   0.668149   0.57750\n",
              "4   0.663269   0.58500\n",
              "5   0.654567   0.58375\n",
              "6   0.641257   0.67500\n",
              "7   0.626428   0.70125\n",
              "8   0.603831   0.74875\n",
              "9   0.571403   0.77375\n",
              "10  0.540443   0.76500\n",
              "11  0.501504   0.78375\n",
              "12  0.468332   0.79750\n",
              "13  0.411301   0.84500\n",
              "14  0.362506   0.91250\n",
              "15  0.320904   0.93125\n",
              "16  0.284708   0.94875\n",
              "17  0.259720   0.95250\n",
              "18  0.237469   0.95625\n",
              "19  0.213520   0.96625\n",
              "20  0.193820   0.97750\n",
              "21  0.175244   0.97375\n",
              "22  0.161893   0.97875\n",
              "23  0.154989   0.97750\n",
              "24  0.148973   0.97625"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "leeMsAkSsoZJ",
        "outputId": "4a19567f-090d-4841-a8db-b7ab4de315f7"
      },
      "source": [
        "# Plot the loss curves\n",
        "pd.DataFrame(history.history).plot()\n",
        "plt.title(\"Model_8 loss curves\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Model_8 loss curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Osb8uf4Ws_FZ"
      },
      "source": [
        "## Finding the best learning rate\n",
        "\n",
        "To finf the ideal learning rate ( the learning rate where the loss decreases the most during training ) we're going to use the following steps :\n",
        "* A learning rate **callback** : you can think of a callback as an extra piece of functionality, you can add to your *while* its training.\n",
        "* Another model\n",
        "* A modified loss curves plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wmFOJ_l4DnV",
        "outputId": "322025f1-675f-40e4-9def-b6251af15cfe"
      },
      "source": [
        "# Let'r recreate a model to fit on the training data and evaluate on the test data\n",
        "\n",
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model using the Sequential API\n",
        "model_9 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid),\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_9.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.01),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# Create a learning rate callback\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch:1e-4 *10**(epoch/20))\n",
        "\n",
        "# Fit the model\n",
        "history = model_9.fit(X_train,y_train, epochs=100, callbacks=[lr_scheduler])\n",
        "\n",
        "# Evaluate the model\n",
        "model_9.evaluate(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6909 - accuracy: 0.5016\n",
            "Epoch 2/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6929 - accuracy: 0.5065\n",
            "Epoch 3/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6925 - accuracy: 0.5094\n",
            "Epoch 4/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6945 - accuracy: 0.4864\n",
            "Epoch 5/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6968 - accuracy: 0.4857\n",
            "Epoch 6/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6848 - accuracy: 0.5191\n",
            "Epoch 7/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6799 - accuracy: 0.5292\n",
            "Epoch 8/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6845 - accuracy: 0.5055\n",
            "Epoch 9/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6934 - accuracy: 0.4750\n",
            "Epoch 10/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6906 - accuracy: 0.4965\n",
            "Epoch 11/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6864 - accuracy: 0.5259\n",
            "Epoch 12/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6859 - accuracy: 0.5104\n",
            "Epoch 13/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6856 - accuracy: 0.5138\n",
            "Epoch 14/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6910 - accuracy: 0.4744\n",
            "Epoch 15/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6833 - accuracy: 0.5307\n",
            "Epoch 16/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6856 - accuracy: 0.5072\n",
            "Epoch 17/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6858 - accuracy: 0.4996\n",
            "Epoch 18/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6847 - accuracy: 0.5168\n",
            "Epoch 19/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6806 - accuracy: 0.5278\n",
            "Epoch 20/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6790 - accuracy: 0.5394\n",
            "Epoch 21/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6810 - accuracy: 0.5379\n",
            "Epoch 22/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6804 - accuracy: 0.5430\n",
            "Epoch 23/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6828 - accuracy: 0.5314\n",
            "Epoch 24/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6744 - accuracy: 0.5556\n",
            "Epoch 25/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6765 - accuracy: 0.5559\n",
            "Epoch 26/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6818 - accuracy: 0.5207\n",
            "Epoch 27/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6721 - accuracy: 0.5742\n",
            "Epoch 28/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6762 - accuracy: 0.5452\n",
            "Epoch 29/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6764 - accuracy: 0.5424\n",
            "Epoch 30/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6744 - accuracy: 0.5542\n",
            "Epoch 31/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6772 - accuracy: 0.5404\n",
            "Epoch 32/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6696 - accuracy: 0.5664\n",
            "Epoch 33/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6705 - accuracy: 0.5502\n",
            "Epoch 34/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6657 - accuracy: 0.5524\n",
            "Epoch 35/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6695 - accuracy: 0.5621\n",
            "Epoch 36/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6652 - accuracy: 0.5660\n",
            "Epoch 37/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6552 - accuracy: 0.5703\n",
            "Epoch 38/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6531 - accuracy: 0.5819\n",
            "Epoch 39/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6485 - accuracy: 0.6921\n",
            "Epoch 40/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6142 - accuracy: 0.7194\n",
            "Epoch 41/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5983 - accuracy: 0.7564\n",
            "Epoch 42/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5866 - accuracy: 0.7109\n",
            "Epoch 43/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5444 - accuracy: 0.7511\n",
            "Epoch 44/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.4765 - accuracy: 0.7952\n",
            "Epoch 45/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3716 - accuracy: 0.8875\n",
            "Epoch 46/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2789 - accuracy: 0.9369\n",
            "Epoch 47/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2261 - accuracy: 0.9392\n",
            "Epoch 48/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1354 - accuracy: 0.9691\n",
            "Epoch 49/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1294 - accuracy: 0.9682\n",
            "Epoch 50/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1028 - accuracy: 0.9710\n",
            "Epoch 51/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1123 - accuracy: 0.9665\n",
            "Epoch 52/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1396 - accuracy: 0.9507\n",
            "Epoch 53/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0664 - accuracy: 0.9919\n",
            "Epoch 54/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0996 - accuracy: 0.9652\n",
            "Epoch 55/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0749 - accuracy: 0.9637\n",
            "Epoch 56/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0925 - accuracy: 0.9599\n",
            "Epoch 57/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0958 - accuracy: 0.9559\n",
            "Epoch 58/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0588 - accuracy: 0.9791\n",
            "Epoch 59/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1413 - accuracy: 0.9414\n",
            "Epoch 60/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1977 - accuracy: 0.9402\n",
            "Epoch 61/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3667 - accuracy: 0.8807\n",
            "Epoch 62/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2341 - accuracy: 0.9493\n",
            "Epoch 63/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0756 - accuracy: 0.9825\n",
            "Epoch 64/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0693 - accuracy: 0.9810\n",
            "Epoch 65/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1079 - accuracy: 0.9588\n",
            "Epoch 66/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1035 - accuracy: 0.9684\n",
            "Epoch 67/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6146 - accuracy: 0.8081\n",
            "Epoch 68/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1624 - accuracy: 0.9460\n",
            "Epoch 69/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0454 - accuracy: 0.9914\n",
            "Epoch 70/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2045 - accuracy: 0.9373\n",
            "Epoch 71/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3916 - accuracy: 0.8553\n",
            "Epoch 72/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.4679 - accuracy: 0.7587\n",
            "Epoch 73/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2813 - accuracy: 0.9027\n",
            "Epoch 74/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.4714 - accuracy: 0.8190\n",
            "Epoch 75/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3401 - accuracy: 0.8340\n",
            "Epoch 76/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3725 - accuracy: 0.8432\n",
            "Epoch 77/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5277 - accuracy: 0.7565\n",
            "Epoch 78/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.6324 - accuracy: 0.6524\n",
            "Epoch 79/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5383 - accuracy: 0.7588\n",
            "Epoch 80/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.4788 - accuracy: 0.7951\n",
            "Epoch 81/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5341 - accuracy: 0.7797\n",
            "Epoch 82/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7141 - accuracy: 0.5729\n",
            "Epoch 83/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7045 - accuracy: 0.5434\n",
            "Epoch 84/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7012 - accuracy: 0.4991\n",
            "Epoch 85/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7127 - accuracy: 0.5118\n",
            "Epoch 86/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7355 - accuracy: 0.5060\n",
            "Epoch 87/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7913 - accuracy: 0.5157\n",
            "Epoch 88/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7510 - accuracy: 0.4987\n",
            "Epoch 89/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7871 - accuracy: 0.5255\n",
            "Epoch 90/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7385 - accuracy: 0.5122\n",
            "Epoch 91/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.8160 - accuracy: 0.5047\n",
            "Epoch 92/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7325 - accuracy: 0.4932\n",
            "Epoch 93/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7312 - accuracy: 0.5028\n",
            "Epoch 94/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7768 - accuracy: 0.4681\n",
            "Epoch 95/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.8776 - accuracy: 0.4622\n",
            "Epoch 96/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.8233 - accuracy: 0.4632\n",
            "Epoch 97/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.7647 - accuracy: 0.4644\n",
            "Epoch 98/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.9182 - accuracy: 0.4847\n",
            "Epoch 99/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.9738 - accuracy: 0.4745\n",
            "Epoch 100/100\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.8422 - accuracy: 0.4568\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 0.6934 - accuracy: 0.4950\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6934390068054199, 0.4950000047683716]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "AR2OX5zr4MbN",
        "outputId": "d647ec29-d6db-4a61-e876-c2683ddc35e1"
      },
      "source": [
        "# Checkout the history\n",
        "pd.DataFrame(history.history).plot(figsize=(10,7), xlabel=\"epochs\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae4c729d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "wypbeGwQ5fkD",
        "outputId": "29e409cb-0969-4611-b234-03ab28295c17"
      },
      "source": [
        "# Plot the learning rate versus the loss\n",
        "lrs = 1e-4 * (10** (tf.range(100)/20))\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.semilogx(lrs, history.history[\"loss\"])\n",
        "plt.xlabel(\"Learning Rate\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Learning rate vs title\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Learning rate vs title')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4r8fr7DCJPYB",
        "outputId": "cbad2e1f-7d48-4cd7-9342-7b8d89df62b3"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_10 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(4, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_10.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.02),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = model_10.fit(X_train,y_train, epochs=20, verbose=0)\n",
        "\n",
        "# 4. Evaluate the model\n",
        "model_10.evaluate(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.0574 - accuracy: 0.9900\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05740185081958771, 0.9900000095367432]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "HR2HdgiBLEX0",
        "outputId": "5d92920f-b39f-491f-c428-17d51efadf17"
      },
      "source": [
        "# Plot the decision boundaries for the training and test data sets\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_10,X=X_train,y=y_train)\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_10,X=X_test,y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "doing binary classifcation...\n",
            "doing binary classifcation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQe5gld7Mi-I"
      },
      "source": [
        "## More classification evaluation methods\n",
        "\n",
        "Alongside visualizing our model results as much as possible, there are a handful of other classification evaluaton methods and metrics you should be familiar with :\n",
        "* Accuracy\n",
        "* Precision\n",
        "* Recall\n",
        "* F1-score\n",
        "* Confusion matrix\n",
        "* Classification report(from sklearn)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4MYP7y46OPzQ",
        "outputId": "feb5bcb9-7bce-4303-c34b-55cf398d8b2c"
      },
      "source": [
        "# Check the accuracy of our model\n",
        "loss, accuracy = model_10.evaluate(X_test, y_test)\n",
        "print(f\"Model loss on the test set: {(loss):.5f}\")\n",
        "print(f\"Model accuracy on the test set: {(accuracy*100):.2f}%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.0574 - accuracy: 0.9900\n",
            "Model loss on the test set: 0.05740\n",
            "Model accuracy on the test set: 99.00%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15FBWXjfO82S"
      },
      "source": [
        "### How about a confusion matrix ?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "aMTEtuJFPUAW",
        "outputId": "f665324f-1290-46e6-858f-c1cdfd75bf3d"
      },
      "source": [
        "# Create a confusion matrix \n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Make predictions\n",
        "y_preds = model_10.predict(X_test)\n",
        "\n",
        "# Create confusion matrx\n",
        "confusion_matrix(y_test,y_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-50-012f5bc16be4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Create confusion matrx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_preds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \"\"\"\n\u001b[0;32m--> 268\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n\u001b[0;32m---> 90\u001b[0;31m                          \"and {1} targets\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of binary and continuous targets"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dIDSgu2P5Uq"
      },
      "source": [
        "y_test[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-2VHD-EQKF_"
      },
      "source": [
        "y_preds[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TejUc6TIQL5x"
      },
      "source": [
        "Oops... looks like our prediction array has come out in **prediction probability** form... the standard output from the sigmoid(or softmax) activation functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aXsAJK0GQfZo"
      },
      "source": [
        "# Convert prediction probabilities to binary format and view the first 10\n",
        "tf.round(y_preds)[:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YCA2RGVQ0ZR"
      },
      "source": [
        "# Create a confusion matrix\n",
        "confusion_matrix(y_test, tf.round(y_preds))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wQ9b32lsQ_ni"
      },
      "source": [
        "### How about we prettify our confusion matrix ?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWDl_KUhRgAD"
      },
      "source": [
        "import itertools\n",
        "\n",
        "figsize=(8,8)\n",
        "\n",
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_test, tf.round(y_preds))\n",
        "cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:,np.newaxis]   # Normalize our cm\n",
        "n_classes = cm.shape[0]\n",
        "\n",
        "# Let's prettify it\n",
        "fig, ax = plt.subplots(figsize=figsize)\n",
        "# Create a matrix plot\n",
        "cax = ax.matshow(cm, cmap=plt.cm.Blues)\n",
        "fig.colorbar(cax)\n",
        "\n",
        "# Create classes\n",
        "classes = False\n",
        "if classes:\n",
        "  labels=classes\n",
        "else:\n",
        "  labels= np.arange(cm.shape[0])\n",
        "\n",
        "# Label the axes\n",
        "ax.set(title=\"Confusion Matrix\",\n",
        "       xlabel=\"Predicted Label\",\n",
        "       ylabel=\"True Label\",\n",
        "       xticks=np.arange(n_classes),\n",
        "       yticks=np.arange(n_classes),\n",
        "       xticklabels=labels,\n",
        "       yticklabels=labels)\n",
        "\n",
        "# Set x-axis labels to bottom\n",
        "ax.xaxis.set_label_position(\"bottom\")\n",
        "ax.xaxis.tick_bottom()\n",
        "\n",
        "# Adjust label size\n",
        "ax.yaxis.label.set_size(15)\n",
        "ax.xaxis.label.set_size(15)\n",
        "ax.title.set_size(20)\n",
        "\n",
        "# Set threshold for different colors\n",
        "threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "# Plot the text on each cell\n",
        "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "  plt.text(j,i, f\"{cm[i,j]}({cm_norm[i,j]*100:.1f}%\",\n",
        "           horizontalalignment = \"center\",\n",
        "           color=\"white\" if cm[i,j]> threshold else \"black\",\n",
        "           size=15)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVQhWM3IUqpp"
      },
      "source": [
        "# Working with a larger example (multiclass classificaton)\n",
        "\n",
        "To practice multi-class classification, we're going to build a neural network to classify images of different items of clothing ."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIbkQm1mAaQz",
        "outputId": "b942874f-d913-4cf8-bd54-d33da25433e1"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "# The data has already been sorted into training and test sets for us\n",
        "(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6zyiamI8BvH1",
        "outputId": "1e81fda2-d4e5-4fb6-d9b4-76aec36027c2"
      },
      "source": [
        "# Show the first training example\n",
        "print(f\"Training sample:\\n{train_data[0]}\\n\")\n",
        "print(f\"Training label:\\n{train_labels[0]}\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training sample:\n",
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
            "    0   1   4   0   0   0   0   1   1   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
            "   54   0   0   0   1   3   4   0   0   3]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
            "  144 123  23   0   0   0   0  12  10   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
            "  107 156 161 109  64  23  77 130  72  15]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
            "  216 163 127 121 122 146 141  88 172  66]\n",
            " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
            "  223 223 215 213 164 127 123 196 229   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
            "  235 227 224 222 224 221 223 245 173   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
            "  180 212 210 211 213 223 220 243 202   0]\n",
            " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
            "  169 227 208 218 224 212 226 197 209  52]\n",
            " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
            "  198 221 215 213 222 220 245 119 167  56]\n",
            " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
            "  232 213 218 223 234 217 217 209  92   0]\n",
            " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
            "  222 221 216 223 229 215 218 255  77   0]\n",
            " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
            "  211 218 224 223 219 215 224 244 159   0]\n",
            " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
            "  224 234 176 188 250 248 233 238 215   0]\n",
            " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
            "  255 255 221 234 221 211 220 232 246   0]\n",
            " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
            "  188 154 191 210 204 209 222 228 225   0]\n",
            " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
            "  168 219 221 215 217 223 223 224 229  29]\n",
            " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
            "  239 223 218 212 209 222 220 221 230  67]\n",
            " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
            "  199 206 186 181 177 172 181 205 206 115]\n",
            " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
            "  195 191 198 192 176 156 167 177 210  92]\n",
            " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
            "  210 210 211 188 188 194 192 216 170   0]\n",
            " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
            "  182 182 181 176 166 168  99  58   0   0]\n",
            " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n",
            "\n",
            "Training label:\n",
            "9\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cc2-3lkvB_p0",
        "outputId": "2b469538-ec42-491c-ab4a-89a8854208ae"
      },
      "source": [
        "# Check the shape of a single example\n",
        "train_data[0].shape, train_labels[0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28, 28), ())"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "NSYJtkvQDGZK",
        "outputId": "318d0ee1-992f-42d2-c8e7-932958041a77"
      },
      "source": [
        "# Plot a single sample\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(train_data[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fae4bfcfa50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUFElEQVR4nO3da2yc1ZkH8P8z4/ElzjiJk+CE4BIuoZDCEqhJuIlSKDREVQOli4gQCxLaoF3otl0+gGhXZb+sEFpAaNntroEsYVWoWhUERREFzCULlDQmpOS2ITeHxDi2ExPbcTz2XJ794Bdqgs/zmnnnRs7/J1kezzNn5njGf78zc+acI6oKIjr+xcrdASIqDYadyBMMO5EnGHYiTzDsRJ6oKuWNVUuN1qK+lDdJ5JUUhjCqIzJRLVLYRWQpgEcAxAE8rqr3W5evRT2WyJVRbpKIDOu0zVnL+2m8iMQB/DuAawAsBLBCRBbme31EVFxRXrMvBrBTVXer6iiAXwNYXphuEVGhRQn7PAD7xv28Pzjvc0RkpYi0i0h7GiMRbo6Ioij6u/Gq2qqqLarakkBNsW+OiByihL0TQPO4n08KziOiChQl7OsBLBCRU0SkGsCNAF4oTLeIqNDyHnpT1YyI3AngDxgbelulqlsK1jMiKqhI4+yqugbAmgL1hYiKiB+XJfIEw07kCYadyBMMO5EnGHYiTzDsRJ5g2Ik8wbATeYJhJ/IEw07kCYadyBMMO5EnGHYiT5R0KWkqA5lwVeG/iLixZ3xmo1n/5LtnOGsNT78b6bbDfjepSjhrmh6NdttRhT0uljwfMx7ZiTzBsBN5gmEn8gTDTuQJhp3IEww7kScYdiJPcJz9OCfxuFnXTMasxxbZe3Vuu32q3X7YXUsMLTbbVg3nzHri5XazHmksPWwMP+R+hdjH0Sh9kyojtsbDySM7kScYdiJPMOxEnmDYiTzBsBN5gmEn8gTDTuQJjrMf58wxWYSPs+/77nSzftNF/2vW3+491VnbWzPHbKt1ZhlV37nIrJ/xH53OWqbjI/vKQ+aMh91vYeIzZriL2azZNjsw4C4a3Y4UdhHpADAIIAsgo6otUa6PiIqnEEf2b6vqwQJcDxEVEV+zE3kiatgVwMsi8p6IrJzoAiKyUkTaRaQ9jZGIN0dE+Yr6NP5SVe0UkRMAvCIi/6eqa8dfQFVbAbQCQIM0RlvdkIjyFunIrqqdwfceAM8BsKcxEVHZ5B12EakXkeSnpwFcDWBzoTpGRIUV5Wl8E4DnZGzebxWAp1X1pYL0igoml0pFaj963hGz/sNp9pzy2ljaWXszZs9X73yt2axn/8ru296Hks5a7v2LzbYzN9tj3Q3vd5n1g5fNM+u933S/om0KWU5/xqu7nDXpc0c677Cr6m4A5+bbnohKi0NvRJ5g2Ik8wbATeYJhJ/IEw07kCdGIW/Z+GQ3SqEvkypLdnjesZY9DHt8jN1xo1q/5+Rtm/azaj836YK7WWRvVaB/gfHT7t8z60O5pzlpsNGTL5JBytsleClrT9nF0xgb37163vNtsK4/NdtY+aHsER/r2Tdh7HtmJPMGwE3mCYSfyBMNO5AmGncgTDDuRJxh2Ik9wnL0ShGwPHEnI43v2e/b/+x/MsKewhokbaxsPabXZ9nC2PtJt92bcU1zTIWP8j++wp8AeMcbwASCWsR/Tq779vrN2feN6s+0Dp53jrK3TNgxoH8fZiXzGsBN5gmEn8gTDTuQJhp3IEww7kScYdiJPcMvmSlDCzzoca8eRE8z6oYapZv1Axt7SeWbcvdxzMjZstp2fsPcL7c26x9EBIJ5wL1U9qnGz7T9/4/dmPXVWwqwnxF6K+mJjHYC/3vo3Ztt67DbrLjyyE3mCYSfyBMNO5AmGncgTDDuRJxh2Ik8w7ESe4Di752bX2Nse14p7y2UAqJaMWf84PcNZ2zH8dbPthwP2ZwCWNm0x62ljLN2aZw+Ej5OfmPjErKfUHoe37tVLmuxx9I1m1S30yC4iq0SkR0Q2jzuvUUReEZEdwXf3I0pEFWEyT+OfBLD0mPPuAdCmqgsAtAU/E1EFCw27qq4F0HfM2csBrA5OrwZwbYH7RUQFlu9r9iZV7QpOHwDQ5LqgiKwEsBIAajElz5sjoqgivxuvYytWOt/tUNVWVW1R1ZYEaqLeHBHlKd+wd4vIXAAIvvcUrktEVAz5hv0FALcEp28B8HxhukNExRL6ml1EngFwOYBZIrIfwC8A3A/gNyJyG4C9AG4oZiePeyHrxkvcnnutGfdYd3yGPSr6rembzHpvtsGsH87a78NMjx911gYz7r3bAaBv2L7uM2u6zPqGo/OdtdnV9ji51W8A6BidZdYX1Bww6w90u/dPaK499v3wz8tceZmzpuv+6KyFhl1VVzhK3O2B6CuEH5cl8gTDTuQJhp3IEww7kScYdiJPcIprJQhZSlqq7IfJGnrbd9tZZtsrpthLJr+TmmfWZ1cNmnVrmuncmn6zbbIpZdbDhv0aq9zTdwezdWbbKbERsx72e59fbS+D/dNXz3fWkmcfMts2JIxjtDGKyyM7kScYdiJPMOxEnmDYiTzBsBN5gmEn8gTDTuQJjrNXAElUm/Vcyh5vtszaNGrWD2btJY+nx+ypntUhSy5bWyNf3LjHbNsbMha+YfgUs56Mu7eEnh2zx8mbE/ZY96ZUs1lfM3S6Wb/te686a8+0XmW2rX7pHWdN1P148chO5AmGncgTDDuRJxh2Ik8w7ESeYNiJPMGwE3niqzXObiy5LFX2eLHEQ/6vxex6LmXMb87ZY81hNG2PhUfxyH89atb3Zaab9QNpux625HLWmGD97vA0s21tzN4uenbVgFkfyNnj9JbBnL3MtTVPHwjv+90zdzhrz/Z/x2ybLx7ZiTzBsBN5gmEn8gTDTuQJhp3IEww7kScYdiJPVNQ4e5T10cPGqtUe9iyr4eWLzfq+a+1x/JvO+5OzdiCTNNu+b2xrDADTjDnhAFAfsr56St2ff/h41N5OOmys2loXHgBOMMbhs2of5zrTdt/ChH3+YH/GWNP++/Zc++lP5dWl8CO7iKwSkR4R2TzuvPtEpFNENgZfy/K7eSIqlck8jX8SwNIJzn9YVRcFX2sK2y0iKrTQsKvqWgB9JegLERVRlDfo7hSRD4Kn+c4XOCKyUkTaRaQ9Dfv1HREVT75h/yWA0wAsAtAF4EHXBVW1VVVbVLUlgZo8b46Iosor7KrarapZVc0BeAyA/XYyEZVdXmEXkbnjfrwOwGbXZYmoMoSOs4vIMwAuBzBLRPYD+AWAy0VkEQAF0AHg9kJ0xhpHj6pq7hyznj6lyaz3neXeC/zoHGNTbACLlm0z67c2/bdZ7802mPWEGPuzp2eabc+b0mHWX+tfaNYPVk0169Y4/cX17jndAHA4Z++/fmLVJ2b97p0/dNaapthj2Y+fbA8wpTVn1ren7Zes/Tn3fPh/WPi62fY5zDbrLqFhV9UVE5z9RF63RkRlw4/LEnmCYSfyBMNO5AmGncgTDDuRJypqiuvINReY9RN+tttZW9Sw32y7sO4ts57K2UtRW9Mttw7PM9sezdlbMu8YtYcF+zP2EFRc3MNAPaP2FNcH99jLFrct/k+z/vOPJ5oj9RexOnXWDmXtYbvrp9pLRQP2Y3b719Y6a6dW95htXxyaa9Y/DpkC25ToN+vzE73O2g+SH5pt8x1645GdyBMMO5EnGHYiTzDsRJ5g2Ik8wbATeYJhJ/JEacfZxV4uesm/rDebX5nc4qwdVXtKYdg4eti4qWValb1s8Ejavpt70vYU1jBn1Bxw1q5r2Gi2XfvoErN+aepHZn3XFfb03LZh91TO3oz9e9+45wqzvuGjZrN+4fw9zto5yU6zbdhnG5LxlFm3ph0DwFDO/ff6bsr+/EG+eGQn8gTDTuQJhp3IEww7kScYdiJPMOxEnmDYiTwhqu75xoVWN6dZT7v5H5311jv+zWz/dN+Fzlpzrb0d3cnVB836zLi9/a8lGbPHXL+esMdcXxw6yay/cfhMs/7NZIezlhB7u+fLp+w067f+9C6znqm1l9EemO8+nmTq7b+9hnMPmfUfnf6aWa82fvfDWXscPex+C9uSOYy1BkEyZm+T/eCy65y1P3Y8if7hrgkfFB7ZiTzBsBN5gmEn8gTDTuQJhp3IEww7kScYdiJPlHQ+eywNTOl2jy++OLDIbH9qnXut7YNpe330Pxw5x6yfVGdv/2ttPXy6MZ8cADamppv1l3q/YdZPrLPXT+9OT3PWDqXrzbZHjXnVAPDEww+Z9Qe77XXnr2vc4KydW22Pox/O2ceirSHr7Q/map21lNrrG/SHjMMnjb8HAEirHa24seXz9Jg9hj9wjnsb7my3+3ZDj+wi0iwir4vIVhHZIiI/Ds5vFJFXRGRH8D3/1R+IqOgm8zQ+A+AuVV0I4EIAd4jIQgD3AGhT1QUA2oKfiahChYZdVbtUdUNwehDANgDzACwHsDq42GoA1xark0QU3Zd6g05E5gM4D8A6AE2q2hWUDgBocrRZKSLtItKeGRmK0FUiimLSYReRqQB+B+Anqvq5d4x0bDbNhLMaVLVVVVtUtaWqxn6ziIiKZ1JhF5EExoL+K1V9Nji7W0TmBvW5AOxtMYmorEKH3kREADwBYJuqjh+HeQHALQDuD74/H3Zd8dEckvtGnPWc2tMlXzvonurZVDtotl2U3GfWtx+1h3E2DZ/orG2o+prZti7u3u4ZAKZV21Nk66vc9xkAzEq4f/dTauz/wdY0UABYn7J/t7+b/YZZ/yjjHqT5/dAZZtutR933OQDMCFnCe9OAu/3RjL2N9kjWjkYqYw/lTquxH9MLGvc6a9thbxfde64xbfhtd7vJjLNfAuBmAJtE5NNFyO/FWMh/IyK3AdgL4IZJXBcRlUlo2FX1LQCuQ+6Vhe0OERULPy5L5AmGncgTDDuRJxh2Ik8w7ESeKO2WzUeGEXvzfWf5ty9fYjb/p+W/ddbeDFlu+cUD9rjowKg91XP2FPdHfRuMcW4AaEzYHxMO2/K5NmT7308y7k8mjsTsqZxZ50DLmAMj7umzAPB2boFZT+fcWzaPGDUg/PMJfaOzzPqJdf3O2mDGPf0VADoGG836wX57W+XUFDtab2VPc9aWznFvTQ4AdT3uxyxm/KnwyE7kCYadyBMMO5EnGHYiTzDsRJ5g2Ik8wbATeaKkWzY3SKMukfwnyvXf5N6y+dS/3262XTx9j1nfMGDP2/7IGHdNhyx5nIi5lw0GgCmJUbNeGzLeXB13z0mPTbyA0GdyIePs9XG7b2Fz7Ruq3PO6k3F7znfM2NZ4MuLG7/6n/vmRrjsZ8ntn1P6buGjaLmdt1Z6LzbbTlrm32V6nbRjQPm7ZTOQzhp3IEww7kScYdiJPMOxEnmDYiTzBsBN5ovTj7PGr3RfI2WuYRzF0/RKzvuTe9XY96R4XPbO622ybgD1eXBsynlwfs8fCU8ZjGPbf/K3hZrOeDbmG1z45y6ynjfHm7qMNZtuE8fmBybD2IRjOhGzZPGzPd4/H7Nyk3rDn2s/c6v7sRM0a+2/RwnF2ImLYiXzBsBN5gmEn8gTDTuQJhp3IEww7kSdCx9lFpBnAUwCaACiAVlV9RETuA/C3AHqDi96rqmus64o6n71SyQX2mvTDc+rMes0he2704Ml2+4Zd7nXpYyP2mvO5P28z6/TVYo2zT2aTiAyAu1R1g4gkAbwnIq8EtYdV9V8L1VEiKp7J7M/eBaArOD0oItsAzCt2x4iosL7Ua3YRmQ/gPADrgrPuFJEPRGSViMxwtFkpIu0i0p6G/XSViIpn0mEXkakAfgfgJ6o6AOCXAE4DsAhjR/4HJ2qnqq2q2qKqLQnY+6kRUfFMKuwiksBY0H+lqs8CgKp2q2pWVXMAHgOwuHjdJKKoQsMuIgLgCQDbVPWhcefPHXex6wBsLnz3iKhQJvNu/CUAbgawSUQ2BufdC2CFiCzC2HBcB4Dbi9LDrwBdv8ms25MlwzW8k3/baIsx0/FkMu/GvwVMuLi4OaZORJWFn6Aj8gTDTuQJhp3IEww7kScYdiJPMOxEnmDYiTzBsBN5gmEn8gTDTuQJhp3IEww7kScYdiJPMOxEnijpls0i0gtg77izZgE4WLIOfDmV2rdK7RfAvuWrkH07WVVnT1Qoadi/cOMi7araUrYOGCq1b5XaL4B9y1ep+san8USeYNiJPFHusLeW+fYtldq3Su0XwL7lqyR9K+trdiIqnXIf2YmoRBh2Ik+UJewislREtovIThG5pxx9cBGRDhHZJCIbRaS9zH1ZJSI9IrJ53HmNIvKKiOwIvk+4x16Z+nafiHQG991GEVlWpr41i8jrIrJVRLaIyI+D88t63xn9Ksn9VvLX7CISB/AhgKsA7AewHsAKVd1a0o44iEgHgBZVLfsHMETkMgBHADylqmcH5z0AoE9V7w/+Uc5Q1bsrpG/3AThS7m28g92K5o7fZhzAtQBuRRnvO6NfN6AE91s5juyLAexU1d2qOgrg1wCWl6EfFU9V1wLoO+bs5QBWB6dXY+yPpeQcfasIqtqlqhuC04MAPt1mvKz3ndGvkihH2OcB2Dfu5/2orP3eFcDLIvKeiKwsd2cm0KSqXcHpAwCaytmZCYRu411Kx2wzXjH3XT7bn0fFN+i+6FJVPR/ANQDuCJ6uViQdew1WSWOnk9rGu1Qm2Gb8M+W87/Ld/jyqcoS9E0DzuJ9PCs6rCKraGXzvAfAcKm8r6u5Pd9ANvveUuT+fqaRtvCfaZhwVcN+Vc/vzcoR9PYAFInKKiFQDuBHAC2XoxxeISH3wxglEpB7A1ai8rahfAHBLcPoWAM+XsS+fUynbeLu2GUeZ77uyb3+uqiX/ArAMY+/I7wLws3L0wdGvUwH8OfjaUu6+AXgGY0/r0hh7b+M2ADMBtAHYAeBVAI0V1Lf/AbAJwAcYC9bcMvXtUow9Rf8AwMbga1m57zujXyW53/hxWSJP8A06Ik8w7ESeYNiJPMGwE3mCYSfyBMNO5AmGncgT/w8K8iUImXY9pQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSVIV5iCD809",
        "outputId": "be87694b-9979-4471-f3ac-2983da9df04b"
      },
      "source": [
        "# Check out samples label\n",
        "train_labels[7]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oq9xjPykEZMk",
        "outputId": "80e1532b-4e4c-4ce4-df08-9e36a681c1e9"
      },
      "source": [
        "# Create a small list so we can index onto our training labels so they're human-readable\n",
        "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]\n",
        "len(class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "YhUbrLPiFF3Q",
        "outputId": "dfe8e5e9-ab92-457b-8238-83aaec5ba5ec"
      },
      "source": [
        "# Plot an example image and its labels\n",
        "index_of_choice = 1700\n",
        "plt.imshow(train_data[index_of_choice], cmap=plt.cm.binary)\n",
        "plt.title(class_names[train_labels[index_of_choice]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ankle boot')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "pYzEJPLzGGrM",
        "outputId": "ffd0ea4e-a8c5-4b99-cf58-eb13e9e7712a"
      },
      "source": [
        "# Plot multiple random images of fashion MNIST\n",
        "import random\n",
        "plt.figure(figsize=(8,8))\n",
        "for i in range(10):\n",
        "  ax = plt.subplot(5,5,i+1)\n",
        "  rand_index = random.choice(range(len(train_data)))\n",
        "  plt.imshow(train_data[rand_index], cmap=plt.cm.binary)\n",
        "  plt.title(class_names[train_labels[rand_index]])\n",
        "  plt.axis(False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x576 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6a2snMHHLno"
      },
      "source": [
        "## Building a multi-class classification model\n",
        "\n",
        "For our model, we can use a similar architecture to our binary classifiers, however, we're going to have to tweak a few things :\n",
        "* Input shape = 28 x 28 (the shape of one image)\n",
        "* Output shape = 10 (one per class of clothing)\n",
        "* Loss function = tf.keras.losses.CategoricalCrossentropy()\n",
        "  * If your labels are one-hot encoded, use CategoricalCrossentropy()\n",
        "  * If your labels are integer form use SparseCategoricalCrossentropy()\n",
        "* Output layer activation = Softmax (not sigmoid)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKHCdfJyILLx",
        "outputId": "b773acb9-0486-4e77-c597-3b163dd9e871"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_11 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\"),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_11.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "non_norm_history = model_11.fit(train_data,\n",
        "                                tf.one_hot(train_labels,depth=10),\n",
        "                                epochs=10,\n",
        "                                validation_data=(test_data, tf.one_hot(test_labels, depth=10)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.8251 - accuracy: 0.1333 - val_loss: 1.8075 - val_accuracy: 0.2064\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.7467 - accuracy: 0.2279 - val_loss: 1.6509 - val_accuracy: 0.2824\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.6495 - accuracy: 0.2819 - val_loss: 1.6052 - val_accuracy: 0.3025\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.6095 - accuracy: 0.2867 - val_loss: 1.6011 - val_accuracy: 0.2986\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.6079 - accuracy: 0.2936 - val_loss: 1.5992 - val_accuracy: 0.3195\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.5938 - accuracy: 0.3074 - val_loss: 1.5662 - val_accuracy: 0.3144\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.5845 - accuracy: 0.3174 - val_loss: 1.5740 - val_accuracy: 0.3221\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.5742 - accuracy: 0.3202 - val_loss: 1.5540 - val_accuracy: 0.3383\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.5821 - accuracy: 0.3177 - val_loss: 1.5621 - val_accuracy: 0.3316\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.5699 - accuracy: 0.3182 - val_loss: 1.5761 - val_accuracy: 0.2959\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EpNw5bhJ5xj",
        "outputId": "a5561c55-e7e4-4a6c-e627-6ede16d44874"
      },
      "source": [
        "# Check the model summary\n",
        "model_11.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_26 (Dense)             (None, 4)                 3140      \n",
            "_________________________________________________________________\n",
            "dense_27 (Dense)             (None, 4)                 20        \n",
            "_________________________________________________________________\n",
            "dense_28 (Dense)             (None, 10)                50        \n",
            "=================================================================\n",
            "Total params: 3,210\n",
            "Trainable params: 3,210\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hn0VYbSROnoL",
        "outputId": "e5988a68-b5c4-4eb7-81de-0ad397eb90b7"
      },
      "source": [
        "#Check the min and max values of the training data\n",
        "train_data.min(), train_data.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 255)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G27Fk24EPaI3"
      },
      "source": [
        "Neural networks prefer data to be scaled(or normalized), this means they like to have the numbers in the tensors between 0 and 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bVAOSgj_PtZx",
        "outputId": "1b3faee3-0772-4448-a768-98d2aa5cb04b"
      },
      "source": [
        "# We can get our training and testing data between 0 & 1 by dividing by the maximum\n",
        "train_data_norm = train_data /255.0\n",
        "test_data_norm = test_data /255.0\n",
        "\n",
        "train_data_norm.min(), train_data_norm.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igN_kiOPQXDf",
        "outputId": "a3fbde86-ad37-4be4-c5ae-4d1dff6bdb9d"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_12 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(4, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\"),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_12.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "norm_history = model_12.fit(train_data_norm,\n",
        "                                tf.one_hot(train_labels,depth=10),\n",
        "                                epochs=10,\n",
        "                                validation_data=(test_data_norm, tf.one_hot(test_labels, depth=10)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.4476 - accuracy: 0.5004 - val_loss: 0.6937 - val_accuracy: 0.7617\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6479 - accuracy: 0.7706 - val_loss: 0.6400 - val_accuracy: 0.7820\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5984 - accuracy: 0.7902 - val_loss: 0.6247 - val_accuracy: 0.7783\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5767 - accuracy: 0.7986 - val_loss: 0.6078 - val_accuracy: 0.7881\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5611 - accuracy: 0.8017 - val_loss: 0.6169 - val_accuracy: 0.7881\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5562 - accuracy: 0.8030 - val_loss: 0.5855 - val_accuracy: 0.7951\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5458 - accuracy: 0.8076 - val_loss: 0.6097 - val_accuracy: 0.7836\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5432 - accuracy: 0.8086 - val_loss: 0.5787 - val_accuracy: 0.7971\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5457 - accuracy: 0.8065 - val_loss: 0.5698 - val_accuracy: 0.7977\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5289 - accuracy: 0.8149 - val_loss: 0.5658 - val_accuracy: 0.8014\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNJ3-ur5Rx6H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "248db2dd-06a1-4026-ab44-8314d195c43c"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model using the Sequential API\n",
        "model_13 = 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(128, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(10, activation=\"softmax\"),\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_13.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "norm_history_2 = model_13.fit(train_data_norm,\n",
        "                                tf.one_hot(train_labels,depth=10),\n",
        "                                epochs=10,\n",
        "                                validation_data=(test_data_norm, tf.one_hot(test_labels, depth=10)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6204 - accuracy: 0.7815 - val_loss: 0.4035 - val_accuracy: 0.8547\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3606 - accuracy: 0.8671 - val_loss: 0.4262 - val_accuracy: 0.8529\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3328 - accuracy: 0.8752 - val_loss: 0.3682 - val_accuracy: 0.8657\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3022 - accuracy: 0.8891 - val_loss: 0.3712 - val_accuracy: 0.8681\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2909 - accuracy: 0.8901 - val_loss: 0.3494 - val_accuracy: 0.8732\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2723 - accuracy: 0.8976 - val_loss: 0.3519 - val_accuracy: 0.8700\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2575 - accuracy: 0.9028 - val_loss: 0.3731 - val_accuracy: 0.8640\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2474 - accuracy: 0.9089 - val_loss: 0.3189 - val_accuracy: 0.8862\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2430 - accuracy: 0.9085 - val_loss: 0.3321 - val_accuracy: 0.8830\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2294 - accuracy: 0.9118 - val_loss: 0.3271 - val_accuracy: 0.8838\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "xkxcLZF2QfGJ",
        "outputId": "bf4675f7-b39c-4068-a687-acb2e4686e59"
      },
      "source": [
        "import pandas as pd\n",
        "# Plot non-normalized data loss curves\n",
        "pd.DataFrame(non_norm_history.history).plot(title=\"Non-normalized data\")\n",
        "# Plot normalized data loss curves\n",
        "pd.DataFrame(norm_history.history).plot(title=\"Normalized data\")\n",
        "pd.DataFrame(norm_history_2.history).plot(title=\"Normalized data 2\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae3bcf61d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IO0l1yf2uSVY"
      },
      "source": [
        "## Finding the ideal learning rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mGAP0hrHwJt",
        "outputId": "542d2690-9765-41b0-f54d-26676fe375e4"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model using the Sequential API\n",
        "model_14 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "  tf.keras.layers.Dense(128, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(10, activation=tf.keras.activations.softmax),\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_14.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# Create a learning rate callback\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch:1e-3 *10**(epoch/20))\n",
        "\n",
        "# Fit the model\n",
        "find_lr_history = model_14.fit(train_data_norm,\n",
        "                               train_labels,\n",
        "                               epochs=40,\n",
        "                               callbacks=[lr_scheduler],\n",
        "                               validation_data=(test_data_norm,test_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6232 - accuracy: 0.7845 - val_loss: 0.4185 - val_accuracy: 0.8481\n",
            "Epoch 2/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3813 - accuracy: 0.8619 - val_loss: 0.4478 - val_accuracy: 0.8464\n",
            "Epoch 3/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3485 - accuracy: 0.8713 - val_loss: 0.3857 - val_accuracy: 0.8554\n",
            "Epoch 4/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3227 - accuracy: 0.8835 - val_loss: 0.3982 - val_accuracy: 0.8560\n",
            "Epoch 5/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3125 - accuracy: 0.8834 - val_loss: 0.3685 - val_accuracy: 0.8661\n",
            "Epoch 6/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3001 - accuracy: 0.8904 - val_loss: 0.3974 - val_accuracy: 0.8531\n",
            "Epoch 7/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2868 - accuracy: 0.8928 - val_loss: 0.3792 - val_accuracy: 0.8660\n",
            "Epoch 8/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2835 - accuracy: 0.8949 - val_loss: 0.3566 - val_accuracy: 0.8721\n",
            "Epoch 9/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2872 - accuracy: 0.8938 - val_loss: 0.3607 - val_accuracy: 0.8754\n",
            "Epoch 10/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2827 - accuracy: 0.8930 - val_loss: 0.3640 - val_accuracy: 0.8729\n",
            "Epoch 11/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2772 - accuracy: 0.8967 - val_loss: 0.4296 - val_accuracy: 0.8539\n",
            "Epoch 12/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2865 - accuracy: 0.8948 - val_loss: 0.3695 - val_accuracy: 0.8735\n",
            "Epoch 13/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2834 - accuracy: 0.8971 - val_loss: 0.4097 - val_accuracy: 0.8655\n",
            "Epoch 14/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2899 - accuracy: 0.8940 - val_loss: 0.3742 - val_accuracy: 0.8696\n",
            "Epoch 15/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2940 - accuracy: 0.8908 - val_loss: 0.4058 - val_accuracy: 0.8633\n",
            "Epoch 16/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3017 - accuracy: 0.8901 - val_loss: 0.4551 - val_accuracy: 0.8487\n",
            "Epoch 17/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3098 - accuracy: 0.8869 - val_loss: 0.4042 - val_accuracy: 0.8676\n",
            "Epoch 18/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3213 - accuracy: 0.8840 - val_loss: 0.4086 - val_accuracy: 0.8662\n",
            "Epoch 19/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3309 - accuracy: 0.8835 - val_loss: 0.4106 - val_accuracy: 0.8595\n",
            "Epoch 20/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3444 - accuracy: 0.8788 - val_loss: 0.4804 - val_accuracy: 0.8555\n",
            "Epoch 21/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3684 - accuracy: 0.8688 - val_loss: 0.4366 - val_accuracy: 0.8491\n",
            "Epoch 22/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3804 - accuracy: 0.8677 - val_loss: 0.4510 - val_accuracy: 0.8514\n",
            "Epoch 23/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3963 - accuracy: 0.8605 - val_loss: 0.5954 - val_accuracy: 0.8096\n",
            "Epoch 24/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4220 - accuracy: 0.8563 - val_loss: 0.5060 - val_accuracy: 0.8459\n",
            "Epoch 25/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4197 - accuracy: 0.8542 - val_loss: 0.5105 - val_accuracy: 0.8323\n",
            "Epoch 26/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4377 - accuracy: 0.8537 - val_loss: 0.6445 - val_accuracy: 0.8415\n",
            "Epoch 27/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5052 - accuracy: 0.8338 - val_loss: 0.5486 - val_accuracy: 0.8378\n",
            "Epoch 28/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5053 - accuracy: 0.8368 - val_loss: 0.5980 - val_accuracy: 0.8079\n",
            "Epoch 29/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5412 - accuracy: 0.8238 - val_loss: 0.5693 - val_accuracy: 0.8250\n",
            "Epoch 30/40\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5799 - accuracy: 0.8124 - val_loss: 0.8551 - val_accuracy: 0.7871\n",
            "Epoch 31/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6376 - accuracy: 0.8089 - val_loss: 0.7234 - val_accuracy: 0.8006\n",
            "Epoch 32/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7053 - accuracy: 0.7830 - val_loss: 1.1188 - val_accuracy: 0.7208\n",
            "Epoch 33/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.7642 - accuracy: 0.7441 - val_loss: 1.0740 - val_accuracy: 0.6733\n",
            "Epoch 34/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.8727 - accuracy: 0.7023 - val_loss: 0.9726 - val_accuracy: 0.6904\n",
            "Epoch 35/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9013 - accuracy: 0.6983 - val_loss: 0.9884 - val_accuracy: 0.6729\n",
            "Epoch 36/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0690 - accuracy: 0.6348 - val_loss: 1.2631 - val_accuracy: 0.5218\n",
            "Epoch 37/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.3452 - accuracy: 0.5161 - val_loss: 1.6135 - val_accuracy: 0.4986\n",
            "Epoch 38/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.5365 - accuracy: 0.4436 - val_loss: 1.7326 - val_accuracy: 0.3930\n",
            "Epoch 39/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.5587 - accuracy: 0.3966 - val_loss: 1.5902 - val_accuracy: 0.3407\n",
            "Epoch 40/40\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.7261 - accuracy: 0.3409 - val_loss: 1.7330 - val_accuracy: 0.3302\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "qNghYOX0JigX",
        "outputId": "60601b31-7e40-4f90-bc4d-24f758037cc3"
      },
      "source": [
        "# Plot the learning rate decay curve\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "lrs = 1e-3 * (10**(tf.range(40)/20))\n",
        "plt.semilogx(lrs, find_lr_history.history[\"loss\"])\n",
        "plt.xlabel(\"Learning rate\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Finding the ideal learning rate\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Finding the ideal learning rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSc6ltZaK2xh",
        "outputId": "ad60af90-48c4-4e53-8b17-d21bb649e449"
      },
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model using the Sequential API\n",
        "model_15 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "  tf.keras.layers.Dense(128, activation=tf.keras.activations.relu),\n",
        "  tf.keras.layers.Dense(10, activation=tf.keras.activations.softmax),\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_15.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history_14 = model_15.fit(train_data_norm,\n",
        "                               train_labels,\n",
        "                               epochs=20,\n",
        "                               validation_data=(test_data_norm,test_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6232 - accuracy: 0.7845 - val_loss: 0.4185 - val_accuracy: 0.8481\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3781 - accuracy: 0.8632 - val_loss: 0.4491 - val_accuracy: 0.8467\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3429 - accuracy: 0.8737 - val_loss: 0.3874 - val_accuracy: 0.8555\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3104 - accuracy: 0.8872 - val_loss: 0.3737 - val_accuracy: 0.8642\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2966 - accuracy: 0.8893 - val_loss: 0.3629 - val_accuracy: 0.8690\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2789 - accuracy: 0.8973 - val_loss: 0.3695 - val_accuracy: 0.8655\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2614 - accuracy: 0.9026 - val_loss: 0.3533 - val_accuracy: 0.8749\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2509 - accuracy: 0.9075 - val_loss: 0.3435 - val_accuracy: 0.8798\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2485 - accuracy: 0.9080 - val_loss: 0.3260 - val_accuracy: 0.8849\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2365 - accuracy: 0.9106 - val_loss: 0.3487 - val_accuracy: 0.8836\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2232 - accuracy: 0.9150 - val_loss: 0.3379 - val_accuracy: 0.8843\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2197 - accuracy: 0.9194 - val_loss: 0.3477 - val_accuracy: 0.8822\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2125 - accuracy: 0.9218 - val_loss: 0.3398 - val_accuracy: 0.8861\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2056 - accuracy: 0.9226 - val_loss: 0.3255 - val_accuracy: 0.8944\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2014 - accuracy: 0.9239 - val_loss: 0.3605 - val_accuracy: 0.8795\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1957 - accuracy: 0.9269 - val_loss: 0.3629 - val_accuracy: 0.8823\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1894 - accuracy: 0.9284 - val_loss: 0.3415 - val_accuracy: 0.8897\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1842 - accuracy: 0.9292 - val_loss: 0.3456 - val_accuracy: 0.8904\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1828 - accuracy: 0.9317 - val_loss: 0.3509 - val_accuracy: 0.8881\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1775 - accuracy: 0.9326 - val_loss: 0.4002 - val_accuracy: 0.8784\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfMhIWAdMYN7"
      },
      "source": [
        "## Evaluating our multi-class classification model\n",
        "\n",
        "To evaluate our multi-class classication model we could :\n",
        "* Evaluate its performance using other classification metrics (such as a confusion matrix)\n",
        "* Assess some of its predictions (through visualizations)\n",
        "* Improve its results (by training it for longer or changing the architecture)\n",
        "* Save and export it for use in an application\n",
        "\n",
        "Let's go through the top 2...."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZlBwKMWNhlS"
      },
      "source": [
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "def make_confusion_matrix(y_true,y_pred, classes=None, figsize=(8,8), text_size=15):\n",
        "  # Create the confusion matrix\n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "  cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:,np.newaxis]   # Normalize our cm\n",
        "  n_classes = cm.shape[0]\n",
        "\n",
        "  # Let's prettify it\n",
        "  fig, ax = plt.subplots(figsize=figsize)\n",
        "  # Create a matrix plot\n",
        "  cax = ax.matshow(cm, cmap=plt.cm.Blues)\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  # Set labels to be classes\n",
        "  if classes:\n",
        "    labels=classes\n",
        "  else:\n",
        "    labels= np.arange(cm.shape[0])\n",
        "\n",
        "  # Label the axes\n",
        "  ax.set(title=\"Confusion Matrix\",\n",
        "        xlabel=\"Predicted Label\",\n",
        "        ylabel=\"True Label\",\n",
        "        xticks=np.arange(n_classes),\n",
        "        yticks=np.arange(n_classes),\n",
        "        xticklabels=labels,\n",
        "        yticklabels=labels)\n",
        "\n",
        "  # Set x-axis labels to bottom\n",
        "  ax.xaxis.set_label_position(\"bottom\")\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Adjust label size\n",
        "  ax.yaxis.label.set_size(text_size)\n",
        "  ax.xaxis.label.set_size(text_size)\n",
        "  ax.title.set_size(text_size)\n",
        "\n",
        "  # Set threshold for different colors\n",
        "  threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "  # Plot the text on each cell\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j,i, f\"{cm[i,j]}({cm_norm[i,j]*100:.1f}%\",\n",
        "            horizontalalignment = \"center\",\n",
        "            color=\"white\" if cm[i,j]> threshold else \"black\",\n",
        "            size=text_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HztV3os0Qyh0",
        "outputId": "7d4d0f1f-ca3a-4fc6-a233-8b43b92c9d9a"
      },
      "source": [
        "# Make some predictions with our model\n",
        "y_probs = model_15.predict(test_data_norm)    # Prediction on normalized data as model is on normalized train data\n",
        "\n",
        "# View the first 5 predictions\n",
        "y_probs[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.12000222e-07, 1.76992934e-15, 6.89703461e-10, 5.62301670e-13,\n",
              "        9.19181509e-10, 1.00368788e-05, 1.51214926e-11, 4.15677205e-03,\n",
              "        2.10184634e-08, 9.95833099e-01],\n",
              "       [5.59432156e-05, 1.37382117e-13, 9.99399900e-01, 4.50634515e-18,\n",
              "        5.43707400e-04, 2.50688665e-18, 3.62922975e-07, 2.16759949e-28,\n",
              "        6.61902051e-17, 1.75640830e-19],\n",
              "       [1.56108511e-15, 1.00000000e+00, 2.25604499e-16, 1.07084304e-13,\n",
              "        1.50146578e-13, 5.81620674e-27, 2.56735788e-14, 3.86670121e-30,\n",
              "        1.01831679e-21, 4.09808251e-29],\n",
              "       [1.82198332e-12, 1.00000000e+00, 9.00878493e-15, 1.82863213e-09,\n",
              "        2.65479669e-11, 9.33654772e-22, 2.67607936e-10, 1.85144152e-27,\n",
              "        1.58372645e-17, 2.64937918e-26],\n",
              "       [2.89262146e-01, 5.99733414e-12, 3.59929563e-03, 7.90870308e-06,\n",
              "        2.02983247e-05, 1.44180512e-09, 7.07110226e-01, 4.78181121e-08,\n",
              "        1.48310866e-08, 4.75560650e-12]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mngZcDzSRF1M",
        "outputId": "5e3d5515-e23d-4d9d-f596-7d75e3905fef"
      },
      "source": [
        "y_probs[0], tf.argmax(y_probs[0]), class_names[tf.argmax(y_probs[0])]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1.1200022e-07, 1.7699293e-15, 6.8970346e-10, 5.6230167e-13,\n",
              "        9.1918151e-10, 1.0036879e-05, 1.5121493e-11, 4.1567720e-03,\n",
              "        2.1018463e-08, 9.9583310e-01], dtype=float32),\n",
              " <tf.Tensor: shape=(), dtype=int64, numpy=9>,\n",
              " 'Ankle boot')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nw5rDHYgRqBl",
        "outputId": "6c23a023-5d93-4149-f72a-36fa82c4d24c"
      },
      "source": [
        "# Convert all of the prediction probabilities into integers\n",
        "y_preds = y_probs.argmax(axis=1)\n",
        "\n",
        "# View the first 10 prediction labels\n",
        "y_preds[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "niacoYjhSRjV",
        "outputId": "1e168a5d-5b17-4991-f9e7-1bdbc72cf96e"
      },
      "source": [
        "test_labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, ..., 8, 1, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05P0pf9PSd6c",
        "outputId": "42113bcd-d08c-4f4e-d1ff-504fabb48848"
      },
      "source": [
        "confusion_matrix(y_true=test_labels,\n",
        "                 y_pred=y_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[829,   4,  18,  18,   7,   0, 115,   0,   9,   0],\n",
              "       [  3, 983,   1,   8,   4,   0,   1,   0,   0,   0],\n",
              "       [ 14,   1, 769,   6, 166,   0,  42,   0,   2,   0],\n",
              "       [ 24,   6,  13, 864,  66,   0,  23,   0,   4,   0],\n",
              "       [  0,   0,  60,   9, 904,   0,  26,   0,   1,   0],\n",
              "       [  2,   0,   0,   1,   0, 931,   0,  36,   1,  29],\n",
              "       [120,   0,  94,  23, 141,   0, 615,   0,   7,   0],\n",
              "       [  0,   0,   0,   0,   0,   6,   0, 981,   0,  13],\n",
              "       [  4,   0,   3,   4,   9,   1,   4,   4, 971,   0],\n",
              "       [  0,   0,   0,   0,   0,   1,   1,  61,   0, 937]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFJamGqJSqfZ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "0aWovCQFSzJU",
        "outputId": "762da7e1-524e-4d70-e777-8d2b5972621f"
      },
      "source": [
        "# Make a prettier confusion matrix\n",
        "make_confusion_matrix(y_true=test_labels,\n",
        "                      y_pred=y_preds,\n",
        "                      classes=class_names,\n",
        "                      figsize=(16,16),\n",
        "                      text_size=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyR2Em9VS9rZ"
      },
      "source": [
        "How about we create a fun little function for :\n",
        "* Plot a random image\n",
        "* Make a prediction on said image\n",
        "* Label the plot with the truth label & the predicted label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLGOgAz6UF8n"
      },
      "source": [
        "import random\n",
        "\n",
        "def plot_random_image(model, images, true_labels, classes):\n",
        "  \"\"\"\n",
        "  Picks a random image, plots it and labels it with a prediction and truth label\n",
        "  \"\"\"\n",
        "  # Set up random integer\n",
        "  i = random.randint(0, len(images))\n",
        "\n",
        "  # Create predictions and targets\n",
        "  target_image = images[i]\n",
        "  pred_probs = model.predict(target_image.reshape(1,28,28))\n",
        "  pred_label = classes[pred_probs.argmax()]\n",
        "  true_label = classes[true_labels[i]]\n",
        "\n",
        "  # Plot the image\n",
        "  plt.imshow(target_image, cmap=plt.cm.binary)\n",
        "\n",
        "  # Change the color of the titles depending on if the prediction is right or wrong\n",
        "  if pred_label == true_label:\n",
        "    color = \"green\"\n",
        "  else:\n",
        "    color = \"red\"\n",
        "  # Add xlabel information (prediction/true label)\n",
        "  plt.xlabel(\"Pred:{} {:2.0f}% (True:{})\".format(pred_label,\n",
        "                                                 100*tf.reduce_max(pred_probs),\n",
        "                                                 true_label),\n",
        "             color=color)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "NEO6rWDGWN1V",
        "outputId": "8b086185-c1ae-4ecc-cc31-d3c7106c04ff"
      },
      "source": [
        "# Check out a random image as well as its prediction\n",
        "plot_random_image(model=model_15,\n",
        "                  images=test_data_norm,\n",
        "                  true_labels=test_labels,\n",
        "                  classes=class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8UBjSKOWen5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}