Basic_ANN,_Learning_Curves_and_Confusion_Matrix_.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Basic ANN, Learning Curves and Confusion Matrix .ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_nd-Q9MdswA",
        "colab_type": "text"
      },
      "source": [
        "**Installing TensorFlow**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2MLvINtJ_9e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "9ac5b251-56c8-4198-d7ab-5e9830058fe4"
      },
      "source": [
        "!pip install tensorflow==2.0.0-rc0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow==2.0.0-rc0 in /usr/local/lib/python3.6/dist-packages (2.0.0rc0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (0.8.1)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.0.8)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (0.8.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.1.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.1.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (3.10.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (0.33.6)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (0.2.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (3.1.0)\n",
            "Requirement already satisfied: tb-nightly<1.15.0a20190807,>=1.15.0a20190806 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.15.0a20190806)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.11.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.12.0)\n",
            "Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019080602,>=1.14.0.dev2019080601 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.14.0.dev2019080601)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.17.4)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (1.15.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (0.1.8)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==2.0.0-rc0) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==2.0.0-rc0) (42.0.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow==2.0.0-rc0) (0.16.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow==2.0.0-rc0) (3.1.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwEtXV456Ks1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDSTv1p4eEzV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCR-QEO49sOD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist = keras.datasets.fashion_mnist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qApWLzVA9xod",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sMWClXYP_-HQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "92820e49-2999-4de6-d5fc-7952f86557f7"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "(60000,)\n",
            "(10000, 28, 28)\n",
            "(10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjvP37LOAP0x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5927b93c-9357-4627-d903-48da9e49aaf8"
      },
      "source": [
        "print(np.max(X_train))\n",
        "print(np.max(X_test))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "255\n",
            "255\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-6M9rMAAkiH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "bf1462d7-a234-41de-beb8-4ac1e5e35d35"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(X_train[0])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f75b1f39908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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de64xbfhtd7vJjLNfAuBmAJtE5NNFyO/FWMh/IyK3AdgL4IZJXBcRlUlo2FX1LQCuQ+6V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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgk-m0U9BU7J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwQHz6phCU8W",
        "colab_type": "text"
      },
      "source": [
        "**Neural Network**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCIQO1Wa9DUA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Flatten, Dense"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hc8vZcjJBx9Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Flatten(input_shape=X_train[0].shape))\n",
        "model.add(Dense(200, activation='relu'))\n",
        "model.add(Dense(120, activation='relu'))\n",
        "model.add(Dense(120, activation='relu'))\n",
        "model.add(Dense(120, activation='relu'))\n",
        "model.add(Dense(50, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yP2x_1cDE4fg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "56381ac9-bb78-41ea-fce9-eb8df69eeabc"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 200)               157000    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 120)               24120     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 120)               14520     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 120)               14520     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 50)                6050      \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                510       \n",
            "=================================================================\n",
            "Total params: 216,720\n",
            "Trainable params: 216,720\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQSeoJOcGBAf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', metrics=['accuracy'], loss='sparse_categorical_crossentropy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DgXw0AQqRu9Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eb600304-31bf-406d-8251-a9edeaaeb8e9"
      },
      "source": [
        "history = model.fit(X_train, y_train, batch_size=10000, epochs=150)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/150\n",
            "60000/60000 [==============================] - 3s 46us/sample - loss: 2.0789 - accuracy: 0.3049\n",
            "Epoch 2/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 1.3404 - accuracy: 0.5925\n",
            "Epoch 3/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.8668 - accuracy: 0.6929\n",
            "Epoch 4/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.7290 - accuracy: 0.7370\n",
            "Epoch 5/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.6406 - accuracy: 0.7631\n",
            "Epoch 6/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.5769 - accuracy: 0.7873\n",
            "Epoch 7/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.5405 - accuracy: 0.8034\n",
            "Epoch 8/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.5146 - accuracy: 0.8139\n",
            "Epoch 9/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.5003 - accuracy: 0.8214\n",
            "Epoch 10/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.4784 - accuracy: 0.8319\n",
            "Epoch 11/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.4618 - accuracy: 0.8382\n",
            "Epoch 12/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.4464 - accuracy: 0.8441\n",
            "Epoch 13/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.4316 - accuracy: 0.8509\n",
            "Epoch 14/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.4181 - accuracy: 0.8551\n",
            "Epoch 15/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.4087 - accuracy: 0.8580\n",
            "Epoch 16/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.3976 - accuracy: 0.8616\n",
            "Epoch 17/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.3888 - accuracy: 0.8645\n",
            "Epoch 18/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3803 - accuracy: 0.8676\n",
            "Epoch 19/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.3750 - accuracy: 0.8684\n",
            "Epoch 20/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.3662 - accuracy: 0.8713\n",
            "Epoch 21/150\n",
            "60000/60000 [==============================] - 2s 30us/sample - loss: 0.3597 - accuracy: 0.8735\n",
            "Epoch 22/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3516 - accuracy: 0.8775\n",
            "Epoch 23/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3478 - accuracy: 0.8781\n",
            "Epoch 24/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3449 - accuracy: 0.8776\n",
            "Epoch 25/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.3444 - accuracy: 0.8774\n",
            "Epoch 26/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3391 - accuracy: 0.8795\n",
            "Epoch 27/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3428 - accuracy: 0.8771\n",
            "Epoch 28/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3453 - accuracy: 0.8753\n",
            "Epoch 29/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.3446 - accuracy: 0.8761\n",
            "Epoch 30/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3319 - accuracy: 0.8798\n",
            "Epoch 31/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.3220 - accuracy: 0.8846\n",
            "Epoch 32/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3114 - accuracy: 0.8884\n",
            "Epoch 33/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3086 - accuracy: 0.8884\n",
            "Epoch 34/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3065 - accuracy: 0.8892\n",
            "Epoch 35/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3042 - accuracy: 0.8903\n",
            "Epoch 36/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3047 - accuracy: 0.8891\n",
            "Epoch 37/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.3019 - accuracy: 0.8905\n",
            "Epoch 38/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2945 - accuracy: 0.8934\n",
            "Epoch 39/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2888 - accuracy: 0.8954\n",
            "Epoch 40/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2846 - accuracy: 0.8971\n",
            "Epoch 41/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2836 - accuracy: 0.8970\n",
            "Epoch 42/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2853 - accuracy: 0.8959\n",
            "Epoch 43/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2826 - accuracy: 0.8971\n",
            "Epoch 44/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2830 - accuracy: 0.8961\n",
            "Epoch 45/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2770 - accuracy: 0.8989\n",
            "Epoch 46/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2729 - accuracy: 0.8994\n",
            "Epoch 47/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2725 - accuracy: 0.9002\n",
            "Epoch 48/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2758 - accuracy: 0.8983\n",
            "Epoch 49/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2898 - accuracy: 0.8923\n",
            "Epoch 50/150\n",
            "60000/60000 [==============================] - 2s 30us/sample - loss: 0.2814 - accuracy: 0.8961\n",
            "Epoch 51/150\n",
            "60000/60000 [==============================] - 2s 30us/sample - loss: 0.2721 - accuracy: 0.9003\n",
            "Epoch 52/150\n",
            "60000/60000 [==============================] - 2s 30us/sample - loss: 0.2671 - accuracy: 0.9019\n",
            "Epoch 53/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2612 - accuracy: 0.9047\n",
            "Epoch 54/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2609 - accuracy: 0.9053\n",
            "Epoch 55/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2558 - accuracy: 0.9066\n",
            "Epoch 56/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2607 - accuracy: 0.9044\n",
            "Epoch 57/150\n",
            "60000/60000 [==============================] - 2s 30us/sample - loss: 0.2534 - accuracy: 0.9071\n",
            "Epoch 58/150\n",
            "60000/60000 [==============================] - 2s 31us/sample - loss: 0.2482 - accuracy: 0.9098\n",
            "Epoch 59/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2488 - accuracy: 0.9085\n",
            "Epoch 60/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2446 - accuracy: 0.9108\n",
            "Epoch 61/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2429 - accuracy: 0.9112\n",
            "Epoch 62/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2533 - accuracy: 0.9069\n",
            "Epoch 63/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2467 - accuracy: 0.9086\n",
            "Epoch 64/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2423 - accuracy: 0.9109\n",
            "Epoch 65/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2347 - accuracy: 0.9147\n",
            "Epoch 66/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2342 - accuracy: 0.9139\n",
            "Epoch 67/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2340 - accuracy: 0.9147\n",
            "Epoch 68/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2318 - accuracy: 0.9152\n",
            "Epoch 69/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2370 - accuracy: 0.9136\n",
            "Epoch 70/150\n",
            "60000/60000 [==============================] - 2s 27us/sample - loss: 0.2406 - accuracy: 0.9116\n",
            "Epoch 71/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2307 - accuracy: 0.9148\n",
            "Epoch 72/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2250 - accuracy: 0.9186\n",
            "Epoch 73/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2231 - accuracy: 0.9176\n",
            "Epoch 74/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2236 - accuracy: 0.9165\n",
            "Epoch 75/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2225 - accuracy: 0.9171\n",
            "Epoch 76/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2260 - accuracy: 0.9162\n",
            "Epoch 77/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2215 - accuracy: 0.9188\n",
            "Epoch 78/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2177 - accuracy: 0.9204\n",
            "Epoch 79/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2143 - accuracy: 0.9211\n",
            "Epoch 80/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2119 - accuracy: 0.9223\n",
            "Epoch 81/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2105 - accuracy: 0.9240\n",
            "Epoch 82/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2082 - accuracy: 0.9240\n",
            "Epoch 83/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2088 - accuracy: 0.9222\n",
            "Epoch 84/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2093 - accuracy: 0.9227\n",
            "Epoch 85/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2095 - accuracy: 0.9221\n",
            "Epoch 86/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2031 - accuracy: 0.9261\n",
            "Epoch 87/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1997 - accuracy: 0.9263\n",
            "Epoch 88/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1975 - accuracy: 0.9275\n",
            "Epoch 89/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1959 - accuracy: 0.9288\n",
            "Epoch 90/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1949 - accuracy: 0.9291\n",
            "Epoch 91/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2047 - accuracy: 0.9239\n",
            "Epoch 92/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2036 - accuracy: 0.9239\n",
            "Epoch 93/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1993 - accuracy: 0.9258\n",
            "Epoch 94/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1930 - accuracy: 0.9282\n",
            "Epoch 95/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1890 - accuracy: 0.9310\n",
            "Epoch 96/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1893 - accuracy: 0.9306\n",
            "Epoch 97/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1937 - accuracy: 0.9287\n",
            "Epoch 98/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1887 - accuracy: 0.9305\n",
            "Epoch 99/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1845 - accuracy: 0.9330\n",
            "Epoch 100/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1891 - accuracy: 0.9302\n",
            "Epoch 101/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.2046 - accuracy: 0.9230\n",
            "Epoch 102/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1999 - accuracy: 0.9258\n",
            "Epoch 103/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1931 - accuracy: 0.9284\n",
            "Epoch 104/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1907 - accuracy: 0.9305\n",
            "Epoch 105/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1817 - accuracy: 0.9336\n",
            "Epoch 106/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1848 - accuracy: 0.9322\n",
            "Epoch 107/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1815 - accuracy: 0.9337\n",
            "Epoch 108/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1857 - accuracy: 0.9315\n",
            "Epoch 109/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1813 - accuracy: 0.9330\n",
            "Epoch 110/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1739 - accuracy: 0.9369\n",
            "Epoch 111/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1721 - accuracy: 0.9373\n",
            "Epoch 112/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1702 - accuracy: 0.9370\n",
            "Epoch 113/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1657 - accuracy: 0.9391\n",
            "Epoch 114/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1678 - accuracy: 0.9382\n",
            "Epoch 115/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1638 - accuracy: 0.9405\n",
            "Epoch 116/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1624 - accuracy: 0.9413\n",
            "Epoch 117/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1592 - accuracy: 0.9419\n",
            "Epoch 118/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1607 - accuracy: 0.9410\n",
            "Epoch 119/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1604 - accuracy: 0.9423\n",
            "Epoch 120/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1654 - accuracy: 0.9387\n",
            "Epoch 121/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1827 - accuracy: 0.9312\n",
            "Epoch 122/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.2043 - accuracy: 0.9244\n",
            "Epoch 123/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1936 - accuracy: 0.9280\n",
            "Epoch 124/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1812 - accuracy: 0.9336\n",
            "Epoch 125/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1663 - accuracy: 0.9393\n",
            "Epoch 126/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1584 - accuracy: 0.9431\n",
            "Epoch 127/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1560 - accuracy: 0.9441\n",
            "Epoch 128/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1529 - accuracy: 0.9448\n",
            "Epoch 129/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1510 - accuracy: 0.9463\n",
            "Epoch 130/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1503 - accuracy: 0.9457\n",
            "Epoch 131/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1476 - accuracy: 0.9464\n",
            "Epoch 132/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1468 - accuracy: 0.9469\n",
            "Epoch 133/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1487 - accuracy: 0.9453\n",
            "Epoch 134/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1453 - accuracy: 0.9473\n",
            "Epoch 135/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1466 - accuracy: 0.9460\n",
            "Epoch 136/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1450 - accuracy: 0.9470\n",
            "Epoch 137/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1463 - accuracy: 0.9470\n",
            "Epoch 138/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1489 - accuracy: 0.9454\n",
            "Epoch 139/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1522 - accuracy: 0.9438\n",
            "Epoch 140/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1398 - accuracy: 0.9501\n",
            "Epoch 141/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1457 - accuracy: 0.9472\n",
            "Epoch 142/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1390 - accuracy: 0.9504\n",
            "Epoch 143/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1392 - accuracy: 0.9491\n",
            "Epoch 144/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1776 - accuracy: 0.9324\n",
            "Epoch 145/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1835 - accuracy: 0.9299\n",
            "Epoch 146/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1585 - accuracy: 0.9413\n",
            "Epoch 147/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1484 - accuracy: 0.9448\n",
            "Epoch 148/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1381 - accuracy: 0.9505\n",
            "Epoch 149/150\n",
            "60000/60000 [==============================] - 2s 28us/sample - loss: 0.1374 - accuracy: 0.9499\n",
            "Epoch 150/150\n",
            "60000/60000 [==============================] - 2s 29us/sample - loss: 0.1369 - accuracy: 0.9504\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrQ-x_bUUC9c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcVf18oQVq_b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict_classes(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmAnWVJFVyjA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8a43581c-c0ce-4cea-9b10-5e0c0822e374"
      },
      "source": [
        "print(accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8873\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujN0Cz7dV2Ii",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b81dd9fc-a960-492d-b902-fe513a4ec08d"
      },
      "source": [
        "print(y_test)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[9 2 1 ... 8 1 5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxK1rm0fWgnO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1c508af1-2a6f-498f-94a6-e79f23f76a20"
      },
      "source": [
        "print(y_pred)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[9 2 1 ... 8 1 5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSGscMBNbDBJ",
        "colab_type": "text"
      },
      "source": [
        "**Learning Curves**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8hFHjh5bk2Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Accuracy with number of Epochs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCQejIU_Wjsp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "bf03e5c6-03ed-4607-e78b-7bff1556bf8e"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.label('Model Accuracy')\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Accuracy')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-a3b59ac10308>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\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----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model Accuracy'\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      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No. of Epochs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'label'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ge5o6aDjRw7ptR/n5liMEgs75ZQV89IoK7r6qRtdci0hcaYQeZ0fbevn27w7w1PqDdEQm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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jjzqw99rbrrn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Loss with number of Epochs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqXEOegtbr3t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "997b224c-c986-43e0-95a1-ca94ef6d8a4a"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history.history['loss'])\n",
        "plt.label('Model Loss')\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Loss')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-7cdf165b1d64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\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----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model Loss'\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      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No. of Epochs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'label'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4M26C/oKZlSTTzopmzb4RkfFl3AT9+fWZAVn16UVkvBk3QV9ZluC06jL+9PqefJciInJS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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Y2L_QFab_k5",
        "colab_type": "text"
      },
      "source": [
        "**Confusion Matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nD9J0mPbsDF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "9da204b4-cb06-480a-859e-b88c1ea579ce"
      },
      "source": [
        "!pip install mlxtend"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.6/dist-packages (0.14.0)\n",
            "Requirement already satisfied: matplotlib>=1.5.1 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (3.1.2)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (0.21.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from mlxtend) (42.0.2)\n",
            "Requirement already satisfied: scipy>=0.17 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (1.3.3)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (1.17.4)\n",
            "Requirement already satisfied: pandas>=0.17.1 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (0.25.3)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->mlxtend) (2.4.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->mlxtend) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->mlxtend) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->mlxtend) (2.6.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->mlxtend) (0.14.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.1->mlxtend) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=1.5.1->mlxtend) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOlgi6oTbsKn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mlxtend.plotting import plot_confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eo0mpTM5bsB9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hyaTlVObsAD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mat = confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xfi5ihHfbr-a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "b1e550b4-aed3-4295-8a52-192a3e506f67"
      },
      "source": [
        "plot_confusion_matrix(conf_mat=mat, figsize=(8, 8))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 576x576 with 1 Axes>,\n",
              " <matplotlib.axes._subplots.AxesSubplot at 0x7f759c265a90>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tNFNQzUps3JVE2u9ZuFw2a7b8Qq82Qez+JZk9B49dtHxqeibrtx8kPSPLQNrc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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wa1SRq2Obr2i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upVUcICabeGh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}