CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN.ipynb",
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBbKxkUKlYed",
        "colab_type": "code",
        "outputId": "6f5329b9-ecf8-467c-cea0-1a6c879e77f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0-rc0) (0.33.6)\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: 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: 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: 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",
            "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HMpAKSwoSrm",
        "colab_type": "code",
        "outputId": "d2b94e87-82ae-4573-9508-9fca7c2008b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "!pip install mlxtend==0.17.0"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mlxtend==0.17.0 in /usr/local/lib/python3.6/dist-packages (0.17.0)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17.0) (3.1.2)\n",
            "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17.0) (1.3.3)\n",
            "Requirement already satisfied: numpy>=1.16.2 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17.0) (1.17.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17.0) (42.0.2)\n",
            "Requirement already satisfied: scikit-learn>=0.20.3 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17.0) (0.21.3)\n",
            "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17.0) (0.14.1)\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17.0) (0.25.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->mlxtend==0.17.0) (0.10.0)\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>=3.0.0->mlxtend==0.17.0) (2.4.5)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->mlxtend==0.17.0) (2.6.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->mlxtend==0.17.0) (1.1.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.2->mlxtend==0.17.0) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=3.0.0->mlxtend==0.17.0) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6S27olagoe3I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPool2D, Dropout"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGzmZIQBp2Fi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.datasets import cifar10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFI7liY8qSHh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQrpdjdEqeug",
        "colab_type": "code",
        "outputId": "1c2bc450-8b0e-46ca-e279-4e73b256356f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "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": [
            "(50000, 32, 32, 3)\n",
            "(50000, 1)\n",
            "(10000, 32, 32, 3)\n",
            "(10000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBN9i6e5wTqB",
        "colab_type": "code",
        "outputId": "d08e5785-5e80-4c06-e29f-dd0dcd75bb3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "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": "6CKoPiJEwT5o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train=X_train/np.max(X_train)\n",
        "X_test=X_test/np.max(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TW3CsCJ_qq3F",
        "colab_type": "code",
        "outputId": "5de8c389-3375-44b3-b256-4b6febc16681",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(X_train[0])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f54147ffa90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8nKkQ4peCrTyzf9rMnjWzh8yMfzcTQlwTXG2wfw3AYQBHAMwB+BL7RTM7ZmYnzOxEucKf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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRhdW4BrQUbo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ec191459-f3ed-4e02-b683-21dfd6bfc631"
      },
      "source": [
        "X_train[0].shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9UBIwggrBdH",
        "colab_type": "text"
      },
      "source": [
        "**CNN Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D134B1MOqxyb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(filters=32, kernel_size=(2, 2), strides=(1, 1), padding='same', activation='relu', input_shape=X_train[0].shape))\n",
        "model.add(Conv2D(filters=32, kernel_size=(2, 2), strides=(1, 1), padding='same', activation='relu'))\n",
        "model.add(Conv2D(filters=32, kernel_size=(2, 2), strides=(1, 1), padding='same', activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(3, 3), strides=(1, 1), padding='valid'))\n",
        "model.add(Conv2D(filters=32, kernel_size=(2, 2), strides=(1, 1), padding='same', activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding='valid'))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(150, activation='relu'))\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wx6EhlzCs3kH",
        "colab_type": "code",
        "outputId": "7b5ae4db-c7ad-475f-d2f9-90cbb452cdf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 32)        416       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        4128      \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        4128      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 30, 30, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 30, 30, 32)        4128      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 29, 29, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 26912)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 150)               4036950   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               15100     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 4,065,860\n",
            "Trainable params: 4,065,860\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikBHwrU4s6M4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpsiKSe6twAj",
        "colab_type": "code",
        "outputId": "80dfa55a-6b6a-4246-9943-b41a6edea77a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model.fit(X_train, y_train, batch_size=10000, epochs=20)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples\n",
            "Epoch 1/20\n",
            "50000/50000 [==============================] - 24s 472us/sample - loss: 0.5201 - accuracy: 0.8220\n",
            "Epoch 2/20\n",
            "50000/50000 [==============================] - 24s 472us/sample - loss: 0.4880 - accuracy: 0.8340\n",
            "Epoch 3/20\n",
            "50000/50000 [==============================] - 23s 470us/sample - loss: 0.4659 - accuracy: 0.8459\n",
            "Epoch 4/20\n",
            "50000/50000 [==============================] - 23s 467us/sample - loss: 0.4550 - accuracy: 0.8484\n",
            "Epoch 5/20\n",
            "50000/50000 [==============================] - 23s 465us/sample - loss: 0.4461 - accuracy: 0.8530\n",
            "Epoch 6/20\n",
            "50000/50000 [==============================] - 24s 475us/sample - loss: 0.4329 - accuracy: 0.8574\n",
            "Epoch 7/20\n",
            "50000/50000 [==============================] - 23s 466us/sample - loss: 0.4347 - accuracy: 0.8559\n",
            "Epoch 8/20\n",
            "50000/50000 [==============================] - 23s 465us/sample - loss: 0.4476 - accuracy: 0.8485\n",
            "Epoch 9/20\n",
            "50000/50000 [==============================] - 23s 467us/sample - loss: 0.5734 - accuracy: 0.8036\n",
            "Epoch 10/20\n",
            "50000/50000 [==============================] - 23s 465us/sample - loss: 0.5920 - accuracy: 0.7885\n",
            "Epoch 11/20\n",
            "50000/50000 [==============================] - 24s 471us/sample - loss: 0.5099 - accuracy: 0.8270\n",
            "Epoch 12/20\n",
            "50000/50000 [==============================] - 23s 469us/sample - loss: 0.4735 - accuracy: 0.8413\n",
            "Epoch 13/20\n",
            "50000/50000 [==============================] - 23s 468us/sample - loss: 0.4449 - accuracy: 0.8507\n",
            "Epoch 14/20\n",
            "50000/50000 [==============================] - 24s 473us/sample - loss: 0.4214 - accuracy: 0.8613\n",
            "Epoch 15/20\n",
            "50000/50000 [==============================] - 23s 469us/sample - loss: 0.4092 - accuracy: 0.8663\n",
            "Epoch 16/20\n",
            "50000/50000 [==============================] - 24s 473us/sample - loss: 0.4019 - accuracy: 0.8676\n",
            "Epoch 17/20\n",
            "50000/50000 [==============================] - 24s 473us/sample - loss: 0.3899 - accuracy: 0.8741\n",
            "Epoch 18/20\n",
            "50000/50000 [==============================] - 24s 472us/sample - loss: 0.3804 - accuracy: 0.8782\n",
            "Epoch 19/20\n",
            "50000/50000 [==============================] - 24s 483us/sample - loss: 0.3842 - accuracy: 0.8752\n",
            "Epoch 20/20\n",
            "50000/50000 [==============================] - 24s 474us/sample - loss: 0.3715 - accuracy: 0.8816\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_xxbCtB8d8A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2Pl7w--8mWk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict_classes(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-xgTWoX8mUC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c8fac0b2-6a8c-4663-f69c-239f463e57dc"
      },
      "source": [
        "print(accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.714\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vAw7dFz8mRj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "a29250b8-87d6-4645-ad28-24dbcbd8d94b"
      },
      "source": [
        "print(y_test)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3]\n",
            " [8]\n",
            " [8]\n",
            " ...\n",
            " [5]\n",
            " [1]\n",
            " [7]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "se3PaCaT8mPj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0958851-1403-4341-dc2d-1b0c1b915a89"
      },
      "source": [
        "print(y_pred)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3 8 0 ... 5 1 7]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gWbgjUs7ZAO",
        "colab_type": "text"
      },
      "source": [
        "**Learning Curve**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcy8y8Y5t7rC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Accuracy Score with Number of Epochs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNDH8jKB7fRZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "be2ae9ba-ef69-419b-b51b-30eef57917af"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.title(\"Model Accuracy\")\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Accuracy')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y63UoJsYFU+8xVFW/JyJnq+pTIvIc8EkwT66qc3Aan/233e53fxlw7H6eY0Yw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\nMMYYE5AlCGOMMQH9P6ZLEOr994u2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVv2q9ZJ7xFj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Loss with Number of Epochs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPJtfrV670Bp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "outputId": "b0c0105d-b504-4140-c127-fbf885e39960"
      },
      "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": 26,
      "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-26-150c8d2a01d5>\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": {
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8QN658XvERoVz5bMreGTJDpq92JSzoeQIP5izlLfWlDB7Shbzr59AepL2jbfC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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WI8dQGkd8BUD",
        "colab_type": "text"
      },
      "source": [
        "**Confusion Matix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0bhGIN57_i7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_HrlKYT8Zdg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "ab1e93bc-c401-498a-84e8-ad401a9814a5"
      },
      "source": [
        "mat = confusion_matrix(y_test, y_pred)\n",
        "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 0x7f53c54a9390>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HTpo29+eZ+o665nwcKytrUlJyT/ZLSUnBUsfDNk/i5OTMmnWbiNmZRK++/ahX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\nVLgiIiIOUOGKiIg44P8A143+jD0w6KEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwY-XdKuVx0T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}