RecognizeDoodling_AI.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RecognizeDoodling_AI.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "neo8RaQJ67xW",
        "u2rVk4cRiT7i"
      ],
      "machine_shape": "hm",
      "mount_file_id": "1OPtzzmdQaD0a4Q2_54nVaOBh2n74s8jv",
      "authorship_tag": "ABX9TyNc4hRqcAqh42yHRxKbl59A"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FcKhT8X2WmTX",
        "colab_type": "text"
      },
      "source": [
        "#### Importing all the necessary libraries\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RY-WlZJ0Wjep",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import numpy as np\n",
        "import os\n",
        "import pickle\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle\n",
        "from keras.layers import Dense,Flatten, Conv2D\n",
        "from keras.layers import MaxPooling2D, Dropout\n",
        "from keras.utils import np_utils, print_summary\n",
        "import tensorflow as tf\n",
        "from keras.models import Sequential\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.callbacks import TensorBoard\n",
        "#from tensorflow.keras import \n",
        "\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn import metrics\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIt24S--WwMI",
        "colab_type": "text"
      },
      "source": [
        "####  Listing all files and directories present in the specified directory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pp0JmQckXHgx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = os.listdir(\"/content/drive/My Drive/Artificial Intelligence/Data/\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbfW34qXXKRe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "75ad954f-3085-48ee-b760-756d2b49a146"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['full_numpy_bitmap_apple.npy',\n",
              " 'full_numpy_bitmap_bowtie.npy',\n",
              " 'full_numpy_bitmap_candle.npy',\n",
              " 'full_numpy_bitmap_door.npy',\n",
              " 'full_numpy_bitmap_envelope.npy',\n",
              " 'full_numpy_bitmap_fish.npy',\n",
              " 'full_numpy_bitmap_guitar.npy',\n",
              " 'full_numpy_bitmap_ice cream.npy',\n",
              " 'full_numpy_bitmap_lightning.npy',\n",
              " 'full_numpy_bitmap_moon.npy',\n",
              " 'full_numpy_bitmap_mountain.npy',\n",
              " 'full_numpy_bitmap_star.npy',\n",
              " 'full_numpy_bitmap_tent.npy',\n",
              " 'full_numpy_bitmap_toothbrush.npy',\n",
              " 'full_numpy_bitmap_wristwatch.npy']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DuPGb_InX57z",
        "colab_type": "text"
      },
      "source": [
        "#### Creating an empty list to store the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PwO658t-XzXS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=[]\n",
        "feature = []\n",
        "x = []\n",
        "label = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHcDYmAuYHnK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_data():\n",
        "    count = 0\n",
        "    for file in dataset:\n",
        "        file = \"/content/drive/My Drive/Artificial Intelligence/Data/\" + file\n",
        "        # Load arrays from .npy files\n",
        "        data = np.load(file)\n",
        "        # Casting the int numpy.ndarray to float numpy.ndarray\n",
        "        # Dividing it by 255 helps to get the RGB color\n",
        "        data = data.astype('float32') / 255.\n",
        "        # Extracting top 10000 rows and all columns\n",
        "        data = data[0:10000, :]\n",
        "        # Storing the extracted data from each file as feature\n",
        "        feature.append(data)\n",
        "        x = [count for _ in range(10000)]\n",
        "        count += 1\n",
        "        x = np.array(x).astype('float32')\n",
        "        # Reshaping the list to a single row \n",
        "        x = x.reshape(x.shape[0], 1)\n",
        "        # Loading teh created data as label\n",
        "        label.append(x)\n",
        "\n",
        "    return feature, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kkZtdQ6h74I",
        "colab_type": "text"
      },
      "source": [
        "Storing the loaded feature and label of each file as features and labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bxq9b1tWh4V8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features, labels = load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BIpVZ5xejW2Y",
        "colab_type": "text"
      },
      "source": [
        "Casting the features and labels to float"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t68hJ_EDjAno",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = np.array(features).astype('float32')\n",
        "labels = np.array(labels).astype('float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMy_a8mhj1q0",
        "colab_type": "text"
      },
      "source": [
        "#### Reshaping labels and features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgNnpEGDjrIA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=features.reshape(features.shape[0]*features.shape[1],features.shape[2])\n",
        "labels=labels.reshape(labels.shape[0]*labels.shape[1],labels.shape[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAnmS-JLlBR8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "122706c4-9db7-4fa2-914e-c6f537ea4ae4"
      },
      "source": [
        "features.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIS9KUEDlDiX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0e2bd838-8776-4d10-fcb7-b3ad9a46d13f"
      },
      "source": [
        "labels.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150000, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QjQHXA1-kdfh",
        "colab_type": "text"
      },
      "source": [
        "#### To store the python objects as  byte stream"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3DU3a2HBbgo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open(\"features\", \"wb\") as f:\n",
        "    pickle.dump(features, f, protocol=4)\n",
        "with open(\"labels\", \"wb\") as f:\n",
        "    pickle.dump(labels, f, protocol=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-oqaFM7EIoG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def keras_model(image_x, image_y):\n",
        "    num_of_classes = 15\n",
        "    model = Sequential()\n",
        "    model.add(Conv2D(32, (5, 5), input_shape=(image_x,image_y,1), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))\n",
        "    model.add(Conv2D(64, (5, 5), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(512, activation='relu'))\n",
        "    model.add(Dropout(0.6))\n",
        "    model.add(Dense(128, activation='relu'))\n",
        "    model.add(Dropout(0.6))\n",
        "    model.add(Dense(num_of_classes, activation='softmax'))\n",
        "\n",
        "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    filepath = \"QuickDraw.h5\"\n",
        "    checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
        "    callbacks_list = [checkpoint]\n",
        "\n",
        "    return model, callbacks_list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvseJK4Bli9r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def loadFromPickle():\n",
        "    with open(\"features\", \"rb\") as f:\n",
        "        features = np.array(pickle.load(f))\n",
        "    with open(\"labels\", \"rb\") as f:\n",
        "        labels = np.array(pickle.load(f))\n",
        "\n",
        "    return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bR-wkJSyloj6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def augmentData(features, labels):\n",
        "    features = np.append(features, features[:, :, ::-1], axis=0)\n",
        "    labels = np.append(labels, -labels, axis=0)\n",
        "    return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMaYhy7-lrV2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prepress_labels(labels):\n",
        "    labels = np_utils.to_categorical(labels)\n",
        "    return labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEdAYNmToJfD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features, labels = loadFromPickle()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pyIwlwPoSRX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features, labels = shuffle(features, labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DEV2kqproYBY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels=prepress_labels(labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNDVA0-5lwqc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_x, test_x, train_y, test_y = train_test_split(features, labels, random_state=0,test_size=0.1)\n",
        "train_x = train_x.reshape(train_x.shape[0], 28, 28, 1)\n",
        "test_x = test_x.reshape(test_x.shape[0], 28, 28, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gp4dIVs_l01k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "outputId": "9af6c8b9-b394-446b-d2f5-f3516476b2b6"
      },
      "source": [
        "model, callbacks_list = keras_model(28,28)\n",
        "print_summary(model)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
            "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 24, 24, 32)        832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 8, 8, 64)          51264     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               524800    \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               65664     \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 15)                1935      \n",
            "=================================================================\n",
            "Total params: 644,495\n",
            "Trainable params: 644,495\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ygBEz9Vooaz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "56374a30-44c1-404a-94fb-a85b8f706333"
      },
      "source": [
        "t1 = time.time()\n",
        "model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=3, batch_size=64,\n",
        "callbacks=[TensorBoard(log_dir=\"QuickDraw\")])\n",
        "print(time.time() - t1)\n",
        "model.save('QuickDraw.h5')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 135000 samples, validate on 15000 samples\n",
            "Epoch 1/3\n",
            "135000/135000 [==============================] - 34s 249us/step - loss: 0.3482 - acc: 0.9065 - val_loss: 0.2570 - val_acc: 0.9288\n",
            "Epoch 2/3\n",
            "135000/135000 [==============================] - 34s 249us/step - loss: 0.2946 - acc: 0.9219 - val_loss: 0.2444 - val_acc: 0.9307\n",
            "Epoch 3/3\n",
            "135000/135000 [==============================] - 33s 245us/step - loss: 0.2637 - acc: 0.9289 - val_loss: 0.2403 - val_acc: 0.9348\n",
            "100.41230726242065\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reyqFdLznpia",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(test_x, test_y, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLmwjd0Do96T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4afbfbf5-0c3a-4a71-fca4-34fd3afcbdfc"
      },
      "source": [
        "score"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.24029617153505484, 0.9348]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBcjJde2w7pU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_y = model.predict(test_x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-ZFbhVExyhN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "fa20e4b4-7e85-4ed2-fbf2-241e4393e3c1"
      },
      "source": [
        "test_y"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 1.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxWse0HhzVmW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_test_labels = np.argmax(test_y, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePhbgrqCzeBd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eae80a5c-2863-4559-ad39-6570f6b23ee2"
      },
      "source": [
        "y_test_labels"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([14,  7, 11, ...,  7, 12, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RaTibJjJx1F1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "947a32e9-cf4b-4433-d7eb-46fa3b25d38e"
      },
      "source": [
        "pred_y"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.5061885e-02, 6.2014576e-02, 2.5316304e-02, ..., 1.7570507e-02,\n",
              "        8.2752235e-02, 3.7670061e-01],\n",
              "       [9.4490033e-18, 3.3035684e-12, 7.0308592e-13, ..., 1.2752712e-18,\n",
              "        1.6873249e-14, 2.6353845e-14],\n",
              "       [3.5870132e-10, 3.2076554e-07, 9.7344528e-11, ..., 1.7585699e-06,\n",
              "        1.7094895e-10, 3.6272218e-07],\n",
              "       ...,\n",
              "       [1.2107124e-13, 3.8942463e-09, 3.2966035e-10, ..., 3.9708617e-14,\n",
              "        2.9422208e-11, 1.9226820e-11],\n",
              "       [3.8123180e-09, 4.4635023e-08, 1.5825236e-08, ..., 9.9887949e-01,\n",
              "        9.5868039e-08, 1.3806570e-08],\n",
              "       [1.5285250e-08, 3.5947588e-07, 2.2460590e-07, ..., 6.8379022e-02,\n",
              "        2.6654238e-06, 8.0743270e-09]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHpudf2mziPQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_labels = np.argmax(pred_y, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2whCaqikznwU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5575c9b8-5ce8-4412-aa60-1110d2d9a01f"
      },
      "source": [
        "y_pred_labels"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([14,  7, 11, ...,  7, 12, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-EmZ04RFxvXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "con = metrics.confusion_matrix(y_test_labels,y_pred_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syHazudLwvdN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "2be34a5d-d47b-42fe-b2c9-7e4504b61c6b"
      },
      "source": [
        "#con = metrics.confusion_matrix(test_y,pred_y)\n",
        "def cm2df(cm, labels):\n",
        "    df = pd.DataFrame()\n",
        "    # rows\n",
        "    for i, row_label in enumerate(labels):\n",
        "        rowdata={}\n",
        "        # columns\n",
        "        for j, col_label in enumerate(labels): \n",
        "            rowdata[col_label]=cm[i,j]\n",
        "        df = df.append(pd.DataFrame.from_dict({row_label:rowdata}, orient='index'))\n",
        "    return df[labels]\n",
        "labels=[i.split('.')[0].split('_')[-1] for i in dataset] ## list comprehension\n",
        "df_con = cm2df(con, labels)\n",
        "plt.figure(figsize=(15,10))\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "sns.heatmap(df_con, cmap=cmap, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted',size =15)\n",
        "plt.ylabel('Actual', size =15)\n",
        "plt.title('CNN-Adam Optimizer',size=20)\n",
        "#plt.show()\n",
        "plt.savefig('CNNAdam_CM')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1sAAAKTCAYAAADv8BJoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gb2hGcs4pJbs",
        "colab_type": "text"
      },
      "source": [
        "#### SVM Implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrFSXNBevQPI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=[]\n",
        "feature = []\n",
        "x = []\n",
        "label = []\n",
        "def load_data():\n",
        "    count = 0\n",
        "    for file in dataset:\n",
        "        file = \"/content/drive/My Drive/Artificial Intelligence/Data/\" + file\n",
        "        # Load arrays from .npy files\n",
        "        data = np.load(file)\n",
        "        # Casting the int numpy.ndarray to float numpy.ndarray\n",
        "        # Dividing it by 255 helps to get the RGB color\n",
        "        data = data.astype('float32') / 255.\n",
        "        # Extracting top 10000 rows and all columns\n",
        "        data = data[0:10000, :]\n",
        "        # Storing the extracted data from each file as feature\n",
        "        feature.append(data)\n",
        "        x = [count for _ in range(10000)]\n",
        "        count += 1\n",
        "        x = np.array(x).astype('float32')\n",
        "        # Reshaping the list to a single row \n",
        "        x = x.reshape(x.shape[0], 1)\n",
        "        # Loading the created data as label\n",
        "        label.append(x)\n",
        "\n",
        "    return feature, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Asf3XH9BvYd_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features, labels = load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXX5svNIvhqK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = np.array(features).astype('float32')\n",
        "labels = np.array(labels).astype('float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48OXDSyDvmDj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=features.reshape(features.shape[0]*features.shape[1],features.shape[2])\n",
        "labels=labels.reshape(labels.shape[0]*labels.shape[1],labels.shape[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmIKUiUXpJCm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(features, labels, random_state=0,test_size=0.5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAfYEbTqotZ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = SVC(kernel='rbf')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQLyKkaIpffw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "6f810a0c-17c2-4c51-c496-eeee97baf62f"
      },
      "source": [
        "t1 = time.time()\n",
        "clf.fit(x_train,y_train)\n",
        "print(time.time() - t1)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2890.0483858585358\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V70FZSHpsjq9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d7c079b-5bc6-4662-e2c3-4086e06161a9"
      },
      "source": [
        "y_pred = clf.predict(x_test)\n",
        "print(accuracy_score(y_test,y_pred))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8909066666666666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrybSMlbOgzE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "57a92eb2-7c26-4f35-9998-031f7efdfc77"
      },
      "source": [
        "con = metrics.confusion_matrix(y_test,y_pred)\n",
        "def cm2df(cm, labels):\n",
        "    df = pd.DataFrame()\n",
        "    # rows\n",
        "    for i, row_label in enumerate(labels):\n",
        "        rowdata={}\n",
        "        # columns\n",
        "        for j, col_label in enumerate(labels): \n",
        "            rowdata[col_label]=cm[i,j]\n",
        "        df = df.append(pd.DataFrame.from_dict({row_label:rowdata}, orient='index'))\n",
        "    return df[labels]\n",
        "labels=[i.split('.')[0].split('_')[-1] for i in dataset] ## list comprehension\n",
        "df_con = cm2df(con, labels)\n",
        "plt.figure(figsize=(15,10))\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "sns.heatmap(df_con, cmap=cmap, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted',size =15)\n",
        "plt.ylabel('Actual', size =15)\n",
        "plt.title('SVM',size=20)\n",
        "#plt.show()\n",
        "plt.savefig('SVM_CM')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2EAAAKTCAYAAACQIQCZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "neo8RaQJ67xW",
        "colab_type": "text"
      },
      "source": [
        "### Logistic Regression Implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EX-X42z-6-e2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=[]\n",
        "feature = []\n",
        "x = []\n",
        "label = []\n",
        "def load_data():\n",
        "    count = 0\n",
        "    for file in dataset:\n",
        "        file = \"/content/drive/My Drive/Artificial Intelligence/Data/\" + file\n",
        "        # Load arrays from .npy files\n",
        "        data = np.load(file)\n",
        "        # Casting the int numpy.ndarray to float numpy.ndarray\n",
        "        # Dividing it by 255 helps to get the RGB color\n",
        "        data = data.astype('float32') / 255.\n",
        "        # Extracting top 10000 rows and all columns\n",
        "        data = data[0:10000, :]\n",
        "        # Storing the extracted data from each file as feature\n",
        "        feature.append(data)\n",
        "        x = [count for _ in range(10000)]\n",
        "        count += 1\n",
        "        x = np.array(x).astype('float32')\n",
        "        # Reshaping the list to a single row \n",
        "        x = x.reshape(x.shape[0], 1)\n",
        "        # Loading teh created data as label\n",
        "        label.append(x)\n",
        "\n",
        "    return feature, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4trjvcRmvIJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features, labels = load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hsqgsFb27Dwj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = np.array(features).astype('float32')\n",
        "labels = np.array(labels).astype('float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDHTjfvKXlca",
        "colab_type": "code",
        "outputId": "611314c2-6cf3-49a1-ead7-271bffdd49f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "features.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 10000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcWReuYjXny2",
        "colab_type": "code",
        "outputId": "8dd8287d-e105-414d-d863-688d32835b9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "labels.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 10000, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oLV8qhZ7MU9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=features.reshape(features.shape[0]*features.shape[1],features.shape[2])\n",
        "labels=labels.reshape(labels.shape[0]*labels.shape[1],labels.shape[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7QggJWAXe8T",
        "colab_type": "code",
        "outputId": "4ba39156-51ca-490d-c4af-9d1b1c168459",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "features.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syysvcYCXiDe",
        "colab_type": "code",
        "outputId": "1ce88c88-31d3-4cc6-88e5-7849b2827284",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "labels.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150000, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDSJa_UR7Oo9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(features, labels, random_state=0,test_size=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_uDbJ3ktYlkv",
        "colab_type": "code",
        "outputId": "a5771230-f48a-4d0d-ca57-22642653d274",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(135000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2rbeT2yYxRi",
        "colab_type": "code",
        "outputId": "501a6044-871d-425f-ab79-4b52693dd55e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_test.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V12y17aC7QdJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = LogisticRegression(solver = 'lbfgs' , multi_class= 'multinomial', max_iter=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IL84_HCm7YDH",
        "colab_type": "code",
        "outputId": "4ad177c6-9dd6-43cb-e6be-f0ec6ced60d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "clf.fit(x_train,y_train)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='multinomial', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vH3iDr3K7kHy",
        "colab_type": "code",
        "outputId": "f394be72-9f91-4ebc-b4c1-4034e714f02c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_pred = clf.predict(x_test)\n",
        "print(accuracy_score(y_test,y_pred))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7562666666666666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXXZs5qkVy6J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f1687141-cfce-4373-85f1-77e6beb87b46"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEYZyWfsUXjl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e097c799-670f-4311-c230-911896871b08"
      },
      "source": [
        "y_pred.shape"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2rVk4cRiT7i",
        "colab_type": "text"
      },
      "source": [
        "## Create dataset to view Actual and Predicted values in a table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGfhpCAHUZ9I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.DataFrame({'Actual': temp_test[0], 'Predicted': y_pred})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeQ59R53U_92",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "0cb83d29-8fcb-4ddd-b1d7-b9b7cf324366"
      },
      "source": [
        "df"
      ],
      "execution_count": 44,
      "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14999</th>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Actual  Predicted\n",
              "0         1.0        1.0\n",
              "1        14.0       14.0\n",
              "2         7.0        7.0\n",
              "3         0.0        0.0\n",
              "4         8.0        8.0\n",
              "...       ...        ...\n",
              "14995    12.0       12.0\n",
              "14996     5.0        5.0\n",
              "14997     9.0        9.0\n",
              "14998     6.0        6.0\n",
              "14999    11.0       11.0\n",
              "\n",
              "[15000 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5fKKw6himpk",
        "colab_type": "text"
      },
      "source": [
        "## Create Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYXFx-jLZ32w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "con = metrics.confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ShwTPPEZjHDU",
        "colab_type": "text"
      },
      "source": [
        "#### Converting Confusion Matrix to Pandas Dataframe\n",
        "#### This is done to add labels to rows and columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IRFGJcBbFLR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cm2df(cm, labels):\n",
        "    df = pd.DataFrame()\n",
        "    # rows\n",
        "    for i, row_label in enumerate(labels):\n",
        "        rowdata={}\n",
        "        # columns\n",
        "        for j, col_label in enumerate(labels): \n",
        "            rowdata[col_label]=cm[i,j]\n",
        "        df = df.append(pd.DataFrame.from_dict({row_label:rowdata}, orient='index'))\n",
        "    return df[labels]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1k4j7yf9jqR0",
        "colab_type": "text"
      },
      "source": [
        "#### Extracting labels from the paths in dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qXx4X6-fell",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels=[i.split('.')[0].split('_')[-1] for i in dataset] ## list comprehension"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKmysobVgSde",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "47a0a927-cbed-43c4-b418-a85d65b4f82a"
      },
      "source": [
        "labels"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apple',\n",
              " 'bowtie',\n",
              " 'candle',\n",
              " 'door',\n",
              " 'envelope',\n",
              " 'fish',\n",
              " 'guitar',\n",
              " 'ice cream',\n",
              " 'lightning',\n",
              " 'moon',\n",
              " 'mountain',\n",
              " 'star',\n",
              " 'tent',\n",
              " 'toothbrush',\n",
              " 'wristwatch']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n44PgmWJgTiu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_con = cm2df(con, labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jTU66D4ogsRb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "34585d64-3827-4dfc-d6da-90a495623bc0"
      },
      "source": [
        "df_con"
      ],
      "execution_count": 84,
      "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>apple</th>\n",
              "      <th>bowtie</th>\n",
              "      <th>candle</th>\n",
              "      <th>door</th>\n",
              "      <th>envelope</th>\n",
              "      <th>fish</th>\n",
              "      <th>guitar</th>\n",
              "      <th>ice cream</th>\n",
              "      <th>lightning</th>\n",
              "      <th>moon</th>\n",
              "      <th>mountain</th>\n",
              "      <th>star</th>\n",
              "      <th>tent</th>\n",
              "      <th>toothbrush</th>\n",
              "      <th>wristwatch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>apple</th>\n",
              "      <td>888</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>31</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bowtie</th>\n",
              "      <td>1</td>\n",
              "      <td>763</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>67</td>\n",
              "      <td>19</td>\n",
              "      <td>9</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>25</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>candle</th>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>845</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>10</td>\n",
              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>door</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>866</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>envelope</th>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>868</td>\n",
              "      <td>38</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fish</th>\n",
              "      <td>2</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>729</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
              "      <td>29</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>guitar</th>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>47</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>758</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ice cream</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>822</td>\n",
              "      <td>54</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightning</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>47</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "      <td>87</td>\n",
              "      <td>608</td>\n",
              "      <td>24</td>\n",
              "      <td>28</td>\n",
              "      <td>49</td>\n",
              "      <td>24</td>\n",
              "      <td>64</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moon</th>\n",
              "      <td>36</td>\n",
              "      <td>11</td>\n",
              "      <td>16</td>\n",
              "      <td>33</td>\n",
              "      <td>13</td>\n",
              "      <td>39</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>46</td>\n",
              "      <td>662</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>29</td>\n",
              "      <td>24</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mountain</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>781</td>\n",
              "      <td>24</td>\n",
              "      <td>77</td>\n",
              "      <td>37</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>star</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>25</td>\n",
              "      <td>18</td>\n",
              "      <td>43</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>782</td>\n",
              "      <td>47</td>\n",
              "      <td>16</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tent</th>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "      <td>142</td>\n",
              "      <td>33</td>\n",
              "      <td>734</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>toothbrush</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>20</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>38</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>47</td>\n",
              "      <td>2</td>\n",
              "      <td>24</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>755</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wristwatch</th>\n",
              "      <td>39</td>\n",
              "      <td>24</td>\n",
              "      <td>22</td>\n",
              "      <td>6</td>\n",
              "      <td>17</td>\n",
              "      <td>31</td>\n",
              "      <td>89</td>\n",
              "      <td>27</td>\n",
              "      <td>92</td>\n",
              "      <td>81</td>\n",
              "      <td>37</td>\n",
              "      <td>44</td>\n",
              "      <td>14</td>\n",
              "      <td>56</td>\n",
              "      <td>483</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            apple  bowtie  candle  door  ...  star  tent  toothbrush  wristwatch\n",
              "apple         888       2      13     6  ...     8    10           9          20\n",
              "bowtie          1     763       1     2  ...     9     8          25          17\n",
              "candle         18       0     845     1  ...     5     5           6          12\n",
              "door            2       2       7   866  ...     4     8          12           8\n",
              "envelope        2      28       0    12  ...    11     7           1          16\n",
              "fish            2      71       1     2  ...    21    22          29          26\n",
              "guitar          2      18      47     3  ...    10     8          11          70\n",
              "ice cream       4       0      14     9  ...    10     1          17           9\n",
              "lightning       2       5      47    28  ...    49    24          64          19\n",
              "moon           36      11      16    33  ...    15    29          24          37\n",
              "mountain        4       5      12     0  ...    24    77          37          16\n",
              "star            4       1       7     5  ...   782    47          16          41\n",
              "tent            3       7      18    11  ...    33   734          19          15\n",
              "toothbrush      1       9      20    11  ...     6    10         755          47\n",
              "wristwatch     39      24      22     6  ...    44    14          56         483\n",
              "\n",
              "[15 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNXovlIpgtI0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "58222241-5801-48a9-cb46-68482c50831c"
      },
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "sns.heatmap(df_con, cmap=cmap, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted',size =15)\n",
        "plt.ylabel('Actual', size =15)\n",
        "plt.title('Logistic Regression')\n",
        "#plt.show()\n",
        "plt.savefig('Logistic_CM')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-a29dcdbbd4a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\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[0m\u001b[1;32m      2\u001b[0m \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiverging_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m220\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_con\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mannot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'd'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\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'Predicted'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m15\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'Actual'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDCHqC4dgx6w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}