ECG_Classification.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ECG_Classification.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "BN2fgQ4ortBp",
        "colab_type": "code",
        "outputId": "9ca98c14-4ee2-4f03-a280-64cfa73c9d08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "cell_type": "code",
      "source": [
        "pip install biosppy"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: biosppy in /usr/local/lib/python3.6/dist-packages (0.6.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from biosppy) (1.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from biosppy) (1.11.0)\n",
            "Requirement already satisfied: shortuuid in /usr/local/lib/python3.6/dist-packages (from biosppy) (0.5.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from biosppy) (0.20.3)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from biosppy) (2.8.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from biosppy) (3.0.3)\n",
            "Requirement already satisfied: bidict in /usr/local/lib/python3.6/dist-packages (from biosppy) (0.18.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from biosppy) (1.14.6)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->biosppy) (1.0.1)\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->biosppy) (2.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->biosppy) (2.5.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->biosppy) (0.10.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->biosppy) (40.8.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r7-63SJMRGt2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import normalize\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import csv\n",
        "import itertools\n",
        "import collections\n",
        "import glob\n",
        "import pywt\n",
        "import math\n",
        "import biosppy\n",
        "import pandas as pd\n",
        "from scipy import signal\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sXjWLcQbRGt6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Plot Setting"
      ]
    },
    {
      "metadata": {
        "id": "N0vaUmV4RGt6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (30,6)\n",
        "plt.rcParams['lines.linewidth'] = 1\n",
        "plt.rcParams['lines.color'] = 'b'\n",
        "plt.rcParams['axes.grid'] = True "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wLGHQ3-RRGt9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Functions"
      ]
    },
    {
      "metadata": {
        "id": "lgX9cGRwRGt-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Train Inputs\n",
        "def get_train_inputs():\n",
        "    x = tf.constant(X_train)\n",
        "    y = tf.constant(y_train)\n",
        "    return x, y\n",
        "\n",
        "# Test Inputs\n",
        "def get_test_inputs():\n",
        "    x = tf.constant(X_test)\n",
        "    y = tf.constant(y_test)\n",
        "    return x, y\n",
        "\n",
        "# Eval data\n",
        "def get_eval_data():\n",
        "    return tf.constant(X_test)\n",
        "\n",
        "# Plot matrix\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    plt.figure()\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        #cm[i, j] = 0 if np.isnan(cm[i, j]) else cm[i, j]\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eDBzZ_-rRGuA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Comment\n",
        "\n",
        "### Arrhythmia Type List\n",
        "- Normal = 'N', 'L', 'R', 'e', 'j'\n",
        "- SVEB = 'A', 'a', 'J', 'S'\n",
        "- VEB = 'V', 'E'\n",
        "- F = 'F'\n",
        "- Q = '/', 'f'\n",
        "\n",
        "### Arrhythmia Annotation\n",
        "- N = Normal beat\n",
        "- L = Left bundle branch block beat\n",
        "- R = Right bundle branch block beat\n",
        "- e = Atrial escape beat\n",
        "- j = Nodal (junctional) escape beat\n",
        "- A = Atrial premature beat\n",
        "- a = Aberrated atrial premature beat\n",
        "- J = Nodal (junctional) premature beat\n",
        "- S = Supraventricular premature or ectopic beat (atrial or nodal)\n",
        "- V = Premature ventricular contraction\n",
        "- E = Ventricular escape beat\n",
        "- F = Fusion of ventricular and normal beat\n",
        "- / = Paced beat\n",
        "- f = Fusion of paced and normal beat\n",
        "\n",
        "### The Number of Each Arrhythmia Type\n",
        "- #### N = 10001\n",
        "- #### L = 8075\n",
        "- #### R = 7259\n",
        "- e = 16\n",
        "- j = 229\n",
        "- #### A = 2546\n",
        "- a = 150\n",
        "- J = 83\n",
        "- S = 2\n",
        "- #### V = 7130\n",
        "- E = 106\n",
        "- F = 803\n",
        "- #### / = 7028\n",
        "- f = 982"
      ]
    },
    {
      "metadata": {
        "id": "4C1duvLERGuB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Read Dataset"
      ]
    },
    {
      "metadata": {
        "id": "rpedHnAyRNV-",
        "colab_type": "code",
        "outputId": "d1838a7d-ed28-4a0b-cd22-cf0c52a08ab7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "W3VlsPTlRGuC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Variables\n",
        "\n",
        "path = \"gdrive/My Drive/Graduate/Courses/EE8610/mitDB/\"\n",
        "window_size = 160\n",
        "maximum_counting = 10000\n",
        "\n",
        "classes = ['N', 'L', 'R', 'A', 'V', '/']\n",
        "n_classes = len(classes)\n",
        "count_classes = [0]*n_classes\n",
        "\n",
        "X = list()\n",
        "y = list()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-ou5qQQktwGR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Processing"
      ]
    },
    {
      "metadata": {
        "id": "gOYn9Vo3RGuE",
        "colab_type": "code",
        "outputId": "8e6f2176-e39d-418c-b7eb-fad273a3fef3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        }
      },
      "cell_type": "code",
      "source": [
        "# Read files\n",
        "# paths = glob.glob('gdrive/My Drive/Graduate/Courses/EE8610/mitDB/*.csv')\n",
        "filenames = next(os.walk(path))[2]\n",
        "\n",
        "filenames.sort()\n",
        "\n",
        "# Split and save .csv , .txt \n",
        "records = list()\n",
        "annotations = list()\n",
        "\n",
        "for f in filenames:\n",
        "    filename, file_extension = os.path.splitext(f)\n",
        "    # *.csv\n",
        "    if(file_extension == '.csv'):\n",
        "        records.append(path + filename + file_extension)\n",
        "\n",
        "    # *.txt\n",
        "    else:\n",
        "        annotations.append(path + filename + file_extension)  \n",
        "# Records\n",
        "for r in range(0,len(records)):\n",
        "    signals = []\n",
        "\n",
        "    with open(records[r], 'rt') as csvfile:\n",
        "        spamreader = csv.reader(csvfile, delimiter=',', quotechar='|') # read CSV file\\\n",
        "        row_index = -1\n",
        "        for row in spamreader:\n",
        "            if(row_index >= 0):\n",
        "                signals.insert(row_index, int(row[1]))\n",
        "            row_index += 1  \n",
        "    \n",
        "    \n",
        "    \n",
        "    ###### DWT w/ thresholding #####\n",
        "    w = pywt.Wavelet('coif5')\n",
        "    nl = 6\n",
        "    coeffs = pywt.wavedec(signals, w, level=nl)\n",
        "    \n",
        "    # Apply the threshold.  Pseudocode here.\n",
        "    new_coeffs = []\n",
        "    for nc in coeffs:\n",
        "      threshVal = (np.std(signals)/75)*(math.sqrt(2*math.log(len(signals))))\n",
        "      tcoeff = pywt.threshold(nc, threshVal , mode='soft')\n",
        "      new_coeffs.append(tcoeff)\n",
        "    \n",
        "    # IDWT\n",
        "    renc = pywt.waverec(new_coeffs, w);  \n",
        "        \n",
        "    if r is 0:\n",
        "        # Plot first patient's signal\n",
        "  #         plt.subplot(2,1,1)\n",
        "        plt.title(\"Sample Wave\")\n",
        "        plt.plot(signals[:1000])\n",
        "        plt.show()\n",
        "\n",
        "  #         plt.subplot(2,1,2)\n",
        "        plt.title(\"Denoised Sample Wave\")\n",
        "        plt.plot(renc[:1000])\n",
        "        plt.show()\n",
        "\n",
        "    # Read anotations: R position and Arrhythmia class\n",
        "    with open(annotations[r], 'r') as fileID:\n",
        "        data = fileID.readlines()\n",
        "        beat = list()\n",
        "\n",
        "        for d in range(1, len(data)): # 0 index is Chart Head\n",
        "            splitted = data[d].split(' ')\n",
        "            splitted = filter(None, splitted)\n",
        "            next(splitted) # Time... Clipping\n",
        "            pos = int(next(splitted)) # Sample ID\n",
        "            arrhythmia_type = next(splitted) # Type\n",
        "\n",
        "            if(arrhythmia_type in classes):\n",
        "                arrhythmia_index = classes.index(arrhythmia_type)\n",
        "                if count_classes[arrhythmia_index] > maximum_counting: # avoid overfitting\n",
        "                    pass\n",
        "                else:\n",
        "                    count_classes[arrhythmia_index] += 1\n",
        "                    if(window_size < pos and pos < (len(renc) - window_size)):\n",
        "                        beat = signal.resample(renc[pos-window_size+1:pos+window_size],130)\n",
        "                        X.append(beat)\n",
        "                        y.append(arrhythmia_index)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3rpqplS9oRQAA7K3d9j4qtQAATZblO7fQjWOCWvCHoBbgWeby3dsPUsoLAOiQ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\nGQTGRAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 2160x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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29tF+EADQN9PnkYRK01xFQWALANBOPs0mzkNmphaaRFALaIFpDzlxZKjUAgD0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            "text/plain": [
              "<Figure size 2160x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "K39h1rjGRGuJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Training & Test"
      ]
    },
    {
      "metadata": {
        "id": "_TMyXH0FRGuK",
        "colab_type": "code",
        "outputId": "6447f484-e049-4396-fe6f-b0daea6765b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Train Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n",
        "\n",
        "print(\"X_train : \", len(X_train))\n",
        "print(\"X_test  : \", len(X_test))\n",
        "print(\"y_train : \", collections.Counter(y_train))\n",
        "print(\"y_test  : \", collections.Counter(y_test))\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train :  28154\n",
            "X_test  :  13867\n",
            "y_train :  Counter({0: 6653, 1: 5434, 2: 4904, 5: 4721, 4: 4720, 3: 1722})\n",
            "y_test  :  Counter({0: 3344, 1: 2637, 4: 2409, 2: 2351, 5: 2302, 3: 824})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "N3jE3QkhRGuO",
        "colab_type": "code",
        "outputId": "2b104967-ae40-46bb-b74e-d0301f92a5e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "print(np.shape(X_train[0]))\n",
        "print(np.shape(y_train))\n",
        "print(np.shape(X_test))\n",
        "print(np.shape(y_test))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(130,)\n",
            "(28154,)\n",
            "(13867, 130)\n",
            "(13867,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CFBPcR8PP6yl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "c26ea6c0-2a3f-4035-f2c8-cf2683471986"
      },
      "cell_type": "code",
      "source": [
        "model_m = tf.keras.models.Sequential()\n",
        "model_m.add(tf.keras.layers.Conv1D(18, 7, activation='sigmoid', input_shape=(130, 1)))\n",
        "model_m.add(tf.keras.layers.MaxPooling1D(2))\n",
        "model_m.add(tf.keras.layers.Conv1D(18, 7, activation='sigmoid'))\n",
        "model_m.add(tf.keras.layers.MaxPooling1D(2))\n",
        "\n",
        "model_m.add(tf.keras.layers.GlobalAveragePooling1D())\n",
        "model_m.add(tf.keras.layers.Dropout(0.5))\n",
        "model_m.add(tf.keras.layers.Dense(n_classes, activation='softmax'))\n",
        "\n",
        "print(model_m.summary())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py:143: 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",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d (Conv1D)              (None, 124, 18)           144       \n",
            "_________________________________________________________________\n",
            "max_pooling1d (MaxPooling1D) (None, 62, 18)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 56, 18)            2286      \n",
            "_________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1 (None, 28, 18)            0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d (Gl (None, 18)                0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 18)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 6)                 114       \n",
            "=================================================================\n",
            "Total params: 2,544\n",
            "Trainable params: 2,544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sDXX5o17Z5wE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "1051d16d-009b-4673-fce1-a0a431db18ff"
      },
      "cell_type": "code",
      "source": [
        "model_m.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "BATCH_SIZE = 100\n",
        "EPOCHS = 50\n",
        "\n",
        "X_train = np.expand_dims(X_train, axis=2)\n",
        "# X_train = np.reshape(X_train, (np.shape(X_train)[0], np.shape(X_train)[1], 1))\n",
        "y_binary = to_categorical(y_train,num_classes=n_classes)\n",
        "\n",
        "display(np.shape(X_train))\n",
        "history = model_m.fit(X_train,y_binary)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(28154, 130, 1)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "28154/28154 [==============================] - 10s 346us/sample - loss: 1.7503 - acc: 0.2207\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lFy50kNI3Kr5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "e1f560e5-9a0b-4681-f3e9-cf74bf3654b7"
      },
      "cell_type": "code",
      "source": [
        "print(history.history.keys())\n",
        "print(history.history['loss'])\n",
        "print(history.history['acc'])\n",
        "print(history.history['val_loss'])\n",
        "print(history.history['val_acc'])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'acc'])\n",
            "[1.7503416190781174]\n",
            "[0.22071464]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-09d440f714a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\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[1;32m      3\u001b[0m \u001b[0mprint\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'acc'\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----> 4\u001b[0;31m \u001b[0mprint\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'val_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[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\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'val_acc'\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;31mKeyError\u001b[0m: 'val_loss'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "l6cBAtYQzpqo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_test = np.reshape(X_test, (np.shape(X_test)[0], np.shape(X_test)[1], 1))\n",
        "\n",
        "\n",
        "# accuracy_score = model_m.evaluate(X_test,y_test,batch_size=10)[1]\n",
        "predictions = model_m.predict(X_test)\n",
        "display(predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-6s3HxJARGuW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "\n",
        "# Results"
      ]
    },
    {
      "metadata": {
        "id": "1j9bL6mARGuX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "    \n",
        "cm = confusion_matrix(y_test, predictions.argmax(axis=1))\n",
        "\n",
        "print(\"\\nTest Accuracy: {0:f}%\\n\".format(accuracy_score*100))\n",
        "plot_confusion_matrix(cm, classes, normalize=True)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}