DL_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DL_1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPq6H-rcS7Es",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "689c548d-cfb7-4b7c-a818-4d3a47f7b4ac"
      },
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"\n",
        "@author: Shaon Bhatta Shuvo\n",
        "\"\"\"\n",
        "#Importing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn.datasets as skd\n",
        "from sklearn import preprocessing\n",
        "import tensorflow.keras as tfk\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from mlxtend.plotting import plot_decision_regions\n",
        "\n",
        "\n",
        "#Generating synthetic linear dataset\n",
        "X,y = skd.make_regression(n_samples=100, n_features=1, n_targets=1, bias=0.5, noise=5.5, random_state=42)\n",
        "\n",
        "#Reading dataset from csv file\n",
        "#dataframe = pd.read_csv(\"datasetName.csv\", header=None) #header=0 if first row/line is the header of the dataset\n",
        "#dataset = dataframe.values\n",
        "#Split into input (X) and output (Y) variables,\n",
        "#Lets say the dataset have n number of columns where the last column is the target value\n",
        "#therefore we have first n-2 columns as independent variables (inputs) and the (n-1)th column is the as output since index starts from 0\n",
        "#X = dataset[:,0:n-1] # when range is [start:end] value is read from start to end-1 index\n",
        "#y = dataset[:,n-1] # here it is not range is specific index\n",
        "\n",
        "# Visulalizing the synthetic dataset\n",
        "print(\"\\nVisualizing the Synthetic Dataset\")\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.scatter(X,y,color='red',edgecolors=\"green\")\n",
        "plt.title(\"Synthetic Dataset\")\n",
        "plt.xlabel(\"X\", fontsize=20)\n",
        "plt.ylabel(\"y\",rotation = 0, fontsize = 20)\n",
        "plt.show()\n",
        "\n",
        "#reshaping the y values into 2D matrix of 1 column\n",
        "y = y.reshape(-1,1) #if y is not an array then use, np.asanyarray(y).reshape(-1,1)\n",
        "# Equivalent code y = np.reshape(y,(-1,1))\n",
        "\n",
        "#Feature Scaling (Standardization : needs 2D array as input)\n",
        "#Here your data Z is rescaled such that μ = 0 and 𝛔 = 1, and is done through this formula: z= (Xi - μ)/𝛔\n",
        "#sc = preprocessing.StandardScaler()\n",
        "#X = sc.fit_transform(X)\n",
        "#y = sc.fit_transform(y)\n",
        "\n",
        "# Visulalizing the synthetic dataset after standardization\n",
        "#print(\"\\nVisualizing the Synthetic Dataset after Standardization\")\n",
        "#plt.style.use(\"ggplot\")\n",
        "#plt.scatter(X,y,color='red',edgecolors=\"green\")\n",
        "#plt.title(\"Synthetic Dataset\")\n",
        "#plt.xlabel(\"X\", fontsize=20)\n",
        "#plt.ylabel(\"y\",rotation = 0, fontsize = 20)\n",
        "#plt.show()\n",
        "\n",
        "#Spliting the dataset into Training and Testset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
        "\n",
        "#Creating the deep learning model with hyperbolic tangent activation function\n",
        "\n",
        "#A shape (5,2,8) means an array or tensor with 3 dimensions, containing 5 elements in the first dimension,\n",
        "#2 in the second and 8 in the third, totaling 30*4*10 = 1200 elements or numbers.\n",
        "#What flows between layers are tensors. Tensors can be seen as matrices, with shapes.\n",
        "#In Keras, the input layer itself is not a layer, but a tensor. It's the starting tensor you send to the first\n",
        "#hidden layer. This tensor must have the same shape as your training data.\n",
        "#In our example input data is one dimentional and also has only one element (column).\n",
        "model = tfk.Sequential([\n",
        "        tfk.Input(shape = (1,)),\n",
        "        tfk.layers.Dense(50, activation='tanh'),  #first hidden layer\n",
        "        tfk.layers.Dense(100, activation='tanh'), #second hidden layer\n",
        "        tfk.layers.Dense(1,) #activation linear by default, also can add: activation ='linear'\n",
        "        ])\n",
        "#model Looks like:  1 input -> [50 units in layer1] ->[100 units in layer2] -> 1 output\n",
        "\n",
        "#Compiling the model with Stochatstic Gradient Discent optimizer and MSE as the loss function\n",
        "model.compile(optimizer=tfk.optimizers.SGD(lr=0.001), loss='mean_squared_error')\n",
        "#Model's Summary\n",
        "model.summary()\n",
        "#Training the model\n",
        "training = model.fit(X_train,y_train, epochs = 50, batch_size =10)\n",
        "#Testing the models performance\n",
        "y_pred = model.predict(X_test)\n",
        "mse = metrics.mean_squared_error(y_test,y_pred)\n",
        "print(\"Testset Result: \\n---------------\")\n",
        "print(\"MSE: \", mse)\n",
        "\n",
        "#Visualizing training loss values\n",
        "plt.plot(training.history['loss'], label='Training Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "#Reshapping the matrix into array to pass the value into np.linspace() for 2D visualizaiton.\n",
        "x_train_arr = np.asarray(X_train).reshape(-1)\n",
        "y_train_arr = np.asarray(y_train).reshape(-1)\n",
        "x_test_arr = np.asarray(X_test).reshape(-1)\n",
        "y_test_arr = np.asarray(y_test).reshape(-1)\n",
        "#Creating evenly spaced values for smooth visulatization\n",
        "xp_train = np.linspace(x_train_arr.min(), x_train_arr.max())\n",
        "xp_test = np.linspace(x_test_arr.min(), x_test_arr.max())\n",
        "\n",
        "#Visulalizing training and testing plots.\n",
        "fig, ax = plt.subplots (nrows=1, ncols=2, figsize=(8, 4))\n",
        "ax[0].scatter (X_train, y_train, color='red', edgecolors='green', label='Synthetic Data Points')\n",
        "ax[0].plot(xp_train,model.predict(xp_train.reshape(-1)),color='blue', label='Regression Line')\n",
        "ax[0].set_title(\"NN Regression Plot (Training Set)\")\n",
        "ax[0].set_xlabel(\"X_train\", fontsize=20)\n",
        "ax[0].set_ylabel(\"y_train\", fontsize = 20)\n",
        "ax[0].legend()\n",
        "ax[1].scatter(X_test,y_test,color='red', edgecolors='green', label='Synthetic Data Points')\n",
        "ax[1].plot(xp_test,model.predict(xp_test.reshape(-1)),color='blue',label='Regression Line')\n",
        "ax[1].set_title(\"NN Regression Plot (Testing Set)\")\n",
        "ax[1].set_xlabel(\"X_test\", fontsize=20)\n",
        "ax[1].set_ylabel(\"y_test\", fontsize = 20)\n",
        "ax[1].legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "#Creating Linear Regression Model\n",
        "lr_model = LinearRegression()\n",
        "#Training the model\n",
        "lr_model.fit(X_train,y_train)\n",
        "#Testing the model's performance\n",
        "y_pred_lr = lr_model.predict(X_test)\n",
        "mse = metrics.mean_squared_error(y_test,y_pred_lr)\n",
        "print(\"MSE: \", mse)\n",
        "\n",
        "#Visualizing the Training and Testset performance of Linear Regression\n",
        "fig, ax = plt.subplots (nrows=1, ncols=2, figsize=(8, 4))\n",
        "ax[0].scatter (X_train, y_train, color='red', edgecolors='green', label='Synthetic Data Points')\n",
        "ax[0].plot(X_train,lr_model.predict(X_train),color='blue', label='Regression Line')\n",
        "ax[0].set_title(\"Linear Regression Plot (Training Set)\")\n",
        "ax[0].set_xlabel(\"X_train\", fontsize=20)\n",
        "ax[0].set_ylabel(\"y_train\", fontsize = 20)\n",
        "ax[0].legend()\n",
        "ax[1].scatter(X_test,y_test,color='red', edgecolors='green', label='Synthetic Data Points')\n",
        "ax[1].plot(X_test,lr_model.predict(X_test),color='blue',label='Regression Line')\n",
        "ax[1].set_title(\"Linear Regression Plot (Testing Set)\")\n",
        "ax[1].set_xlabel(\"X_test\", fontsize=20)\n",
        "ax[1].set_ylabel(\"y_test\", fontsize = 20)\n",
        "ax[1].legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "#Generating synthetic non-linear data to solve classification problem\n",
        "X,y = skd.make_circles(n_samples=100, shuffle=False, noise=None, random_state=None, factor=0.5)\n",
        "\n",
        "#Following classes will not be shapped as circle, parameters can be changed to make it more non-linear\n",
        "#X, y = skd.make_classification(n_samples=100, n_features=2, n_redundant=0, n_informative=2,\n",
        "#                             n_clusters_per_class=1,class_sep=0.5,flip_y=0.2, random_state=1,shuffle=False)\n",
        "\n",
        "#Finding and counting unique elements.\n",
        "unique_elements, counts_elements = np.unique(y, return_counts=True)\n",
        "print(\"Frequency of unique class of the dataset:\")\n",
        "print(np.asarray((unique_elements, counts_elements)))\n",
        "\n",
        "#Visualizing the synthetic dataset of Class 1 and Class -1:\n",
        "plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], 'g^', label='Class: 0')\n",
        "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], 'ro' , label=\"Class: 1\")\n",
        "plt.title(\"Visualizing the synthetic dataset of class 1 and 0\")\n",
        "plt.xlabel(\"X1\")\n",
        "plt.ylabel(\"X2\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "#Spliting the dataset into train and testset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
        "\n",
        "print(\"Frequency of unique class of elements in the test set:\")\n",
        "unique_elements_test, count_elements_test=np.unique(y_test, return_counts=True)\n",
        "print(unique_elements_test, count_elements_test)\n",
        "\n",
        "#Creating validation set  by copying last 10 elements from the training set\n",
        "X_val = X_train[70:]\n",
        "y_val = y_train[70:]\n",
        "#Removing the validation set (last 10 elements) from training set\n",
        "X_train = X_train[:70]\n",
        "y_train = y_train[:70]\n",
        "\n",
        "#Creating the deep learning model (Lets try a differnt apprach, can be used same approach shown earlier)\n",
        "model = tfk.Sequential()\n",
        "model.add(tfk.layers.Dense(50,input_shape=(2,), activation='relu')) #First Hidden Layer\n",
        "model.add(tfk.layers.Dense(100, activation='relu')) #Second  Hidden Layer\n",
        "model.add(tfk.layers.Dense(1, activation='sigmoid')) #Output Layer\n",
        "\n",
        "#Model can be crated using following approach as well\n",
        "#input_units = tfk.Input(shape=(2,))\n",
        "#hidden_layer1 = tfk.layers.Dense(100, activation ='relu')((input_units))\n",
        "#hidden_layer2 = tfk.layers.Dense(50, activation ='relu')(hidden_layer1)\n",
        "#prediction = tfk.layers.Dense(1, activation ='sigmoid')(hidden_layer2)\n",
        "#model = tfk.models.Model(inputs=input_units, outputs=prediction)\n",
        "\n",
        "#model Looks like:  2 input -> [50 units in layer1] ->[100 units in layer2] -> 1 output\n",
        "\n",
        "# Compiling the model for binary classification # Use loss = categorical_crossentropy for multiclass prediction.\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "#Model's Summary\n",
        "model.summary()\n",
        "\n",
        "#Training the model\n",
        "training = model.fit(X_train,y_train, epochs = 50, batch_size =10, validation_data =(X_val,y_val))\n",
        "\n",
        "#Visulaizing the Training and Validation Sets Loss and Accuracy\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8,4))\n",
        "#Plot training and validation accuracy values\n",
        "#axes[0].set_ylim(0,1) #if we want to limit axis in certain range\n",
        "axes[0].plot(training.history['accuracy'], label='Train')\n",
        "axes[0].plot(training.history['val_accuracy'], label='Validation')\n",
        "axes[0].set_title('Model Accuracy')\n",
        "axes[0].set_xlabel('Epoch')\n",
        "axes[0].set_ylabel('Accuracy')\n",
        "axes[0].legend()\n",
        "#Plot training and validation loss values\n",
        "#axes[1].set_ylim(0,1)\n",
        "axes[1].plot(training.history['loss'], label='Train')\n",
        "axes[1].plot(training.history['val_loss'], label='Validation')\n",
        "axes[1].set_title('Model Loss')\n",
        "axes[1].set_xlabel('Epoch')\n",
        "axes[1].set_ylabel('Loss')\n",
        "axes[1].legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Evaluating the performance on the Test set\n",
        "test_loss_accuracy = model.evaluate(X_test, y_test, verbose=2)\n",
        "\n",
        "# Visualising the Training and Test set plot decision area\n",
        "fig, axes = plt.subplots (nrows=1, ncols=2, figsize=(8, 4))\n",
        "fig1 = plot_decision_regions(X_train, y_train, clf=model, ax=axes[0], legend=0)\n",
        "fig2 = plot_decision_regions(X_test, y_test, clf=model, ax=axes[1], legend=0)\n",
        "axes[0].set_title('NN Plot Decision Region (Training set)')\n",
        "axes[0].set_xlabel('x1')\n",
        "axes[0].set_ylabel('x2')\n",
        "axes[1].set_title('NN Plot Decision Region (Test set)')\n",
        "axes[1].set_xlabel('x1')\n",
        "axes[1].set_ylabel('x2')\n",
        "\n",
        "handles, labels = fig1.get_legend_handles_labels()\n",
        "fig1.legend(handles,\n",
        "          ['class 0', 'class 1'])\n",
        "fig2.legend(handles,\n",
        "          ['class 0', 'class 1'])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = model.predict(X_test)\n",
        "# Converting the predicted result into desired class level\n",
        "# Singmoid produce the output between 0 and 1. Therefore, the decision boundary for sigmoid is 0.5\n",
        "for i in range(0, len(y_pred)):\n",
        "    if(y_pred[i]>0.5):\n",
        "        y_pred[i] = 1\n",
        "    else:\n",
        "        y_pred[i] = 0\n",
        "\n",
        "# Generating confusion matrics, details classification report\n",
        "cm = metrics.confusion_matrix(y_test,y_pred)\n",
        "print(\"Confusion Matrix for Neural Network Model:\\n \",cm)\n",
        "print( \"{0}\".format(metrics.classification_report(y_test,y_pred)))\n",
        "# Generating accuracy in %,\n",
        "# Similary precision_score and recall_score can be used to generate precision and recall seperately\n",
        "accuracy_test = metrics.accuracy_score(y_test,y_pred)*100\n",
        "print('Accuracy:%.2f' % accuracy_test,\"%\")\n",
        "\n",
        "# Using non-linear svm calssifier , use kernel=linear for linear classifier.\n",
        "from sklearn import svm\n",
        "classifier = svm.SVC(kernel='rbf') #rbf = 'radial basis function' for non-linear classification\n",
        "classifier.fit(X_train,y_train)\n",
        "\n",
        "# Predicting Teset set result\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Generating confusion matrics, details classification report\n",
        "cm = metrics.confusion_matrix(y_test,y_pred)\n",
        "print(\"Confusion Matrix for SVM Clssifer:\\n \",cm)\n",
        "print( \"{0}\".format(metrics.classification_report(y_test,y_pred)))\n",
        "# Generating accuracy in %,\n",
        "# Similary precision_score and recall_score can be used to generate precision and recall seperately\n",
        "accuracy_test = metrics.accuracy_score(y_test,y_pred)*100\n",
        "print('Accuracy:%.2f' % accuracy_test,\"%\")\n",
        "\n",
        "# Visualising the Training and Test set plot decision area\n",
        "fig, axes = plt.subplots (nrows=1, ncols=2, figsize=(8, 4))\n",
        "fig1 = plot_decision_regions(X_train, y_train, clf=classifier, ax=axes[0], legend=0)\n",
        "fig2 = plot_decision_regions(X_test, y_test, clf=classifier, ax=axes[1], legend=0)\n",
        "axes[0].set_title('SVM Plot Decision Region (Training set)')\n",
        "axes[0].set_xlabel('x1')\n",
        "axes[0].set_ylabel('x2')\n",
        "axes[1].set_title('SVM Plot Decision Region (Test set)')\n",
        "axes[1].set_xlabel('x1')\n",
        "axes[1].set_ylabel('x2')\n",
        "handles, labels = fig1.get_legend_handles_labels()\n",
        "fig1.legend(handles,\n",
        "          ['class 0', 'class 1'])\n",
        "fig2.legend(handles,\n",
        "          ['class 0', 'class 1'])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Visualizing the Synthetic Dataset\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 50)                100       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 100)               5100      \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 5,301\n",
            "Trainable params: 5,301\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 1392.2367\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 932.0399\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 409.5587\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 215.0930\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 169.8694\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 0s 1ms/step - loss: 149.8187\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 131.1446\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 120.3923\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 0s 1ms/step - loss: 110.9230\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 102.3944\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 96.9132\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 91.4578\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 81.0621\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 79.2439\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 70.7905\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 72.0974\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 69.9414\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 63.4767\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 62.1073\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 60.1652\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 60.1665\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 60.7055\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 56.6949\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 56.4988\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 69.2097\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 73.4341\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 58.3874\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 55.2435\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 59.7323\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 54.2761\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 54.7991\n",
            "Epoch 32/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 56.1136\n",
            "Epoch 33/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 52.2566\n",
            "Epoch 34/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 58.9960\n",
            "Epoch 35/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 64.5148\n",
            "Epoch 36/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 46.7199\n",
            "Epoch 37/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 47.9841\n",
            "Epoch 38/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 66.6056\n",
            "Epoch 39/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 49.4953\n",
            "Epoch 40/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 48.2625\n",
            "Epoch 41/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 57.2668\n",
            "Epoch 42/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 63.0883\n",
            "Epoch 43/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 51.4222\n",
            "Epoch 44/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 46.9850\n",
            "Epoch 45/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 49.3981\n",
            "Epoch 46/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 60.7584\n",
            "Epoch 47/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 52.3891\n",
            "Epoch 48/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 44.9207\n",
            "Epoch 49/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 69.3773\n",
            "Epoch 50/50\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 47.3162\n",
            "Testset Result: \n",
            "---------------\n",
            "MSE:  120.27430004450682\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MSE:  43.23366621507749\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Frequency of unique class of the dataset:\n",
            "[[ 0  1]\n",
            " [50 50]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Frequency of unique class of elements in the test set:\n",
            "[0 1] [10 10]\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_6 (Dense)              (None, 50)                150       \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 100)               5100      \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 5,351\n",
            "Trainable params: 5,351\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "7/7 [==============================] - 0s 50ms/step - loss: 0.7123 - accuracy: 0.4857 - val_loss: 0.6919 - val_accuracy: 0.6000\n",
            "Epoch 2/50\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.6908 - accuracy: 0.5429 - val_loss: 0.6764 - val_accuracy: 0.7000\n",
            "Epoch 3/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.6780 - accuracy: 0.5000 - val_loss: 0.6629 - val_accuracy: 0.5000\n",
            "Epoch 4/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.6663 - accuracy: 0.5000 - val_loss: 0.6514 - val_accuracy: 0.5000\n",
            "Epoch 5/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.6566 - accuracy: 0.5000 - val_loss: 0.6428 - val_accuracy: 0.5000\n",
            "Epoch 6/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.6471 - accuracy: 0.5000 - val_loss: 0.6355 - val_accuracy: 0.5000\n",
            "Epoch 7/50\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.6393 - accuracy: 0.5000 - val_loss: 0.6279 - val_accuracy: 0.5000\n",
            "Epoch 8/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.6308 - accuracy: 0.5000 - val_loss: 0.6201 - val_accuracy: 0.5000\n",
            "Epoch 9/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.6229 - accuracy: 0.5000 - val_loss: 0.6115 - val_accuracy: 0.5000\n",
            "Epoch 10/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.6137 - accuracy: 0.5000 - val_loss: 0.6021 - val_accuracy: 0.5000\n",
            "Epoch 11/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.6023 - accuracy: 0.5286 - val_loss: 0.5916 - val_accuracy: 0.6000\n",
            "Epoch 12/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.5914 - accuracy: 0.6143 - val_loss: 0.5799 - val_accuracy: 0.7000\n",
            "Epoch 13/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.5780 - accuracy: 0.7429 - val_loss: 0.5671 - val_accuracy: 0.7000\n",
            "Epoch 14/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.5649 - accuracy: 0.7714 - val_loss: 0.5538 - val_accuracy: 0.7000\n",
            "Epoch 15/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.5485 - accuracy: 0.8143 - val_loss: 0.5359 - val_accuracy: 0.8000\n",
            "Epoch 16/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.5310 - accuracy: 0.8286 - val_loss: 0.5175 - val_accuracy: 0.8000\n",
            "Epoch 17/50\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.5119 - accuracy: 0.8429 - val_loss: 0.4971 - val_accuracy: 0.8000\n",
            "Epoch 18/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.4912 - accuracy: 0.8429 - val_loss: 0.4749 - val_accuracy: 0.9000\n",
            "Epoch 19/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.4670 - accuracy: 0.9286 - val_loss: 0.4498 - val_accuracy: 0.9000\n",
            "Epoch 20/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.4421 - accuracy: 0.9857 - val_loss: 0.4248 - val_accuracy: 1.0000\n",
            "Epoch 21/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.4154 - accuracy: 1.0000 - val_loss: 0.3983 - val_accuracy: 1.0000\n",
            "Epoch 22/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.3883 - accuracy: 1.0000 - val_loss: 0.3717 - val_accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.3601 - accuracy: 1.0000 - val_loss: 0.3440 - val_accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.3298 - accuracy: 1.0000 - val_loss: 0.3146 - val_accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.3044 - accuracy: 1.0000 - val_loss: 0.2873 - val_accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.2741 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.2462 - accuracy: 1.0000 - val_loss: 0.2330 - val_accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.2199 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.1977 - accuracy: 1.0000 - val_loss: 0.1861 - val_accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.1731 - accuracy: 1.0000 - val_loss: 0.1640 - val_accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.1534 - accuracy: 1.0000 - val_loss: 0.1460 - val_accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.1347 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.1181 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.1042 - accuracy: 1.0000 - val_loss: 0.1012 - val_accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.0913 - accuracy: 1.0000 - val_loss: 0.0900 - val_accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.0810 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.0713 - accuracy: 1.0000 - val_loss: 0.0703 - val_accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.0634 - accuracy: 1.0000 - val_loss: 0.0632 - val_accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.0560 - accuracy: 1.0000 - val_loss: 0.0564 - val_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.0502 - accuracy: 1.0000 - val_loss: 0.0505 - val_accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.0447 - accuracy: 1.0000 - val_loss: 0.0460 - val_accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.0417 - val_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.0366 - accuracy: 1.0000 - val_loss: 0.0379 - val_accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.0331 - accuracy: 1.0000 - val_loss: 0.0349 - val_accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.0303 - accuracy: 1.0000 - val_loss: 0.0319 - val_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.0278 - accuracy: 1.0000 - val_loss: 0.0292 - val_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.0254 - accuracy: 1.0000 - val_loss: 0.0271 - val_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.0234 - accuracy: 1.0000 - val_loss: 0.0254 - val_accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.0216 - accuracy: 1.0000 - val_loss: 0.0235 - val_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.0200 - accuracy: 1.0000 - val_loss: 0.0218 - val_accuracy: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1/1 - 0s - loss: 0.0229 - accuracy: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix for Neural Network Model:\n",
            "  [[10  0]\n",
            " [ 0 10]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        10\n",
            "           1       1.00      1.00      1.00        10\n",
            "\n",
            "    accuracy                           1.00        20\n",
            "   macro avg       1.00      1.00      1.00        20\n",
            "weighted avg       1.00      1.00      1.00        20\n",
            "\n",
            "Accuracy:100.00 %\n",
            "Confusion Matrix for SVM Clssifer:\n",
            "  [[10  0]\n",
            " [ 0 10]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        10\n",
            "           1       1.00      1.00      1.00        10\n",
            "\n",
            "    accuracy                           1.00        20\n",
            "   macro avg       1.00      1.00      1.00        20\n",
            "weighted avg       1.00      1.00      1.00        20\n",
            "\n",
            "Accuracy:100.00 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}