Randomly_assigned_weights_inrange.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "Randomly_assigned_weights_inrange.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ueaKksALyNin",
        "outputId": "96cb1224-252c-4ce9-ee57-509462ef7c97"
      },
      "source": [
        "#Reference : https://heartbeat.fritz.ai/building-a-neural-network-from-scratch-using-python-part-1-6d399df8d432\n",
        "#https://towardsdatascience.com/math-neural-network-from-scratch-in-python-d6da9f29ce65\n",
        "#https://zerowithdot.com/mlp-backpropagation/\n",
        "import numpy as np\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "class NeuralNet():\n",
        "    '''\n",
        "    A two layer neural network\n",
        "    '''\n",
        "        \n",
        "    def __init__(self, layers=[3,4,1], lr=0.01, epoch=150):\n",
        "        self.params = {}\n",
        "        self.lr = lr\n",
        "        self.epoch = epoch\n",
        "        self.loss = []\n",
        "        self.sample_size = None\n",
        "        self.layers = layers\n",
        "        self.X = None\n",
        "        self.y = None\n",
        "                \n",
        "    def init_weights(self):\n",
        "        '''\n",
        "        Initialize the weights from a random normal distribution\n",
        "        '''\n",
        "        np.random.seed(1) # Seed the random number generator\n",
        "        self.params[\"W1\"] = np.random.randn(self.layers[0], self.layers[1])\n",
        "        for i in  range(self.layers[0]):\n",
        "            for j in range(self.layers[1]):\n",
        "                self.params[\"W1\"][i][j] = random.uniform(-0.01, 0.01)    #weights between -0.01 and 0.01\n",
        "        self.params['b1']  = np.random.randint(1,2,size = (self.layers[1],))\n",
        "\n",
        "        self.params['W2'] = np.random.randn(self.layers[1],self.layers[2]) \n",
        "        for i in  range(self.layers[1]):\n",
        "            for j in range(self.layers[2]):\n",
        "                self.params[\"W2\"][i][j] = random.uniform(-0.01, 0.01)   #weights between -0.01 and 0.01\n",
        "        \n",
        "        self.params['b2'] = np.random.randint(1,2,size = (self.layers[2],))\n",
        "\n",
        "\n",
        "    def eta(self, x):\n",
        "      ETA = 0.0000000001\n",
        "      return np.maximum(x, ETA)\n",
        "\n",
        "#Sigmoid Activation function\n",
        "    def sigmoid(self,Z):\n",
        "        '''\n",
        "        The sigmoid function takes in real numbers in any range and \n",
        "        squashes it to a real-valued output between 0 and 1.\n",
        "        '''\n",
        "        return 1/(1+np.exp(-Z))\n",
        "\n",
        "    def entropy_loss(self,y, yhat):\n",
        "        nsample = len(y)\n",
        "        yhat_inv = 1.0 - yhat\n",
        "        y_inv = 1.0 - y\n",
        "        yhat = self.eta(yhat) ## clips value to avoid NaNs in log\n",
        "        yhat_inv = self.eta(yhat_inv) \n",
        "        loss = -1/nsample * (np.sum(np.multiply(np.log(yhat), y) + np.multiply((y_inv), np.log(yhat_inv))))\n",
        "        return loss\n",
        "\n",
        "    def forward_propagation(self):\n",
        "        '''\n",
        "        Performs the forward propagation\n",
        "        '''\n",
        "        Z1 = np.dot(self.X, self.params['W1']) + self.params['b1']\n",
        "        A1 = self.sigmoid(Z1)\n",
        "        Z2 = np.dot(A1, self.params['W2']) + self.params['b2']\n",
        "        yhat = self.sigmoid(Z2)\n",
        "        loss = self.entropy_loss(self.y,yhat)\n",
        "\n",
        "        # save calculated parameters     \n",
        "        self.params['Z1'] = Z1\n",
        "        self.params['Z2'] = Z2\n",
        "        self.params['A1'] = A1\n",
        "\n",
        "        return yhat,loss\n",
        "\n",
        "    def back_propagation(self,yhat):\n",
        "        '''\n",
        "        Computes the derivatives and update weights and bias according.\n",
        "        '''\n",
        "        y_inv = 1 - self.y\n",
        "        yhat_inv = 1 - yhat\n",
        "        dl_wrt_yhat = np.divide(y_inv, self.eta(yhat_inv)) - np.divide(self.y, self.eta(yhat))\n",
        "        dl_wrt_sig = yhat * (yhat_inv)\n",
        "        dl_wrt_z2 = np.dot(dl_wrt_yhat, dl_wrt_sig)\n",
        "\n",
        "        dl_wrt_A1 = np.dot(dl_wrt_z2, self.params['W2'].T)\n",
        "        dl_wrt_w2 = np.dot(self.params['A1'].T, dl_wrt_z2)\n",
        "        dl_wrt_b2 = np.sum(dl_wrt_z2, axis=0, keepdims=True)\n",
        "\n",
        "        dl_wrt_z1 = dl_wrt_A1 * self.params['A1']*(1-self.params['A1'])\n",
        "        dl_wrt_w1 = np.dot(self.X.T, dl_wrt_z1)\n",
        "        dl_wrt_b1 = np.sum(dl_wrt_z1, axis=0, keepdims=True)\n",
        "\n",
        "        #update the weights and bias\n",
        "        self.params['W1'] = self.params['W1'] - self.lr * dl_wrt_w1\n",
        "        self.params['W2'] = self.params['W2'] - self.lr * dl_wrt_w2\n",
        "        self.params['b1'] = self.params['b1'] - self.lr * dl_wrt_b1\n",
        "        self.params['b2'] = self.params['b2'] - self.lr * dl_wrt_b2\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        '''\n",
        "        Trains the neural network using the specified data and labels\n",
        "        '''\n",
        "        self.X = X\n",
        "        self.y = y\n",
        "        self.init_weights() #initialize weights and bias\n",
        "        misclassified_ = [] \n",
        "        misclassified = 0\n",
        "\n",
        "\n",
        "        for i in range(self.epoch):\n",
        "            \n",
        "            yhat, loss = self.forward_propagation()\n",
        "            for i in range(yhat.shape[0]):\n",
        "                if np.round(yhat[i][0])-y[i] != 0:\n",
        "                    misclassified += 1\n",
        "            misclassified_.append(misclassified)\n",
        "            self.back_propagation(yhat)\n",
        "            self.loss.append(loss)\n",
        "        return misclassified_\n",
        "\n",
        "    def predict(self, X):\n",
        "        '''\n",
        "        Predicts on a test data\n",
        "        '''\n",
        "        Z1 = np.dot(X, self.params['W1']) + self.params['b1']\n",
        "        A1 = self.sigmoid(Z1)\n",
        "        Z2 = np.dot(A1, self.params['W2']) + self.params['b2']\n",
        "        pred = self.sigmoid(Z2)\n",
        "        return np.round(pred) \n",
        "\n",
        "    def acc(self, y, yhat):\n",
        "        '''\n",
        "        Calculates the accutacy between the predicted valuea and the truth labels\n",
        "        '''\n",
        "        acc = int(sum(y == yhat) / len(y) * 100)\n",
        "        return acc\n",
        "\n",
        "\n",
        "    def plot_loss(self):\n",
        "        '''\n",
        "        Plots the loss curve\n",
        "        '''\n",
        "        plt.plot(self.loss)\n",
        "        plt.xlabel(\"Iteration\")\n",
        "        plt.ylabel(\"log_loss\")\n",
        "        plt.title(\"Loss curve for training\")\n",
        "        plt.show()\n",
        "        \n",
        "#OR dataset      \n",
        "training_inputs = []\n",
        "training_inputs_or = np.array([[1, 1, 1],\n",
        "        [1, 1, -1],\n",
        "        [1, -1, 1],\n",
        "        [1, -1, -1],\n",
        "        [-1, 1, 1],\n",
        "        [-1, 1, -1],\n",
        "        [-1, -1, 1],\n",
        "        [-1, -1, -1]])\n",
        "\n",
        "labels_or = np.array([1, 1, 1, 1, 1, 1, 1, 0])\n",
        "\n",
        "neu_net_or = NeuralNet()\n",
        "\n",
        "misclassified_ = neu_net_or.fit(training_inputs_or, labels_or)\n",
        "\n",
        "neu_net_or.plot_loss()\n",
        "\n",
        "iterations = np.arange(1, 150+1)\n",
        "plt.plot(iterations, misclassified_)\n",
        "plt.xlabel('iterations')\n",
        "plt.ylabel('misclassified for OR dataset')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#XOR dataset\n",
        "training_inputs_xor = np.array([[1, 1, 1],\n",
        "        [1, 1, -1],\n",
        "        [1, -1, 1],\n",
        "        [1, -1, -1],\n",
        "        [-1, 1, 1],\n",
        "        [-1, 1, -1],\n",
        "        [-1, -1, 1],\n",
        "        [-1, -1, -1]])\n",
        "\n",
        "labels_xor = np.array([0, 1, 1, 1, 1, 1, 1, 0])\n",
        "\n",
        "neu_net_xor = NeuralNet()\n",
        "\n",
        "misclassified_ = neu_net_xor.fit(training_inputs_xor, labels_xor)\n",
        "\n",
        "neu_net_xor.plot_loss()\n",
        "\n",
        "iterations = np.arange(1, 150+1)\n",
        "plt.plot(iterations, misclassified_)\n",
        "plt.xlabel('iterations')\n",
        "plt.ylabel('misclassified for XOR dataset')\n",
        "plt.show()\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMhwvThZyNiz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}