ANN-2-hidden-layer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dcb46dbb-74ac-495e-ae09-aedc15e6243e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial w: [array([[0.189, 0.797],\n",
      "        [0.601, 0.048],\n",
      "        [0.777, 0.752],\n",
      "        [0.957, 0.902]]) array([[0.788, 0.575, 0.852, 0.818],\n",
      "                                [0.637, 0.07 , 0.328, 0.452]])\n",
      " array([0.881, 0.089])]\n",
      "-----------------------------\n",
      "Final w: [array([[0.189, 0.797],\n",
      "        [0.6  , 0.048],\n",
      "        [0.777, 0.752],\n",
      "        [0.957, 0.901]]) array([[0.786, 0.574, 0.851, 0.816],\n",
      "                                [0.636, 0.07 , 0.327, 0.452]])\n",
      " array([0.855, 0.067])]\n",
      "-----------------------------\n",
      "Error: 0.000 A: 0.700\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the necessary packages and modules\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('./sci.mplstyle')\n",
    "import numpy as np\n",
    "\n",
    "# Define activation function\n",
    "def sigmoid(y):\n",
    "    return 1.0/(1+np.exp(-1*y))\n",
    "# Define error function\n",
    "def error(A, Y):\n",
    "    return np.power(A-Y, 2)\n",
    "# Define derivative of error function\n",
    "def d_error_A(A, Y):\n",
    "    return 2*(A-Y)\n",
    "# Define derivative of activation function\n",
    "def d_A_y(A):\n",
    "    return A*(1.0-A)\n",
    "# Define derivative of linear function\n",
    "def d_y_w(x):\n",
    "    return x\n",
    "# Define the gradient of weight\n",
    "def update_w(w,grad,r):\n",
    "    return w-r*grad\n",
    "\n",
    "# Input value\n",
    "x = np.array([0.5, 2.4])\n",
    "# Output value\n",
    "Y = np.array([0.7])\n",
    "\n",
    "# Learning rate\n",
    "r = 0.5\n",
    "\n",
    "# Number of inputs, number of neurons per each hidden layer, number of output neurons\n",
    "network = np.array([x.shape[0],4,2,1])\n",
    "\n",
    "# Initializing the weights of the entire networks\n",
    "w = []\n",
    "w_temp = []\n",
    "for layer_counter in np.arange(network.shape[0]-1):\n",
    "    for neuron_counter in np.arange(network[layer_counter + 1]):\n",
    "        w_temp.append(np.random.rand(network[layer_counter]))\n",
    "    w.append(np.array(w_temp))\n",
    "    w_temp = []\n",
    "w = np.array(w, dtype=object)\n",
    "w[-1] = w[-1][0] #Last set of weights are of shape (n, 1). For simplicity, first dim is removed.\n",
    "w_old = w\n",
    "\n",
    "np.set_printoptions(precision=3)\n",
    "print(\"Initial w:\", w)\n",
    "print(\"-----------------------------\")\n",
    "\n",
    "step = []\n",
    "err_step = []\n",
    "layer_idx = 0\n",
    "\n",
    "for k in range (100):\n",
    "    # Forward Pass\n",
    "    # First hidden layer calculations\n",
    "    y1 = np.matmul(w[layer_idx], x)\n",
    "    A1 = sigmoid(y1)\n",
    "    \n",
    "    # Second hidden layer calculations\n",
    "    layer_idx = layer_idx + 1 # =1\n",
    "    y2 = np.matmul(w[layer_idx], A1)\n",
    "    A2 = sigmoid(y2)\n",
    "    \n",
    "    # Output layer calculations\n",
    "    layer_idx = layer_idx + 1 # =2\n",
    "    y3 = np.sum(w[layer_idx]*A2)\n",
    "    A3 = sigmoid(y3)\n",
    "    err = error(A3, Y)\n",
    "    \n",
    "    # Backward Pass\n",
    "    g1 = d_error_A(A3, Y)\n",
    "    g2 = d_A_y(A3)\n",
    "    g3 = d_y_w(A2)\n",
    "    \n",
    "    # Weights between second hidden and output layers\n",
    "    grad_hidden_output = g3*g2*g1\n",
    "    \n",
    "    w[layer_idx] = update_w(w[layer_idx],grad_hidden_output,r)\n",
    "    \n",
    "    # Weights between first and second hidden layers\n",
    "    g3 = d_y_w(w_old[layer_idx])\n",
    "    g4 = d_A_y(A2)\n",
    "    g5 = d_y_w(A1)\n",
    "    \n",
    "    layer_idx = layer_idx -1 # =1 \n",
    "    for i in np.arange(g3.shape[0]): # neuron index of second hidden layer\n",
    "        grad_hidden_input = g5*g4[i]*g3[i]*g2*g1\n",
    "        w[layer_idx][i] = update_w(w[layer_idx][i],grad_hidden_input,r)\n",
    "     \n",
    "    # Weights between first hidden and input layers\n",
    "    g6 = d_A_y(A1)\n",
    "    g7 = d_y_w(x)\n",
    "    \n",
    "    layer_idx = layer_idx -1 # =0\n",
    "    for i in np.arange(g3.shape[0]): # neuron index of second hidden layer\n",
    "        g5 = d_y_w(w_old[layer_idx+1][i])\n",
    "        for j in np.arange(g5.shape[0]): # neuron index of first hidden layer\n",
    "            grad_hidden_input = g7*g6[j]*g5[j]*g4[i]*g3[i]*g2*g1\n",
    "            w[layer_idx][j] = update_w(w[layer_idx][j],grad_hidden_input,r)\n",
    "    \n",
    "    w_old = w\n",
    "    \n",
    "    #Store results\n",
    "    step.append((k))\n",
    "    err_step.append((err))\n",
    "\n",
    "print(\"Final w:\", w)\n",
    "print(\"-----------------------------\")\n",
    "print(\"Error: %5.3f\"%err, \"A: %5.3f\"%A3)\n",
    "    \n",
    "# Create figure object\n",
    "plt.figure()\n",
    "# Add the x and y-axis labels\n",
    "plt.xlabel('Iteration Number')\n",
    "plt.ylabel('Error')\n",
    "# Plot\n",
    "plt.plot(step, err_step)\n",
    "# Save figure\n",
    "plt.savefig('ANN-2-hidden-layer.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "356ff072-3cfd-4594-8618-4d0c927e3de4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.10"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}