{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Initial parameters\n",
    "weights = np.array([-3.0, -1.0, 2.0])\n",
    "bias = 1.0\n",
    "inputs = np.array([1.0, -2.0, 3.0])\n",
    "target_output = 0.0\n",
    "learning_rate = 0.001\n",
    "\n",
    "def relu(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "def relu_derivative(x):\n",
    "    return np.where(x > 0, 1.0, 0.0)\n",
    "\n",
    "for iteration in range(200):\n",
    "    # Forward pass\n",
    "    linear_output = np.dot(weights, inputs) + bias\n",
    "    output = relu(linear_output)\n",
    "    loss = (output - target_output) ** 2\n",
    "\n",
    "    # Backward pass\n",
    "    dloss_doutput = 2 * (output - target_output)\n",
    "    doutput_dlinear = relu_derivative(linear_output)\n",
    "    dlinear_dweights = inputs\n",
    "    dlinear_dbias = 1.0\n",
    "\n",
    "    dloss_dlinear = dloss_doutput * doutput_dlinear\n",
    "    dloss_dweights = dloss_dlinear * dlinear_dweights\n",
    "    dloss_dbias = dloss_dlinear * dlinear_dbias\n",
    "\n",
    "    # Update weights and bias\n",
    "    weights -= learning_rate * dloss_dweights\n",
    "    bias -= learning_rate * dloss_dbias\n",
    "\n",
    "    # Print the loss for this iteration\n",
    "    print(f\"Iteration {iteration + 1}, Loss: {loss}\")\n",
    "\n",
    "print(\"Final weights:\", weights)\n",
    "print(\"Final bias:\", bias)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0, Loss: 466.56000000000006\n",
      "Iteration 20, Loss: 5.32959636083938\n",
      "Iteration 40, Loss: 0.41191523404899866\n",
      "Iteration 60, Loss: 0.031836212079467595\n",
      "Iteration 80, Loss: 0.002460565465389601\n",
      "Iteration 100, Loss: 0.000190172825660145\n",
      "Iteration 120, Loss: 1.4698126966451542e-05\n",
      "Iteration 140, Loss: 1.1359926717815175e-06\n",
      "Iteration 160, Loss: 8.779889800154524e-08\n",
      "Iteration 180, Loss: 6.7858241357822796e-09\n",
      "Final weights:\n",
      " [[-0.00698895 -0.01397789 -0.02096684 -0.02795579]\n",
      " [ 0.25975286  0.11950572 -0.02074143 -0.16098857]\n",
      " [ 0.53548461  0.27096922  0.00645383 -0.25806156]]\n",
      "Final biases:\n",
      " [-0.00698895 -0.04024714 -0.06451539]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Initial inputs\n",
    "inputs = np.array([1, 2, 3, 4])\n",
    "\n",
    "# Initial weights and biases\n",
    "weights = np.array([\n",
    "    [0.1, 0.2, 0.3, 0.4],\n",
    "    [0.5, 0.6, 0.7, 0.8],\n",
    "    [0.9, 1.0, 1.1, 1.2]\n",
    "])\n",
    "\n",
    "biases = np.array([0.1, 0.2, 0.3])\n",
    "\n",
    "# Learning rate\n",
    "learning_rate = 0.001\n",
    "\n",
    "# ReLU activation function and its derivative\n",
    "def relu(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "def relu_derivative(x):\n",
    "    return np.where(x > 0, 1, 0)\n",
    "\n",
    "# Training loop\n",
    "for iteration in range(200):\n",
    "    # Forward pass\n",
    "    z = np.dot(weights, inputs) + biases\n",
    "    a = relu(z)\n",
    "    y = np.sum(a)\n",
    "\n",
    "    # Calculate loss\n",
    "    loss = y ** 2\n",
    "\n",
    "    # Backward pass\n",
    "    # Gradient of loss with respect to output y\n",
    "    dL_dy = 2 * y\n",
    "\n",
    "    # Gradient of y with respect to a\n",
    "    dy_da = np.ones_like(a)\n",
    "\n",
    "    # Gradient of loss with respect to a\n",
    "    dL_da = dL_dy * dy_da\n",
    "\n",
    "    # Gradient of a with respect to z (ReLU derivative)\n",
    "    da_dz = relu_derivative(z)\n",
    "\n",
    "    # Gradient of loss with respect to z\n",
    "    dL_dz = dL_da * da_dz\n",
    "\n",
    "    # Gradient of z with respect to weights and biases\n",
    "    dL_dW = np.outer(dL_dz, inputs)\n",
    "    dL_db = dL_dz\n",
    "\n",
    "    # Update weights and biases\n",
    "    weights -= learning_rate * dL_dW\n",
    "    biases -= learning_rate * dL_db\n",
    "\n",
    "    # Print the loss every 20 iterations\n",
    "    if iteration % 20 == 0:\n",
    "        print(f\"Iteration {iteration}, Loss: {loss}\")\n",
    "\n",
    "# Final weights and biases\n",
    "print(\"Final weights:\\n\", weights)\n",
    "print(\"Final biases:\\n\", biases)\n",
    "\n"
   ]
  }
 ],
 "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}