Lecture 1 Homework 'Random Weight Spread'.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will try to visualize the neural network while I change its random weight's spread.\n",
    "This neural network won't have any hidden layer in order to obtain the results of changing random weight's spread."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.81197574])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biases = np.random.uniform(low = -1, high = 1, size = 1)  # Generating bisases randomly\n",
    "# y_in = np.random.uniform(low = -5, high = 5, size = 20)   # generating the input array randomly\n",
    "#biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_weights(spread):   # Defining a function so I can change the spread of the random weights\n",
    "    weights = np.random.uniform(low = -spread, high = spread, size = 2)\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_layer(y_in, w, b):    # Defining the function that applies weighted sum and feeds the result through sigmoid function\n",
    "    z = np.dot(w, y_in) + b\n",
    "    return (1 / (1 + np.exp(-z)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weights = random_weights(10)\n",
    "# apply_layer(y_in, weights, biases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.1       , -0.1       ],\n",
       "       [-0.09949875, -0.1       ],\n",
       "       [-0.09899749, -0.1       ],\n",
       "       ...,\n",
       "       [ 0.09899749,  0.1       ],\n",
       "       [ 0.09949875,  0.1       ],\n",
       "       [ 0.1       ,  0.1       ]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = 400  # Mesh size\n",
    "batchSize = M ** 2\n",
    "y_in = np.zeros([batchSize, 2])\n",
    "v0, v1 = np.meshgrid(np.linspace(-0.1, 0.1, M), np.linspace(-0.1, 0.1, M))\n",
    "y_in[ : , 0] = v0.flatten()\n",
    "y_in[ : , 1] = v1.flatten()\n",
    "# np.shape(y_in)\n",
    "#y_in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = random_weights(1)\n",
    "y_out = np.zeros([batchSize, 2])\n",
    "for i in range(len(y_in)):\n",
    "    y_out[i] = apply_layer(weights, y_in[i], biases)\n",
    "y_out_2D = np.reshape(y_out[ : , 0], [M, M])\n",
    "y_out_2D\n",
    "plt.imshow(y_out,origin='lower',extent=(-0.5,0.5,-0.5,0.5))\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit",
   "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": "3.8.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "4c1bb4c646da37a2bb17303adb9552ad74559de15c477677eeea613e7ac7a00a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}