Second_chapter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.8, 1.21, 2.385]\n"
     ]
    }
   ],
   "source": [
    "inputs = [ 1, 2, 3, 2.5 ]\n",
    "weights = [[ 0.2, 0.8,- 0.5,1],[ 0.5, -0.91, 0.26,-0.5],[-0.26,-0.27,0.17,0.87]]\n",
    "biases = [ 2 , 3 , 0.5]\n",
    "\n",
    "layer_outputs = []\n",
    "\n",
    "for neuron_weight, n_biases in zip(weights, biases):\n",
    "    neuron_output=0\n",
    "    for n_input, n_weight in zip(inputs, neuron_weight):\n",
    "        neuron_output += n_input*n_weight\n",
    "    #add bias    \n",
    "    neuron_output += n_biases\n",
    "    #call empty list and add those\n",
    "    layer_outputs.append(neuron_output)\n",
    "    \n",
    "print(layer_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#array \n",
    "a=[[[2,4,6,1],\n",
    "   [2,3,6,2]],\n",
    "   [[12,3,4,6],\n",
    "   [1,4,4,6]],\n",
    "   [[3,4.5,6,2],\n",
    "   [2,3,4,1]]]\n",
    "\n",
    "#3 dimnsion, 2 list and 4 element "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)\n",
    "# a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#python a 1-dimensional array represent an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First layer with Numpy library\n",
    "inputs = [ 1, 2, 3, 2.5 ]\n",
    "weights = [[ 0.2, 0.8,- 0.5,1],\n",
    "           [ 0.5, -0.91, 0.26,-0.5],\n",
    "           [-0.26,-0.27,0.17,0.87]]\n",
    "biases = [ 2 , 3 , 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.8   1.21  2.385]\n"
     ]
    }
   ],
   "source": [
    "# output =np.dot(inputs,weights)+biases #not working\n",
    "output =np.dot(weights,inputs)+biases\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [2, 3, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array([[2,3,4,5]])   \n",
    "#array not first dimension in python bcoz first dimension in python is list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=np.array([a])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 4)\n",
      "[[[2 3 4 5]]]\n"
     ]
    }
   ],
   "source": [
    "print(b.shape)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=np.expand_dims(np.array(a), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 1, 4)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[2, 3, 4, 5]]]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[2,3,4]\n",
    "b=[4,7,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array([a])\n",
    "b=np.array([b]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.dot(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[37]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SortCut Way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = [ [1, 2, 3, 2.5],[1,4,5,6],[2,5,7,2]]\n",
    "weights = [[ 0.2, 0.8,- 0.5,1],[ 0.5, -0.91, 0.26,-0.5],[-0.26,-0.27,0.17,0.87]]\n",
    "biases = [ 2 , 3 , 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.8  ,  1.21 ,  2.385],\n",
       "       [ 8.9  , -1.84 ,  5.23 ],\n",
       "       [ 4.9  ,  0.27 ,  1.56 ]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(inputs, np.array(weights).T)+biases   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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