Neural Networks 1.ipynb

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   "source": [
    "## creating neural network class in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetwork:\n",
    "    def __init__(self,x,y):\n",
    "        self.input=x\n",
    "        self.weights1=np.random.rand(self.input.shape[1],4)\n",
    "        self.weights2=np.random.rand(4,1)\n",
    "        self.y=y\n",
    "        self.output=np.zeros(y.shape)\n",
    "        # for simplicity we have assumed the biases to be 0\n",
    "    def feedforward(self):\n",
    "        self.layer1=sigmoid(np.dot(self.input,self.weights1))\n",
    "        self.output=sigmoid(np.dot(self.layer1,self.weights2))\n",
    "    def backprop(self):\n",
    "        # application of chain rule to find derivative of the loss function with the \n",
    "        #respect to the weights1 and weights2\n",
    "        d_weights2=np.dot(self.layer1.T,(2*(self.y-self.output)*sigmoid_derivative(self.output)))\n",
    "        d_weights1=np.dot(self.input.T,(2*(self.y-self.output)*sigmoid_derivative(self.output)))\n",
    "        #update the weights with the derivative of the loss function\n",
    "        self.weights1+=d_weights1\n",
    "        self.weights2+=d_weights2\n",
    "        "
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1.0/(1+ np.exp(-x))\n",
    "\n",
    "def sigmoid_derivative(x):\n",
    "    return x * (1.0 - x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## training the neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.09602904]\n",
      " [0.63063135]\n",
      " [0.63067015]\n",
      " [0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    X = np.array([[0,0,1],\n",
    "                  [0,1,1],\n",
    "                  [1,0,1],\n",
    "                  [1,1,1]])\n",
    "    y = np.array([[0],[1],[1],[0]])\n",
    "    nn = NeuralNetwork(X,y)\n",
    "\n",
    "    for i in range(1500):\n",
    "        nn.feedforward()\n",
    "        nn.backprop()\n",
    "\n",
    "    print(nn.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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