Neural Network Projects with Python

Feedforward

As we've seen in the preceding sequential graph, feedforward is just simple calculus, and for a basic two-layer neural network, the output of the neural network is as follows:

Let's add a feedforward function in our Python code to do exactly that. Note that for simplicity, we have assumed the biases to be 0:

import numpy as npdef sigmoid(x):    return 1.0/(1 + np.exp(-x))class NeuralNetwork:    def __init__(self, x, y):        self.input    = x        self.weights1 = np.random.rand(self.input.shape[1],4)         self.weights2 = np.random.rand(4,1)         self.y        = y        self.output   = np.zeros(self.y.shape)    def feedforward(self): self.layer1 = sigmoid(np.dot(self.input, ...