fully_connected_layer.py

import numpy as np
class FullyConnectedLayer:

    def __init__(self, num_inputs, num_outputs, learning_rate, name):
        self.weights = 0.01*np.random.rand(num_inputs, num_outputs)
        self.bias = np.zeros((num_outputs, 1))
        self.lr = learning_rate
        self.name = name

    def forward(self, inputs):
        self.inputs = inputs
        return np.dot(self.inputs, self.weights) + self.bias.T

    def backward(self, dy):

        if dy.shape[0] == self.inputs.shape[0]:
            dy = dy.T
        dw = dy.dot(self.inputs)
        db = np.sum(dy, axis=1, keepdims=True)
        dx = np.dot(dy.T, self.weights.T)

        self.weights -= self.lr * dw.T
        self.bias -= self.lr * db

        return dx

    def extract(self):
        return {self.name+'.weights':self.weights, self.name+'.bias':self.bias}

    def feed(self, weights, bias):
        self.weights = weights
        self.bias = bias