Python Deep Learning Cookbook

How to do it...

1. In this basic example, we will implement our simple RNN with NumPy:

import numpy as np

2. Let's start with creating the dummy dataset that we will be using:

X = []X.append([1,0,0,0])X.append([0,1,0,0])X.append([0,0,1,0])X.append([0,0,0,1])X.append([0,0,0,1])X.append([1,0,0,0])X.append([0,1,0,0])X.append([0,0,1,0])X.append([0,0,0,1])y = [0.20, 0.30, 0.40, 0.50, 0.05, 0.10, 0.20,0.30, 0.40]

3. For this regression problem, we will be using a sigmoid activation function:

def sigmoid(x):    return 1 / (1 + np.exp(-x))def sigmoid_der(x):    return 1.0 - x**2

4. Next, we initialize the layers and weights:

layers = []# 4 input variables, 16 hidden units and 1 output variablen_units = (4, 16, 1)n_layers = len(n_units)layers.append(np.ones(n_units[0]+1+n_units[1])) ...