We start with a simple form of a recurrent neural network to understand the basic idea of RNNs. In this example, we will feed the RNN four binary variables. These represent the weather types on a certain day. For example, [1, 0, 0, 0] stands for sunny and [1, 0, 1, 0] stands for sunny and windy. The target value is a double representing the percentage of rain on that day. For this problem, we can say that the quantity of rain on a certain day also depends on the values of the previous day. This makes this problem well suited for a 4-to-1 RNN model.
- In this basic example, we will our simple RNN with NumPy:
import numpy as np
- 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]
- For this regression...