Training an RNN

As we discussed at the beginning of the chapter, the applications of RNNs are wide and varied across a plethora of industries. In our case, we will only perform a quick example in order to more firmly understand the basic mechanics of RNNs.

The input data that we will be trying to model with our RNN is the mathematical cosine function.

So first let's define our input data and store it into a NumPy array.

import numpy as np
import math
import matplotlib.pyplot as plt
input_data = np.array([math.cos(x) for x in np.arange(200)])
plt.plot(input_data[:50])
plt.show

The preceding statement will plot the data so we can visualize what our input data looks like. You should get an output like this:

Figure 8: Visualization of input data

Let's now split the input data into two sets so we can use one portion for training and another portion for validation. Perhaps not the optimal split from a training standpoint, but to keep...