Y_values = p.getY()
number_of_instances = p.getNumberOfInstances()
#X the array of inputs, Y the array of outputs
X = np.array(X_values)
Y = np.array(Y_values)
neuralNetwork = NeuralNetwork(X, Y, 2)
neuralNetwork.loss = []
iterations = []
for i in range(1000): # noOfIterations
neuralNetwork.feedforward()
# learning rate = 0.0000001
neuralNetwork.backprop(0.0000001)
iterations.append(i)
#In this plot we can see how the loss function decreases as iterations increase
plt.pyplot.plot(iterations,
neuralNetwork.loss,
label='loss value vs iteration')
plt.pyplot.xlabel('Iterations')
plt.pyplot.ylabel('loss function')
plt.pyplot.legend()
plt.pyplot.show()
print(neuralNetwork.output)
