inputSize = rnnHand.buildVocab()
inputs, targets = rnnHand.buildInputTarget()
print(inputs)
print(targets)
hiddenPoss = int(inputSize * 0.75)
net = RNN(learningRate=0.01, hiddenSize=hiddenPoss, decayRate=0.1)
net.addLayer(inputLayer=True, inputSize=inputSize)
net.addLayer()
net.addLayer()
net.addLayer()
# net.addLayer()
net.addLayer(outputLayer=True, outputSize=inputSize)
net.trainNetwork(inputs, targets, epochs=31)
net.saveNeuralNet("rnn_net")
lastOut = []
# Makes prediciton for next word
def makePrediction(wordInput):
return net.predict(wordInput)
for i in range(0, 31):
# print(lastOut)
# If there is a prev prediction, feed it into network
# and set the last prediction
if len(lastOut):
