def testSystem():
t = Tokenizer()
xTrain, yTrain = t.getData()
np.random.seed(10)
model = RNN(15000)
o, s = model.forwardPropagation(xTrain[30])
predictions = model.predict(xTrain[30])
print(o.shape)
print(o)
print(predictions.shape)
print(predictions)
print("Expected Loss: \n" + str(np.log(model.vocab)))
print("Actual Loss:")
print(model.calculateLoss(xTrain[:100], yTrain[:100]))
def testTrain ():
print ("Starting Test")
np.random.seed(10)
print ("Starting Tokenization")
t = Tokenizer(vocabSize=15000)
print ("Tokenizer Complete")
vocabSize = t.getVocabSize()
print ("Vocab Size: " + str(vocabSize))
xTrain, yTrain = t.getData()
print ("Constructing Model")
model = RNN(vocabSize)
print ("Starting Timer")
start = time.clock()
model.sgdStep(xTrain[10], yTrain[10], .005)
end = time.clock()
print ("One Step Time: " + str(end-start))
print ("Starting Training")
reset = open ("Data/Log.txt", "w")
reset.write("")
losses = trainWithSGD(model,xTrain, yTrain, cycles=50, evalAfterLoss=1)
save("Data/Fakespeare.npz", model)
