#loop over each training example
for x, y in train_data.items():
inputs = createInputs(x)
target = int(y)
#Forward
out, __ = rnn.forward(inputs)
probs = softmax(out)
#Build dL/dy
d_L_d_y = probs
d_L_d_y -= 1
#Backward
rnn.backprop(d_L_d_y)
def processData(data, backprop = True):
'''
Returns the RNN's loss and accuracy for the given data.
- data is a dictionary mapping text to True or False.
- backprop determines if the backward phase should be run.
'''
items = list(data.items())
random.shuffle(items)
loss = 0
num_correct = 0
