GRU.py

## Numpy implementation of GRU
# Params
ht = []
for t, xt in enumerate(train_data):
  if t == 0: h.append(t)
  if t >= 1:
    ht_1 = ht[t-1]
    zt = sigmoid(np.dot(xt, Wz) + np.dot(ht_1, Uz) + bz) # Update Gate Vector
    rt = sigmoid(np.dot(xt, Wr)+ np.dot(ht_1, Ur) + br) # Forget/Reset Gate Vector
    ht.append((1 - z) * ht_1 + z * np.tanh(np.dot(xt, Wh) 
                                  + np.dot(r * ht_1, Uh) + bh)) # Output Vector