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def sumLoss(xs, hs, ys, ps, t):

  xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation

  xs[t][inputs[t]] = 1

  hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state

  ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars

  ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars

  return -np.log(ps[t][targets[t],0])

 for t in range(len(inputs)):

   loss+=sumLoss(xs, hs, ys, ps, t)

res = Parallel(n_jobs=num_cores)(delayed(sumLoss)(xs, hs, ys, ps, t) for t in range(len(inputs)))

loss = sum(res)

def sumLoss(xs, hs, ys, ps, t):

  xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation

  xs[t][inputs[t]] = 1

  hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state

  ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars

  ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars

  return -np.log(ps[t][targets[t],0])

 for t in range(len(inputs)):

   loss+=sumLoss(xs, hs, ys, ps, t)

res = Parallel(n_jobs=num_cores)(delayed(sumLoss)(xs, hs, ys, ps, t) for t in range(len(inputs)))

loss = sum(res)

def sumLoss(xs, hs, ys, ps, t):

  xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation

  xs[t][inputs[t]] = 1

  hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state

  ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars

  ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars

  return -np.log(ps[t][targets[t],0])

 for t in range(len(inputs)):

   loss+=sumLoss(xs, hs, ys, ps, t)

res = Parallel(n_jobs=num_cores)(delayed(sumLoss)(xs, hs, ys, ps, t) for t in range(len(inputs)))

loss = sum(res)

def sumLoss(xs, hs, ys, ps, t):

  xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation

  xs[t][inputs[t]] = 1

  hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state

  ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars

  ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars

  return -np.log(ps[t][targets[t],0])

 for t in range(len(inputs)):

   loss+=sumLoss(xs, hs, ys, ps, t)

res = Parallel(n_jobs=num_cores)(delayed(sumLoss)(xs, hs, ys, ps, t) for t in range(len(inputs)))

loss = sum(res)

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