def adagrad_optimizer(learning_rate, epsilon, n_win=10):
 def optimizer(loss, param):
  updates = OrderedDict()
  if (param is not list):
   param = list(param)
  for param_ in param:
   i = theano.shared(floatX(np.array(0)))
   i_int = i.astype('int64')
   value = param_.get_value(borrow=True)
   accu = theano.shared(np.zeros((value.shape + (n_win,)), dtype=value.dtype))
   grad = tt.grad(loss, param_)
   accu_new = tt.set_subtensor(accu[:, i_int], (grad ** 2))
   i_new = tt.switch(((i + 1) < n_win), (i + 1), 0)
   updates[accu] = accu_new
   updates[i] = i_new
   accu_sum = accu_new.sum(axis=1)
   updates[param_] = (param_ - ((learning_rate * grad) / tt.sqrt((accu_sum + epsilon))))
  return updates
 return optimizer

def adagrad_optimizer ( learning_rate , epsilon , n_win = 10 ) : def optimizer ( loss , param ) : updates = OrderedDict ( ) if ( param is not list ) : param = list ( param ) for param_ in param : i = theano . shared ( floatX ( np . array ( 0 ) ) ) i_int = i . astype ( 'int64' ) value = param_ . get_value ( borrow = True ) accu = theano . shared ( np . zeros ( ( value . shape + ( n_win , ) ) , dtype = value . dtype ) ) grad = tt . grad ( loss , param_ ) accu_new = tt . set_subtensor ( accu [ : , i_int ] , ( grad ** 2 ) ) i_new = tt . switch ( ( ( i + 1 ) < n_win ) , ( i + 1 ) , 0 ) updates [ accu ] = accu_new updates [ i ] = i_new accu_sum = accu_new . sum ( axis = 1 ) updates [ param_ ] = ( param_ - ( ( learning_rate * grad ) / tt . sqrt ( ( accu_sum + epsilon ) ) ) ) return updates return optimizer
returns a function that returns parameter updates .