def get_sample_func(model, layer_to_state, x):
 theano_rng = MRG_RandomStreams(((2012 + 9) + 18))
 if (x > 0):
  sampling_updates = model.get_sampling_updates(layer_to_state, theano_rng, layer_to_clamp={model.visible_layer: True}, num_steps=x)
  t1 = time.time()
  sample_func = function([], updates=sampling_updates)
  t2 = time.time()
  print('Clamped sampling function compilation took', (t2 - t1))
  sample_func()
 sampling_updates = model.get_sampling_updates(layer_to_state, theano_rng)
 assert (layer_to_state[model.visible_layer] in sampling_updates)
 t1 = time.time()
 sample_func = function([], updates=sampling_updates)
 t2 = time.time()
 print('Sampling function compilation took', (t2 - t1))
 return sample_func

def get_sample_func ( model , layer_to_state , x ) : theano_rng = MRG_RandomStreams ( ( ( 2012 + 9 ) + 18 ) ) if ( x > 0 ) : sampling_updates = model . get_sampling_updates ( layer_to_state , theano_rng , layer_to_clamp = { model . visible_layer : True } , num_steps = x ) t1 = time . time ( ) sample_func = function ( [ ] , updates = sampling_updates ) t2 = time . time ( ) print ( 'Clamped sampling function compilation took' , ( t2 - t1 ) ) sample_func ( ) sampling_updates = model . get_sampling_updates ( layer_to_state , theano_rng ) assert ( layer_to_state [ model . visible_layer ] in sampling_updates ) t1 = time . time ( ) sample_func = function ( [ ] , updates = sampling_updates ) t2 = time . time ( ) print ( 'Sampling function compilation took' , ( t2 - t1 ) ) return sample_func
construct the sample theano function .