def _test_loop(self, f, ins, batch_size=128, verbose=0):
'''Abstract method to loop over some data in batches.
'''
nb_sample = len(ins[0])
outs = []
if verbose == 1:
progbar = Progbar(target=nb_sample)
batches = make_batches(nb_sample, batch_size)
index_array = np.arange(nb_sample)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
ins_batch = slice_X(ins, batch_ids)
batch_outs = f(ins_batch)
if type(batch_outs) == list:
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i, _ in enumerate(outs):
outs[i] /= nb_sample
return outs
def on_epoch_begin(self, epoch, logs={}):
if self.verbose:
print('Epoch %d/%d' % (epoch + 1, self.nb_epoch))
self.progbar = Progbar(target=self.params['nb_sample'],
verbose=self.verbose)
self.seen = 0
self.totals = {}
def _predict_loop(self, f, ins, batch_size=128, verbose=0):
'''Abstract method to loop over some data in batches.
'''
nb_sample = len(ins[0])
outs = []
if verbose == 1:
progbar = Progbar(target=nb_sample)
batches = make_batches(nb_sample, batch_size)
index_array = np.arange(nb_sample)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
ins_batch = slice_X(ins, batch_ids)
batch_outs = f(ins_batch)
if type(batch_outs) != list:
batch_outs = [batch_outs]
if batch_index == 0:
for batch_out in batch_outs:
shape = (nb_sample, ) + batch_out.shape[1:]
outs.append(np.zeros(shape))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
return outs
