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 _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
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 _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
class BaseLogger(Callback):
'''Callback that prints events to the standard output.
This callback is automatically applied to
every Keras model (it is the basis of the verbosity modes
in models).
'''
def on_train_begin(self, logs={}):
self.verbose = self.params['verbose']
self.nb_epoch = self.params['nb_epoch']
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 on_batch_begin(self, batch, logs={}):
if self.seen < self.params['nb_sample']:
self.log_values = []
def on_batch_end(self, batch, logs={}):
batch_size = logs.get('size', 0)
self.seen += batch_size
for k, v in logs.items():
if k in self.totals:
self.totals[k] += v * batch_size
else:
self.totals[k] = v * batch_size
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
# skip progbar update for the last batch;
# will be handled by on_epoch_end
if self.verbose and self.seen < self.params['nb_sample']:
self.progbar.update(self.seen, self.log_values)
def on_epoch_end(self, epoch, logs={}):
for k in self.params['metrics']:
if k in self.totals:
self.log_values.append((k, self.totals[k] / self.seen))
if k in logs:
self.log_values.append((k, logs[k]))
if self.verbose:
self.progbar.update(self.seen, self.log_values)
class BaseLogger(Callback):
'''Callback that prints events to the standard output.
This callback is automatically applied to
every Keras model (it is the basis of the verbosity modes
in models).
'''
def on_train_begin(self, logs={}):
self.verbose = self.params['verbose']
self.nb_epoch = self.params['nb_epoch']
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 on_batch_begin(self, batch, logs={}):
if self.seen < self.params['nb_sample']:
self.log_values = []
def on_batch_end(self, batch, logs={}):
batch_size = logs.get('size', 0)
self.seen += batch_size
for k, v in logs.items():
if k in self.totals:
self.totals[k] += v * batch_size
else:
self.totals[k] = v * batch_size
for k in self.params['metrics']:
if k in logs:
self.log_values.append((k, logs[k]))
# skip progbar update for the last batch;
# will be handled by on_epoch_end
if self.verbose and self.seen < self.params['nb_sample']:
self.progbar.update(self.seen, self.log_values)
def on_epoch_end(self, epoch, logs={}):
for k in self.params['metrics']:
if k in self.totals:
self.log_values.append((k, self.totals[k] / self.seen))
if k in logs:
self.log_values.append((k, logs[k]))
if self.verbose:
self.progbar.update(self.seen, self.log_values)