def create_member(self):
train_set, train_weights = self.resampler.make_new_train(self.params.resample_size)
if self.member_number > 0 :
resampled = [
train_set,
self.resampler.get_valid(),
self.resampler.get_test()
]
else:
train_weights = None
resampled = [
self.resampler.get_train(),
self.resampler.get_valid(),
self.resampler.get_test()
]
m = mlp.sequential_model(resampled, self.params,
member_number = self.member_number,
sample_weight = train_weights)
orig_train = self.resampler.get_train()
errors = common.errors(m, orig_train[0], orig_train[1])
e = np.sum((errors * self.D))
alpha = .5 * math.log((1-e)/e)
w = np.where(errors == 1,
self.D * math.exp(alpha),
self.D * math.exp(-alpha))
self.D = w / w.sum()
self.resampler.update_weights(self.D)
self.alphas.append(alpha)
self.member_number += 1
return m
def create_member(self):
train_weights = None
if self.member_number > 0:
train_set, train_weights = self.resampler.make_new_train(
self.params.resample_size)
resampled = [
train_set,
self.resampler.get_valid(),
self.resampler.get_test()
]
else:
resampled = [
self.resampler.get_train(),
self.resampler.get_valid(),
self.resampler.get_test()
]
m = mlp.sequential_model(resampled,
self.params,
member_number=self.member_number,
sample_weight=train_weights)
orig_train = self.resampler.get_train()
errors = common.errors(m, orig_train[0], orig_train[1])
e = np.sum((errors * self.D))
alpha = .5 * math.log((1 - e) / e)
w = np.where(errors == 1, self.D * math.exp(alpha),
self.D * math.exp(-alpha))
self.D = w / w.sum()
self.resampler.update_weights(self.D)
self.alphas.append(alpha)
self.member_number += 1
return (m.to_yaml(), m.get_weights())
def create_member(self):
self.set_defaults()
if self.member_number > 0 :
train_set, train_weights = self.resampler.make_new_train(self.params.resample_size)
resampled = [
train_set,
self.resampler.get_valid(),
self.resampler.get_test()
]
else:
train_weights = None
resampled = [
self.resampler.get_train(),
self.resampler.get_valid(),
self.resampler.get_test()
]
if self.member_number > 0:
self.params.n_epochs = self.n_epochs_after_first
if not self.use_sample_weights:
train_weights = None
m = mlp.sequential_model(resampled, self.params,
member_number = self.member_number, model_weights = self.weights,
#the copy is because there is a bug in Keras that deletes names
model_config = copy.deepcopy(self.model_config),
frozen_layers = self.frozen_layers,
sample_weight = train_weights)
self.weights = [l.get_weights() for l in m.layers]
injection_index = self.incremental_index + self.member_number
if self.incremental_layers is not None:
if injection_index == -1:
injection_index = len(self.model_config)
new_layers = []
for i,l in enumerate(self.incremental_layers):
new_layers.append(copy.deepcopy(l))
#make residual block
new_block = self._residual_block(injection_index, new_layers, m,
self.member_number)
new_model_config = self.model_config[:injection_index] + [new_block] + self.model_config[injection_index:]
if self.freeze_old_layers:
self.frozen_layers = range(0,injection_index)
self.model_config = copy.deepcopy(new_model_config)
self.weights = self.weights[:injection_index]
orig_train = self.resampler.get_train()
errors = common.errors(m, orig_train[0], orig_train[1])
e = np.sum((errors * self.D))
alpha = .5 * math.log((1-e)/e)
w = np.where(errors == 1,
self.D * math.exp(alpha),
self.D * math.exp(-alpha))
self.D = w / w.sum()
self.resampler.update_weights(self.D)
self.alphas.append(alpha)
self.member_number += 1
return m
def create_member(self):
self.set_defaults()
if self.member_number > 0 :
train_set, train_weights = self.resampler.make_new_train(self.params.resample_size)
resampled = [
train_set,
self.resampler.get_valid(),
self.resampler.get_test()
]
else:
resampled = [
self.resampler.get_train(),
self.resampler.get_valid(),
self.resampler.get_test()
]
if self.member_number > 0:
self.params.n_epochs = self.n_epochs_after_first
if 'lr_after_first' in self.params.__dict__:
self.params.optimizer['config']['lr'] = self.params.lr_after_first
if not self.use_sample_weights:
train_weights = None
m = mlp.sequential_model(resampled, self.params,
member_number = self.member_number, model_weights = self.weights,
#the copy is because there is a bug in Keras that deletes names
model_config = copy.deepcopy(self.model_config),
sample_weight = train_weights)
self.weights = [l.get_weights() for l in m.layers]
injection_index = self.incremental_index + self.member_number * len(self.incremental_layers)
if self.incremental_layers is not None:
if injection_index == -1:
injection_index = len(self.model_config)
new_layers = []
for i,l in enumerate(self.incremental_layers):
l['config']['name'] = "DIB-incremental-{0}-{1}".format(
self.member_number, i)
new_layers.append(l)
new_model_config = self.model_config[:injection_index] + new_layers + self.model_config[injection_index:]
self.model_config = copy.deepcopy(new_model_config)
self.weights = self.weights[:injection_index]
orig_train = self.resampler.get_train()
errors = common.errors(m, orig_train[0], orig_train[1])
e = np.sum((errors * self.D))
alpha = .5 * math.log((1-e)/e)
w = np.where(errors == 1,
self.D * math.exp(alpha),
self.D * math.exp(-alpha))
self.D = w / w.sum()
self.resampler.update_weights(self.D)
self.alphas.append(alpha)
self.member_number += 1
return m
def create_member(self):
self.set_defaults()
if self.member_number > 0:
if self.resample:
train_set, train_weights = self.resampler.make_new_train(
self.params.resample_size)
resampled = [
train_set,
self.resampler.get_valid(),
self.resampler.get_test()
]
else:
train_weights = self.D
resampled = [
self.resampler.get_train(),
self.resampler.get_valid(),
self.resampler.get_test()
]
else:
train_weights = None
resampled = [
self.resampler.get_train(),
self.resampler.get_valid(),
self.resampler.get_test()
]
if self.member_number > 0:
self.params.n_epochs = self.n_epochs_after_first
if not self.use_sample_weights:
train_weights = None
m = mlp.sequential_model(
resampled,
self.params,
member_number=self.member_number,
model_weights=self.weights,
#the copy is because there is a bug in Keras that deletes names
model_config=copy.deepcopy(self.model_config),
frozen_layers=self.frozen_layers,
sample_weight=train_weights)
self.weights = [l.get_weights() for l in m.layers]
injection_index = self.incremental_index + self.member_number
if self.incremental_layers is not None:
if injection_index == -1:
injection_index = len(self.model_config)
new_layers = []
for i, l in enumerate(self.incremental_layers):
new_layers.append(copy.deepcopy(l))
#make residual block
new_block = self._residual_block(injection_index, new_layers, m,
self.member_number)
new_model_config = self.model_config[:injection_index] + [
new_block
] + self.model_config[injection_index:]
if self.freeze_old_layers:
self.frozen_layers = range(0, injection_index)
self.model_config = copy.deepcopy(new_model_config)
self.weights = self.weights[:injection_index]
orig_train = self.resampler.get_train()
errors = common.errors(m, orig_train[0], orig_train[1])
e = np.sum((errors * self.D))
alpha = .5 * math.log((1 - e) / e)
w = np.where(errors == 1, self.D * math.exp(alpha),
self.D * math.exp(-alpha))
self.D = w / w.sum()
self.resampler.update_weights(self.D)
self.alphas.append(alpha)
self.member_number += 1
return (m.to_yaml(), m.get_weights())
def create_member(self):
self.set_defaults()
if self.member_number > 0:
train_set, train_weights = self.resampler.make_new_train(
self.params.resample_size)
resampled = [
train_set,
self.resampler.get_valid(),
self.resampler.get_test()
]
else:
train_weights = None
resampled = [
self.resampler.get_train(),
self.resampler.get_valid(),
self.resampler.get_test()
]
if self.member_number > 0:
self.params.n_epochs = self.n_epochs_after_first
if 'lr_after_first' in self.params.__dict__:
self.params.optimizer['config'][
'lr'] = self.params.lr_after_first
if not self.use_sample_weights:
train_weights = None
m = mlp.sequential_model(
resampled,
self.params,
member_number=self.member_number,
model_weights=self.weights,
#the copy is because there is a bug in Keras that deletes names
model_config=copy.deepcopy(self.model_config),
sample_weight=train_weights)
self.weights = [l.get_weights() for l in m.layers]
injection_index = self.incremental_index + self.member_number * len(
self.incremental_layers)
if self.incremental_layers is not None:
if injection_index == -1:
injection_index = len(self.model_config)
new_layers = []
for i, l in enumerate(self.incremental_layers):
l['config']['name'] = "DIB-incremental-{0}-{1}".format(
self.member_number, i)
new_layers.append(l)
new_model_config = self.model_config[:
injection_index] + new_layers + self.model_config[
injection_index:]
self.model_config = copy.deepcopy(new_model_config)
self.weights = self.weights[:injection_index]
orig_train = self.resampler.get_train()
errors = common.errors(m, orig_train[0], orig_train[1])
e = np.sum((errors * self.D))
alpha = .5 * math.log((1 - e) / e)
w = np.where(errors == 1, self.D * math.exp(alpha),
self.D * math.exp(-alpha))
self.D = w / w.sum()
self.resampler.update_weights(self.D)
self.alphas.append(alpha)
self.member_number += 1
m_yaml = m.to_yaml()
m_weights = m.get_weights()
del m
return (m_yaml, m_weights)