def _initOptimizer(self):
modelParallel = self.getModelParallel()
args = self.getArgs()
optimizer = SGD(modelParallel.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
# load optimizer pre-trained state dict if exists
if self.optimizerStateDict:
optimizer.load_state_dict(self.optimizerStateDict)
return optimizer
class RestrictedBoltzmannMachines(nn.Module, ObservableData):
'''
Restricted Boltzmann Machines(RBM).
According to graph theory, the structure of RBM corresponds to
a complete bipartite graph which is a special kind of bipartite
graph where every node in the visible layer is connected to every
node in the hidden layer. Based on statistical mechanics and
thermodynamics(Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. 1985),
the state of this structure can be reflected by the energy function.
In relation to RBM, the Contrastive Divergence(CD) is a method for
approximation of the gradients of the log-likelihood(Hinton, G. E. 2002).
This algorithm draws a distinction between a positive phase and a
negative phase. Conceptually, the positive phase is to the negative
phase what waking is to sleeping.
The procedure of this method is similar to Markov Chain Monte Carlo method(MCMC).
However, unlike MCMC, the visbile variables to be set first in visible layer is
not randomly initialized but the observed data points in training dataset are set
to the first visbile variables. And, like Gibbs sampler, drawing samples from hidden
variables and visible variables is repeated k times. Empirically (and surprisingly),
`k` is considered to be `1`.
**Note** that this class does not support a *Hybrid* of imperative and symbolic
programming. Only `mxnet.ndarray` is supported.
References:
- Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive science, 9(1), 147-169.
- Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural computation, 14(8), 1771-1800.
- Le Roux, N., & Bengio, Y. (2008). Representational power of restricted Boltzmann machines and deep belief networks. Neural computation, 20(6), 1631-1649.
'''
# The list of losses.
__loss_arr = []
# Learning rate.
__learning_rate = 0.5
# Batch size in learning.
__batch_size = 0
# Batch size in inference(recursive learning or not).
__r_batch_size = 0
def __init__(
self,
computable_loss,
initializer_f=None,
optimizer_f=None,
visible_activation=torch.nn.Sigmoid(),
hidden_activation=torch.nn.Sigmoid(),
visible_dim=1000,
hidden_dim=100,
learning_rate=0.005,
visible_dropout_rate=0.0,
hidden_dropout_rate=0.0,
visible_batch_norm=None,
hidden_batch_norm=None,
regularizatable_data_list=[],
ctx="cpu",
):
'''
Init.
Args:
computable_loss: is-a `ComputableLoss`.
visible_activation: `mxnet.ndarray.Activation` or `mxnet.symbol.Activation` in visible layer.
hidden_activation: `mxnet.ndarray.Activation` or `mxnet.symbol.Activation` in hidden layer.
visible_dim: `int` of dimension in visible layer.
hidden_dim: `int` of dimension in hidden layer.
initializer: is-a `mxnet.initializer` for parameters of model. If `None`, it is drawing from the Xavier distribution.
optimizer_name: `str` of name of optimizer.
learning_rate: `float` of learning rate.
learning_attenuate_rate: `float` of attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`.
attenuate_epoch: `int` of attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`.
visible_dropout_rate: `float` of dropout rate in visible layer.
hidden_dropout_rate: `float` of dropout rate in hidden layer.
visible_batch_norm: `gluon.nn.BatchNorm` in visible layer.
hidden_batch_norm: `gluon.nn.BatchNorm` in hidden layer.
regularizatable_data_list: `list` of `RegularizatableData`s.
ctx: `mx.gpu()` or `mx.cpu()`.
'''
super(RestrictedBoltzmannMachines, self).__init__()
for v in regularizatable_data_list:
if isinstance(v, RegularizatableData) is False:
raise TypeError(
"The type of values of `regularizatable_data_list` must be `RegularizatableData`."
)
self.__regularizatable_data_list = regularizatable_data_list
self.__computable_loss = computable_loss
self.visible_activation = visible_activation
self.hidden_activation = hidden_activation
self.__visible_unit = nn.Linear(
visible_dim,
hidden_dim,
bias=True,
)
self.visible_dropout_forward = None
if visible_dropout_rate > 0:
self.visible_dropout_forward = nn.Dropout(p=visible_dropout_rate)
self.hidden_dropout_forward = None
if hidden_dropout_rate > 0:
self.hidden_dropout_forward = nn.Dropout(p=hidden_dropout_rate)
self.visible_batch_norm = visible_batch_norm
self.hidden_batch_norm = hidden_batch_norm
self.__ctx = ctx
self.to(self.__ctx)
if initializer_f is not None:
self.__visible_unit.weight = initializer_f(
self.__visible_unit.weight)
else:
self.__visible_unit.weight = torch.nn.init.xavier_normal_(
self.__visible_unit.weight, gain=1.0)
if optimizer_f is not None:
self.optimizer = optimizer_f(self.parameters())
else:
self.optimizer = SGD(
self.parameters(),
lr=self.__learning_rate,
)
self.__learning_rate = learning_rate
self.__loss_arr = np.array([])
logger = getLogger("accelbrainbase")
self.__logger = logger
self.__loss_list = []
self.__test_loss_list = []
self.epoch = 0
def learn(self, iteratable_data):
'''
Learn samples drawn by `IteratableData.generate_learned_samples()`.
Args:
iteratable_data: is-a `IteratableData`.
'''
if isinstance(iteratable_data, IteratableData) is False:
raise TypeError(
"The type of `iteratable_data` must be `IteratableData`.")
self.__loss_list = []
self.__test_loss_list = []
try:
epoch = self.epoch
iter_n = 0
for observed_arr, label_arr, test_observed_arr, test_label_arr in iteratable_data.generate_learned_samples(
):
self.batch_size = observed_arr.shape[0]
observed_arr = observed_arr.reshape((self.batch_size, -1))
test_observed_arr = test_observed_arr.reshape(
(self.batch_size, -1))
self.optimizer.zero_grad()
visible_activity_arr = self.inference(observed_arr)
loss = self.compute_loss(observed_arr, visible_activity_arr)
loss.backward()
self.optimizer.step()
self.regularize()
if (iter_n + 1) % int(
iteratable_data.iter_n / iteratable_data.epochs) == 0:
with torch.inference_mode():
test_visible_activity_arr = self.inference(
test_observed_arr)
test_loss = self.compute_loss(
test_observed_arr, test_visible_activity_arr)
_loss = loss.to('cpu').detach().numpy().copy()
_test_loss = test_loss.to('cpu').detach().numpy().copy()
self.__loss_list.append(_loss)
self.__test_loss_list.append(_test_loss)
self.__logger.debug("Epoch: " + str(epoch + 1) +
" Train loss: " +
str(self.__loss_list[-1]) +
" Test loss: " +
str(self.__test_loss_list[-1]))
epoch += 1
iter_n += 1
except KeyboardInterrupt:
self.__logger.debug("Interrupt.")
self.__logger.debug("end. ")
self.__loss_arr = np.c_[
np.array(self.__loss_list[:len(self.__test_loss_list)]),
np.array(self.__test_loss_list)]
self.epoch = epoch
def inference(self, observed_arr):
'''
Inference samples drawn by `IteratableData.generate_inferenced_samples()`.
Args:
observed_arr: rank-2 Array like or sparse matrix as the observed data points.
The shape is: (batch size, feature points)
Returns:
`mxnet.ndarray` of inferenced feature points.
'''
return self(observed_arr)
def compute_loss(self, pred_arr, labeled_arr):
'''
Compute loss.
Args:
pred_arr: `mxnet.ndarray` or `mxnet.symbol`.
labeled_arr: `mxnet.ndarray` or `mxnet.symbol`.
Returns:
loss.
'''
return self.__computable_loss(pred_arr, labeled_arr)
def extract_learned_dict(self):
'''
Extract (pre-) learned parameters.
Returns:
`dict` of the parameters.
'''
params_dict = {}
for k in self.state_dict().keys():
params_dict.setdefault(k, self.state_dict()[k])
return params_dict
def forward(self, x):
'''
Forward with Gluon API.
Args:
F: `mxnet.ndarray` or `mxnet.symbol`.
x: `mxnet.ndarray` of observed data points.
Returns:
`mxnet.ndarray` or `mxnet.symbol` of inferenced feature points.
'''
self.batch_size = x.shape[0]
x = x.reshape((self.batch_size, -1))
self.__visible_activity_arr = x
x = self.__visible_unit(x)
if self.visible_activation == "identity_adjusted":
x = x / torch.sum(torch.ones_like(x))
elif self.visible_activation != "identity":
x = self.visible_activation(x)
if self.visible_dropout_forward is not None:
x = self.visible_dropout_forward(x)
if self.visible_batch_norm is not None:
x = self.visible_batch_norm(x)
self.__hidden_activity_arr = x
self.__diff_weights_arr = torch.mm(
self.__visible_activity_arr.T,
self.__hidden_activity_arr,
)
#self.__visible_diff_bias_arr += nd.nansum(self.__visible_activity_arr, axis=0)
#self.__hidden_diff_bias_arr += nd.nansum(self.__hidden_activity_arr, axis=0)
params_dict = self.extract_learned_dict()
weight_keys_list = [
key for key in params_dict.keys() if "weight" in key
]
weights_arr = params_dict[weight_keys_list[0]]
self.__visible_activity_arr = torch.mm(
self.__hidden_activity_arr,
weights_arr,
)
x = self.__visible_activity_arr
if self.hidden_activation == "identity_adjusted":
x = x / torch.sum(torch.ones_like(x))
elif self.hidden_activation != "identity":
x = self.hidden_activation(x)
if self.hidden_dropout_forward is not None:
x = self.hidden_dropout_forward(x)
if self.hidden_batch_norm is not None:
x = self.hidden_batch_norm(x)
self.__visible_activity_arr = x
self.__hidden_activity_arr = self.__visible_unit(
self.__visible_activity_arr)
x = self.__hidden_activity_arr
if self.visible_activation == "identity_adjusted":
x = x / torch.sum(torch.ones_like(x))
elif self.visible_activation != "identity":
x = self.visible_activation(x)
if self.visible_dropout_forward is not None:
x = self.visible_dropout_forward(x)
if self.visible_batch_norm is not None:
x = self.visible_batch_norm(x)
self.__hidden_activity_arr = x
self.__diff_weights_arr = self.__diff_weights_arr - torch.mm(
self.__visible_activity_arr.T,
self.__hidden_activity_arr,
)
#self.__visible_diff_bias_arr -= nd.nansum(self.__visible_activity_arr, axis=0)
#self.__hidden_diff_bias_arr -= nd.nansum(self.__hidden_activity_arr, axis=0)
return self.__visible_activity_arr
def regularize(self):
'''
Regularization.
'''
if len(self.__regularizatable_data_list) > 0:
params_dict = self.extract_learned_dict()
for regularizatable in self.__regularizatable_data_list:
params_dict = regularizatable.regularize(params_dict)
for k, params in params_dict.items():
self.load_state_dict({k: params}, strict=False)
def save_parameters(self, filename):
'''
Save parameters to files.
Args:
filename: File name.
'''
torch.save(
{
'epoch': self.epoch,
'model_state_dict': self.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'loss': self.loss_arr,
}, filename)
def load_parameters(self, filename, ctx=None, strict=True):
'''
Load parameters to files.
Args:
filename: File name.
ctx: Context-manager that changes the selected device.
strict: Whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: `True`.
'''
checkpoint = torch.load(filename)
self.load_state_dict(checkpoint['model_state_dict'], strict=strict)
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
self.epoch = checkpoint['epoch']
self.__loss_list = checkpoint['loss'].tolist()
if ctx is not None:
self.to(ctx)
self.__ctx = ctx
def set_readonly(self, value):
''' setter '''
raise TypeError("This is read-only.")
def get_loss_list(self):
''' getter for `list` of losses in training. '''
return self.__loss_list
loss_list = property(get_loss_list, set_readonly)
def get_test_loss_arr(self):
''' getter for `list` of losses in test. '''
return self.__test_loss_list
test_loss_list = property(get_test_loss_arr, set_readonly)
def get_loss_arr(self):
''' getter for losses. '''
return self.__loss_arr
loss_arr = property(get_loss_arr, set_readonly)
def get_feature_points_arr(self):
''' getter for `mxnet.narray` of feature points in middle hidden layer. '''
return self.__hidden_activity_arr
feature_points_arr = property(get_feature_points_arr, set_readonly)
def get_weights_arr(self):
''' getter for `mxnet.ndarray` of weights matrics. '''
return self.__weights_arr
def set_weights_arr(self, value):
''' setter for `mxnet.ndarray` of weights matrics.'''
self.__weights_arr = value
weights_arr = property(get_weights_arr, set_weights_arr)
def get_visible_bias_arr(self):
''' getter for `mxnet.ndarray` of biases in visible layer.'''
return self.__visible_bias_arr
def set_visible_bias_arr(self, value):
''' setter for `mxnet.ndarray` of biases in visible layer.'''
self.__visible_bias_arr = value
visible_bias_arr = property(get_visible_bias_arr, set_visible_bias_arr)
def get_hidden_bias_arr(self):
''' getter for `mxnet.ndarray` of biases in hidden layer.'''
return self.__hidden_bias_arr
def set_hidden_bias_arr(self, value):
''' setter for `mxnet.ndarray` of biases in hidden layer.'''
self.__hidden_bias_arr = value
hidden_bias_arr = property(get_hidden_bias_arr, set_hidden_bias_arr)
def get_visible_activity_arr(self):
''' getter for `mxnet.ndarray` of activities in visible layer.'''
return self.__visible_activity_arr
def set_visible_activity_arr(self, value):
''' setter for `mxnet.ndarray` of activities in visible layer.'''
self.__visible_activity_arr = value
visible_activity_arr = property(get_visible_activity_arr,
set_visible_activity_arr)
def get_hidden_activity_arr(self):
''' getter for `mxnet.ndarray` of activities in hidden layer.'''
return self.__hidden_activity_arr
def set_hidden_activity_arr(self, value):
''' setter for `mxnet.ndarray` of activities in hidden layer.'''
self.__hidden_activity_arr = value
hidden_activity_arr = property(get_hidden_activity_arr,
set_hidden_activity_arr)
def main():
# ============================= args setting =================================
args = ArgumentsTrainVal().parse_args()
print('***************************Arguments****************************')
print(args)
checkpoint = None
resume_model = None
resume_optimizer = None
if args.resume_model is not None:
checkpoint = torch.load(args.resume_model)
if 'model' in checkpoint.keys():
resume_model = checkpoint['model']
else:
resume_model = checkpoint
if 'optimizer' in checkpoint.keys():
resume_optimizer = checkpoint['optimizer']
# ============================= model setting =================================
model = construct_model(args, resume_model)
print('--------------------------Model Info----------------------------')
print(model)
# ============================= training setting =================================
train_iterator, validate_iterator = construct_train_dataloaders(args)
criterion = utilities.loss.SoftMax2D(ignore_index=train_iterator.dataset.ignore_lbl)
optimizer = SGD(model.parameters(), args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay)
if resume_optimizer is not None:
optimizer.load_state_dict(resume_optimizer)
engine_set = dict(gpu_ids=args.gpu_ids,
network=model,
criterion=criterion,
train_iterator=train_iterator,
validate_iterator=validate_iterator,
optimizer=optimizer)
# learning rate points
lr_points = []
if args.num_classes == 100:
lr_points = [150, 225]
print('==> Set lr_points: {}'.format(lr_points))
if not os.path.exists(args.checkpoint_save_path):
os.system('mkdir {}'.format(args.checkpoint_save_path))
engine_args = dict(checkpoint_iter_freq=args.checkpoint_iter_freq,
checkpoint_epoch_freq=args.checkpoint_epoch_freq,
checkpoint_save_path=args.checkpoint_save_path,
iter_log_freq=args.iter_log_freq,
environment=args.environment,
lr_points=lr_points,
num_classes=args.num_classes)
engine = construct_engine(engine_set, **engine_args)
engine.resume(args.maxepoch, args.resume_epoch, args.resume_iteration)
