def pretrain(self, train_set, validation_set=None):
self.do_pretrain = True
def set_params_func(rbmmachine, rbmgraph):
params = rbmmachine.get_model_parameters(graph=rbmgraph)
self.encoding_w_.append(params['W'])
self.encoding_b_.append(params['bh_'])
return SupervisedModel.pretrain_procedure(self, self.rbms, self.rbm_graphs, set_params_func=set_params_func,
train_set=train_set, validation_set=validation_set)
def pretrain(self, train_set, validation_set=None):
self.do_pretrain = True
def set_params_func(autoenc, autoencgraph):
params = autoenc.get_model_parameters(graph=autoencgraph)
self.encoding_w_.append(params['enc_w'])
self.encoding_b_.append(params['enc_b'])
return SupervisedModel.pretrain_procedure(self, self.autoencoders, self.autoencoder_graphs,
set_params_func=set_params_func, train_set=train_set,
validation_set=validation_set)
def pretrain(self, train_set, validation_set=None):
"""Perform Unsupervised pretraining of the autoencoder."""
self.do_pretrain = True
def set_params_func(autoenc, autoencgraph):
params = autoenc.get_model_parameters(graph=autoencgraph)
self.encoding_w_.append(params['enc_w'])
self.encoding_b_.append(params['enc_b'])
return SupervisedModel.pretrain_procedure(
self, self.autoencoders, self.autoencoder_graphs,
set_params_func=set_params_func, train_set=train_set,
validation_set=validation_set)
def pretrain(self, train_set, validation_set=None):
self.do_pretrain = True
def set_params_func(rbmmachine, rbmgraph):
params = rbmmachine.get_model_parameters(graph=rbmgraph)
self.encoding_w_.append(params['W'])
self.encoding_b_.append(params['bh_'])
return SupervisedModel.pretrain_procedure(
self,
self.rbms,
self.rbm_graphs,
set_params_func=set_params_func,
train_set=train_set,
validation_set=validation_set)
def pretrain(self, train_set, validation_set=None):
"""Perform Unsupervised pretraining of the autoencoder."""
self.do_pretrain = True
def set_params_func(autoenc, autoencgraph):
params = autoenc.get_model_parameters(graph=autoencgraph)
self.encoding_w_.append(params["enc_w"])
self.encoding_b_.append(params["enc_b"])
return SupervisedModel.pretrain_procedure(
self,
self.autoencoders,
self.autoencoder_graphs,
set_params_func=set_params_func,
train_set=train_set,
validation_set=validation_set,
)
