def continue_finetuning(self, epochs, binary_output=False):
"""
Continue finetuning. This will only run if the finetuning has already run.
@param epochs: Number of epochs to run the finetuning.
@param binary_output: If the DBN must output binary values.
"""
self.weight_matrices = load_dbn_weights()
self.load_finetuning_output_txt()
self.print_output('Fine Tuning (continued)')
timer = time()
fine_tuning = FineTuning(self.weight_matrices,
self.batches,
self.print_output,
binary_output=binary_output)
fine_tuning.run_finetuning(epochs)
fine_tuning_error_train = fine_tuning.train_error
fine_tuning_error_test = fine_tuning.test_error
self.print_output('Time ' + str(time() - timer))
save_dbn(fine_tuning.weight_matrices_added_biases,
fine_tuning_error_train, fine_tuning_error_test)
self.output_dbn_errors(fine_tuning_error_train, fine_tuning_error_test)
self.save_output()
def run_finetuning(self, load_from_serialization=False):
"""
Run the finetuning of the DBN. This will only run if the pretraining has been run.
@param load_from_serialization: If the weight matrices are not loaded into memory, this should be True.
"""
try:
if load_from_serialization:
self.weight_matrices, self.hidden_biases, self.visible_biases = load_rbm_weights(
)
except IOError:
print 'Please run pretraining before executing finetuning.'
return
self.print_output('Fine Tuning')
timer = time()
fine_tuning = FineTuning(self.weight_matrices,
self.batches,
self.print_output,
self.hidden_biases,
self.visible_biases,
binary_output=self.binary_output)
fine_tuning.run_finetuning(self.max_epochs)
fine_tuning_error_train = fine_tuning.train_error
fine_tuning_error_test = fine_tuning.test_error
self.print_output('Time ' + str(time() - timer))
save_dbn(fine_tuning.weight_matrices_added_biases,
fine_tuning_error_train, fine_tuning_error_test)
self.output_dbn_errors(fine_tuning_error_train, fine_tuning_error_test)
self.save_output()
def run_finetuning(self, load_from_serialization = False):
"""
Run the finetuning of the DBN. This will only run if the pretraining has been run.
@param load_from_serialization: If the weight matrices are not loaded into memory, this should be True.
"""
try:
if load_from_serialization:
self.weight_matrices,self.hidden_biases,self.visible_biases = load_rbm_weights()
except IOError:
print 'Please run pretraining before executing finetuning.'
return
self.print_output('Fine Tuning')
timer = time()
fine_tuning = FineTuning(self.weight_matrices, self.batches,self.print_output,self.increment_progress,
self.hidden_biases,self.visible_biases,binary_output = self.binary_output)
fine_tuning.run_finetuning(self.max_epochs)
fine_tuning_error_train = fine_tuning.train_error
fine_tuning_error_test = fine_tuning.test_error
if self.plot:
self.plot_finetuning_error(fine_tuning_error_train, fine_tuning_error_test)
self.print_output('Time '+str(time()-timer))
save_dbn(fine_tuning.weight_matrices_added_biases,fine_tuning_error_train,fine_tuning_error_test)
self.output_dbn_errors(fine_tuning_error_train,fine_tuning_error_test)
self.save_output()
def continue_finetuning(self,epochs,binary_output = False):
"""
Continue finetuning. This will only run if the finetuning has already run.
@param epochs: Number of epochs to run the finetuning.
@param binary_output: If the DBN must output binary values.
"""
self.weight_matrices = load_dbn_weights()
self.load_finetuning_output_txt()
self.print_output('Fine Tuning (continued)')
timer = time()
fine_tuning = FineTuning(self.weight_matrices, self.batches,self.print_output,self.increment_progress,binary_output = binary_output)
fine_tuning.run_finetuning(epochs)
fine_tuning_error_train = fine_tuning.train_error
fine_tuning_error_test = fine_tuning.test_error
self.print_output('Time '+str(time()-timer))
save_dbn(fine_tuning.weight_matrices_added_biases,fine_tuning_error_train,fine_tuning_error_test)
self.output_dbn_errors(fine_tuning_error_train,fine_tuning_error_test)
self.save_output()
