except IOError, e:
logger.error("IOError:" + str(e))
logger.error('Model cannot be initialize from input file ')
exit(2)
########################
# FINETUNING THE MODEL #
########################
if model_config['processes']['finetuning']:
fineTunning(cnn, model_config, data_spec)
########################
# TESTING THE MODEL #
########################
if model_config['processes']['testing']:
testing(cnn, data_spec)
##########################
## Export Features ##
##########################
if model_config['processes']['export_data']:
exportFeatures(cnn, model_config, data_spec)
logger.info('Saving model to ' + str(model_config['output_file']) + '....')
cnn.save(filename=model_config['output_file'])
logger.info('Saved model to ' + str(model_config['output_file']))
"""
##############################
## Plotting Layer output ##
##############################
if model_config['processes']['plotting']:
if model_config['processes']['pretraining']:
train_sets = read_dataset(data_spec['training'])
preTraining(dbn, train_sets, model_config['pretrain_params'])
del train_sets
########################
# FINETUNING THE MODEL #
########################
if model_config['processes']['finetuning']:
fineTunning(dbn, model_config, data_spec)
########################
# TESTING THE MODEL #
########################
if model_config['processes']['testing']:
testing(dbn, data_spec)
##########################
# Export Features ##
##########################
if model_config['processes']['export_data']:
exportFeatures(dbn, model_config, data_spec)
logger.info('Saving model to ' + str(model_config['output_file']) + '....')
dbn.save(filename=model_config['output_file'])
logger.info('Saved model to ' + str(model_config['output_file']))
if __name__ == '__main__':
import sys
setLogger()
runRBM(sys.argv[1])
exit(2)
########################
# FINETUNING THE MODEL #
########################
if model_config['processes']['finetuning']:
fineTunning(cnn,model_config,data_spec)
########################
# TESTING THE MODEL #
########################
if model_config['processes']['testing']:
testing(cnn,data_spec)
##########################
## Export Features ##
##########################
if model_config['processes']['export_data']:
exportFeatures(cnn,model_config,data_spec)
logger.info('Saving model to ' + str(model_config['output_file'])+ '....')
cnn.save(filename=model_config['output_file']);
logger.info('Saved model to ' + str(model_config['output_file']))
"""
##############################
if model_config['processes']['pretraining']:
train_sets = read_dataset(data_spec['training'])
preTraining(dbn,train_sets,model_config['pretrain_params'])
del train_sets;
########################
# FINETUNING THE MODEL #
########################
if model_config['processes']['finetuning']:
fineTunning(dbn,model_config,data_spec)
########################
# TESTING THE MODEL #
########################
if model_config['processes']['testing']:
testing(dbn,data_spec)
##########################
# Export Features ##
##########################
if model_config['processes']['export_data']:
exportFeatures(dbn,model_config,data_spec)
logger.info('Saving model to ' + str(model_config['output_file']) + '....')
dbn.save(filename=model_config['output_file'])
logger.info('Saved model to ' + str(model_config['output_file']))
if __name__ == '__main__':
import sys
setLogger();
runRBM(sys.argv[1])
except IOError, e:
logger.error("IOError:"+str(e));
logger.error('Model cannot be initialize from input file ')
sys.exit(2)
########################
# FINETUNING THE MODEL #
########################
if model_config['processes']['finetuning']:
fineTunning(dnn,model_config,data_spec)
########################
# TESTING THE MODEL #
########################
if model_config['processes']['testing']:
testing(dnn,data_spec)
##########################
## Export Features ##
##########################
if model_config['processes']['export_data']:
exportFeatures(dnn,model_config,data_spec)
logger.info('Saving model to ' + str(model_config['output_file']) + '....')
dnn.save(filename=model_config['output_file'])
logger.info('Saved model to ' + str(model_config['output_file']))
if __name__ == '__main__':
import sys
def runSdA(arg):
if type(arg) is dict:
model_config = arg
else :
model_config = load_model(arg,'SDA')
sda_config = load_sda_spec(model_config['nnet_spec'])
data_spec = load_data_spec(model_config['data_spec'],model_config['batch_size']);
# numpy random generator
numpy_rng = numpy.random.RandomState(model_config['random_seed'])
#theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
#get Activation function
activationFn = parse_activation(sda_config['activation']);
createDir(model_config['wdir']);
#create working dir
logger.info('building the model')
# construct the stacked denoising autoencoder class
sda = SDA(numpy_rng=numpy_rng, n_ins=model_config['n_ins'],
hidden_layers_sizes=sda_config['hidden_layers'],
n_outs=model_config['n_outs'],activation=activationFn)
batch_size = model_config['batch_size'];
#########################
# PRETRAINING THE MODEL #
#########################
if model_config['processes']['pretraining']:
train_sets = read_dataset(data_spec['training'])
pretraining_config = model_config['pretrain_params']
corruption_levels = sda_config['corruption_levels']
preTraining(sda,train_sets,corruption_levels,pretraining_config);
del train_sets;
########################
# FINETUNING THE MODEL #
########################
if model_config['processes']['finetuning']:
fineTunning(sda,model_config,data_spec)
########################
# TESTING THE MODEL #
########################
if model_config['processes']['testing']:
testing(sda,data_spec)
##########################
## Export Features ##
##########################
if model_config['processes']['export_data']:
exportFeatures(sda,model_config,data_spec)
# save the pretrained nnet to file
logger.info('Saving model to ' + str(model_config['output_file']) + '....')
sda.save(filename=model_config['output_file'], withfinal=True)
logger.info('Saved model to ' + str(model_config['output_file']))
