def objective(args):
params = {}
params['l1_reg'] = args['l1_reg']
params['l2_reg'] = args['l2_reg']
params['num_layers'] = args['num_layers']
params['layer_size'] = args['layer_size']
params['learning_rate'] = args['learning_rate']
params['batch_size'] = args['batch_size']
params['dropout_keep_probability'] = args['dropout_keep_probability']
params['validation_window'] = args['validation_window']
params['total_columns'] = total_columns
with tf.Graph().as_default():
inverse_valid_auc, train_loss, train_auc, valid_loss = run_MLP(
params, trows, vrows)
return {
'loss': inverse_valid_auc,
'train_accu_str': train_loss,
'train_auc': train_auc,
'valid_loss': valid_loss,
'valid_auc': 1 - inverse_valid_auc,
'status': STATUS_OK
}
def objective(args):
config = config_reader.read_config(utils.abs_path_of("config/default.ini"))
params = {}
params['l1_reg'] = args['l1_reg']
params['l2_reg'] = args['l2_reg']
params['num_layers'] = int(args['num_layers'])
params['layer_size'] = int(args['layer_size'])
params['learning_rate'] = args['learning_rate']
params['batch_size'] = args['batch_size']
params['dropout_keep_probability'] = args['dropout_keep_probability']
params['validation_window'] = args['validation_window']
trows = csv_reader.read_csv_dataframe(
config.get_rel_path("PATHS", "training_file"))
vrows = csv_reader.read_csv_dataframe(
config.get_rel_path("PATHS", "validation_file"))
with open(config.get_rel_path("PATHS", "training_file")) as f:
temporary_reader = csv.reader(f, delimiter=',')
total_columns = len(next(temporary_reader))
params['total_columns'] = total_columns
with tf.Graph().as_default():
loss = run_MLP(params, trows, vrows)
return loss
def objective(args):
params = {}
params['l1_reg'] = args['l1_reg']
params['l2_reg'] = args['l2_reg']
params['num_layers'] = int(args['num_layers'])
params['layer_size'] = int(args['layer_size'])
params['learning_rate'] = args['learning_rate']
params['batch_size'] = args['batch_size']
params['dropout_keep_probability'] = args['dropout_keep_probability']
params['validation_window'] = args['validation_window']
with tf.Graph().as_default():
loss = run_MLP(params)
return loss
def objective(args):
config = config_reader.read_config(utils.abs_path_of("config/default.ini"))
params = {}
params['l1_reg'] = args['l1_reg']
params['l2_reg'] = args['l2_reg']
params['num_layers'] = args['num_layers']
params['layer_size'] = args['layer_size']
params['learning_rate'] = args['learning_rate']
params['batch_size'] = args['batch_size']
params['dropout_keep_probability'] = args['dropout_keep_probability']
params['validation_window'] = args['validation_window']
trows = csv_reader.read_csv_dataframe(
config.get_rel_path("PATHS", "training_file"))
vrows = csv_reader.read_csv_dataframe(
config.get_rel_path("PATHS", "validation_file"))
with tf.Graph().as_default():
loss = run_MLP(params, trows, vrows)
return loss