y_pred) # I haven't checked the log_loss
return logloss
''' # for testing the classification accuracy
predictions_binary = []
for item in y_pred:
if item > 0.5:
predictions_binary.append(1.0)
else:
predictions_binary.append(0.0)
Accuracy = accuracy_score(y_true, predictions_binary)
return Accuracy '''
if __name__ == '__main__':
# Data loading
args = parse_args()
data = DATA.LoadData_cut_dire(args.path, args.dataset, args.loss_type)
if args.verbose > 0:
print(
"Neural FM: dataset=%s, hidden_factor=%d, dropout_keep=%s, layers=%s, loss_type=%s, pretrain=%d, #epoch=%d, batch=%d, lr=%.4f, lambda=%.4f, optimizer=%s, batch_norm=%d, activation=%s, early_stop=%d"
% (args.dataset, args.hidden_factor, args.keep_prob, args.layers,
args.loss_type, args.pretrain, args.epoch, args.batch_size,
args.lr, args.lamda, args.optimizer, args.batch_norm,
args.activation, args.early_stop))
activation_function = tf.nn.relu
if args.activation == 'sigmoid':
activation_function = tf.sigmoid
elif args.activation == 'tanh':
activation_function == tf.tanh
elif args.activation == 'identity':
activation_function = tf.identity
