parser.add_argument("--expr_nb_variables", type=int, default=5)
-parser.add_argument("--expr_sequence_length", type=int, default=30)
+parser.add_argument("--expr_sequence_length", type=int, default=40)
+
+parser.add_argument("--expr_operand_max", type=int, default=9)
+
+parser.add_argument("--expr_result_max", type=int, default=99)
+
+parser.add_argument("--expr_input_file", type=str, default=None)
######################################################################
"nb_test_samples": 1000,
},
"expr": {
- "nb_epochs": 50,
+ "nb_epochs": 40,
"batch_size": 25,
- "nb_train_samples": 250000,
+ "nb_train_samples": 1000000,
"nb_test_samples": 10000,
},
}
nb_test_samples=args.nb_test_samples,
nb_variables=args.expr_nb_variables,
sequence_length=args.expr_sequence_length,
+ operand_max=args.expr_operand_max,
+ result_max=args.expr_result_max,
batch_size=args.batch_size,
device=device,
)
######################################################################
+if args.task == "expr" and args.expr_input_file is not None:
+ task.produce_results(
+ nb_epochs_finished,
+ model,
+ args.result_dir,
+ log_string,
+ args.deterministic_synthesis,
+ args.expr_input_file,
+ )
+
+ exit(0)
+
+######################################################################
+
nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
# Compute the entropy of the training tokens