def train_model(prefix,
interpreter: Interpreter,
io_examples_tr,
io_examples_val,
io_examples_test,
task_info,
save=False,
dir_path=None,
last_recogniser_ti=None,
last_rnn_ti=None,
load=False):
if task_info.task_type == TaskType.Recognise:
output_type = ProgramOutputType.SIGMOID
else:
output_type = ProgramOutputType.INTEGER
data_loader_tr = interpreter._get_data_loader(io_examples_tr)
data_loader_val = interpreter._get_data_loader(io_examples_val)
data_loader_test = interpreter._get_data_loader(io_examples_test)
program, new_fns_dict, parameters = get_model(task_info,
dir_path,
last_recogniser_ti,
last_rnn_ti,
load=load)
new_fns_dict, max_accuracy_val, evaluations_np = interpreter.learn_neural_network_(
program,
output_type=output_type,
new_fns_dict=new_fns_dict,
trainable_parameters=list(parameters),
data_loader_tr=data_loader_tr,
data_loader_val=data_loader_val,
data_loader_test=data_loader_test)
max_accuracy_test = interpreter._get_accuracy(program, data_loader_test,
output_type, new_fns_dict)
print(max_accuracy_test)
num_examples = io_examples_tr[0].shape[0] if type(
io_examples_tr) == tuple else io_examples_tr[0][0].shape[0]
np.save(
"{}/_{}__{}evaluations_np.npy".format(dir_path, prefix, num_examples),
evaluations_np)
if save:
for key, value in new_fns_dict.items():
value.save("{}/Models/".format(dir_path))
return {"accuracy": max_accuracy_test}
def train_summer(type,
interpreter: Interpreter,
io_examples_tr,
io_examples_val,
io_examples_test,
dir_path,
save=False):
output_type = ProgramOutputType.INTEGER
if type == "sa" or type == "wt":
program, new_fns_dict, parameters = get_model_summer(dir_path,
load=type == "wt")
else:
program, new_fns_dict, parameters = get_model_summer_pnn(dir_path)
# output_type = ProgramOutputType.INTEGER
data_loader_tr = interpreter._get_data_loader(io_examples_tr)
data_loader_val = interpreter._get_data_loader(io_examples_val)
data_loader_test = interpreter._get_data_loader(io_examples_test)
new_fns_dict, max_accuracy_val, _, evaluations_np = interpreter.learn_neural_network_(
program,
output_type=output_type,
new_fns_dict=new_fns_dict,
trainable_parameters=list(parameters),
data_loader_tr=data_loader_tr,
data_loader_val=data_loader_val,
data_loader_test=data_loader_test)
max_accuracy_test = interpreter._get_accuracy(program, data_loader_test,
output_type, new_fns_dict)
print(max_accuracy_test)
# num_examples = io_examples_tr[0].shape[0] if type(io_examples_tr) == tuple else io_examples_tr[0][0].shape[0]
# np.save("{}/_{}__{}evaluations_np.npy".format(dir_path, prefix, num_examples), evaluations_np)
if save:
for key, value in new_fns_dict.items():
value.save("{}/Models/".format(dir_path))
return {"accuracy": max_accuracy_test}