def create(data_nm, dim=1): data_nm = data_nm.lower() if data_nm == "mnist": if dim == 1: return MNISTDataset.get_tf_dataset_1d() elif data_nm == "era_dataset": return EraDataset.get()
def load(data_nm, dim=1): data_nm = data_nm.lower() if data_nm == "mnist": try: return MNISTDataset.load(dim, True) except Exception as e: print("dimension is max 3") return None, None if data_nm == "cifar10": if dim == 1: return Cifar10Dataset.get_tf_dataset_1d()
if __name__ == '__main__': parameters = { ## model parameters "model_nm": "DNN-test", "algorithm_type": "classifier", "job_type": "learn", ## learning parameters "global_step": "10", "early_type": "none", "min_step": "10", "early_key": "accuracy", "early_value": "0.98", ## algorithm parameters "input_units": "784", "output_units": "10", "hidden_units": "100, 200, 100", "dropout_prob": "0.1", "optimizer_fn": "Adam", "learning_rate": "0.01", "initial_weight": "0.1", "act_fn": "tanh", } dnn = DNN(parameters) dnn.build() from hps.dataset.MNISTDataset import MNISTDataset ds_learn, ds_test = MNISTDataset.get_tf_dataset_1d() dnn.learn(ds_learn) print(dnn.predict(ds_test))