import torch import pandas as pd from torch.utils.data import DataLoader from torch.utils.data import TensorDataset from output_writer import OutputWriter ow = OutputWriter(5) ow.write_to_csv(verbose=True) # Create Tensors to hold dependent/independent variable data train_csv = ow.get_cached_csv("train") train_ind = pd.read_csv(train_csv)[["f1", "f2", "f3", "f4", "f5"]] train_dep = pd.read_csv(train_csv)[["phone_class_index"]] x = torch.from_numpy(train_ind.values).float() y = torch.from_numpy(train_dep.values).long() print(x) print(y) # Create a TensorDataset and DataLoader to provide the model with batches of data train_ds = TensorDataset(x, y) train_dl = DataLoader(train_ds, batch_size=32) ##### Set model layer dimensions ### D_in is the input dimension (5, one for each estimated formant) D_in = x.shape[1] ### H is the hidden layer dimension H = 16 ### C is the number of final categories (there are 14 monophthongs) C = 14