| import os |
| import torch |
| import torch.nn as nn |
| import pytorch_lightning as pl |
| from sklearn import metrics |
| from transformers import AutoModelForAudioClassification |
| import numpy as np |
|
|
| class FeedforwardModel(nn.Module): |
| def __init__(self, input_size, output_size): |
| super(FeedforwardModel, self).__init__() |
| self.model = nn.Sequential( |
| nn.Linear(input_size, 1024), |
| nn.BatchNorm1d(1024), |
| nn.ReLU(), |
| nn.Dropout(0.3), |
|
|
| nn.Linear(1024, 512), |
| nn.BatchNorm1d(512), |
| nn.ReLU(), |
| nn.Dropout(0.3), |
|
|
| nn.Linear(512, 256), |
| nn.BatchNorm1d(256), |
| nn.ReLU(), |
| nn.Dropout(0.3), |
|
|
| nn.Linear(256, 128), |
| nn.BatchNorm1d(128), |
| nn.ReLU(), |
| nn.Dropout(0.3), |
|
|
| nn.Linear(128, output_size), |
| ) |
|
|
| def forward(self, x): |
| logit = self.model(x) |
| return logit |
| |
|
|
