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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 | |