Hello all,
I am trying to make a CNN model for Encoder Parameter Suggestion for Audio data
Input: Audio File(Length : 480000 samples)
Output: Parameter(Length : 469, eg :[0001111222001233312000] )
I am getting very low Validation and Test Accuracy
class AudioClassifier(nn.Module):
    def __init__(self):
        super(AudioClassifier, self).__init__()

        self.conv11 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=Kernel_size_conv, stride=1,
                                bias=True, padding=int((Kernel_size_conv - 1) / 2))
        self.conv12 = nn.Conv1d(in_channels=16, out_channels=16, kernel_size=Kernel_size_conv, stride=1,
                                bias=True, padding=int((Kernel_size_conv - 1) / 2))
        self.conv13 = nn.Conv1d(in_channels=16, out_channels=4, kernel_size=Kernel_size_conv,
                                stride=1024, bias=True)

    def forward(self, X):
        out1 = torch.tanh(self.conv11(X))
        out1 = torch.tanh(self.conv12(out1))
        out1 = (self.conv13(out1))
        return out1

optimizer = torch.optim.Adam(model.parameters(),lr=1e-2,  weight_decay=1e-5)
loss_fn = nn.CrossEntropyLoss()
    for epoch in range(E):
        for batch in range(B)
            pred_1= model(training_data)
            loss = loss_fn(pred_1, Target)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

Can Anybody suggest any other approach to rectify it.