def __init__(self):
super(baseline_lstm, self).__init__()
self.encoder_fc = layers.SequenceWise(nn.Linear(60, 32))
self.encoder_dropout = layers.SequenceWise(nn.Dropout(0.3))
self.seq_model = nn.LSTM(32, 32, 1, bidirectional=True, batch_first=True)
self.final_fc = nn.Linear(32, 3)
def __init__(self):
super(attentionlstm, self).__init__()
self.encoder_fc = layers.SequenceWise(nn.Linear(60, 32))
self.encoder_dropout = layers.SequenceWise(nn.Dropout(0.3))
self.seq_model = nn.LSTM(32,
64,
1,
bidirectional=True,
batch_first=True)
self.final_fc = nn.Linear(128, 3)
self.scale = 1. / math.sqrt(60)
def __init__(self):
super(baseline_lstm, self).__init__()
self.encoder_fc = layers.SequenceWise(nn.Linear(60, 32))
self.encoder_dropout = layers.SequenceWise(nn.Dropout(0.7))
self.seq_model = nn.LSTM(64,
64,
1,
bidirectional=True,
batch_first=True)
self.prefinal_fc = layers.SequenceWise(nn.Linear(128, 32))
self.final_fc = nn.Linear(128, 3)
self.cnn = nn.Conv1d(in_channels=32,
out_channels=64,
kernel_size=3,
stride=2) #3,5,7