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Add ALSTM config

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Jactus
2020-11-25 19:29:30 +08:00
parent 05599d1de8
commit a99db6a1dc
10 changed files with 139 additions and 53 deletions
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42
qlib/contrib/model/pytorch_alstm.py
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@@ -345,7 +345,6 @@ class GRUModel(nn.Module):
return self.fc_out(out[:, -1, :]).squeeze()
class ALSTMModel(nn.Module):
def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, rnn_type="GRU"):
super().__init__()
@@ -360,33 +359,36 @@ class ALSTMModel(nn.Module):
try:
klass = getattr(nn, self.rnn_type.upper())
except:
raise ValueError('unknown rnn_type `%s`' % self.rnn_type)
raise ValueError("unknown rnn_type `%s`" % self.rnn_type)
self.net = nn.Sequential()
self.net.add_module('fc_in', nn.Linear(in_features=self.input_size, out_features=self.hid_size))
self.net.add_module('act', nn.Tanh())
self.rnn = klass(input_size=self.hid_size,
hidden_size=self.hid_size,
num_layers=self.rnn_layer,
batch_first=True,
dropout=self.dropout)
self.fc_out = nn.Linear(in_features=self.hid_size*2, out_features=1)
self.net.add_module("fc_in", nn.Linear(in_features=self.input_size, out_features=self.hid_size))
self.net.add_module("act", nn.Tanh())
self.rnn = klass(
input_size=self.hid_size,
hidden_size=self.hid_size,
num_layers=self.rnn_layer,
batch_first=True,
dropout=self.dropout,
)
self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1)
# self.fc_out = nn.Linear(in_features=self.hid_size, out_features=1)
self.att_net = nn.Sequential()
self.att_net.add_module('att_fc_in', nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size/2)))
self.att_net.add_module('att_dropout', torch.nn.Dropout(self.dropout))
self.att_net.add_module('att_act', nn.Tanh())
self.att_net.add_module('att_fc_out', nn.Linear(in_features=int(self.hid_size/2), out_features=1, bias=False))
self.att_net.add_module('att_softmax', nn.Softmax(dim=1))
self.att_net.add_module("att_fc_in", nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)))
self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout))
self.att_net.add_module("att_act", nn.Tanh())
self.att_net.add_module("att_fc_out", nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False))
self.att_net.add_module("att_softmax", nn.Softmax(dim=1))
def forward(self, inputs):
# inputs: [batch_size, input_size*input_day]
inputs = inputs.view(len(inputs), self.input_size, -1)
inputs = inputs.permute(0, 2, 1) # [batch, input_size, seq_len] -> [batch, seq_len, input_size]
rnn_out, _ = self.rnn(self.net(inputs)) # [batch, seq_len, num_directions * hidden_size]
attention_score = self.att_net(rnn_out) # [batch, seq_len, 1]
inputs = inputs.permute(0, 2, 1) # [batch, input_size, seq_len] -> [batch, seq_len, input_size]
rnn_out, _ = self.rnn(self.net(inputs)) # [batch, seq_len, num_directions * hidden_size]
attention_score = self.att_net(rnn_out) # [batch, seq_len, 1]
out_att = torch.mul(rnn_out, attention_score)
out_att = torch.sum(out_att, dim=1)
out = self.fc_out(torch.cat((rnn_out[:, -1, :], out_att), dim=1)) # [batch, seq_len, num_directions * hidden_size] -> [batch, 1]
out = self.fc_out(
torch.cat((rnn_out[:, -1, :], out_att), dim=1)
) # [batch, seq_len, num_directions * hidden_size] -> [batch, 1]
# out = self.fc_out(rnn_out[:, -1, :] + out_att)
return out[..., 0]