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Fix code with block.

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This commit is contained in:
lwwang1995
2020-11-27 22:44:28 +08:00
parent c5a3b74a96
commit 1353e81b5b
7 changed files with 42 additions and 136 deletions
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32
qlib/contrib/model/pytorch_alstm.py
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@@ -124,9 +124,7 @@ class ALSTM(Model):
elif optimizer.lower() == "gd":
self.train_optimizer = optim.SGD(self.ALSTM_model.parameters(), lr=self.lr)
else:
raise NotImplementedError(
"optimizer {} is not supported!".format(optimizer)
)
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
self._fitted = False
if self.use_gpu:
@@ -171,12 +169,8 @@ class ALSTM(Model):
if len(indices) - i < self.batch_size:
break
feature = torch.from_numpy(
x_train_values[indices[i : i + self.batch_size]]
).float()
label = torch.from_numpy(
y_train_values[indices[i : i + self.batch_size]]
).float()
feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float()
label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float()
if self.use_gpu:
feature = feature.cuda()
@@ -208,9 +202,7 @@ class ALSTM(Model):
if len(indices) - i < self.batch_size:
break
feature = torch.from_numpy(
x_values[indices[i : i + self.batch_size]]
).float()
feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float()
label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float()
if self.use_gpu:
@@ -320,9 +312,7 @@ class ALSTM(Model):
class ALSTMModel(nn.Module):
def __init__(
self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, rnn_type="GRU"
):
def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, rnn_type="GRU"):
super().__init__()
self.hid_size = hidden_size
self.input_size = d_feat
@@ -337,9 +327,7 @@ class ALSTMModel(nn.Module):
except:
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("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,
@@ -365,12 +353,8 @@ class ALSTMModel(nn.Module):
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]
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)