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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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80
qlib/contrib/model/pytorch_sfm.py
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@@ -56,52 +56,30 @@ class SFM_Model(nn.Module):
self.hidden_dim = hidden_size
self.device = device
self.W_i = nn.Parameter(
init.xavier_uniform_(torch.empty((self.input_dim, self.hidden_dim)))
)
self.U_i = nn.Parameter(
init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim))
)
self.W_i = nn.Parameter(init.xavier_uniform_(torch.empty((self.input_dim, self.hidden_dim))))
self.U_i = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
self.b_i = nn.Parameter(torch.zeros(self.hidden_dim))
self.W_ste = nn.Parameter(
init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim))
)
self.U_ste = nn.Parameter(
init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim))
)
self.W_ste = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim)))
self.U_ste = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
self.b_ste = nn.Parameter(torch.ones(self.hidden_dim))
self.W_fre = nn.Parameter(
init.xavier_uniform_(torch.empty(self.input_dim, self.freq_dim))
)
self.U_fre = nn.Parameter(
init.orthogonal_(torch.empty(self.hidden_dim, self.freq_dim))
)
self.W_fre = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.freq_dim)))
self.U_fre = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.freq_dim)))
self.b_fre = nn.Parameter(torch.ones(self.freq_dim))
self.W_c = nn.Parameter(
init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim))
)
self.U_c = nn.Parameter(
init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim))
)
self.W_c = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim)))
self.U_c = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
self.b_c = nn.Parameter(torch.zeros(self.hidden_dim))
self.W_o = nn.Parameter(
init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim))
)
self.U_o = nn.Parameter(
init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim))
)
self.W_o = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim)))
self.U_o = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
self.b_o = nn.Parameter(torch.zeros(self.hidden_dim))
self.U_a = nn.Parameter(init.orthogonal_(torch.empty(self.freq_dim, 1)))
self.b_a = nn.Parameter(torch.zeros(self.hidden_dim))
self.W_p = nn.Parameter(
init.xavier_uniform_(torch.empty(self.hidden_dim, self.output_dim))
)
self.W_p = nn.Parameter(init.xavier_uniform_(torch.empty(self.hidden_dim, self.output_dim)))
self.b_p = nn.Parameter(torch.zeros(self.output_dim))
self.activation = nn.Tanh()
@@ -137,12 +115,8 @@ class SFM_Model(nn.Module):
x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o
i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i))
ste = self.inner_activation(
x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste)
)
fre = self.inner_activation(
x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre)
)
ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste))
fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre))
ste = torch.reshape(ste, (-1, self.hidden_dim, 1))
fre = torch.reshape(fre, (-1, 1, self.freq_dim))
@@ -331,9 +305,7 @@ class SFM(Model):
elif optimizer.lower() == "gd":
self.train_optimizer = optim.SGD(self.sfm_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
self.sfm_model.to(self.device)
@@ -356,16 +328,8 @@ class SFM(Model):
if len(indices) - i < self.batch_size:
break
feature = (
torch.from_numpy(x_values[indices[i : i + self.batch_size]])
.float()
.to(self.device)
)
label = (
torch.from_numpy(y_values[indices[i : i + self.batch_size]])
.float()
.to(self.device)
)
feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
pred = self.sfm_model(feature)
loss = self.loss_fn(pred, label)
@@ -391,16 +355,8 @@ class SFM(Model):
if len(indices) - i < self.batch_size:
break
feature = (
torch.from_numpy(x_train_values[indices[i : i + self.batch_size]])
.float()
.to(self.device)
)
label = (
torch.from_numpy(y_train_values[indices[i : i + self.batch_size]])
.float()
.to(self.device)
)
feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
pred = self.sfm_model(feature)
loss = self.loss_fn(pred, label)