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This commit is contained in:
Yuchen Fang
2021-01-28 00:41:02 +08:00
parent 98086e4fdc
commit a03b08bb4c
21 changed files with 154 additions and 563 deletions
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24
examples/trade/model/ppo.py
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@@ -18,24 +18,12 @@ class PPO_Extractor(nn.Module):
self.rnn = nn.GRU(64, hidden_size, batch_first=True)
self.rnn2 = nn.GRU(64, hidden_size, batch_first=True)
self.dnn = nn.Sequential(
nn.Linear(2, 64),
nn.ReLU(),
)
self.cnn = nn.Sequential(
nn.Conv1d(self.cnn_shape[1], 3, 3),
nn.ReLU(),
)
self.raw_fc = nn.Sequential(
nn.Linear((self.cnn_shape[0] - 2) * 3, 64),
nn.ReLU(),
)
self.dnn = nn.Sequential(nn.Linear(2, 64), nn.ReLU(),)
self.cnn = nn.Sequential(nn.Conv1d(self.cnn_shape[1], 3, 3), nn.ReLU(),)
self.raw_fc = nn.Sequential(nn.Linear((self.cnn_shape[0] - 2) * 3, 64), nn.ReLU(),)
self.fc = nn.Sequential(
nn.Linear(hidden_size * 2, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, 32),
nn.ReLU(),
nn.Linear(hidden_size * 2, hidden_size), nn.ReLU(), nn.Linear(hidden_size, 32), nn.ReLU(),
)
def forward(self, inp):
@@ -74,9 +62,7 @@ class PPO_Actor(nn.Module):
def forward(self, obs, state=None, info={}):
self.feature = self.extractor(obs)
assert not (
torch.isnan(self.feature).any() | torch.isinf(self.feature).any()
), f"{self.feature}"
assert not (torch.isnan(self.feature).any() | torch.isinf(self.feature).any()), f"{self.feature}"
out = self.layer_out(self.feature)
return out, state

15
examples/trade/model/qmodel.py
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@@ -18,18 +18,9 @@ class RNNQModel(nn.Module):
self.rnn = nn.GRU(64, hidden_size, batch_first=True)
self.rnn2 = nn.GRU(64, hidden_size, batch_first=True)
self.dnn = nn.Sequential(
nn.Linear(2, 64),
nn.ReLU(),
)
self.cnn = nn.Sequential(
nn.Conv1d(self.cnn_shape[1], 3, 3),
nn.ReLU(),
)
self.raw_fc = nn.Sequential(
nn.Linear((self.cnn_shape[0] - 2) * 3, 64),
nn.ReLU(),
)
self.dnn = nn.Sequential(nn.Linear(2, 64), nn.ReLU(),)
self.cnn = nn.Sequential(nn.Conv1d(self.cnn_shape[1], 3, 3), nn.ReLU(),)
self.raw_fc = nn.Sequential(nn.Linear((self.cnn_shape[0] - 2) * 3, 64), nn.ReLU(),)
self.fc = nn.Sequential(
nn.Linear(hidden_size * 2, hidden_size),

20
examples/trade/model/teacher.py
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@@ -18,24 +18,12 @@ class Teacher_Extractor(nn.Module):
self.rnn = nn.GRU(64, hidden_size, batch_first=True)
self.rnn2 = nn.GRU(64, hidden_size, batch_first=True)
self.dnn = nn.Sequential(
nn.Linear(2, 64),
nn.ReLU(),
)
self.cnn = nn.Sequential(
nn.Conv1d(self.cnn_shape[1], 3, 3),
nn.ReLU(),
)
self.raw_fc = nn.Sequential(
nn.Linear((self.cnn_shape[0] - 2) * 3, 64),
nn.ReLU(),
)
self.dnn = nn.Sequential(nn.Linear(2, 64), nn.ReLU(),)
self.cnn = nn.Sequential(nn.Conv1d(self.cnn_shape[1], 3, 3), nn.ReLU(),)
self.raw_fc = nn.Sequential(nn.Linear((self.cnn_shape[0] - 2) * 3, 64), nn.ReLU(),)
self.fc = nn.Sequential(
nn.Linear(hidden_size * 2, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, 32),
nn.ReLU(),
nn.Linear(hidden_size * 2, hidden_size), nn.ReLU(), nn.Linear(hidden_size, 32), nn.ReLU(),
)
def forward(self, inp):

39
examples/trade/model/util.py
View File

@@ -11,14 +11,9 @@ from tianshou.data import to_torch
class Attention(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.get_w = nn.Sequential(
nn.Linear(in_dim * 2, in_dim), nn.ReLU(), nn.Linear(in_dim, 1)
)
self.get_w = nn.Sequential(nn.Linear(in_dim * 2, in_dim), nn.ReLU(), nn.Linear(in_dim, 1))
self.fc = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.ReLU(),
)
self.fc = nn.Sequential(nn.Linear(in_dim, out_dim), nn.ReLU(),)
def forward(self, value, key):
key = key.unsqueeze(dim=1)
@@ -34,14 +29,9 @@ class Attention(nn.Module):
class MaskAttention(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.get_w = nn.Sequential(
nn.Linear(in_dim * 2, in_dim), nn.ReLU(), nn.Linear(in_dim, 1)
)
self.get_w = nn.Sequential(nn.Linear(in_dim * 2, in_dim), nn.ReLU(), nn.Linear(in_dim, 1))
self.fc = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.ReLU(),
)
self.fc = nn.Sequential(nn.Linear(in_dim, out_dim), nn.ReLU(),)
def forward(self, value, key, seq_len, maxlen=9):
# seq_len: (batch,)
@@ -61,14 +51,9 @@ class MaskAttention(nn.Module):
class TFMaskAttention(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.get_w = nn.Sequential(
nn.Linear(in_dim * 2, in_dim), nn.ReLU(), nn.Linear(in_dim, 1)
)
self.get_w = nn.Sequential(nn.Linear(in_dim * 2, in_dim), nn.ReLU(), nn.Linear(in_dim, 1))
self.fc = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.ReLU(),
)
self.fc = nn.Sequential(nn.Linear(in_dim, out_dim), nn.ReLU(),)
def forward(self, value, key, seq_len, maxlen=9):
device = value.device
@@ -155,14 +140,10 @@ class DARNN(nn.Module):
def forward(self, inputs):
inputs = inputs.view(-1, self.input_length, self.input_size) # [B, T, F]
today_input = inputs[:, : self.today_length, :]
today_input = torch.cat(
(torch.zeros_like(today_input[:, :1, :]), today_input), dim=1
)
today_input = torch.cat((torch.zeros_like(today_input[:, :1, :]), today_input), dim=1)
prev_input = inputs[:, 240 : 240 + self.prev_length, :]
if self.emb_dim != 0:
embedding = self.pos_emb(
torch.arange(end=self.today_length + 1, device=inputs.device)
)
embedding = self.pos_emb(torch.arange(end=self.today_length + 1, device=inputs.device))
embedding = embedding.repeat([today_input.size()[0], 1, 1])
today_input = torch.cat((today_input, embedding), dim=-1)
prev_outs, _ = self.prev_rnn(prev_input)
@@ -205,8 +186,6 @@ def onehot_enc(y, len):
def sequence_mask(lengths, maxlen=None, dtype=torch.bool, device=None):
if maxlen is None:
maxlen = lengths.max()
mask = ~(
torch.ones((len(lengths), maxlen), device=device).cumsum(dim=1).t() > lengths
).t()
mask = ~(torch.ones((len(lengths), maxlen), device=device).cumsum(dim=1).t() > lengths).t()
mask.type(dtype)
return mask