class lstmwrapper(nn.Module):
def __init__(self,
input_size=66529,
output_size=5952,
hidden_size=52,
num_layers=16,
batch_first=True,
dropout=0.1):
super(lstmwrapper, self).__init__()
self.lstm = LSTM(input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=batch_first,
dropout=dropout)
self.output = nn.Linear(hidden_size, output_size)
self.bn = nn.BatchNorm1d(input_size)
self.reset_parameters()
def reset_parameters(self):
self.lstm.reset_parameters()
self.output.reset_parameters()
def forward(self, input, hx=None):
input = self.bn(input)
output, statetuple = self.lstm(input, hx)
return self.output(output)
class LSTM_vocab(nn.Module):
def __init__(self,
vocab_size=50000,
vocab_embed_d=512,
output_size=12,
hidden_size=256,
*args,
**kwargs):
super(LSTM_vocab, self).__init__()
self.src_word_emb = nn.Embedding(vocab_size,
vocab_embed_d,
padding_idx=0)
self.lstm = LSTM(input_size=vocab_embed_d,
hidden_size=hidden_size,
*args,
**kwargs)
self.output = nn.Linear(hidden_size, output_size)
self.reset_parameters()
def reset_parameters(self):
self.lstm.reset_parameters()
self.output.reset_parameters()
def forward(self, input, hx=None):
input = self.src_word_emb(input)
output, statetuple = self.lstm(input, hx)
# this is a design decision that can be experimented with
output = self.output(output)
# output=torch.max(output,dim=1)[0]
output = output[:, -1, :]
return output
class LSTMWrapper(nn.Module):
def __init__(self, output_size=12, hidden_size=256, *args, **kwargs):
super(LSTMWrapper, self).__init__()
self.lstm = LSTM(hidden_size=hidden_size, *args, **kwargs)
self.output = nn.Linear(hidden_size, output_size)
self.reset_parameters()
def reset_parameters(self):
self.lstm.reset_parameters()
self.output.reset_parameters()
def forward(self, input, hx=None):
output, statetuple = self.lstm(input, hx)
# this is a design decision that can be experimented with
output = self.output(output)
# output=torch.max(output,dim=1)[0]
output = output[:, -1, :]
return output
class lstmwrapperJ(nn.Module):
def __init__(self,
input_size=52686,
output_size=2976,
hidden_size=128,
num_layers=16,
batch_first=True,
dropout=0.1):
super(lstmwrapperJ, self).__init__()
self.lstm = LSTM(input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=batch_first,
dropout=dropout)
self.bn = nn.BatchNorm1d(input_size)
self.output = nn.Linear(hidden_size, output_size)
self.reset_parameters()
for name, param in self.named_parameters():
print(name, param.data.shape)
def reset_parameters(self):
self.lstm.reset_parameters()
self.output.reset_parameters()
def forward(self, input, hx=None):
input = input.permute(0, 2, 1).contiguous()
try:
bnout = self.bn(input)
bnout[(bnout != bnout).detach()] = 0
except ValueError:
if step_input.shape[0] == 1:
print("Somehow the batch size is one for this input")
bnout = step_input
else:
raise
input = bnout.permute(0, 2, 1).contiguous()
output, statetuple = self.lstm(input, hx)
output = self.output(output)
# (batch_size, seq_len, target_dim)
# pdb.set_trace()
# output=output.sum(1)
output = output.max(1)[0]
return output
class Stock_LSTM(nn.Module):
"""
I prefer using this Stock LSTM for numerical stability.
"""
def __init__(self, x, R, W, h, L, v_t):
super(Stock_LSTM, self).__init__()
self.x = x
self.R = R
self.W = W
self.h = h
self.L = L
self.v_t= v_t
self.LSTM=LSTM(input_size=self.x+self.R*self.W,hidden_size=h,num_layers=L,batch_first=True,
dropout=0.1)
self.last=nn.Linear(self.h, self.v_t)
self.st=None
def forward(self, input_x):
"""
:param input_x: input and memory values
:return:
"""
assert (self.st is not None)
o, st = self.LSTM(input_x, self.st)
if (st[0]!=st[0]).any():
with open("debug/lstm.pkl") as f:
pickle.dump(self, f)
with open("debug/lstm.pkl") as f:
pickle.dump(input_x, f)
raise ("LSTM produced a NAN, objects dumped.")
return self.last(o), st
def reset_parameters(self):
self.LSTM.reset_parameters()
self.last.reset_parameters()
def assign_states_tuple(self, states_tuple):
self.st=states_tuple
class PriorLSTM(nn.Module):
def __init__(self,
prior,
input_size=52686,
output_size=2976,
hidden_size=128,
num_layers=16,
batch_first=True,
dropout=0.1):
super(PriorLSTM, self).__init__()
self.lstm = LSTM(input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=batch_first,
dropout=dropout)
self.bn = nn.BatchNorm1d(input_size)
self.output = nn.Linear(hidden_size, output_size)
self.reset_parameters()
self.prior = prior
'''prior'''
# this is the prior probability of each label predicting true
# this is added to the logit
self.prior = prior
if isinstance(self.prior, np.ndarray):
self.prior = torch.from_numpy(self.prior).float()
self.prior = Variable(self.prior, requires_grad=False)
elif isinstance(self.prior, torch.Tensor):
self.prior = Variable(self.prior, requires_grad=False)
else:
assert (isinstance(self.prior, Variable))
# transform to logits
# because we are using sigmoid, not softmax, self.prior=log(P(y))-log(P(not y))
# sigmoid_input = z + self.prior
# z = log(P(x|y)) - log(P(x|not y))
# sigmoid output is the posterior positive
self.prior = self.prior.clamp(1e-8, 1 - 1e-8)
self.prior = torch.log(self.prior) - torch.log(1 - self.prior)
a = Variable(torch.Tensor([0]))
self.prior = torch.cat((a, self.prior))
self.prior = self.prior.cuda()
for name, param in self.named_parameters():
print(name, param.data.shape)
print("Using prior lstm")
def reset_parameters(self):
self.lstm.reset_parameters()
self.output.reset_parameters()
def forward(self, input, hx=None):
input = input.permute(0, 2, 1).contiguous()
bnout = self.bn(input)
bnout[(bnout != bnout).detach()] = 0
input = bnout.permute(0, 2, 1).contiguous()
output, statetuple = self.lstm(input, hx)
output = self.output(output)
# (batch_size, seq_len, target_dim)
# pdb.set_trace()
# output=output.sum(1)
output = output.max(1)[0]
output = output + self.prior
return output
