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models.py
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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, embed_size, output_size):
super(LSTM, self).__init__()
self.hidden_size = hidden_size
# input embedding
self.encoder = nn.Embedding(input_size, embed_size)
# lstm weights
self.weight_fh = nn.Linear(hidden_size, hidden_size)
self.weight_ih = nn.Linear(hidden_size, hidden_size)
self.weight_ch = nn.Linear(hidden_size, hidden_size)
self.weight_oh = nn.Linear(hidden_size, hidden_size)
self.weight_fx = nn.Linear(embed_size, hidden_size)
self.weight_ix = nn.Linear(embed_size, hidden_size)
self.weight_cx = nn.Linear(embed_size, hidden_size)
self.weight_ox = nn.Linear(embed_size, hidden_size)
# decoder
self.decoder = nn.Linear(hidden_size, output_size)
def forward(self, inp, h_0, c_0):
# encode the input characters
inp = self.encoder(inp)
# forget gate
f_g = F.sigmoid(self.weight_fx(inp) + self.weight_fh(h_0))
# input gate
i_g = F.sigmoid(self.weight_ix(inp) + self.weight_ih(h_0))
# output gate
o_g = F.sigmoid(self.weight_ox(inp) + self.weight_oh(h_0))
# intermediate cell state
c_tilda = F.tanh(self.weight_cx(inp) + self.weight_ch(h_0))
# current cell state
cx = f_g * c_0 + i_g * c_tilda
# hidden state
hx = o_g * F.tanh(cx)
out = self.decoder(hx.view(1,-1))
return out, hx, cx
def init_hidden(self):
h_0 = Variable(torch.zeros(1, self.hidden_size)).cuda()
c_0 = Variable(torch.zeros(1, self.hidden_size)).cuda()
return h_0, c_0
class miLSTM(nn.Module):
def __init__(self, input_size, hidden_size, embed_size, output_size):
super(miLSTM, self).__init__()
self.hidden_size = hidden_size
# input embedding
self.encoder = nn.Embedding(input_size, embed_size)
# lstm weights
self.weight_fh = nn.Linear(hidden_size, hidden_size)
self.weight_ih = nn.Linear(hidden_size, hidden_size)
self.weight_zh = nn.Linear(hidden_size, hidden_size)
self.weight_oh = nn.Linear(hidden_size, hidden_size)
self.weight_fx = nn.Linear(embed_size, hidden_size)
self.weight_ix = nn.Linear(embed_size, hidden_size)
self.weight_zx = nn.Linear(embed_size, hidden_size)
self.weight_ox = nn.Linear(embed_size, hidden_size)
# alphas and betas
self.alpha_f = nn.Parameter(torch.ones(1,hidden_size))
self.beta_f1 = nn.Parameter(torch.ones(1,hidden_size))
self.beta_f2 = nn.Parameter(torch.ones(1,hidden_size))
self.alpha_i = nn.Parameter(torch.ones(1,hidden_size))
self.beta_i1 = nn.Parameter(torch.ones(1,hidden_size))
self.beta_i2 = nn.Parameter(torch.ones(1,hidden_size))
self.alpha_o = nn.Parameter(torch.ones(1,hidden_size))
self.beta_o1 = nn.Parameter(torch.ones(1,hidden_size))
self.beta_o2 = nn.Parameter(torch.ones(1,hidden_size))
self.alpha_z = nn.Parameter(torch.ones(1,hidden_size))
self.alpha_z = nn.Parameter(torch.ones(1,hidden_size))
self.beta_z1 = nn.Parameter(torch.ones(1,hidden_size))
self.beta_z2 = nn.Parameter(torch.ones(1,hidden_size))
# decoder
self.decoder = nn.Linear(hidden_size, output_size)
def forward(self, inp, h_0, c_0):
# encode the input characters
inp = self.encoder(inp)
# forget gate
f_g = F.sigmoid(self.alpha_f * self.weight_fx(inp) * self.weight_fh(h_0) +
(self.beta_f1 * self.weight_fx(inp)) + (self.beta_f2 * self.weight_fh(h_0)))
# input gate
i_g = F.sigmoid(self.alpha_i * self.weight_ix(inp) * self.weight_ih(h_0) +
(self.beta_i1 * self.weight_ix(inp)) + (self.beta_i2 * self.weight_ih(h_0)))
# output gate
o_g = F.sigmoid(self.alpha_o * self.weight_ox(inp) * self.weight_oh(h_0) +
(self.beta_o1 * self.weight_ox(inp)) + (self.beta_o2 * self.weight_oh(h_0)))
# block input
z_t = F.tanh(self.alpha_z * self.weight_zx(inp) * self.weight_zh(h_0) +
(self.beta_z1 * self.weight_zx(inp)) + (self.beta_z2 * self.weight_zh(h_0)))
# current cell state
cx = f_g * c_0 + i_g * z_t
# hidden state
hx = o_g * F.tanh(cx)
out = self.decoder(hx.view(1,-1))
return out, hx, cx
def init_hidden(self):
h_0 = Variable(torch.zeros(1, self.hidden_size)).cuda()
c_0 = Variable(torch.zeros(1, self.hidden_size)).cuda()
return h_0, c_0
'''
Same models but with layer normalization
'''
class LayerNormalization(nn.Module):
def __init__(self, hidden_size, eps=1e-5):
super(LayerNormalization, self).__init__()
self.eps = eps
self.hidden_size = hidden_size
self.a2 = nn.Parameter(torch.ones(1, hidden_size), requires_grad=True)
self.b2 = nn.Parameter(torch.zeros(1, hidden_size), requires_grad=True)
def forward(self, z):
mu = torch.mean(z, dim=1)
sigma = torch.std(z, dim=1)
ln_out = (z - mu.expand_as(z)) / (sigma.expand_as(z) + self.eps)
ln_out = ln_out * self.a2 + self.b2
return ln_out
class LN_miLSTM(nn.Module):
def __init__(self, input_size, hidden_size, embed_size, output_size):
super(LN_miLSTM, self).__init__()
self.hidden_size = hidden_size
# input embedding
self.encoder = nn.Embedding(input_size, embed_size)
# lstm weights
self.weight_fh = nn.Linear(hidden_size, hidden_size)
self.weight_ih = nn.Linear(hidden_size, hidden_size)
self.weight_zh = nn.Linear(hidden_size, hidden_size)
self.weight_oh = nn.Linear(hidden_size, hidden_size)
self.weight_fx = nn.Linear(embed_size, hidden_size)
self.weight_ix = nn.Linear(embed_size, hidden_size)
self.weight_zx = nn.Linear(embed_size, hidden_size)
self.weight_ox = nn.Linear(embed_size, hidden_size)
# alphas and betas
self.alpha_f = nn.Parameter(torch.ones(1,hidden_size))
self.beta_f1 = nn.Parameter(torch.zeros(1,hidden_size))
self.beta_f2 = nn.Parameter(torch.zeros(1,hidden_size))
self.alpha_i = nn.Parameter(torch.ones(1,hidden_size))
self.beta_i1 = nn.Parameter(torch.zeros(1,hidden_size))
self.beta_i2 = nn.Parameter(torch.zeros(1,hidden_size))
self.alpha_o = nn.Parameter(torch.ones(1,hidden_size))
self.beta_o1 = nn.Parameter(torch.zeros(1,hidden_size))
self.beta_o2 = nn.Parameter(torch.zeros(1,hidden_size))
self.alpha_z = nn.Parameter(torch.ones(1,hidden_size))
self.beta_z1 = nn.Parameter(torch.zeros(1,hidden_size))
self.beta_z2 = nn.Parameter(torch.zeros(1,hidden_size))
# decoder
self.decoder = nn.Linear(hidden_size, output_size)
# layer normalization
self.lnx = LayerNormalization(hidden_size)
self.lnh = LayerNormalization(hidden_size)
self.lno = LayerNormalization(hidden_size)
def forward(self, inp, h_0, c_0):
# encode the input characters
inp = self.encoder(inp)
# forget gate
f_g = F.sigmoid(self.alpha_f * self.lnx(self.weight_fx(inp)) * self.lnh(self.weight_fh(h_0)) +
(self.beta_f1 * self.lnx(self.weight_fx(inp))) + (self.beta_f2 * self.lnh(self.weight_fh(h_0))))
# input gate
i_g = F.sigmoid(self.alpha_i * self.lnx(self.weight_ix(inp)) * self.lnh(self.weight_ih(h_0)) +
(self.beta_i1 * self.lnx(self.weight_ix(inp))) + (self.beta_i2 * self.lnh(self.weight_ih(h_0))))
# output gate
o_g = F.sigmoid(self.alpha_o * self.lnx(self.weight_ox(inp)) * self.lnh(self.weight_oh(h_0)) +
(self.beta_o1 * self.lnx(self.weight_ox(inp))) + (self.beta_o2 * self.lnh(self.weight_oh(h_0))))
# block input
z_t = F.tanh(self.alpha_z * self.lnx(self.weight_zx(inp)) * self.lnh(self.weight_zh(h_0)) +
(self.beta_z1 * self.lnx(self.weight_zx(inp))) + (self.beta_z2 * self.lnh(self.weight_zh(h_0))))
# current cell state
cx = f_g * c_0 + i_g * z_t
# hidden state
hx = o_g * F.tanh(self.lno(cx))
out = self.decoder(hx.view(1,-1))
return out, hx, cx
def init_hidden(self):
h_0 = Variable(torch.zeros(1, self.hidden_size)).cuda()
c_0 = Variable(torch.zeros(1, self.hidden_size)).cuda()
return h_0, c_0