Created
May 24, 2020 22:15
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def forward(self, | |
x, | |
init_states=None): | |
""" | |
assumes x.shape represents (batch_size, sequence_size, input_size) | |
""" | |
bs, seq_sz, _ = x.size() | |
hidden_seq = [] | |
if init_states is None: | |
h_t, c_t = ( | |
torch.zeros(bs, self.hidden_size).to(x.device), | |
torch.zeros(bs, self.hidden_size).to(x.device), | |
) | |
else: | |
h_t, c_t = init_states | |
for t in range(seq_sz): | |
x_t = x[:, t, :] | |
i_t = torch.sigmoid(x_t @ self.U_i + h_t @ self.V_i + self.b_i) | |
f_t = torch.sigmoid(x_t @ self.U_f + h_t @ self.V_f + self.b_f) | |
g_t = torch.tanh(x_t @ self.U_c + h_t @ self.V_c + self.b_c) | |
o_t = torch.sigmoid(x_t @ self.U_o + h_t @ self.V_o + self.b_o) | |
c_t = f_t * c_t + i_t * g_t | |
h_t = o_t * torch.tanh(c_t) | |
hidden_seq.append(h_t.unsqueeze(0)) | |
#reshape hidden_seq p/ retornar | |
hidden_seq = torch.cat(hidden_seq, dim=0) | |
hidden_seq = hidden_seq.transpose(0, 1).contiguous() | |
return hidden_seq, (h_t, c_t) |
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