def __init__(self, inp_size, hidden_size):

super().__init__()

self.inp_size = inp_size

self.hidden_size = hidden_size

self.i2h = nn.Linear(inp_size, 4 * hidden_size)

self.h2h = nn.Linear(hidden_size, 4 * hidden_size)

self.reset_parameters()

def reset_parameters(self):

stdv = 1.0 / math.sqrt(self.hidden_size)

for weight in self.parameters():

weight.data.uniform_(-stdv, stdv)

def forward(self, x, hid_state):

h, c = hid_state

preact = self.i2h(x) self.h2h(h)

gates = preact[:, :3 * self.hidden_size].sigmoid()

g_t = preact[:, 3 * self.hidden_size:].tanh()

i_t = gates[:, :self.hidden_size]

f_t = gates[:, self.hidden_size:2 * self.hidden_size]

o_t = gates[:, -self.hidden_size:]

c_t = torch.mul(c, f_t) torch.mul(i_t, g_t)

h_t = torch.mul(o_t, c_t.tanh())

return h_t, (h_t, c_t)