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rnn_modules.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import os
class GRUCell(nn.Module):
"""Implementation of GRU cell from https://arxiv.org/pdf/1406.1078.pdf."""
def __init__(self, input_size, hidden_size, bias=False):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
# Learnable weights and bias for `update gate`
self.W_z = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b_z = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_z', None)
# Learnable weights and bias for `reset gate`
self.W_r = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b_r = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_r', None)
# Learnable weights and bias for `output gate`
self.W = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b', None)
self.reset_parameters()
def forward(self, x, prev_state):
if prev_state is None:
batch = x.shape[0]
prev_h = torch.zeros((batch, self.hidden_size), device=x.device)
else:
prev_h = prev_state
concat_hx = torch.cat((prev_h, x), dim=1)
z = torch.sigmoid(F.linear(concat_hx, self.W_z, self.b_z))
r = torch.sigmoid(F.linear(concat_hx, self.W_r, self.b_r))
h_tilde = torch.tanh(
F.linear(torch.cat((r * prev_h, x), dim=1), self.W, self.b))
next_h = (1 - z) * prev_h + z * h_tilde
return next_h
def reset_parameters(self):
sqrt_k = (1. / self.hidden_size)**0.5
with torch.no_grad():
for param in self.parameters():
param.uniform_(-sqrt_k, sqrt_k)
return
def extra_repr(self):
return 'input_size={}, hidden_size={}, bias={}'.format(
self.input_size, self.hidden_size, self.bias is not True)
def count_parameters(self):
print('Total Parameters: %d' %
sum(p.numel() for p in self.parameters() if p.requires_grad))
return
class LSTMCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=False):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
#####################################################################
# Learnable weights and bias for `forget gate`
self.W_f = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b_f = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_f', None)
# Learnable weights and bias for `input gate`
self.W_i = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b_i = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_i', None)
# Learnable weights and bias for `output gate`
self.W_o = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b_o = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_o', None)
# Learnable weights and bias for `cell weights`
self.W_c = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b_c = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_c', None)
self.reset_parameters()
#####################################################################
return
def forward(self, x, prev_state):
#####################################################################
# Implement here following the given signature #
if prev_state is None:
batch = x.shape[0]
prev_h = torch.zeros((batch, self.hidden_size), device=x.device)
prev_c = torch.zeros((batch, self.hidden_size), device=x.device)
else:
prev_h, prev_c = prev_state
concat_hx = torch.cat((prev_h, x), dim=1)
f = torch.sigmoid(F.linear(concat_hx, self.W_f, self.b_f))
i = torch.sigmoid(F.linear(concat_hx, self.W_i, self.b_i))
c_tilde = torch.tanh(F.linear(concat_hx, self.W_c, self.b_c))
new_c = (f * prev_c) + (i * c_tilde)
o = torch.sigmoid(F.linear(concat_hx, self.W_o, self.b_o))
h = o * torch.tanh(new_c)
#####################################################################
#return h, new_c
return (h, new_c),(f,i,c_tilde)
def reset_parameters(self):
sqrt_k = (1. / self.hidden_size)**0.5
with torch.no_grad():
for param in self.parameters():
param.uniform_(-sqrt_k, sqrt_k)
return
def extra_repr(self):
return 'input_size={}, hidden_size={}, bias={}'.format(
self.input_size, self.hidden_size, self.bias is not True)
def count_parameters(self):
print('Total Parameters: %d' %
sum(p.numel() for p in self.parameters() if p.requires_grad))
return
class PeepholedLSTMCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=False):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
#####################################################################
# Implement here following the given signature #
self.W_f = nn.Parameter(torch.Tensor(hidden_size, 2*hidden_size + input_size))
if bias:
self.b_f = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_f', None)
# Learnable weights and bias for `input gate`
self.W_i = nn.Parameter(torch.Tensor(hidden_size, 2*hidden_size + input_size))
if bias:
self.b_i = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_i', None)
# Learnable weights and bias for `output gate`
self.W_o = nn.Parameter(torch.Tensor(hidden_size, 2*hidden_size + input_size))
if bias:
self.b_o = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_o', None)
# Learnable weights and bias for `cell weights`
self.W_c = nn.Parameter(torch.Tensor(hidden_size, 2*hidden_size + input_size))
if bias:
self.b_c = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_c', None)
self.reset_parameters()
#####################################################################
return
def forward(self, x, prev_state):
#####################################################################
# Implement here following the given signature #
if prev_state is None:
batch = x.shape[0]
prev_h = torch.zeros((batch, self.hidden_size), device=x.device)
prev_c = torch.zeros((batch, self.hidden_size), device=x.device)
else:
prev_h, prev_c = prev_state
concat_hx = torch.cat((prev_c,prev_h, x), dim=1)
f = torch.sigmoid(F.linear(concat_hx, self.W_f, self.b_f))
i = torch.sigmoid(F.linear(concat_hx, self.W_i, self.b_i))
c_tilde = torch.tanh(F.linear(concat_hx, self.W_c, self.b_c))
new_c = (f * prev_c) + (i * c_tilde)
o = torch.sigmoid(F.linear(torch.cat((new_c,prev_h, x), dim=1), self.W_o, self.b_o))
h = o * torch.tanh(new_c)
#####################################################################
return h, new_c
def reset_parameters(self):
sqrt_k = (1. / self.hidden_size)**0.5
with torch.no_grad():
for param in self.parameters():
param.uniform_(-sqrt_k, sqrt_k)
return
def extra_repr(self):
return 'input_size={}, hidden_size={}, bias={}'.format(
self.input_size, self.hidden_size, self.bias is not True)
def count_parameters(self):
print('Total Parameters: %d' %
sum(p.numel() for p in self.parameters() if p.requires_grad))
return
class CoupledLSTMCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=False):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
#####################################################################
# Implement here following the given signature #
self.W_f = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b_f = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_f', None)
# Learnable weights and bias for `output gate`
self.W_o = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b_o = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_o', None)
# Learnable weights and bias for `cell weights`
self.W_c = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))
if bias:
self.b_c = nn.Parameter(torch.Tensor(hidden_size))
else:
self.register_parameter('b_c', None)
self.reset_parameters()
#####################################################################
return
def forward(self, x, prev_state):
#####################################################################
# Implement here following the given signature #
if prev_state is None:
batch = x.shape[0]
prev_h = torch.zeros((batch, self.hidden_size), device=x.device)
prev_c = torch.zeros((batch, self.hidden_size), device=x.device)
else:
prev_h, prev_c = prev_state
concat_hx = torch.cat((prev_h, x), dim=1)
f = torch.sigmoid(F.linear(concat_hx, self.W_f, self.b_f))
c_tilde = torch.tanh(F.linear(concat_hx, self.W_c, self.b_c))
new_c = (f * prev_c) + ((1 - f) * c_tilde)
o = torch.sigmoid(F.linear(concat_hx, self.W_o, self.b_o))
h = o * torch.tanh(new_c)
#####################################################################
return h, new_c
def reset_parameters(self):
sqrt_k = (1. / self.hidden_size)**0.5
with torch.no_grad():
for param in self.parameters():
param.uniform_(-sqrt_k, sqrt_k)
return
def extra_repr(self):
return 'input_size={}, hidden_size={}, bias={}'.format(
self.input_size, self.hidden_size, self.bias is not True)
def count_parameters(self):
print('Total Parameters: %d' %
sum(p.numel() for p in self.parameters() if p.requires_grad))
return