Thesis/pytorch-dnc
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init rnn hidden states

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This commit is contained in:
ixaxaar 2017-12-10 12:06:01 +05:30
parent 9734e9014e
commit e4eb9a53e6
2 changed files with 10 additions and 3 deletions
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6
dnc/dnc.py
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@ -13,6 +13,8 @@ from torch.nn.utils.rnn import PackedSequence
from .util import * from .util import *
from .memory import * from .memory import *
from torch.nn.init import orthogonal, xavier_uniform
class DNC(nn.Module): class DNC(nn.Module):
@ -114,6 +116,7 @@ class DNC(nn.Module):
# final output layer # final output layer
self.output = nn.Linear(self.nn_output_size, self.input_size) self.output = nn.Linear(self.nn_output_size, self.input_size)
orthogonal(self.output.weight)
if self.gpu_id != -1: if self.gpu_id != -1:
[x.cuda(self.gpu_id) for x in self.rnns] [x.cuda(self.gpu_id) for x in self.rnns]
@ -127,7 +130,8 @@ class DNC(nn.Module):
# initialize hidden state of the controller RNN # initialize hidden state of the controller RNN
if chx is None: if chx is None:
chx = [None for x in range(self.num_layers)] h = cuda(T.zeros(self.num_hidden_layers, batch_size, self.output_size), gpu_id=self.gpu_id)
xavier_uniform(h)
# Last read vectors # Last read vectors
if last_read is None: if last_read is None:

7
dnc/sdnc.py
View File

@ -9,7 +9,7 @@ import numpy as np
from torch.nn.utils.rnn import pad_packed_sequence as pad from torch.nn.utils.rnn import pad_packed_sequence as pad
from torch.nn.utils.rnn import pack_padded_sequence as pack from torch.nn.utils.rnn import pack_padded_sequence as pack
from torch.nn.utils.rnn import PackedSequence from torch.nn.utils.rnn import PackedSequence
from torch.nn.init import orthogonal from torch.nn.init import orthogonal, xavier_uniform
from .util import * from .util import *
from .sparse_memory import SparseMemory from .sparse_memory import SparseMemory
@ -134,7 +134,10 @@ class SDNC(nn.Module):
# initialize hidden state of the controller RNN # initialize hidden state of the controller RNN
if chx is None: if chx is None:
chx = [None for x in range(self.num_layers)] h = cuda(T.zeros(self.num_hidden_layers, batch_size, self.output_size), gpu_id=self.gpu_id)
xavier_uniform(h)
chx = [ (h, h) if self.rnn_type.lower() == 'lstm' else h for x in range(self.num_layers)]
# Last read vectors # Last read vectors
if last_read is None: if last_read is None: