Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

expose h0 in dynamic_lstm #11391

Merged
merged 4 commits into from
Jun 13, 2018
Merged

expose h0 in dynamic_lstm #11391

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
f5a5382
expose h0 in dynamic_lstm
Yancey1989 Jun 12, 2018
2bf7366
update by comment
Yancey1989 Jun 12, 2018
a3e496f
update by comment
Yancey1989 Jun 12, 2018
dd1885c
h0 to H0
Yancey1989 Jun 12, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
30 changes: 24 additions & 6 deletions python/paddle/fluid/layers/nn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -261,9 +261,10 @@ def embedding(input,
return tmp


# TODO(qijun): expose H0 and C0
def dynamic_lstm(input,
size,
h_0=None,
c_0=None,
param_attr=None,
bias_attr=None,
use_peepholes=True,
Expand Down Expand Up @@ -324,6 +325,13 @@ def dynamic_lstm(input,
(T X 4D), where T is the total time steps in this
mini-batch, D is the hidden size.
size(int): 4 * hidden size.
h_0(Variable): The initial hidden state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size and D is the hidden size.
c_0(Variable): The initial cell state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size. `h_0` and `c_0` can be NULL but only at the same time.

param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weights.

Expand Down Expand Up @@ -387,12 +395,20 @@ def dynamic_lstm(input,
cell = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype)
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
batch_size = input.shape[0]
if h_0:
assert h_0.shape == (batch_size, size), \
'The shape of h0 should be (batch_size, %d)' % size
inputs['H0'] = h_0
if c_0:
assert c_0.shape == (batch_size, size), \
'The shape of c0 should be (batch_size, %d)' % size
inputs['C0'] = c_0

helper.append_op(
type='lstm',
inputs={'Input': input,
'Weight': weight,
'Bias': bias},
inputs=inputs,
outputs={
'Hidden': hidden,
'Cell': cell,
Expand Down Expand Up @@ -676,11 +692,13 @@ def dynamic_gru(input,
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
bias = helper.create_parameter(
attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
batch_size = input.shape[0]
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
if h_0 != None:
assert h_0.shape == (
size, size), 'The shape of h0 should be(%d, %d)' % (size, size)
inputs['h0'] = h_0
batch_size, size
), 'The shape of h0 should be(batch_size, %d)' % size
inputs['H0'] = h_0

hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
Expand Down