提交 734e87e5 编写于 作者: Y yangyaming

Add python wrapper for lstm unit op.

上级 c13805e9
......@@ -188,12 +188,6 @@ beam_search_decode
:noindex:
lstm
---------
.. autofunction:: paddle.v2.fluid.layers.lstm
:noindex:
lod_rank_table
---------
.. autofunction:: paddle.v2.fluid.layers.lod_rank_table
......@@ -300,3 +294,8 @@ conv2d_transpose
.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose
:noindex:
lstm_unit
---------
.. autofunction:: paddle.v2.fluid.layers.lstm_unit
:noindex:
......@@ -5,12 +5,13 @@ All layers just related to the neural network.
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable
from tensor import concat
__all__ = [
'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
'batch_norm', 'beam_search_decode', 'conv2d_transpose'
'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'lstm_unit'
]
......@@ -392,7 +393,7 @@ def chunk_eval(input,
excluded_chunk_types=None,
**kwargs):
"""
This function computes and outputs the precision, recall and
This function computes and outputs the precision, recall and
F1-score of chunk detection.
"""
helper = LayerHelper("chunk_eval", **kwargs)
......@@ -789,3 +790,110 @@ def conv2d_transpose(input,
attrs=op_attr)
return out
def lstm_unit(x_t,
hidden_t_prev,
cell_t_prev,
forget_bias=0.0,
main_program=None,
startup_program=None):
"""Lstm unit layer. The equation of a lstm step is:
.. math::
i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)
f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)
c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)
o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)
h_t & = o_t tanh(c_t)
The inputs of lstm unit includes :math:`x_t`, :math:`h_{t-1}` and
:math:`c_{t-1}`. The implementation separates the linear transformation
and non-linear transformation apart. Here, we take :math:`i_t` as an
example. The linear transformation is applied by calling a `fc` layer and
the equation is:
.. math::
L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i
The non-linear transformation is applied by calling `lstm_unit_op` and the
equation is:
.. math::
i_t = \sigma(L_{i_t})
This layer has two outputs including :math:`o_t` and :math:`h_t`.
Args:
x_t (Variable): The input value of current step.
hidden_t_prev (Variable): The hidden value of lstm unit.
cell_t_prev (Variable): The cell value of lstm unit.
forget_bias (float): The forget bias of lstm unit.
main_program (Program): The main program.
startup_program (Program): the startup program.
Returns:
tuple: The cell value and hidden value of lstm unit.
Raises:
ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**\
not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev** \
and **cell_t_prev** not be the same.
Examples:
.. code-block:: python
x_t = fluid.layers.fc(input=x_t_data, size=10)
prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=20)
prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
cell_value, hidden_value = fluid.layers.lstm_unit(x_t=x_t,
hidden_t_prev=prev_hidden,
cell_t_prev=prev_cell)
"""
helper = LayerHelper('lstm_unit', **locals())
if len(x_t.shape) != 2:
raise ValueError("Rank of x_t must be 2.")
if len(hidden_t_prev.shape) != 2:
raise ValueError("Rank of hidden_t_prev must be 2.")
if len(cell_t_prev.shape) != 2:
raise ValueError("Rank of cell_t_prev must be 2.")
if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[
0] != cell_t_prev.shape[0]:
raise ValueError("The 1s dimension of x_t, hidden_t_prev and "
"cell_t_prev must be the same.")
size = cell_t_prev.shape[1]
concat_out = concat(
input=[x_t, hidden_t_prev],
axis=1,
main_program=main_program,
startup_program=startup_program)
fc_out = fc(input=concat_out,
size=4 * size,
main_program=main_program,
startup_program=startup_program)
dtype = x_t.dtype
c = helper.create_tmp_variable(dtype)
h = helper.create_tmp_variable(dtype)
helper.append_op(
type='lstm_unit',
inputs={"X": fc_out,
"C_prev": cell_t_prev},
outputs={"C": c,
"H": h},
attrs={"forget_bias": forget_bias})
return c, h
......@@ -161,6 +161,23 @@ class TestBook(unittest.TestCase):
x=dat, label=lbl))
print(str(program))
def test_lstm_unit(self):
program = Program()
with program_guard(program):
x_t_data = layers.data(
name='x_t_data', shape=[10, 10], dtype='float32')
x_t = layers.fc(input=x_t_data, size=10)
prev_hidden_data = layers.data(
name='prev_hidden_data', shape=[10, 20], dtype='float32')
prev_hidden = layers.fc(input=prev_hidden_data, size=20)
prev_cell_data = layers.data(
name='prev_cell', shape=[10, 30], dtype='float32')
prev_cell = layers.fc(input=prev_cell_data, size=30)
self.assertIsNotNone(
layers.lstm_unit(
x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
print(str(program))
if __name__ == '__main__':
unittest.main()
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