未验证 提交 f3c42f60 编写于 作者: S Siddharth Goyal 提交者: GitHub

Add doc for gru_unit op (in fluid) (#7151)

* Add squared error layers doc

* Add doc for gru_unit

* Remove cdot which isn't supported

* Update layers.rst

* Update layers.rst (minor)
上级 564dba17
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...@@ -307,6 +307,12 @@ sequence_expand ...@@ -307,6 +307,12 @@ sequence_expand
:noindex: :noindex:
gru_unit
--------
.. autofunction:: paddle.v2.fluid.layers.gru_unit
:noindex:
lstm_unit lstm_unit
--------- ---------
.. autofunction:: paddle.v2.fluid.layers.lstm_unit .. autofunction:: paddle.v2.fluid.layers.lstm_unit
......
...@@ -236,21 +236,47 @@ def gru_unit(input, ...@@ -236,21 +236,47 @@ def gru_unit(input,
activation='tanh', activation='tanh',
gate_activation='sigmoid'): gate_activation='sigmoid'):
""" """
GRUUnit Operator implements partial calculations of the GRU unit as following: GRU unit layer. The equation of a gru step is:
$$ .. math::
update \ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\ u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
reset \ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r) \\
output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\ r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)
output: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t)
$$ ch_t & = actNode(xc_t + W_c dot(r_t, h_{t-1}) + b_c)
h_t & = dot((1-u_t), ch_{t-1}) + dot(u_t, h_t)
which is same as one time step of GRU Operator. The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
of the equation above, the :math:`z_t` is split into 3 parts -
:math:`xu_t`, :math:`xr_t` and :math:`xc_t`. This means that in order to
implement a full GRU unit operator for an input, a fully
connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`.
This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t - 1})`
and concatenation of :math:`u_t`, :math:`r_t` and :math:`ch_t`.
Args:
input (Variable): The fc transformed input value of current step.
hidden (Variable): The hidden value of lstm unit from previous step.
size (integer): The input dimension value.
weight (ParamAttr): The weight parameters for gru unit. Default: None
bias (ParamAttr): The bias parameters for gru unit. Default: None
activation (string): The activation type for cell (actNode). Default: 'tanh'
gate_activation (string): The activation type for gates (actGate). Default: 'sigmoid'
Returns:
tuple: The hidden value, reset-hidden value and gate values.
Examples:
.. code-block:: python
@note To implement the complete GRU unit, fully-connected operator must be # assuming we have x_t_data and prev_hidden of size=10
used before to feed xu, xr and xc as the Input of GRUUnit operator. x_t = fluid.layers.fc(input=x_t_data, size=30)
hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
hidden = prev_hidden)
TODO(ChunweiYan) add more document here
""" """
activation_dict = dict( activation_dict = dict(
identity=0, identity=0,
......
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