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体验新版 GitCode,发现更多精彩内容 >>
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db1f6a59
编写于
1月 23, 2018
作者:
Y
Yibing Liu
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Update doc in lstmp_op
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-37
paddle/operators/lstmp_op.cc
paddle/operators/lstmp_op.cc
+49
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paddle/operators/lstmp_op.cc
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db1f6a59
...
...
@@ -120,7 +120,7 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
LSTMPOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Input"
,
"(LoDTensor) the
first input is a LodTensor, which support
"
"(LoDTensor) the
input for sequence data, which supports
"
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X 4D), where T is the "
"total time steps in this mini-batch, D is the hidden size."
);
...
...
@@ -132,21 +132,23 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"C0"
,
"(Tensor, optional) the initial cell state is an optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size. `H0` and `C0` can be NULL but only at the same time"
)
"batch size. Only one of `H0` and `C0` can be NULL at the same "
"time."
)
.
AsDispensable
();
AddInput
(
"Weight"
,
"(Tensor) the learnable hidden-hidden weights."
" - The shape is (P x 4D), where P is the
recurrent projection
"
"
layer size and D is the hidden size.
"
" - The shape is (P x 4D), where P is the
projection layer size
"
"
and D is the hidden size.
"
" - Weight = {W_cr, W_ir, W_fr, W_or}"
);
AddInput
(
"ProjWeight"
,
"(Tensor) the learnable weight
`W_rh`
of the projection layer."
"(Tensor) the learnable weight of the projection layer."
" - The shape is (D x P), where P is the recurrent projection "
"layer size and D is the hidden size."
);
"layer size and D is the hidden size."
" - ProjWeight = {W_rh}"
);
AddInput
(
"Bias"
,
"(Tensor) the learnable
weight
s, which contains two parts: "
"input-hidden bias
weight and peephole connections weight
if "
"setting `use_peepholes`
True
. "
"(Tensor) the learnable
biase
s, which contains two parts: "
"input-hidden bias
es and peephole connections weights
if "
"setting `use_peepholes`
to `True`
. "
"1. `use_peepholes = False` "
" - The shape is (1 x 4D). "
" - Bias = {b_c, b_i, b_f, b_o}."
...
...
@@ -155,27 +157,28 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}."
);
AddOutput
(
"Projection"
,
"(LoDTensor) the projection of the hidden state of LSTMP "
"operator. The shape is (T x P), and
lod
is the same with the "
"operator. The shape is (T x P), and
LoD
is the same with the "
"`Input`."
);
AddOutput
(
"Cell"
,
"(LoDTensor) the cell state of LSTMP operator. "
"The shape is (T x D), and lod is the same with the `Input`."
);
AddOutput
(
"BatchGate"
,
"(LoDTensor) This LoDTensor contains input gate, forget gate "
"and output gate after the nonlinear computation. This "
"LoDTensor has the same shape as the reorganized input, which "
"is also be called batch input. The LoD size is 2. The first "
"LoD is the batch offsets and the second LoD contains the "
"indexes, which denote the position of reorganized sequence "
"in the raw input."
)
"and output gate after the activations. This LoDTensor has the "
"same shape as the reorganized input, which is also be called "
"batch input. The LoD size is 2. The first-level LoD is the "
"batch offsets and the second contains the indices, which "
"denotes the position of reorganized sequence in the raw input."
)
.
AsIntermediate
();
AddOutput
(
"BatchCellPreAct"
,
"(LoDTensor) This LoDTensor is obtained in the forward and used "
"in the backward."
)
"(LoDTensor) the pre-activation cell state reorganized in batch. "
"This LoDTensor is obtained in the forward and used in the "
"backward."
)
.
AsIntermediate
();
AddOutput
(
"BatchHidden"
,
"(LoDTensor) This LoDTensor is obtained in the forward and used "
"in the backward."
)
"(LoDTensor) the hidden state reorganized in batch. "
"This LoDTensor is obtained in the forward and used in the "
"backward."
)
.
AsIntermediate
();
AddOutput
(
"OrderedP0"
,
"(Tensor) the projection of the initial hidden state "
...
...
@@ -190,12 +193,6 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, defalut: False) "
"whether to compute reversed LSTMP."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"share_cell_act"
,
"(bool, defalut: True) "
"whether to share activation with cell output. "
"If false, the projection would be linear, else "
"through an activation same with the cell output."
)
.
SetDefault
(
true
);
AddAttr
<
std
::
string
>
(
"gate_activation"
,
"(string, default: sigmoid)"
...
...
@@ -214,11 +211,21 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
"`tanh` by default."
)
.
SetDefault
(
"tanh"
)
.
InEnum
({
"sigmoid"
,
"tanh"
,
"relu"
,
"identity"
});
AddAttr
<
bool
>
(
"share_cell_act"
,
"(bool, defalut: True) "
"whether to share the activation of cell output with the "
"projection layer. When set to `False`, the projection "
"is simple linear, otherwise it will go through an "
"activation function same as `cell_activation`."
)
.
SetDefault
(
true
);
AddComment
(
R"DOC(
Long-Short Term Memory with
Recurrent Projection
(LSTMP) Operator.
Long-Short Term Memory with
recurrent Projection layer
(LSTMP) Operator.
LSTMP is stand LSTM appended by a recurrent projection layer to reduce the
number of parameters, espeacially when the output size is relative large.
LSTMP has a separate projection layer after the LSTM layer, projecting the
original hidden state to a lower-dimensional one, which is proposed to reduce
the number of total parameters and furthermore computational complexity for
the LSTM, espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
The formula is as follows:
$$
...
...
@@ -226,13 +233,15 @@ i_t = \sigma(W_{ix}x_{t} + W_{ih}r_{t-1} + W_{ic}c_{t-1} + b_i) \\
f_t = \sigma(W_{fx}x_{t} + W_{fh}r_{t-1} + W_{fc}c_{t-1} + b_f) \\
c_t = f_t \odot c_{t-1} + i_t \odot
act_g(W_{cx}x_t + W_{ch}r_{t-1} + b_c) \\
\tilde{c_t} =
act_g(W_{cx}x_t + W_{ch}r_{t-1} + b_c) \\
o_t = \sigma(W_{ox}x_{t} + W_{oh}r_{t-1} + W_{oc}c_t + b_o) \\
c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}
h_t = o_t \odot act_h(c_t)
r_t =
act_{h'
}(W_{rh}h_t)
r_t =
\overline{act_h
}(W_{rh}h_t)
$$
where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
...
...
@@ -240,20 +249,23 @@ of weights from the input gate to the input), $W_{ic}, W_{fc}, W_{oc}$
are diagonal weight matrices for peephole connections. In our implementation,
we use vectors to reprenset these diagonal weight matrices. The b terms
denote bias vectors ($b_i$ is the input gate bias vector), $\sigma$
is the
non-line activations
, such as logistic sigmoid function, and
is the
activation
, such as logistic sigmoid function, and
$i, f, o$ and $c$ are the input gate, forget gate, output gate,
and cell activation vectors, respectively, all of which have the same size as
the cell output activation vector $h$. $r$ denotes the recurrent projection
layer.
the cell output activation vector $h$. Here $h$ is usually called the hidden
state and $r$ denotes its recurrent projection. And $\tilde{c_t}$ is also
called the candidate hidden state, whose computation is based on the current
input and previous hidden state.
The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
are the cell input and cell output activation functions and `tanh` is usually
used for them. If `share_cell_act` setted to `False`, $act_h'$ will be linear
else will be same with $act_h$.
used for them. $\overline{act_h}$ is the activation function for the projection
layer. When `share_cell_act` set to `False`, $\overline{act_h}$ is an
identity activation, otherwise it will be same as $act_h$.
Note that these $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
operations on the input $x_{t}$ are NOT included in this operator.
Users can choose to use fully-connect operator before LSTMP operator.
Users can choose to use fully-connect
ed
operator before LSTMP operator.
)DOC"
);
}
...
...
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