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Set the sequence length if necessary.

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This commit is contained in:
marcus
2015-11-12 14:50:19 +01:00
parent 263688c567
commit 605697fbb7
1 changed files with 46 additions and 34 deletions
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src/mlpack/methods/ann/rnn.hpp
+46 -34
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@@ -79,19 +79,16 @@ class RNN
void FeedBackward(const InputType& input, const ErrorType& error)
{
// Iterate through the input sequence and perform the feed backward pass.
for (seqNum = 0; seqNum < input.n_elem; seqNum += inputSize)
for (seqNum = seqLen - 1; seqNum >= 0; seqNum--)
{
// Load the network activation for the upcoming backward pass.
if (seqNum > 0)
{
LoadActivations(input.rows(input.n_elem - seqNum - 1,
(input.n_elem - seqNum - 1 + inputSize) - 1), network);
}
LoadActivations(input.rows(seqNum * inputSize, (seqNum + 1) *
inputSize - 1), network);
// Perform the backward pass.
if (seqOutput)
{
ErrorType seqError = error.unsafe_col(error.n_cols - seqNum - 1);
ErrorType seqError = error.unsafe_col(seqNum);
Backward(seqError, network);
}
else
@@ -102,6 +99,9 @@ class RNN
// Link the parameters and update the gradients.
LinkParameter(network);
UpdateGradients<>(network);
if (seqNum == 0) break;
}
}
@@ -127,15 +127,15 @@ class RNN
void Predict(const DataType& input, DataType& output)
{
deterministic = true;
seqLen = input.n_rows / inputSize;
ResetParameter(network);
seqLen = input.n_rows / inputSize;
// Iterate through the input sequence and perform the feed forward pass.
for (seqNum = 0; seqNum < input.n_elem; seqNum += inputSize)
for (seqNum = 0; seqNum < seqLen; seqNum++)
{
// Perform the forward pass and save the activations.
Forward(input.rows(seqNum, (seqNum + inputSize) - 1), network);
Forward(input.rows(seqNum * inputSize, (seqNum + 1) * inputSize - 1),
network);
SaveActivations(network);
// Retrieve output of the subsequence.
@@ -170,29 +170,27 @@ class RNN
InitLayer(input, target, network);
double networkError = 0;
seqLen = input.n_rows / inputSize;
deterministic = false;
ResetParameter(network);
seqLen = input.n_rows / inputSize;
error = ErrorType(outputSize, outputSize < target.n_elem ? seqLen : 1);
// Iterate through the input sequence and perform the feed forward pass.
for (seqNum = 0, seqOutputNum = 0; seqNum < input.n_elem;
seqNum += inputSize)
for (seqNum = 0; seqNum < seqLen; seqNum++)
{
// Perform the forward pass and save the activations.
Forward(input.rows(seqNum, (seqNum + inputSize) - 1), network);
Forward(input.rows(seqNum * inputSize, (seqNum + 1) * inputSize - 1),
network);
SaveActivations(network);
// Retrieve output error of the subsequence.
if (seqOutput)
{
arma::mat seqError = error.unsafe_col(seqNum);
arma::mat seqTarget = target.submat(seqOutputNum, 0,
seqOutputNum + outputSize - 1, 0);
arma::mat seqTarget = target.submat(seqNum * outputSize, 0,
(seqNum + 1) * outputSize - 1, 0);
networkError += OutputError(seqTarget, seqError, network);
seqOutputNum += outputSize;
}
}
@@ -230,6 +228,7 @@ class RNN
ResetParameter(std::tuple<Tp...>& t)
{
ResetDeterministic(std::get<I>(t));
ResetSeqLen(std::get<I>(t));
ResetParameter<I + 1, Tp...>(t);
}
@@ -250,6 +249,23 @@ class RNN
!HasDeterministicCheck<T, bool&(T::*)(void)>::value, void>::type
ResetDeterministic(T& /* unused */) { /* Nothing to do here */ }
/**
* Reset the layer sequence length by setting the current seqLen parameter
* for all layer that implement the SeqLen function.
*/
template<typename T>
typename std::enable_if<
HasSeqLenCheck<T, size_t&(T::*)(void)>::value, void>::type
ResetSeqLen(T& t)
{
t.SeqLen() = seqLen;
}
template<typename T>
typename std::enable_if<
!HasSeqLenCheck<T, size_t&(T::*)(void)>::value, void>::type
ResetSeqLen(T& /* unused */) { /* Nothing to do here */ }
/**
* Initialize the network by setting the input size and output size.
*
@@ -322,12 +338,6 @@ class RNN
typename std::enable_if<I < sizeof...(Tp), void>::type
SaveActivations(std::tuple<Tp...>& t)
{
if (activations.size() == I)
{
activations.push_back(new MatType(
std::get<I>(t).OutputParameter().n_rows, seqLen));
}
Save(I, std::get<I>(t), std::get<I>(t).InputParameter());
SaveActivations<I + 1, Tp...>(t);
}
@@ -341,6 +351,9 @@ class RNN
HasRecurrentParameterCheck<T, P&(T::*)()>::value, void>::type
Save(const size_t layerNumber, T& t, P& /* unused */)
{
if (activations.size() == layerNumber)
activations.push_back(new MatType(t.RecurrentParameter().n_rows, seqLen));
activations[layerNumber].unsafe_col(seqNum) = t.RecurrentParameter();
}
@@ -349,6 +362,9 @@ class RNN
!HasRecurrentParameterCheck<T, P&(T::*)()>::value, void>::type
Save(const size_t layerNumber, T& t, P& /* unused */)
{
if (activations.size() == layerNumber)
activations.push_back(new MatType(t.OutputParameter().n_rows, seqLen));
activations[layerNumber].unsafe_col(seqNum) = t.OutputParameter();
}
@@ -383,8 +399,7 @@ class RNN
HasRecurrentParameterCheck<T, P&(T::*)()>::value, void>::type
Load(const size_t layerNumber, T& t, P& /* unused */)
{
t.RecurrentParameter() = activations[layerNumber].unsafe_col(
seqLen - 1 - seqNum);
t.RecurrentParameter() = activations[layerNumber].unsafe_col(seqNum);
}
template<typename T, typename P>
@@ -392,8 +407,7 @@ class RNN
!HasRecurrentParameterCheck<T, P&(T::*)()>::value, void>::type
Load(const size_t layerNumber, T& t, P& /* unused */)
{
t.OutputParameter() = activations[layerNumber].unsafe_col(
seqLen - 1 - seqNum);
t.OutputParameter() = activations[layerNumber].unsafe_col(seqNum);
}
/**
@@ -483,7 +497,7 @@ class RNN
HasRecurrentParameterCheck<T, P&(T::*)()>::value, void>::type
UpdateRecurrent(T& t, P& /* unused */, D& output)
{
t.RecurrentParameter() = output;
t.RecurrentParameter() = output;
}
template<typename T, typename P, typename D>
@@ -491,6 +505,7 @@ class RNN
!HasRecurrentParameterCheck<T, P&(T::*)()>::value, void>::type
UpdateRecurrent(T& /* unused */, P& /* unused */, D& /* unused */)
{
/* Nothing to do here */
}
/*
@@ -611,7 +626,7 @@ class RNN
Update(T1& t1, P1& /* unused */, D1& /* unused */, T2& /* unused */,
P2& /* unused */, D2& delta2)
{
t1.Gradient(delta2, t1.Gradient());
t1.Gradient(delta2, t1.Gradient());
t1.Optimizer().Update();
}
@@ -715,9 +730,6 @@ class RNN
//! The index of the current sequence number.
size_t seqNum;
//! The index of the current sequence target number.
size_t seqOutputNum;
//! Locally stored number of samples in one input sequence.
size_t seqLen;