AI-MXNet
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lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
{
$name = $self->_prefix . $name;
if(not exists $self->_params->{$name})
{
$self->_params->{$name} = AI::MXNet::Symbol->Variable($name, @kwargs);
}
return $self->_params->{$name};
}
package AI::MXNet::RNN::Cell::Base;
=head1 NAME
AI::MXNet::RNNCell::Base
=cut
=head1 DESCRIPTION
Abstract base class for RNN cells
Parameters
----------
prefix : str
prefix for name of layers
(and name of weight if params is undef)
params : AI::MXNet::RNN::Params or undef
container for weight sharing between cells.
created if undef.
=cut
use AI::MXNet::Base;
use Mouse;
use overload "&{}" => sub { my $self = shift; sub { $self->call(@_) } };
has '_prefix' => (is => 'rw', init_arg => 'prefix', isa => 'Str', default => '');
has '_params' => (is => 'rw', init_arg => 'params', isa => 'Maybe[AI::MXNet::RNN::Params]');
has [qw/_own_params
_modified
_init_counter
_counter
/] => (is => 'rw', init_arg => undef);
around BUILDARGS => sub {
my $orig = shift;
my $class = shift;
return $class->$orig(prefix => $_[0]) if @_ == 1;
return $class->$orig(@_);
};
sub BUILD
{
my $self = shift;
if(not defined $self->_params)
{
$self->_own_params(1);
$self->_params(AI::MXNet::RNN::Params->new($self->_prefix));
}
else
{
$self->_own_params(0);
}
$self->_modified(0);
$self->reset;
}
=head2 reset
Reset before re-using the cell for another graph
=cut
method reset()
{
$self->_init_counter(-1);
$self->_counter(-1);
}
=head2 call
Construct symbol for one step of RNN.
Parameters
----------
$inputs : mx->sym->Variable
input symbol, 2D, batch * num_units
$states : mx->sym->Variable or ArrayRef[AI::MXNet::Symbol]
state from previous step or begin_state().
Returns
-------
$output : AI::MXNet::Symbol
output symbol
$states : ArrayRef[AI::MXNet::Symbol]
state to next step of RNN.
Can be called via overloaded &{}: &{$cell}($inputs, $states);
=cut
method call(AI::MXNet::Symbol $inputs, AI::MXNet::Symbol|ArrayRef[AI::MXNet::Symbol] $states)
{
confess("Not Implemented");
}
method _gate_names()
{
[''];
}
=head2 params
Parameters of this cell
=cut
method params()
{
$self->_own_params(0);
return $self->_params;
}
=head2 state_shape
shape(s) of states
=cut
method state_shape()
{
return [map { $_->{shape} } @{ $self->state_info }];
}
=head2 state_info
shape and layout information of states
=cut
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
}
return \%args;
}
=head2 unroll
Unroll an RNN cell across time steps.
Parameters
----------
:$length : Int
number of steps to unroll
:$inputs : AI::MXNet::Symbol, array ref of Symbols, or undef
if inputs is a single Symbol (usually the output
of Embedding symbol), it should have shape
of [$batch_size, $length, ...] if layout == 'NTC' (batch, time series)
or ($length, $batch_size, ...) if layout == 'TNC' (time series, batch).
If inputs is a array ref of symbols (usually output of
previous unroll), they should all have shape
($batch_size, ...).
If inputs is undef, a placeholder variables are
automatically created.
:$begin_state : array ref of Symbol
input states. Created by begin_state()
or output state of another cell. Created
from begin_state() if undef.
:$input_prefix : str
prefix for automatically created input
placehodlers.
:$layout : str
layout of input symbol. Only used if the input
is a single Symbol.
:$merge_outputs : Bool
If 0, returns outputs as an array ref of Symbols.
If 1, concatenates the output across the time steps
and returns a single symbol with the shape
[$batch_size, $length, ...) if the layout equal to 'NTC',
or [$length, $batch_size, ...) if the layout equal tp 'TNC'.
If undef, output whatever is faster
Returns
-------
$outputs : array ref of Symbol or Symbol
output symbols.
$states : Symbol or nested list of Symbol
has the same structure as begin_state()
=cut
method unroll(
Int $length,
Maybe[AI::MXNet::Symbol|ArrayRef[AI::MXNet::Symbol]] :$inputs=,
Maybe[AI::MXNet::Symbol|ArrayRef[AI::MXNet::Symbol]] :$begin_state=,
Str :$input_prefix='',
Str :$layout='NTC',
Maybe[Bool] :$merge_outputs=
)
{
$self->reset;
my $axis = index($layout, 'T');
if(not defined $inputs)
{
$inputs = [
map { AI::MXNet::Symbol->Variable("${input_prefix}t${_}_data") } (0..$length-1)
];
}
elsif(blessed($inputs))
{
assert(
(@{ $inputs->list_outputs() } == 1),
"unroll doesn't allow grouped symbol as input. Please "
."convert to list first or let unroll handle slicing"
);
$inputs = AI::MXNet::Symbol->SliceChannel(
$inputs,
axis => $axis,
num_outputs => $length,
squeeze_axis => 1
);
}
else
{
assert(@$inputs == $length);
}
$begin_state //= $self->begin_state;
my $states = $begin_state;
my $outputs;
my @inputs = @{ $inputs };
for my $i (0..$length-1)
{
my $output;
($output, $states) = &{$self}(
$inputs[$i],
$states
);
push @$outputs, $output;
}
if($merge_outputs)
{
@$outputs = map { AI::MXNet::Symbol->expand_dims($_, axis => $axis) } @$outputs;
$outputs = AI::MXNet::Symbol->Concat(@$outputs, dim => $axis);
}
return($outputs, $states);
}
method _get_activation($inputs, $activation, @kwargs)
{
if(not ref $activation)
{
return AI::MXNet::Symbol->Activation($inputs, act_type => $activation, @kwargs);
}
else
{
return &{$activation}($inputs, @kwargs);
}
}
method _cells_state_shape($cells)
{
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
$self->_hW($self->params->get('h2h_weight'));
$self->_hB($self->params->get('h2h_bias'));
}
method state_info()
{
return [{ shape => [0, $self->_num_hidden], __layout__ => 'NC' }];
}
method call(AI::MXNet::Symbol $inputs, SymbolOrArrayOfSymbols $states)
{
$self->_counter($self->_counter + 1);
my $name = sprintf('%st%d_', $self->_prefix, $self->_counter);
my $i2h = AI::MXNet::Symbol->FullyConnected(
data => $inputs,
weight => $self->_iW,
bias => $self->_iB,
num_hidden => $self->_num_hidden,
name => "${name}i2h"
);
my $h2h = AI::MXNet::Symbol->FullyConnected(
data => @{$states}[0],
weight => $self->_hW,
bias => $self->_hB,
num_hidden => $self->_num_hidden,
name => "${name}h2h"
);
my $output = $self->_get_activation(
$i2h + $h2h,
$self->_activation,
name => "${name}out"
);
return ($output, [$output]);
}
package AI::MXNet::RNN::LSTMCell;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::RNN::Cell';
=head1 NAME
AI::MXNet::RNN::LSTMCell
=cut
=head1 DESCRIPTION
Long-Short Term Memory (LSTM) network cell.
Parameters
----------
num_hidden : int
number of units in output symbol
prefix : str, default 'lstm_'
prefix for name of layers
(and name of weight if params is undef)
params : AI::MXNet::RNN::Params or None
container for weight sharing between cells.
created if undef.
forget_bias : bias added to forget gate, default 1.0.
Jozefowicz et al. 2015 recommends setting this to 1.0
=cut
has '+_prefix' => (default => 'lstm_');
has '+_activation' => (init_arg => undef);
has '+forget_bias' => (is => 'ro', isa => 'Num', default => 1);
method state_info()
{
return [{ shape => [0, $self->_num_hidden], __layout__ => 'NC' } , { shape => [0, $self->_num_hidden], __layout__ => 'NC' }];
}
method _gate_names()
{
[qw/_i _f _c _o/];
}
method call(AI::MXNet::Symbol $inputs, SymbolOrArrayOfSymbols $states)
{
$self->_counter($self->_counter + 1);
my $name = sprintf('%st%d_', $self->_prefix, $self->_counter);
my @states = @{ $states };
my $i2h = AI::MXNet::Symbol->FullyConnected(
data => $inputs,
weight => $self->_iW,
bias => $self->_iB,
num_hidden => $self->_num_hidden*4,
name => "${name}i2h"
);
my $h2h = AI::MXNet::Symbol->FullyConnected(
data => $states[0],
weight => $self->_hW,
bias => $self->_hB,
num_hidden => $self->_num_hidden*4,
name => "${name}h2h"
);
my $gates = $i2h + $h2h;
my @slice_gates = @{ AI::MXNet::Symbol->SliceChannel(
$gates, num_outputs => 4, name => "${name}slice"
) };
my $in_gate = AI::MXNet::Symbol->Activation(
$slice_gates[0], act_type => "sigmoid", name => "${name}i"
);
my $forget_gate = AI::MXNet::Symbol->Activation(
$slice_gates[1], act_type => "sigmoid", name => "${name}f"
);
my $in_transform = AI::MXNet::Symbol->Activation(
$slice_gates[2], act_type => "tanh", name => "${name}c"
);
my $out_gate = AI::MXNet::Symbol->Activation(
$slice_gates[3], act_type => "sigmoid", name => "${name}o"
);
my $next_c = AI::MXNet::Symbol->_plus(
$forget_gate * $states[1], $in_gate * $in_transform,
name => "${name}state"
);
my $next_h = AI::MXNet::Symbol->_mul(
$out_gate,
AI::MXNet::Symbol->Activation(
$next_c, act_type => "tanh"
),
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
use AI::MXNet::Base;
extends 'AI::MXNet::RNN::Cell';
=head1 NAME
AI::MXNet::RNN::GRUCell
=cut
=head1 DESCRIPTION
Gated Rectified Unit (GRU) network cell.
Note: this is an implementation of the cuDNN version of GRUs
(slight modification compared to Cho et al. 2014).
Parameters
----------
num_hidden : int
number of units in output symbol
prefix : str, default 'gru_'
prefix for name of layers
(and name of weight if params is undef)
params : AI::MXNet::RNN::Params or undef
container for weight sharing between cells.
created if undef.
=cut
has '+_prefix' => (default => 'gru_');
method _gate_names()
{
[qw/_r _z _o/];
}
method call(AI::MXNet::Symbol $inputs, SymbolOrArrayOfSymbols $states)
{
$self->_counter($self->_counter + 1);
my $name = sprintf('%st%d_', $self->_prefix, $self->_counter);
my $prev_state_h = @{ $states }[0];
my $i2h = AI::MXNet::Symbol->FullyConnected(
data => $inputs,
weight => $self->_iW,
bias => $self->_iB,
num_hidden => $self->_num_hidden*3,
name => "${name}i2h"
);
my $h2h = AI::MXNet::Symbol->FullyConnected(
data => $prev_state_h,
weight => $self->_hW,
bias => $self->_hB,
num_hidden => $self->_num_hidden*3,
name => "${name}h2h"
);
my ($i2h_r, $i2h_z);
($i2h_r, $i2h_z, $i2h) = @{ AI::MXNet::Symbol->SliceChannel(
$i2h, num_outputs => 3, name => "${name}_i2h_slice"
) };
my ($h2h_r, $h2h_z);
($h2h_r, $h2h_z, $h2h) = @{ AI::MXNet::Symbol->SliceChannel(
$h2h, num_outputs => 3, name => "${name}_h2h_slice"
) };
my $reset_gate = AI::MXNet::Symbol->Activation(
$i2h_r + $h2h_r, act_type => "sigmoid", name => "${name}_r_act"
);
my $update_gate = AI::MXNet::Symbol->Activation(
$i2h_z + $h2h_z, act_type => "sigmoid", name => "${name}_z_act"
);
my $next_h_tmp = AI::MXNet::Symbol->Activation(
$i2h + $reset_gate * $h2h, act_type => "tanh", name => "${name}_h_act"
);
my $next_h = AI::MXNet::Symbol->_plus(
(1 - $update_gate) * $next_h_tmp, $update_gate * $prev_state_h,
name => "${name}out"
);
return ($next_h, [$next_h]);
}
package AI::MXNet::RNN::FusedCell;
use Mouse;
use AI::MXNet::Types;
use AI::MXNet::Base;
extends 'AI::MXNet::RNN::Cell::Base';
=head1 NAME
AI::MXNet::RNN::FusedCell
=cut
=head1 DESCRIPTION
Fusing RNN layers across time step into one kernel.
Improves speed but is less flexible. Currently only
supported if using cuDNN on GPU.
=cut
has '_num_hidden' => (is => 'ro', isa => 'Int', init_arg => 'num_hidden', required => 1);
has '_num_layers' => (is => 'ro', isa => 'Int', init_arg => 'num_layers', default => 1);
has '_dropout' => (is => 'ro', isa => 'Num', init_arg => 'dropout', default => 0);
has '_get_next_state' => (is => 'ro', isa => 'Bool', init_arg => 'get_next_state', default => 0);
has '_bidirectional' => (is => 'ro', isa => 'Bool', init_arg => 'bidirectional', default => 0);
has 'forget_bias' => (is => 'ro', isa => 'Num', default => 1);
has 'initializer' => (is => 'rw', isa => 'Maybe[Initializer]');
has '_mode' => (
is => 'ro',
isa => enum([qw/rnn_relu rnn_tanh lstm gru/]),
init_arg => 'mode',
default => 'lstm'
);
has [qw/_parameter
_directions/] => (is => 'rw', init_arg => undef);
around BUILDARGS => sub {
my $orig = shift;
my $class = shift;
return $class->$orig(num_hidden => $_[0]) if @_ == 1;
return $class->$orig(@_);
};
sub BUILD
{
my $self = shift;
if(not $self->_prefix)
{
$self->_prefix($self->_mode.'_');
}
if(not defined $self->initializer)
{
$self->initializer(
AI::MXNet::Xavier->new(
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
}
for my $gate (@gate_names)
{
my $name = sprintf('%s%s%d_h2h%s_bias', $self->_prefix, $direction, $layer, $gate);
$args{$name} = $arr->slice([$p,$p+$lh-1]);
$p += $lh;
}
}
}
assert($p == $arr->size, "Invalid parameters size for FusedRNNCell");
return %args;
}
method unpack_weights(HashRef[AI::MXNet::NDArray] $args)
{
my %args = %{ $args };
my $arr = delete $args{ $self->_parameter->name };
my $b = @{ $self->_directions };
my $m = $self->_num_gates;
my $h = $self->_num_hidden;
my $num_input = int(int(int($arr->size/$b)/$h)/$m) - ($self->_num_layers - 1)*($h+$b*$h+2) - $h - 2;
my %nargs = $self->_slice_weights($arr, $num_input, $self->_num_hidden);
%args = (%args, map { $_ => $nargs{$_}->copy } keys %nargs);
return \%args
}
method pack_weights(HashRef[AI::MXNet::NDArray] $args)
{
my %args = %{ $args };
my $b = @{ $self->_directions };
my $m = $self->_num_gates;
my @c = @{ $self->_gate_names };
my $h = $self->_num_hidden;
my $w0 = $args{ sprintf('%sl0_i2h%s_weight', $self->_prefix, $c[0]) };
my $num_input = $w0->shape->[1];
my $total = ($num_input+$h+2)*$h*$m*$b + ($self->_num_layers-1)*$m*$h*($h+$b*$h+2)*$b;
my $arr = AI::MXNet::NDArray->zeros([$total], ctx => $w0->context, dtype => $w0->dtype);
my %nargs = $self->_slice_weights($arr, $num_input, $h);
while(my ($name, $nd) = each %nargs)
{
$nd .= delete $args{ $name };
}
$args{ $self->_parameter->name } = $arr;
return \%args;
}
method call(AI::MXNet::Symbol $inputs, SymbolOrArrayOfSymbols $states)
{
confess("AI::MXNet::RNN::FusedCell cannot be stepped. Please use unroll");
}
method unroll(
Int $length,
Maybe[AI::MXNet::Symbol|ArrayRef[AI::MXNet::Symbol]] :$inputs=,
Maybe[AI::MXNet::Symbol|ArrayRef[AI::MXNet::Symbol]] :$begin_state=,
Str :$input_prefix='',
Str :$layout='NTC',
Maybe[Bool] :$merge_outputs=
)
{
$self->reset;
my $axis = index($layout, 'T');
$inputs //= AI::MXNet::Symbol->Variable("${input_prefix}data");
if(blessed($inputs))
{
assert(
(@{ $inputs->list_outputs() } == 1),
"unroll doesn't allow grouped symbol as input. Please "
."convert to list first or let unroll handle slicing"
);
if($axis == 1)
{
AI::MXNet::Logging->warning(
"NTC layout detected. Consider using "
."TNC for RNN::FusedCell for faster speed"
);
$inputs = AI::MXNet::Symbol->SwapAxis($inputs, dim1 => 0, dim2 => 1);
}
else
{
assert($axis == 0, "Unsupported layout $layout");
}
}
else
{
assert(@$inputs == $length);
$inputs = [map { AI::MXNet::Symbol->expand_dims($_, axis => 0) } @{ $inputs }];
$inputs = AI::MXNet::Symbol->Concat(@{ $inputs }, dim => 0);
}
$begin_state //= $self->begin_state;
my $states = $begin_state;
my @states = @{ $states };
my %states;
if($self->_mode eq 'lstm')
{
%states = (state => $states[0], state_cell => $states[1]);
}
else
{
%states = (state => $states[0]);
}
my $rnn = AI::MXNet::Symbol->RNN(
data => $inputs,
parameters => $self->_parameter,
state_size => $self->_num_hidden,
num_layers => $self->_num_layers,
bidirectional => $self->_bidirectional,
p => $self->_dropout,
state_outputs => $self->_get_next_state,
mode => $self->_mode,
name => $self->_prefix.'rnn',
%states
);
my $outputs;
my %attr = (__layout__ => 'LNC');
if(not $self->_get_next_state)
{
($outputs, $states) = ($rnn, []);
}
elsif($self->_mode eq 'lstm')
{
my @rnn = @{ $rnn };
$rnn[1]->_set_attr(%attr);
$rnn[2]->_set_attr(%attr);
($outputs, $states) = ($rnn[0], [$rnn[1], $rnn[2]]);
}
else
{
my @rnn = @{ $rnn };
$rnn[1]->_set_attr(%attr);
($outputs, $states) = ($rnn[0], [$rnn[1]]);
}
if(defined $merge_outputs and not $merge_outputs)
{
AI::MXNet::Logging->warning(
"Call RNN::FusedCell->unroll with merge_outputs=1 "
."for faster speed"
);
$outputs = [@ {
AI::MXNet::Symbol->SliceChannel(
$outputs,
axis => 0,
num_outputs => $length,
squeeze_axis => 1
)
}];
}
elsif($axis == 1)
{
$outputs = AI::MXNet::Symbol->SwapAxis($outputs, dim1 => 0, dim2 => 1);
}
return ($outputs, $states);
}
=head2 unfuse
Unfuse the fused RNN
Returns
-------
$cell : AI::MXNet::RNN::SequentialCell
unfused cell that can be used for stepping, and can run on CPU.
=cut
method unfuse()
{
my $stack = AI::MXNet::RNN::SequentialCell->new;
my $get_cell = {
rnn_relu => sub {
AI::MXNet::RNN::Cell->new(
num_hidden => $self->_num_hidden,
activation => 'relu',
prefix => shift
)
},
rnn_tanh => sub {
AI::MXNet::RNN::Cell->new(
num_hidden => $self->_num_hidden,
activation => 'tanh',
prefix => shift
)
},
lstm => sub {
AI::MXNet::RNN::LSTMCell->new(
num_hidden => $self->_num_hidden,
prefix => shift
)
},
gru => sub {
AI::MXNet::RNN::GRUCell->new(
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
my $in_gate = AI::MXNet::Symbol->Activation(
$slice_gates[0],
act_type => "sigmoid",
name => "${name}i"
);
my $forget_gate = AI::MXNet::Symbol->Activation(
$slice_gates[1],
act_type => "sigmoid",
name => "${name}f"
);
my $in_transform = $self->_get_activation(
$slice_gates[2],
$self->_activation,
name => "${name}c"
);
my $out_gate = AI::MXNet::Symbol->Activation(
$slice_gates[3],
act_type => "sigmoid",
name => "${name}o"
);
my $next_c = AI::MXNet::Symbol->_plus(
$forget_gate * @{$states}[1],
$in_gate * $in_transform,
name => "${name}state"
);
my $next_h = AI::MXNet::Symbol->_mul(
$out_gate, $self->_get_activation($next_c, $self->_activation),
name => "${name}out"
);
return ($next_h, [$next_h, $next_c]);
}
package AI::MXNet::RNN::ConvGRUCell;
use Mouse;
extends 'AI::MXNet::RNN::ConvCell';
has '+_prefix' => (default => 'ConvGRU_');
=head1 NAME
AI::MXNet::RNN::ConvGRUCell
=cut
=head1 DESCRIPTION
Convolutional GRU network cell.
=cut
method _gate_names()
{
return ['_r', '_z', '_o'];
}
method call(AI::MXNet::Symbol $inputs, AI::MXNet::Symbol|ArrayRef[AI::MXNet::Symbol] $states)
{
$self->_counter($self->_counter + 1);
my $name = sprintf('%st%d_', $self->_prefix, $self->_counter);
my ($i2h, $h2h) = $self->_conv_forward($inputs, $states, $name);
my ($i2h_r, $i2h_z, $h2h_r, $h2h_z);
($i2h_r, $i2h_z, $i2h) = @{ AI::MXNet::Symbol->SliceChannel($i2h, num_outputs => 3, name => "${name}_i2h_slice") };
($h2h_r, $h2h_z, $h2h) = @{ AI::MXNet::Symbol->SliceChannel($h2h, num_outputs => 3, name => "${name}_h2h_slice") };
my $reset_gate = AI::MXNet::Symbol->Activation(
$i2h_r + $h2h_r, act_type => "sigmoid",
name => "${name}_r_act"
);
my $update_gate = AI::MXNet::Symbol->Activation(
$i2h_z + $h2h_z, act_type => "sigmoid",
name => "${name}_z_act"
);
my $next_h_tmp = $self->_get_activation($i2h + $reset_gate * $h2h, $self->_activation, name => "${name}_h_act");
my $next_h = AI::MXNet::Symbol->_plus(
(1 - $update_gate) * $next_h_tmp, $update_gate * @{$states}[0],
name => "${name}out"
);
return ($next_h, [$next_h]);
}
package AI::MXNet::RNN::ModifierCell;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::RNN::Cell::Base';
=head1 NAME
AI::MXNet::RNN::ModifierCell
=cut
=head1 DESCRIPTION
Base class for modifier cells. A modifier
cell takes a base cell, apply modifications
on it (e.g. Dropout), and returns a new cell.
After applying modifiers the base cell should
no longer be called directly. The modifer cell
should be used instead.
=cut
has 'base_cell' => (is => 'ro', isa => 'AI::MXNet::RNN::Cell::Base', required => 1);
around BUILDARGS => sub {
my $orig = shift;
my $class = shift;
if(@_%2)
{
my $base_cell = shift;
return $class->$orig(base_cell => $base_cell, @_);
}
return $class->$orig(@_);
};
sub BUILD
{
my $self = shift;
$self->base_cell->_modified(1);
}
method params()
{
$self->_own_params(0);
return $self->base_cell->params;
}
method state_info()
{
return $self->base_cell->state_info;
}
method begin_state(CodeRef :$init_sym=AI::MXNet::Symbol->can('zeros'), @kwargs)
{
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
AI::MXNet::RNN::DropoutCell
=cut
=head1 DESCRIPTION
Apply the dropout on base cell
=cut
method call(AI::MXNet::Symbol $inputs, SymbolOrArrayOfSymbols $states)
{
my ($output, $states) = &{$self->base_cell}($inputs, $states);
if($self->dropout_outputs > 0)
{
$output = AI::MXNet::Symbol->Dropout(data => $output, p => $self->dropout_outputs);
}
if($self->dropout_states > 0)
{
$states = [map { AI::MXNet::Symbol->Dropout(data => $_, p => $self->dropout_states) } @{ $states }];
}
return ($output, $states);
}
package AI::MXNet::RNN::ZoneoutCell;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::RNN::ModifierCell';
has [qw/zoneout_outputs zoneout_states/] => (is => 'ro', isa => 'Num', default => 0);
has 'prev_output' => (is => 'rw', init_arg => undef);
=head1 NAME
AI::MXNet::RNN::ZoneoutCell
=cut
=head1 DESCRIPTION
Apply Zoneout on base cell.
=cut
sub BUILD
{
my $self = shift;
assert(
(not $self->base_cell->isa('AI::MXNet::RNN::FusedCell')),
"FusedRNNCell doesn't support zoneout. ".
"Please unfuse first."
);
assert(
(not $self->base_cell->isa('AI::MXNet::RNN::BidirectionalCell')),
"BidirectionalCell doesn't support zoneout since it doesn't support step. ".
"Please add ZoneoutCell to the cells underneath instead."
);
assert(
(not $self->base_cell->isa('AI::MXNet::RNN::SequentialCell') or not $self->_bidirectional),
"Bidirectional SequentialCell doesn't support zoneout. ".
"Please add ZoneoutCell to the cells underneath instead."
);
}
method reset()
{
$self->SUPER::reset;
$self->prev_output(undef);
}
method call(AI::MXNet::Symbol $inputs, SymbolOrArrayOfSymbols $states)
{
my ($cell, $p_outputs, $p_states) = ($self->base_cell, $self->zoneout_outputs, $self->zoneout_states);
my ($next_output, $next_states) = &{$cell}($inputs, $states);
my $mask = sub {
my ($p, $like) = @_;
AI::MXNet::Symbol->Dropout(
AI::MXNet::Symbol->ones_like(
$like
),
p => $p
);
};
my $prev_output = $self->prev_output // AI::MXNet::Symbol->zeros(shape => [0, 0]);
my $output = $p_outputs != 0
? AI::MXNet::Symbol->where(
&{$mask}($p_outputs, $next_output),
$next_output,
$prev_output
)
: $next_output;
my @states;
if($p_states != 0)
{
zip(sub {
my ($new_s, $old_s) = @_;
push @states, AI::MXNet::Symbol->where(
&{$mask}($p_states, $new_s),
$new_s,
$old_s
);
}, $next_states, $states);
}
$self->prev_output($output);
return ($output, @states ? \@states : $next_states);
}
package AI::MXNet::RNN::ResidualCell;
use Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::RNN::ModifierCell';
=head1 NAME
AI::MXNet::RNN::ResidualCell
=cut
=head1 DESCRIPTION
Adds residual connection as described in Wu et al, 2016
(https://arxiv.org/abs/1609.08144).
Output of the cell is output of the base cell plus input.
=cut
method call(AI::MXNet::Symbol $inputs, SymbolOrArrayOfSymbols $states)
{
my $output;
($output, $states) = &{$self->base_cell}($inputs, $states);
$output = AI::MXNet::Symbol->elemwise_add($output, $inputs, name => $output->name.'_plus_residual');
return ($output, $states)
}
method unroll(
Int $length,
Maybe[AI::MXNet::Symbol|ArrayRef[AI::MXNet::Symbol]] :$inputs=,
Maybe[AI::MXNet::Symbol|ArrayRef[AI::MXNet::Symbol]] :$begin_state=,
Str :$input_prefix='',
Str :$layout='NTC',
Maybe[Bool] :$merge_outputs=
)
{
$self->reset;
$self->base_cell->_modified(0);
my ($outputs, $states) = $self->base_cell->unroll($length, inputs=>$inputs, begin_state=>$begin_state,
layout=>$layout, merge_outputs=>$merge_outputs);
$self->base_cell->_modified(1);
$merge_outputs //= (blessed($outputs) and $outputs->isa('AI::MXNet::Symbol'));
($inputs) = _normalize_sequence($length, $inputs, $layout, $merge_outputs);
if($merge_outputs)
{
$outputs = AI::MXNet::Symbol->elemwise_add($outputs, $inputs, name => $outputs->name . "_plus_residual");
}
else
{
my @temp;
zip(sub {
my ($output_sym, $input_sym) = @_;
push @temp, AI::MXNet::Symbol->elemwise_add($output_sym, $input_sym,
name=>$output_sym->name."_plus_residual");
}, [@{ $outputs }], [@{ $inputs }]);
$outputs = \@temp;
}
return ($outputs, $states);
}
func _normalize_sequence($length, $inputs, $layout, $merge, $in_layout=)
{
assert((defined $inputs),
"unroll(inputs=>undef) has been deprecated. ".
"Please create input variables outside unroll."
);
my $axis = index($layout, 'T');
my $in_axis = defined $in_layout ? index($in_layout, 'T') : $axis;
if(blessed($inputs))
{
if(not $merge)
{
assert(
(@{ $inputs->list_outputs() } == 1),
"unroll doesn't allow grouped symbol as input. Please "
."convert to list first or let unroll handle splitting"
);
$inputs = [ @{ AI::MXNet::Symbol->split(
$inputs,
axis => $in_axis,
num_outputs => $length,
squeeze_axis => 1
) }];
}
}
else
{
assert(not defined $length or @$inputs == $length);
if($merge)
{
$inputs = [map { AI::MXNet::Symbol->expand_dims($_, axis=>$axis) } @{ $inputs }];
$inputs = AI::MXNet::Symbol->Concat(@{ $inputs }, dim=>$axis);
$in_axis = $axis;
}
}
( run in 0.502 second using v1.01-cache-2.11-cpan-39bf76dae61 )