AI-MXNet
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lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
$states : ArrayRef[AI::MXNet::Symbol]
state to next step of RNN.
Can be called via overloaded &{}: &{$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} } @{ $self->state_info }];
}
=head2 state_info
shape and layout information of states
=cut
method state_info()
{
confess("Not Implemented");
}
=head2 begin_state
Initial state for this cell.
Parameters
----------
:$func : sub ref, default is AI::MXNet::Symbol->can('zeros')
Function for creating initial state.
Can be AI::MXNet::Symbol->can('zeros'),
AI::MXNet::Symbol->can('uniform'), AI::MXNet::Symbol->can('Variable') etc.
Use AI::MXNet::Symbol->can('Variable') if you want to directly
feed the input as states.
@kwargs :
more keyword arguments passed to func. For example
mean, std, dtype, etc.
Returns
-------
$states : ArrayRef[AI::MXNet::Symbol]
starting states for first RNN step
=cut
method begin_state(CodeRef :$func=AI::MXNet::Symbol->can('zeros'), @kwargs)
{
assert(
(not $self->_modified),
"After applying modifier cells (e.g. DropoutCell) the base "
."cell cannot be called directly. Call the modifier cell instead."
);
my @states;
my $func_needs_named_name = $func ne AI::MXNet::Symbol->can('Variable');
for my $info (@{ $self->state_info })
{
$self->_init_counter($self->_init_counter + 1);
my @name = (sprintf("%sbegin_state_%d", $self->_prefix, $self->_init_counter));
my %info = %{ $info//{} };
if($func_needs_named_name)
{
unshift(@name, 'name');
}
else
{
if(exists $info{__layout__})
{
$info{kwargs} = { __layout__ => delete $info{__layout__} };
}
}
my %kwargs = (@kwargs, %info);
my $state = &{$func}(
'AI::MXNet::Symbol',
@name,
%kwargs
);
push @states, $state;
}
return \@states;
}
=head2 unpack_weights
Unpack fused weight matrices into separate
weight matrices
Parameters
----------
$args : HashRef[AI::MXNet::NDArray]
hash ref containing packed weights.
usually from AI::MXNet::Module->get_output()
Returns
-------
$args : HashRef[AI::MXNet::NDArray]
hash ref with weights associated with
this cell, unpacked.
=cut
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
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)
{
return [map { @{ $_->state_shape } } @$cells];
}
method _cells_state_info($cells)
{
return [map { @{ $_->state_info } } @$cells];
}
method _cells_begin_state($cells, @kwargs)
{
return [map { @{ $_->begin_state(@kwargs) } } @$cells];
}
method _cells_unpack_weights($cells, $args)
{
$args = $_->unpack_weights($args) for @$cells;
return $args;
}
method _cells_pack_weights($cells, $args)
{
$args = $_->pack_weights($args) for @$cells;
return $args;
}
package AI::MXNet::RNN::Cell;
use Mouse;
extends 'AI::MXNet::RNN::Cell::Base';
=head1 NAME
AI::MXNet::RNN::Cell
=cut
=head1 DESCRIPTION
Simple recurrent neural network cell
Parameters
----------
num_hidden : int
number of units in output symbol
activation : str or Symbol, default 'tanh'
type of activation function
prefix : str, default 'rnn_'
prefix for name of layers
(and name of weight if params is undef)
params : AI::MXNet::RNNParams or undef
container for weight sharing between cells.
created if undef.
=cut
has '_num_hidden' => (is => 'ro', init_arg => 'num_hidden', isa => 'Int', required => 1);
has 'forget_bias' => (is => 'ro', isa => 'Num');
has '_activation' => (
is => 'ro',
init_arg => 'activation',
isa => 'Activation',
default => 'tanh'
);
has '+_prefix' => (default => 'rnn_');
has [qw/_iW _iB
_hW _hB/] => (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;
$self->_iW($self->params->get('i2h_weight'));
$self->_iB(
$self->params->get(
'i2h_bias',
(defined($self->forget_bias)
? (init => AI::MXNet::LSTMBias->new(forget_bias => $self->forget_bias))
: ()
)
)
);
$self->_hW($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"
);
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
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(
$next_c, act_type => "tanh"
),
name => "${name}out"
);
return ($next_h, [$next_h, $next_c]);
}
package AI::MXNet::RNN::GRUCell;
use Mouse;
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(
factor_type => 'in',
magnitude => 2.34
)
);
}
if(not $self->initializer->isa('AI::MXNet::FusedRNN'))
{
$self->initializer(
AI::MXNet::FusedRNN->new(
init => $self->initializer,
num_hidden => $self->_num_hidden,
num_layers => $self->_num_layers,
mode => $self->_mode,
bidirectional => $self->_bidirectional,
forget_bias => $self->forget_bias
)
);
}
$self->_parameter($self->params->get('parameters', init => $self->initializer));
$self->_directions($self->_bidirectional ? [qw/l r/] : ['l']);
}
method state_info()
{
my $b = @{ $self->_directions };
my $n = $self->_mode eq 'lstm' ? 2 : 1;
return [map { +{ shape => [$b*$self->_num_layers, 0, $self->_num_hidden], __layout__ => 'LNC' } } 0..$n-1];
}
method _gate_names()
{
return {
rnn_relu => [''],
rnn_tanh => [''],
lstm => [qw/_i _f _c _o/],
gru => [qw/_r _z _o/]
}->{ $self->_mode };
}
method _num_gates()
{
return scalar(@{ $self->_gate_names })
}
method _slice_weights($arr, $li, $lh)
{
my %args;
my @gate_names = @{ $self->_gate_names };
my @directions = @{ $self->_directions };
my $b = @directions;
my $p = 0;
for my $layer (0..$self->_num_layers-1)
{
for my $direction (@directions)
{
for my $gate (@gate_names)
{
my $name = sprintf('%s%s%d_i2h%s_weight', $self->_prefix, $direction, $layer, $gate);
my $size;
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
{
$size = $li*$lh;
$args{$name} = $arr->slice([$p,$p+$size-1])->reshape([$lh, $li]);
}
$p += $size;
}
for my $gate (@gate_names)
{
my $name = sprintf('%s%s%d_h2h%s_weight', $self->_prefix, $direction, $layer, $gate);
my $size = $lh**2;
$args{$name} = $arr->slice([$p,$p+$size-1])->reshape([$lh, $lh]);
$p += $size;
}
}
}
for my $layer (0..$self->_num_layers-1)
{
for my $direction (@directions)
{
for my $gate (@gate_names)
{
my $name = sprintf('%s%s%d_i2h%s_bias', $self->_prefix, $direction, $layer, $gate);
$args{$name} = $arr->slice([$p,$p+$lh-1]);
$p += $lh;
}
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]);
}
lib/AI/MXNet/RNN/Cell.pm view on Meta::CPAN
my $data = AI::MXNet::Symbol->Variable('data');
my $state_shape = AI::MXNet::Symbol->Convolution(
data => $data,
num_filter => $self->_num_hidden,
kernel => $self->_i2h_kernel,
stride => $self->_i2h_stride,
pad => $self->_i2h_pad,
dilate => $self->_i2h_dilate,
layout => $self->_conv_layout
);
$state_shape = ($state_shape->infer_shape(data=>$self->_input_shape))[1]->[0];
$state_shape->[0] = 0;
$self->_state_shape($state_shape);
}
method state_info()
{
return [
{ shape => $self->_state_shape, __layout__ => $self->_conv_layout },
{ shape => $self->_state_shape, __layout__ => $self->_conv_layout }
];
}
method call($inputs, $states)
{
confess("AI::MXNet::RNN::ConvCell::Base is abstract class for convolutional RNN");
}
package AI::MXNet::RNN::ConvCell;
use Mouse;
extends 'AI::MXNet::RNN::ConvCell::Base';
=head1 NAME
AI::MXNet::RNN::ConvCell
=cut
=head1 DESCRIPTION
Convolutional RNN cells
Parameters
----------
input_shape : array ref of int
Shape of input in single timestep.
num_hidden : int
Number of units in output symbol.
h2h_kernel : array ref of int, default (3, 3)
Kernel of Convolution operator in state-to-state transitions.
h2h_dilate : array ref of int, default (1, 1)
Dilation of Convolution operator in state-to-state transitions.
i2h_kernel : array ref of int, default (3, 3)
Kernel of Convolution operator in input-to-state transitions.
i2h_stride : array ref of int, default (1, 1)
Stride of Convolution operator in input-to-state transitions.
i2h_pad : array ref of int, default (1, 1)
Pad of Convolution operator in input-to-state transitions.
i2h_dilate : array ref of int, default (1, 1)
Dilation of Convolution operator in input-to-state transitions.
activation : str or Symbol,
default functools.partial(symbol.LeakyReLU, act_type='leaky', slope=0.2)
Type of activation function.
prefix : str, default 'ConvRNN_'
Prefix for name of layers (and name of weight if params is None).
params : RNNParams, default None
Container for weight sharing between cells. Created if None.
conv_layout : str, , default 'NCHW'
Layout of ConvolutionOp
=cut
has '+_h2h_kernel' => (default => sub { [3, 3] });
has '+_h2h_dilate' => (default => sub { [1, 1] });
has '+_i2h_kernel' => (default => sub { [3, 3] });
has '+_i2h_stride' => (default => sub { [1, 1] });
has '+_i2h_dilate' => (default => sub { [1, 1] });
has '+_i2h_pad' => (default => sub { [1, 1] });
has '+_prefix' => (default => 'ConvRNN_');
has '+_activation' => (default => sub { sub { AI::MXNet::Symbol->LeakyReLU(@_, act_type => 'leaky', slope => 0.2) } });
has '+i2h_bias_initializer' => (default => 'zeros');
has '+h2h_bias_initializer' => (default => 'zeros');
has 'forget_bias' => (is => 'ro', isa => 'Num');
has [qw/_iW _iB
_hW _hB/] => (is => 'rw', init_arg => undef);
sub BUILD
{
my $self = shift;
$self->_iW($self->_params->get('i2h_weight', init => $self->i2h_weight_initializer));
$self->_hW($self->_params->get('h2h_weight', init => $self->h2h_weight_initializer));
$self->_iB(
$self->params->get(
'i2h_bias',
(defined($self->forget_bias and not defined $self->i2h_bias_initializer)
? (init => AI::MXNet::LSTMBias->new(forget_bias => $self->forget_bias))
: (init => $self->i2h_bias_initializer)
)
)
);
$self->_hB($self->_params->get('h2h_bias', init => $self->h2h_bias_initializer));
}
method _num_gates()
{
scalar(@{ $self->_gate_names() });
}
method _gate_names()
{
return ['']
}
method _conv_forward($inputs, $states, $name)
{
my $i2h = AI::MXNet::Symbol->Convolution(
name => "${name}i2h",
data => $inputs,
num_filter => $self->_num_hidden*$self->_num_gates(),
kernel => $self->_i2h_kernel,
stride => $self->_i2h_stride,
pad => $self->_i2h_pad,
dilate => $self->_i2h_dilate,
weight => $self->_iW,
bias => $self->_iB
);
my $h2h = AI::MXNet::Symbol->Convolution(
name => "${name}h2h",
data => @{ $states }[0],
num_filter => $self->_num_hidden*$self->_num_gates(),
kernel => $self->_h2h_kernel,
stride => [1, 1],
pad => $self->_h2h_pad,
dilate => $self->_h2h_dilate,
weight => $self->_hW,
bias => $self->_hB
);
return ($i2h, $h2h);
}
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 $output = $self->_get_activation($i2h + $h2h, $self->_activation, name => "${name}out");
return ($output, [$output]);
}
package AI::MXNet::RNN::ConvLSTMCell;
use Mouse;
extends 'AI::MXNet::RNN::ConvCell';
has '+forget_bias' => (default => 1);
has '+_prefix' => (default => 'ConvLSTM_');
=head1 NAME
AI::MXNet::RNN::ConvLSTMCell
=cut
=head1 DESCRIPTION
Convolutional LSTM network cell.
Reference:
Xingjian et al. NIPS2015
=cut
method _gate_names()
{
return ['_i', '_f', '_c', '_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 $gates = $i2h + $h2h;
my @slice_gates = @{ AI::MXNet::Symbol->SliceChannel(
$gates,
num_outputs => 4,
axis => index($self->_conv_layout, 'C'),
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 = $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;
}
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