TensorFlow.NET/src/TensorFlowNET.Keras/Layers/Rnn/LSTM.cs at master · MSavameri/TensorFlow.NET · GitHub

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using System.Linq;

using Tensorflow.Keras.ArgsDefinition;

using Tensorflow.Keras.Engine;

using Tensorflow.Common.Types;

using Tensorflow.Common.Extensions;

using Tensorflow.Keras.Saving;

namespace Tensorflow.Keras.Layers

{

/// <summary>

/// Long Short-Term Memory layer - Hochreiter 1997.

///

/// See [the Keras RNN API guide](https://www.tensorflow.org/guide/keras/rnn)

/// for details about the usage of RNN API.

/// </summary>

public class LSTM : RNN

{

LSTMArgs _args;

InputSpec[] _state_spec;

InputSpec _input_spec;

bool _could_use_gpu_kernel;

public LSTMArgs Args { get => _args; }

public LSTM(LSTMArgs args) :

base(CreateCell(args), args)

{

_args = args;

_input_spec = new InputSpec(ndim: 3);

_state_spec = new[] { args.Units, args.Units }.Select(dim => new InputSpec(shape: (-1, dim))).ToArray();

_could_use_gpu_kernel = args.Activation == keras.activations.Tanh

&& args.RecurrentActivation == keras.activations.Sigmoid

&& args.RecurrentDropout == 0 && !args.Unroll && args.UseBias

&& ops.executing_eagerly_outside_functions();

}

private static IRnnCell CreateCell(LSTMArgs lstmArgs)

{

return new LSTMCell(new LSTMCellArgs()

{

Units = lstmArgs.Units,

Activation = lstmArgs.Activation,

RecurrentActivation = lstmArgs.RecurrentActivation,

UseBias = lstmArgs.UseBias,

KernelInitializer = lstmArgs.KernelInitializer,

RecurrentInitializer = lstmArgs.RecurrentInitializer,

UnitForgetBias = lstmArgs.UnitForgetBias,

BiasInitializer = lstmArgs.BiasInitializer,

// TODO(Rinne): kernel_regularizer

// TODO(Rinne): recurrent_regularizer

// TODO(Rinne): bias_regularizer

// TODO(Rinne): kernel_constriant

// TODO(Rinne): recurrent_constriant

// TODO(Rinne): bias_constriant

Dropout = lstmArgs.Dropout,

RecurrentDropout = lstmArgs.RecurrentDropout,

Implementation = lstmArgs.Implementation,

DType = lstmArgs.DType,

Trainable = lstmArgs.Trainable

});

}

protected override Tensors Call(Tensors inputs, Tensors initial_state = null, bool? training = null, IOptionalArgs? optional_args = null)

{

// skip the condition of ragged input

(inputs, initial_state, _) = _process_inputs(inputs, initial_state, null);

Tensor mask = null;

if(optional_args is RnnOptionalArgs rnnArgs)

{

mask = rnnArgs.Mask;

}

var single_input = inputs.Single;

var input_shape = single_input.shape;

var timesteps = _args.TimeMajor ? input_shape[0] : input_shape[1];

_maybe_reset_cell_dropout_mask(Cell);

Func<Tensors, Tensors, (Tensors, Tensors)> step = (inputs, states) =>

{

var res = Cell.Apply(inputs, states, training is null ? true : training.Value);

var (output, state) = res;

return (output, state);

};

var (last_output, outputs, states) = keras.backend.rnn(

step,

inputs,

initial_state,

constants: null,

go_backwards: _args.GoBackwards,

mask: mask,

unroll: _args.Unroll,

input_length: ops.convert_to_tensor(timesteps),

time_major: _args.TimeMajor,

zero_output_for_mask: _args.ZeroOutputForMask,

return_all_outputs: _args.ReturnSequences

);

Tensor output;

if (_args.ReturnSequences)

{

output = keras.backend.maybe_convert_to_ragged(false, outputs, (int)timesteps, _args.GoBackwards);

}

else

{

output = last_output;

}

if (_args.ReturnState)

{

return new Tensor[] { output }.Concat(states).ToArray().ToTensors();

}

else

{

return output;

}

public override IKerasConfig get_config()

{

return _args;

}

TensorFlow.NET/src/TensorFlowNET.Keras/Layers/Rnn/LSTM.cs at master · MSavameri/TensorFlow.NET · GitHub

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