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some RNN

pull/803/head
MPnoy 4 years ago
parent
83bd764b6a
commit
98b4d23053
3 changed files with 170 additions and 119 deletions
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  1. +1
    -0
      src/TensorFlowNET.Core/Keras/ArgsDefinition/StackedRNNCellsArgs.cs
  2. +29
    -17
      src/TensorFlowNET.Keras/Layers/RNN.cs
  3. +140
    -102
      src/TensorFlowNET.Keras/Layers/StackedRNNCells.cs

+ 1
- 0
src/TensorFlowNET.Core/Keras/ArgsDefinition/StackedRNNCellsArgs.cs View File

@@ -5,5 +5,6 @@ namespace Tensorflow.Keras.ArgsDefinition
public class StackedRNNCellsArgs : LayerArgs
{
public IList<RnnCell> Cells { get; set; }
public Dictionary<string, object> Kwargs { get; set; } = null;
}
}

+ 29
- 17
src/TensorFlowNET.Keras/Layers/RNN.cs View File

@@ -2,12 +2,18 @@
using System.Collections.Generic;
using Tensorflow.Keras.ArgsDefinition;
using Tensorflow.Keras.Engine;
// from tensorflow.python.distribute import distribution_strategy_context as ds_context;

namespace Tensorflow.Keras.Layers
{
public class RNN : Layer
{
private RNNArgs args;
private object input_spec = null; // or NoneValue??
private object state_spec = null;
private object _states = null;
private object constants_spec = null;
private int _num_constants = 0;

public RNN(RNNArgs args) : base(PreConstruct(args))
{
@@ -18,16 +24,13 @@ namespace Tensorflow.Keras.Layers
// the input spec will be the list of specs for nested inputs, the structure
// of the input_spec will be the same as the input.

//self.input_spec = None
//self.state_spec = None
//self._states = None
//self.constants_spec = None
//self._num_constants = 0

//if stateful:
// if ds_context.has_strategy():
// raise ValueError('RNNs with stateful=True not yet supported with '
// 'tf.distribute.Strategy.')
//if(stateful)
//{
// if (ds_context.has_strategy()) // ds_context????
// {
// throw new Exception("RNNs with stateful=True not yet supported with tf.distribute.Strategy");
// }
//}
}

private static RNNArgs PreConstruct(RNNArgs args)
@@ -41,16 +44,16 @@ namespace Tensorflow.Keras.Layers
// false case, output from previous timestep is returned for masked timestep.
var zeroOutputForMask = (bool)args.Kwargs.Get("zero_output_for_mask", false);

object input_shape;
var propIS = args.Kwargs.Get("input_shape", null);
var propID = args.Kwargs.Get("input_dim", null);
var propIL = args.Kwargs.Get("input_length", null);
TensorShape input_shape;
var propIS = (TensorShape)args.Kwargs.Get("input_shape", null);
var propID = (int?)args.Kwargs.Get("input_dim", null);
var propIL = (int?)args.Kwargs.Get("input_length", null);

if (propIS == null && (propID != null || propIL != null))
{
input_shape = (
propIL ?? new NoneValue(), // maybe null is needed here
propID ?? new NoneValue()); // and here
input_shape = new TensorShape(
propIL ?? -1,
propID ?? -1);
args.Kwargs["input_shape"] = input_shape;
}

@@ -103,5 +106,14 @@ namespace Tensorflow.Keras.Layers
{
throw new NotImplementedException("");
}

// Check whether the state_size contains multiple states.
public static bool _is_multiple_state(object state_size)
{
var myIndexerProperty = state_size.GetType().GetProperty("Item");
return myIndexerProperty != null
&& myIndexerProperty.GetIndexParameters().Length == 1
&& !(state_size.GetType() == typeof(TensorShape));
}
}
}

+ 140
- 102
src/TensorFlowNET.Keras/Layers/StackedRNNCells.cs View File

@@ -1,5 +1,6 @@
using System;
using System.Collections.Generic;
using System.ComponentModel;
using Tensorflow.Keras.ArgsDefinition;
using Tensorflow.Keras.Engine;

@@ -8,118 +9,155 @@ namespace Tensorflow.Keras.Layers
public class StackedRNNCells : Layer, RNNArgs.IRnnArgCell
{
public IList<RnnCell> Cells { get; set; }
public bool reverse_state_order;

public StackedRNNCells(StackedRNNCellsArgs args) : base(args)
{
if (args.Kwargs == null)
{
args.Kwargs = new Dictionary<string, object>();
}

Cells = args.Cells;
//Cells.reverse_state_order = kwargs.pop('reverse_state_order', False);
// self.reverse_state_order = kwargs.pop('reverse_state_order', False)
// if self.reverse_state_order:
// logging.warning('reverse_state_order=True in StackedRNNCells will soon '
// 'be deprecated. Please update the code to work with the '
// 'natural order of states if you rely on the RNN states, '
// 'eg RNN(return_state=True).')
// super(StackedRNNCells, self).__init__(**kwargs)
throw new NotImplementedException("");
reverse_state_order = (bool)args.Kwargs.Get("reverse_state_order", false);
if (reverse_state_order)
{
throw new WarningException("reverse_state_order=True in StackedRNNCells will soon " +
"be deprecated. Please update the code to work with the " +
"natural order of states if you rely on the RNN states, " +
"eg RNN(return_state=True).");
}
}

public object state_size
{
get => throw new NotImplementedException();
//@property
//def state_size(self) :
// return tuple(c.state_size for c in
// (self.cells[::- 1] if self.reverse_state_order else self.cells))
}

public object output_size
{
get
{
var lastCell = Cells[Cells.Count - 1];

if (lastCell.output_size != -1)
{
return lastCell.output_size;
}
else if (RNN._is_multiple_state(lastCell.state_size))
{
return ((dynamic)Cells[-1].state_size)[0];
}
else
{
return Cells[-1].state_size;
}
}
}

public object get_initial_state()
{
throw new NotImplementedException();
// def get_initial_state(self, inputs= None, batch_size= None, dtype= None) :
// initial_states = []
// for cell in self.cells[::- 1] if self.reverse_state_order else self.cells:
// get_initial_state_fn = getattr(cell, 'get_initial_state', None)
// if get_initial_state_fn:
// initial_states.append(get_initial_state_fn(
// inputs=inputs, batch_size=batch_size, dtype=dtype))
// else:
// initial_states.append(_generate_zero_filled_state_for_cell(
// cell, inputs, batch_size, dtype))

// return tuple(initial_states)
}

public object call()
{
throw new NotImplementedException();
// def call(self, inputs, states, constants= None, training= None, ** kwargs):
// # Recover per-cell states.
// state_size = (self.state_size[::- 1]
// if self.reverse_state_order else self.state_size)
// nested_states = nest.pack_sequence_as(state_size, nest.flatten(states))

// # Call the cells in order and store the returned states.
// new_nested_states = []
// for cell, states in zip(self.cells, nested_states) :
// states = states if nest.is_nested(states) else [states]
//# TF cell does not wrap the state into list when there is only one state.
// is_tf_rnn_cell = getattr(cell, '_is_tf_rnn_cell', None) is not None
// states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
// if generic_utils.has_arg(cell.call, 'training'):
// kwargs['training'] = training
// else:
// kwargs.pop('training', None)
// # Use the __call__ function for callable objects, eg layers, so that it
// # will have the proper name scopes for the ops, etc.
// cell_call_fn = cell.__call__ if callable(cell) else cell.call
// if generic_utils.has_arg(cell.call, 'constants'):
// inputs, states = cell_call_fn(inputs, states,
// constants= constants, ** kwargs)
// else:
// inputs, states = cell_call_fn(inputs, states, ** kwargs)
// new_nested_states.append(states)

// return inputs, nest.pack_sequence_as(state_size,
// nest.flatten(new_nested_states))
}

public void build()
{
throw new NotImplementedException();
// @tf_utils.shape_type_conversion
// def build(self, input_shape) :
// if isinstance(input_shape, list) :
// input_shape = input_shape[0]
// for cell in self.cells:
// if isinstance(cell, Layer) and not cell.built:
// with K.name_scope(cell.name):
// cell.build(input_shape)
// cell.built = True
// if getattr(cell, 'output_size', None) is not None:
// output_dim = cell.output_size
// elif _is_multiple_state(cell.state_size) :
// output_dim = cell.state_size[0]
// else:
// output_dim = cell.state_size
// input_shape = tuple([input_shape[0]] +
// tensor_shape.TensorShape(output_dim).as_list())
// self.built = True
}

//@property
//def state_size(self) :
// return tuple(c.state_size for c in
// (self.cells[::- 1] if self.reverse_state_order else self.cells))

// @property
// def output_size(self) :
// if getattr(self.cells[-1], 'output_size', None) is not None:
// return self.cells[-1].output_size
// elif _is_multiple_state(self.cells[-1].state_size) :
// return self.cells[-1].state_size[0]
// else:
// return self.cells[-1].state_size

// def get_initial_state(self, inputs= None, batch_size= None, dtype= None) :
// initial_states = []
// for cell in self.cells[::- 1] if self.reverse_state_order else self.cells:
// get_initial_state_fn = getattr(cell, 'get_initial_state', None)
// if get_initial_state_fn:
// initial_states.append(get_initial_state_fn(
// inputs=inputs, batch_size=batch_size, dtype=dtype))
// else:
// initial_states.append(_generate_zero_filled_state_for_cell(
// cell, inputs, batch_size, dtype))

// return tuple(initial_states)

// def call(self, inputs, states, constants= None, training= None, ** kwargs):
// # Recover per-cell states.
// state_size = (self.state_size[::- 1]
// if self.reverse_state_order else self.state_size)
// nested_states = nest.pack_sequence_as(state_size, nest.flatten(states))

// # Call the cells in order and store the returned states.
// new_nested_states = []
// for cell, states in zip(self.cells, nested_states) :
// states = states if nest.is_nested(states) else [states]
//# TF cell does not wrap the state into list when there is only one state.
// is_tf_rnn_cell = getattr(cell, '_is_tf_rnn_cell', None) is not None
// states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
// if generic_utils.has_arg(cell.call, 'training'):
// kwargs['training'] = training
// else:
// kwargs.pop('training', None)
// # Use the __call__ function for callable objects, eg layers, so that it
// # will have the proper name scopes for the ops, etc.
// cell_call_fn = cell.__call__ if callable(cell) else cell.call
// if generic_utils.has_arg(cell.call, 'constants'):
// inputs, states = cell_call_fn(inputs, states,
// constants= constants, ** kwargs)
// else:
// inputs, states = cell_call_fn(inputs, states, ** kwargs)
// new_nested_states.append(states)

// return inputs, nest.pack_sequence_as(state_size,
// nest.flatten(new_nested_states))

// @tf_utils.shape_type_conversion
// def build(self, input_shape) :
// if isinstance(input_shape, list) :
// input_shape = input_shape[0]
// for cell in self.cells:
// if isinstance(cell, Layer) and not cell.built:
// with K.name_scope(cell.name):
// cell.build(input_shape)
// cell.built = True
// if getattr(cell, 'output_size', None) is not None:
// output_dim = cell.output_size
// elif _is_multiple_state(cell.state_size) :
// output_dim = cell.state_size[0]
// else:
// output_dim = cell.state_size
// input_shape = tuple([input_shape[0]] +
// tensor_shape.TensorShape(output_dim).as_list())
// self.built = True

// def get_config(self) :
// cells = []
// for cell in self.cells:
// cells.append(generic_utils.serialize_keras_object(cell))
// config = {'cells': cells
//}
//base_config = super(StackedRNNCells, self).get_config()
// return dict(list(base_config.items()) + list(config.items()))

// @classmethod
// def from_config(cls, config, custom_objects = None):
// from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
// cells = []
// for cell_config in config.pop('cells'):
// cells.append(
// deserialize_layer(cell_config, custom_objects = custom_objects))
// return cls(cells, **config)
public override LayerArgs get_config()
{
throw new NotImplementedException();
//def get_config(self):
// cells = []
// for cell in self.cells:
// cells.append(generic_utils.serialize_keras_object(cell))
// config = {'cells': cells}
// base_config = super(StackedRNNCells, self).get_config()
// return dict(list(base_config.items()) + list(config.items()))
}


public void from_config()
{
throw new NotImplementedException();
// @classmethod
// def from_config(cls, config, custom_objects = None):
// from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
// cells = []
// for cell_config in config.pop('cells'):
// cells.append(
// deserialize_layer(cell_config, custom_objects = custom_objects))
// return cls(cells, **config)
}
}
}

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