private static RNNArgs PreConstruct(RNNArgs args)
{
if (args.Kwargs == null)
{
args.Kwargs = new Dictionary <string, object>();
}
// If true, the output for masked timestep will be zeros, whereas in the
// false case, output from previous timestep is returned for masked timestep.
var zeroOutputForMask = (bool)args.Kwargs.Get("zero_output_for_mask", false);
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 = new TensorShape(
propIL ?? -1,
propID ?? -1);
args.Kwargs["input_shape"] = input_shape;
}
return(args);
}
private static RNNArgs PreConstruct(RNNArgs args)
{
if (args.Kwargs == null)
{
args.Kwargs = new Dictionary <string, object>();
}
// If true, the output for masked timestep will be zeros, whereas in the
// 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);
if (propIS == null && (propID != null || propIL != null))
{
input_shape = (
propIL ?? new NoneValue(), // maybe null is needed here
propID ?? new NoneValue()); // and here
args.Kwargs["input_shape"] = input_shape;
}
return(args);
}
public RNN(RNNArgs args) : base(PreConstruct(args))
{
this.args = args;
SupportsMasking = true;
// The input shape is unknown yet, it could have nested tensor inputs, and
// 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.
//if(stateful)
//{
// if (ds_context.has_strategy()) // ds_context????
// {
// throw new Exception("RNNs with stateful=True not yet supported with tf.distribute.Strategy");
// }
//}
}
public RNN(RNNArgs args) : base(PreConstruct(args))
{
this.args = args;
SupportsMasking = true;
// The input shape is unknown yet, it could have nested tensor inputs, and
// 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.')
}
public RNN(RNNArgs args)
: base(args)
{
}
public SimpleRNN(RNNArgs args) : base(args)
{
}
