public override JObject ToJObject()
{
var jobj = new JObject();
jobj["units"] = _units;
if (_inputShape != null)
{
jobj["input_shape"] = new JArray(_inputShape);
}
jobj["use_bias"] = _useBias;
KerasUtils.AddActivation(jobj, _activation);
KerasUtils.AddActivation(jobj, "recurrent_activation", _recurrentActivation);
KerasUtils.AddStringOrObject(jobj, "kernel_initializer", _kernelInitializer);
KerasUtils.AddStringOrObject(jobj, "recurrent_initializer", _recurrentInitializer);
KerasUtils.AddStringOrObject(jobj, "bias_initializer", _biasInitializer);
jobj["unit_forget_bias"] = _unitForgetBias;
KerasUtils.AddStringOrObject(jobj, "kernel_regularizer", _kernelRegularizer);
KerasUtils.AddStringOrObject(jobj, "recurrent_regularizer", _recurrentRegularizer);
KerasUtils.AddStringOrObject(jobj, "bias_regularizer", _biasRegularizer);
KerasUtils.AddStringOrObject(jobj, "activity_regularizer", _activityRegularizer);
jobj["dropout"] = _dropout;
jobj["recurrent_dropout"] = _recurrentDropout;
jobj["op"] = "LSTM";
return(jobj);
}
public override JObject ToJObject()
{
var jobj = new JObject();
jobj["units"] = _units;
if (_inputShape != null)
{
jobj["input_shape"] = new JArray(_inputShape);
}
KerasUtils.AddActivation(jobj, _activation);
if (_kernelInitializer.GetType() == typeof(string))
{
jobj["kernel_initializer"] = (string)_kernelInitializer;
}
else
{
jobj["kernel_initializer"] = (_kernelInitializer as GraphOp).ToJObject();
}
jobj["op"] = "Dense";
return(jobj);
}
public override JObject ToJObject()
{
var jobj = new JObject();
jobj["filters"] = _filters;
jobj["kernel_size"] = new JArray(_kernelSize);
jobj["strides"] = new JArray(_strides);
jobj["use_bias"] = _useBias;
if (_inputShape != null)
{
jobj["input_shape"] = new JArray(_inputShape);
}
KerasUtils.AddActivation(jobj, _activation);
jobj["bias_initializer"] = "zeros";
jobj.Add("op", "Conv2D");
return(jobj);
}
