public override GraphProto GetProto(int inputTensorId, SyftController ctrl)
{
FloatTensor input_tensor = ctrl.floatTensorFactory.Get(inputTensorId);
if (activation != null)
{
this.Forward(input_tensor);
}
NodeProto node = new NodeProto
{
Input = { inputTensorId.ToString() },
Output = { activation.ToString() },
OpType = "Softmax",
Attribute = { new AttributeProto {
Name = "axis",
Type = AttributeProto.Types.AttributeType.Int,
I = this.dim
} }
};
ValueInfoProto input_info = input_tensor.GetValueInfoProto();
GraphProto g = new GraphProto
{
Name = Guid.NewGuid().ToString("N"),
Node = { node },
Initializer = { },
Input = { input_info },
Output = { ctrl.floatTensorFactory.Get(activation).GetValueInfoProto() },
};
return(g);
}
// See https://github.com/onnx/onnx/blob/master/docs/Operators.md#Dropout
public override GraphProto GetProto(int inputTensorId, SyftController ctrl)
{
FloatTensor input_tensor = ctrl.floatTensorFactory.Get(inputTensorId);
this.Forward(input_tensor);
NodeProto node = new NodeProto
{
Input = { inputTensorId.ToString() },
Output = { activation.ToString(), _mask_source.Id.ToString() },
Name = this.name,
OpType = "Dropout",
DocString = ""
};
node.Attribute.Add(new AttributeProto {
Name = "ratio",
Type = AttributeProto.Types.AttributeType.Float,
F = this.rate
});
node.Attribute.Add(new AttributeProto {
Name = "is_test",
Type = AttributeProto.Types.AttributeType.Int,
I = 1
});
ValueInfoProto input_info = input_tensor.GetValueInfoProto();
GraphProto g = new GraphProto
{
Name = Guid.NewGuid().ToString("N"),
Node = { node },
Initializer = { },
Input = { input_info },
Output =
{
ctrl.floatTensorFactory.Get(activation).GetValueInfoProto(),
_mask_source.GetValueInfoProto()
},
};
return(g);
}
// See https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm
public override GraphProto GetProto(int inputTensorId, SyftController ctrl)
{
FloatTensor input_tensor = ctrl.floatTensorFactory.Get(inputTensorId);
this.Forward(input_tensor);
NodeProto node = new NodeProto
{
Input = { inputTensorId.ToString(), _weights.Id.ToString() },
Output = { activation.ToString() },
Name = this.name,
OpType = "Gemm",
DocString = ""
};
if (_biased)
{
node.Input.Add(_bias.Id.ToString());
}
node.Attribute.Add(new AttributeProto {
Name = "alpha",
Type = AttributeProto.Types.AttributeType.Float,
F = 1.0f
});
node.Attribute.Add(new AttributeProto {
Name = "beta",
Type = AttributeProto.Types.AttributeType.Float,
F = 1.0f
});
node.Attribute.Add(new AttributeProto {
Name = "broadcast",
Type = AttributeProto.Types.AttributeType.Int,
I = 1
});
TensorProto w_init = _weights.GetProto();
ValueInfoProto input_info = input_tensor.GetValueInfoProto();
ValueInfoProto w_info = _weights.GetValueInfoProto();
GraphProto g = new GraphProto
{
Name = Guid.NewGuid().ToString("N"),
Node = { node },
Initializer = { w_init },
Input = { input_info, w_info },
Output = { ctrl.floatTensorFactory.Get(activation).GetValueInfoProto() },
};
if (_biased)
{
TensorProto b_init = _bias.GetProto();
ValueInfoProto b_info = _bias.GetValueInfoProto();
g.Initializer.Add(b_init);
g.Input.Add(b_info);
}
else
{
// The Gemm schema, must have 3 inputs (must have a bias)
float[] tmpData = new float[1] {
0
};
int[] tmpDims = new int[1] {
1
};
FloatTensor tmpBias = ctrl.floatTensorFactory.Create(_data: tmpData, _shape: tmpDims, _autograd: false, _keepgrads: false);
g.Initializer.Add(tmpBias.GetProto());
g.Input.Add(tmpBias.GetValueInfoProto());
g.Node[0].Input.Add(tmpBias.Id.ToString());
}
return(g);
}
