Пример #1
0
    protected ValueTuple <Tensor, List <Tensor> > CreateVisualEncoder(Tensor visualInput, List <SimpleDenseLayerDef> denseLayers, string scope)
    {
        //use the same encoder as in UnityML's python codes
        Tensor        temp;
        List <Tensor> returnWeights = new List <Tensor>();

        using (Current.K.name_scope(scope))
        {
            var conv1 = new Conv2D(16, new int[] { 8, 8 }, new int[] { 4, 4 }, use_bias: visualEncoderBias, kernel_initializer: new GlorotUniform(scale: visualEncoderInitialScale), activation: new ELU());
            var conv2 = new Conv2D(32, new int[] { 4, 4 }, new int[] { 2, 2 }, use_bias: visualEncoderBias, kernel_initializer: new GlorotUniform(scale: visualEncoderInitialScale), activation: new ELU());

            temp = conv1.Call(visualInput)[0];
            temp = conv2.Call(temp)[0];

            var flatten = new Flatten();
            //temp = Current.K.batch_flatten(temp);
            temp = flatten.Call(temp)[0];
            returnWeights.AddRange(conv1.weights);
            returnWeights.AddRange(conv2.weights);
        }

        var output     = BuildSequentialLayers(denseLayers, temp, scope);
        var hiddenFlat = output.Item1;

        returnWeights.AddRange(output.Item2);


        return(ValueTuple.Create(hiddenFlat, returnWeights));
    }
Пример #2
0
        public override NDArrayOrSymbol HybridForward(NDArrayOrSymbol x, params NDArrayOrSymbol[] args)
        {
            var residual = x;

            x = bn1.Call(x, args);
            if (x.IsNDArray)
            {
                x = nd.Activation(x.NdX, ActivationType.Relu);
            }
            else
            {
                x = sym.Activation(x.SymX, ActivationType.Relu);
            }

            if (ds != null)
            {
                residual = ds.Call(x, args);
            }

            x = conv1.Call(x, args);

            x = bn2.Call(x, args);
            if (x.IsNDArray)
            {
                x = nd.Activation(x.NdX, ActivationType.Relu);
            }
            else
            {
                x = sym.Activation(x.SymX, ActivationType.Relu);
            }
            x = conv2.Call(x, args);

            x = bn3.Call(x, args);
            if (x.IsNDArray)
            {
                x = nd.Activation(x.NdX, ActivationType.Relu);
            }
            else
            {
                x = sym.Activation(x.SymX, ActivationType.Relu);
            }
            x = conv3.Call(x, args);

            if (x.IsNDArray)
            {
                return(x.NdX + residual.NdX);
            }

            return(x.SymX + residual.SymX);
        }
Пример #3
0
    public void TestConv2D()
    {
        var inputLayer = UnityTFUtils.Input(shape: new int?[] { 32, 32, 3 });

        var conv1 = new Conv2D(16, new int[] { 3, 3 }, padding: PaddingType.Same, activation: new ReLU());
        var conv2 = new Conv2D(3, new int[] { 3, 3 }, padding: PaddingType.Same, activation: new ReLU());

        var target = UnityTFUtils.Input(shape: new int?[] { 32, 32, 3 });


        var pred  = conv2.Call(conv1.Call(inputLayer[0])[0])[0];
        var lossM = new MeanSquareError();

        lossM.Call(target[0], pred);


        ((UnityTFBackend)K).ExportGraphDef("SavedGraph/convLayer.pb");
    }