A tool to visually-design DirectML operators that run in the GPU and to create and train a neural network with it. Uses my DirectML Lib.
Download also binary from Windows App Store
- Undo, Redo, Save, Load, Multiple Sets
- Multiple Visible/Active DirectML operators
- Direct2D Drawing
- Memory Sharing
- Input/Output CSV or binary, Input Random, Output to MessageBox
- Adapter Selection
- Show Adapter Memory Consumed
- Variables
- Generate C++ Code and VS Solution
- Design a neural network
- MNIST-included dataset
- Adapter Selection
- Training on GPU
- Training on CPU
- Testing on GPU/CPU
- Batch training
- Saving/Loading model
- Saving as PTH or ONNX with Python installed
- Customizable network structure
- Customizable activation functions
- Activation: Celu,Elu,Gelu,HardMax,HardSigmoid,Identity,LeakyRelu,Linear,LogSoftmax,ParameterizedRelu,ParametricSoftplus,Relu,ScaledElu,ScaledTanh,Shrink,Sigmoid,Softmax,Softplus,Softsign,Tanh,ThresholdedRelu
- Batch Processing: BatchNormalization, BatchNormalizationGrad, BatchNormalizationTraining, BatchNormalizationTrainingGrad
- Comparison Operators: If, IsInfinity, IsNaN
- A: Abs,ACos,ACosh,Add,And,ASin,ASinh,ATan,ATanh,ATanYX,AveragePooling
- B: BitAnd, BitCount, BitOr, BitNot, BitShiftLeft, BitShiftRight, BitXor
- C: Cast, Ceil, Clip, ClipGrad, Constant, ConvolutionInteger, Cos, Cosh, Cummulative Sum/Product, Convolution
- D: DepthToSpace, Dequantize, DequantizeLinear, DiagonalMatrix, DifferenceSquare, Divide
- E: Erf, Exp, Equals
- F: Floor
- G: Gather, GatherElemends, GatherND, Gemm, GreaterThan, GreaterThanOrEqual, Gru
- I: Identity,
- J: Join
- L: Log, LessThan, LessThanOrEqual, LocalResponseNormalization
- M: Max,MaxPooling,Mean,MeanVarianceNormalization,Min,Multiply,Modulus Floor,Modulus Truncate
- N: Neg, NonZeroCoordinates, Not
- O: OneHot, Or
- P: Padding, Pow
- Q: QuantizedLinearConvolution, QuantizeLinear
- R: RandomGenerator, Recip, Reduce, Resample, ResampleGrad, Round, RoiAlign, RoiAlignGrad, Reintrerpret, ReverseSubsequences
- S: ScatterElements, Slice, SliceGrad, Subtract, Sqrt, Sign, SpaceToDepth
- T: Threshold, TopK
- U: Upsample2D
- V: ValueScale2D
- X: Xor
Complete VS project generation support
Recurrent NN training
Usage of batch DML operations for faster training
Implement Loops