Java Code Examples for org.nd4j.linalg.api.buffer.DataType#isFPType()
The following examples show how to use
org.nd4j.linalg.api.buffer.DataType#isFPType() .
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Example 1
Source File: BaseLossBp.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){ Preconditions.checkState(inputDataTypes.get(0).isFPType(), "Input 0 (predictions) must be a floating point type; inputs datatypes are %s for %s", inputDataTypes, getClass()); DataType dt0 = inputDataTypes.get(0); DataType dt1 = arg(1).dataType(); DataType dt2 = arg(2).dataType(); if(!dt1.isFPType()) dt1 = dt0; if(!dt2.isFPType()) dt2 = dt0; return Arrays.asList(dt0, dt1, dt2); }
Example 2
Source File: MergeAvg.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){ DataType first = dataTypes.get(0); for( int i=1; i<dataTypes.size(); i++ ){ DataType dt = dataTypes.get(i); Preconditions.checkState(first == dt, "All inputs must have same datatype - got %s and %s for inputs 0 and %s respectively", first, dt, i); } //Output type is same as input types if FP, or default FP type otherwise if(first.isFPType()){ return Collections.singletonList(first); } else { return Collections.singletonList(Nd4j.defaultFloatingPointType()); } }
Example 3
Source File: BaseReduceFloatOp.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Override public List<LongShapeDescriptor> calculateOutputShape(OpContext oc) { INDArray x = oc != null ? oc.getInputArray(0) : x(); if(x == null) return Collections.emptyList(); //Calculate reduction shape. Note that reduction on scalar - returns a scalar long[] reducedShape = x.rank() == 0 ? x.shape() : Shape.getReducedShape(x.shape(),dimensions, isKeepDims()); DataType retType = arg().dataType(); if(!retType.isFPType()) retType = Nd4j.defaultFloatingPointType(); return Collections.singletonList(LongShapeDescriptor.fromShape(reducedShape, retType)); }