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shallow delete of assign from SDBase. (#164) 

Signed-off-by: Robert Altena <Rob@Ra-ai.com>
master
Robert Altena 2020-01-04 13:26:39 +09:00 committed by Alex Black
parent 29104083cc
commit 53d3bd1269
2 changed files with 3 additions and 55 deletions
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48
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBaseOps.java
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@ -185,54 +185,6 @@ public abstract class SDBaseOps {
return argmin(null, in, keepDims, dimensions); return argmin(null, in, keepDims, dimensions);
} }
/**
* Assign/copy op: out = x.assign(y). Supports broadcasting
*
* @param x Input variable x
* @param y Input variable y
* @return Output variable
*/
public SDVariable assign(SDVariable x, SDVariable y) {
return assign(null, x, y);
}
/**
* Assign/copy op: out = x.assign(y). Supports broadcasting
*
* @param name Name of the output variable
* @param x Input variable x
* @param y Input variable y
* @return Output variable
*/
public SDVariable assign(String name, SDVariable x, SDVariable y) {
SDVariable ret = f().assign(x, y);
return updateVariableNameAndReference(ret, name);
}
/**
* Return an array with equal shape to the input, but all elements set to 'value'
*
* @param in Input variable
* @param value Value to set
* @return Output variable
*/
public SDVariable assign(SDVariable in, Number value) {
return assign(null, in, value);
}
/**
* Return an array with equal shape to the input, but all elements set to 'value'
*
* @param name Name of the output variable
* @param in Input variable
* @param value Value to set
* @return Output variable
*/
public SDVariable assign(String name, SDVariable in, Number value) {
SDVariable ret = f().assign(in, value);
return updateVariableNameAndReference(ret, name);
}
/** /**
* Matrix multiply a batch of matrices. matricesA and matricesB have to be arrays of same * Matrix multiply a batch of matrices. matricesA and matricesB have to be arrays of same
* length and each pair taken from these sets has to have dimensions (M, N) and (N, K), * length and each pair taken from these sets has to have dimensions (M, N) and (N, K),

8
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/TransformOpValidation.java
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@ -939,9 +939,7 @@ public class TransformOpValidation extends BaseOpValidation {
tc.expectedOutput(t.name(), Transforms.min(ia, 0.5, true)); tc.expectedOutput(t.name(), Transforms.min(ia, 0.5, true));
break; break;
case 65: case 65:
t = sd.assign(in, 0.5); continue; // assign op was removed.
tc.expectedOutput(t.name(), ia.dup().assign(0.5));
break;
case 66: case 66:
t = sd.scalarFloorMod(in, 0.5); t = sd.scalarFloorMod(in, 0.5);
tc.expectedOutput(t.name(), Nd4j.getExecutioner().exec(new ScalarFMod(ia.dup(), 0.5))); tc.expectedOutput(t.name(), Nd4j.getExecutioner().exec(new ScalarFMod(ia.dup(), 0.5)));
@ -1181,9 +1179,7 @@ public class TransformOpValidation extends BaseOpValidation {
tc.expectedOutput(t.name(), Transforms.xor(ia.castTo(DataType.BOOL), ib.castTo(DataType.BOOL))).gradientCheck(false); tc.expectedOutput(t.name(), Transforms.xor(ia.castTo(DataType.BOOL), ib.castTo(DataType.BOOL))).gradientCheck(false);
break; break;
case 18: case 18:
t = sd.assign(in1, in2); continue; //assign op was removed.
tc.expectedOutput(t.name(), ib);
break;
case 19: case 19:
t = sd.math().atan2(in1, in2); t = sd.math().atan2(in1, in2);
tc.expectedOutput(t.name(), Transforms.atan2(ib, ia)); //Note: y,x order for samediff; x,y order for transforms tc.expectedOutput(t.name(), Transforms.atan2(ib, ia)); //Note: y,x order for samediff; x,y order for transforms