org.nd4j.autodiff.samediff.SDVariable Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.autodiff.samediff;
import lombok.*;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.internal.SameDiffOp;
import org.nd4j.autodiff.samediff.internal.Variable;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.blas.params.MMulTranspose;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.common.util.ArrayUtil;
import org.nd4j.weightinit.WeightInitScheme;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Map;
/**
*
* A variable representing a component within a
* {@@link SameDiff} graph.
*
* SDVariable is used for symbolic declaration
* of equations.
*
* @author Adam Gibson
*
*/
@Data
@NoArgsConstructor
@Slf4j
public class SDVariable implements Serializable {
protected SameDiff sameDiff;
@Getter
@Setter
protected String varName;
@Getter
@Setter
protected VariableType variableType;
@Setter(AccessLevel.NONE)
protected long[] shape;
@Getter (AccessLevel.NONE)
@Setter
protected DataType dataType;
private DifferentialFunction creator;
// autogen_tag::sdvars::start
public SDVariable(@NonNull String varName, @NonNull VariableType varType, @NonNull SameDiff sameDiff, long[] shape, DataType dataType){
Preconditions.checkState(dataType != DataType.UNKNOWN, "Unknown datatype is not allowed for SDVariables (variable name: %s)", varName);
varName = sameDiff.generateNewVarName(varName, 0, true);
this.sameDiff = sameDiff;
this.varName = varName;
this.variableType = varType;
this.dataType = dataType;
this.shape = shape;
}
/**
* Get the name of the SDVariable
* @return Name of the variable
*/
public String name(){
return varName;
}
/**
* @deprecated Use {@link #name()}
*/
@Deprecated
public String getVarName(){
return name();
}
/**
* Returns true if this variable is a place holder
* @return
*/
public boolean isPlaceHolder() {
return variableType == VariableType.PLACEHOLDER;
}
public boolean isConstant(){
return variableType == VariableType.CONSTANT;
}
/**
* A getter for the allocated ndarray with this {@link SDVariable}.
*
* This getter will lazy initialize an array if one is not found based on the associated shape and
* {@link WeightInitScheme} - if this is possible. If this is not possible (due to shapes being unknown, etc)
* null is returned
*
* @return the {@link INDArray} associated with this variable.
*/
public INDArray getArr() {
return getArr(false);
}
// autogen_tag::sdvars::end
/**
* A getter for the allocated ndarray with this {@link SDVariable}.
*
* This getter will lazy initialize an array if one is not found based on the associated shape and
* {@link WeightInitScheme} - if this is possible.
* If this is not possible (due to shapes being unknown, etc) either:
* (a) null is returned - if enforceExistence == false, or
* (b) an IllegalStateException is thrown, if enforceExistence == true
*
* @return the {@link INDArray} associated with this variable.
*/
public INDArray getArr(boolean enforceExistence){
if(sameDiff.arrayAlreadyExistsForVarName(getVarName()))
return sameDiff.getArrForVarName(getVarName());
if(variableType == VariableType.ARRAY){
throw new UnsupportedOperationException("Cannot get array for ARRAY type SDVariable - use SDVariable.exec or SameDiff.output instead");
}
INDArray ret = sameDiff.getArrForVarName(getVarName());
if(enforceExistence && ret == null){
throw new IllegalStateException("No array exists for variable \"" + name() + "\"");
}
return ret;
}
/**
* Alias for the gradient variable - same as {@link #getGradient()}.
* The gradient variable is the variable that represents the derivative of the loss function with respect
* to the output of this variable. I.e., if this variable is X and loss function is L, then gradient() returns the
* variable representing dL/dX.
* Note that only floating point variables can have gradients.
*/
public SDVariable gradient() {
return getGradient();
}
/**
* The gradient variable is the variable that represents the derivative of the loss function with respect
* to the output of this variable. I.e., if this variable is X and loss function is L, then gradient() returns the
* variable representing dL/dX
* Note that only floating point variables can have gradients.
* Note also that a gradient may not yet be defined, and/or if no loss function variables have been set.
* You can set the loss function variables using {@link SameDiff#setLossVariables(String...)} and then create the
* gradient functions using {@link SameDiff#createGradFunction()}. Alternatively, the gradient function will be
* created automatically when training is performed.
*/
public SDVariable getGradient() {
Preconditions.checkState(dataType().isFPType(), "Cannot get gradient of %s datatype variable \"%s\": only floating" +
" point variables have gradients", dataType(), getVarName());
return sameDiff.getGradForVariable(getVarName());
}
/**
* Returns the shape of this variable
* @return Shape of the variable
*/
public long[] getShape() {
if (variableType == VariableType.PLACEHOLDER ) {
return shape;
} else if(variableType == VariableType.VARIABLE || variableType == VariableType.CONSTANT){
return getArr().shape();
}
return null;
}
public void setShape(long... shape){
this.shape = shape;
}
public long[] placeholderShape(){
if(variableType != VariableType.PLACEHOLDER){
throw new IllegalStateException("placeholderShape() can only be used for placeholder variables: variable \"" + getVarName()
+ " is a variable of type " + variableType);
}
return shape;
}
public DataType dataType() {
if(this.dataType == null){
//Try to infer datatype instead of returning null
if(variableType != VariableType.ARRAY && getArr() != null){
this.dataType = getArr().dataType();
}
}
return this.dataType;
}
public LongShapeDescriptor getShapeDescriptor() {
return LongShapeDescriptor.fromShape(getShape(), this.dataType());
}
public SDVariable castTo(@NonNull DataType dataType){
return castTo(null, dataType);
}
public SDVariable castTo(String name, @NonNull DataType dataType){
return sameDiff.castTo(name, this, dataType);
}
/**
* Create a new SDVariable, the contents of which is copied from this current variable
* @return The new variable
*/
public SDVariable dup() {
return sameDiff.var(this);
}
/**
* Return a variable with equal shape to the input, but all elements set to the specified value
*
* @param value Value for returned variable
* @return new variable
*/
public SDVariable assign(Number value){
return sameDiff.scalarSet(this, value.doubleValue());
}
/**
* Negate op - returns a new variable with the values of the current variable negated
* @return Negated variable
*/
public SDVariable neg(){
return sameDiff.math.neg(this);
}
/**
* Negate op - returns a new variable with the values of the current variable negated
* @param name Name of the new variable
* @return Negated variable
*/
public SDVariable neg(String name){
return sameDiff.math().neg(name, this);
}
/**
* See {@link #lt(String, double)}
*/
public SDVariable lt(double value){
return lt(null, value);
}
/**
* Less than operation: elementwise {@code this < value}
* Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
* value 0 otherwise
*
* @param name Name of the output variable
* @param value value argument to use in operation
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable lt(String name, double value){
return sameDiff.lt(name, this, value);
}
/**
* See {@link #lte(String, double)}
*/
public SDVariable lte(double value){
return lte(null, value);
}
/**
* Less than or equals operation: elementwise {@code this <= value}
* Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
* value 0 otherwise
*
* @param name Name of the output variable
* @param value value argument to use in operation
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable lte(String name, double value){
return sameDiff.lte(name, this, value);
}
/**
* See {@link #gt(String, double)}
*/
public SDVariable gt(double value){
return gt(null, value);
}
/**
* Greater than operation: elementwise {@code this > value}
* Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
* value 0 otherwise
*
* @param name Name of the output variable
* @param value value argument to use in operation
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable gt(String name, double value){
return sameDiff.gt(name, this, value);
}
/**
* See {@link #gte(String, double)}
*/
public SDVariable gte(double value){
return gte(null, value);
}
/**
* Greater than or equals operation: elementwise {@code this >= value}
* Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
* value 0 otherwise
*
* @param name Name of the output variable
* @param value value argument to use in operation
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable gte(String name, double value){
return sameDiff.gte(name, this, value);
}
/**
* See {@link #eq(String, double)}
*/
public SDVariable eq(double value){
return eq(null, value);
}
/**
* Equals operation: elementwise {@code this == value}
* Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
* value 0 otherwise
*
* @param name Name of the output variable
* @param value value argument to use in operation
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable eq(String name, double value){
return sameDiff.eq(name, this, value);
}
/**
* See {@link #neq(SDVariable)}
*/
public SDVariable neq(double value){
return neq(null, value);
}
/**
* Not equals operation: elementwise {@code this != value}
* Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
* value 0 otherwise
*
* @param name Name of the output variable
* @param value value argument to use in operation
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable neq(String name, double value){
return sameDiff.neq(name, this, value);
}
/**
* See {@link #lt(String, SDVariable)}
*/
public SDVariable lt(SDVariable other){
return lt(null, other);
}
/**
* Less than operation: elementwise {@code this < y}
* If x and y arrays have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if x and y have different shapes and are broadcastable, the output shape is broadcast.
* Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
*
* @param name Name of the output variable
* @param other Variable to compare values against
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable lt(String name, SDVariable other){
return sameDiff.lt(name, this, other);
}
/**
* See {@link #lte(String, SDVariable)}
*/
public SDVariable lte(SDVariable other){
return lte(null, other);
}
/**
* Less than or equal to operation: elementwise {@code this <= y}
* If x and y arrays have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if x and y have different shapes and are broadcastable, the output shape is broadcast.
* Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
*
* @param name Name of the output variable
* @param other Variable to compare values against
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable lte(String name, SDVariable other){
return sameDiff.lte(name, this, other);
}
/**
* See {@link #gt(String, SDVariable)}
*/
public SDVariable gt(SDVariable other){
return gt(null, other);
}
/**
* Greater than operation: elementwise {@code this > y}
* If x and y arrays have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if x and y have different shapes and are broadcastable, the output shape is broadcast.
* Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
*
* @param name Name of the output variable
* @param other Variable to compare values against
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable gt(String name, SDVariable other){
return sameDiff.gt(name, this, other);
}
/**
* See {@link #gte(String, SDVariable)}
*/
public SDVariable gte(SDVariable other){
return gte(null, other);
}
/**
* Greater than or equal to operation: elementwise {@code this >= y}
* If x and y arrays have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if x and y have different shapes and are broadcastable, the output shape is broadcast.
* Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
*
* @param name Name of the output variable
* @param other Variable to compare values against
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable gte(String name, SDVariable other){
return sameDiff.gte(name, this, other);
}
/**
* See {@link #eq(String, SDVariable)}
*/
public SDVariable eq(SDVariable other){
return eq(null, other);
}
/**
* Equal to operation: elementwise {@code this == y}
* If x and y arrays have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if x and y have different shapes and are broadcastable, the output shape is broadcast.
* Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
*
* @param name Name of the output variable
* @param other Variable to compare values against
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable eq(String name, SDVariable other){
return sameDiff.eq(name, this, other);
}
/**
* See {@link #neq(String, SDVariable)}
*/
public SDVariable neq(SDVariable other){
return neq(null, other);
}
/**
* Not equal to operation: elementwise {@code this != y}
* If x and y arrays have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if x and y have different shapes and are broadcastable, the output shape is broadcast.
* Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
*
* @param name Name of the output variable
* @param other Variable to compare values against
* @return Output SDVariable with values 0 (not satisfied) and 1 (where the condition is satisfied)
*/
public SDVariable neq(String name, SDVariable other){
return sameDiff.neq(name, this, other);
}
/**
* See {@link #mmul(String, SDVariable)}
*/
public SDVariable mmul(SDVariable other){
return mmul(null, other);
}
/**
* Matrix multiplication: out = mmul(this,other)
*
* @param name Name of the output variable
* @param other Other variable to perform matrix multiplication with
* @return Output variable (result of mmul)
*/
public SDVariable mmul(String name, SDVariable other){
return sameDiff.mmul(name, this, other);
}
/**
* Matrix multiplication: out = mmul(this,other)
*
* @param name Name of the output variable
* @param other Other variable to perform matrix multiplication with
* @param mMulTranspose Matrix transpose configuration
* @return Output variable (result of mmul)
*/
public SDVariable mmul(String name, SDVariable other, @NonNull MMulTranspose mMulTranspose) {
return sameDiff.mmul(name, this, other, mMulTranspose.isTransposeA(), mMulTranspose.isTransposeB(), mMulTranspose.isTransposeResult());
}
/**
* See {@link #dot(String, SDVariable, int...)}
*/
public SDVariable dot(SDVariable other, int... dimensions){
return dot(null, other, dimensions);
}
/**
* Matrix dot product: out = dot(this,other, dimensions)
*
* @param name Name of the output variable
* @param other Other variable to perform matrix multiplication with
* @return Output variable (result of mmul)
*/
public SDVariable dot(String name, SDVariable other, int... dimensions){
return sameDiff.dot(name, this, other, dimensions);
}
/**
* See {@link #add(String, double)}
*/
public SDVariable add(double scalar) {
return add(null,scalar);
}
/**
* Scalar addition: {@code out = this + scalar}
* Output variable has the same shape as the input variable
*
* @param varName Output variable name
* @param scalar Scalar for operation
* @return Output variable
*/
public SDVariable add(String varName, double scalar) {
val function = sameDiff.math.add(this,scalar);
return sameDiff.updateVariableNameAndReference(function,varName);
}
/**
* See {@link #add(String, SDVariable)}
*/
public SDVariable add(SDVariable other) {
return add(null,other);
}
/**
* Addition operation: elementwise {@code this + x}
* If this and x variables have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if this and x have different shapes and are broadcastable, the output shape is broadcast.
*
* @param name Name of the output variable
* @param x Variable to perform operation with
* @return Output (result) SDVariable
*/
public SDVariable add(String name, SDVariable x) {
val result = sameDiff.math.add(this, x);
return sameDiff.updateVariableNameAndReference(result, name);
}
/**
* For Kotlin operator interop
* @see #add(String, SDVariable)
*/
public SDVariable plus(SDVariable other){
return add(other);
}
/**
* For Kotlin operator interop
* @see #add(String, double)
*/
public SDVariable plus(double other){
return add(other);
}
/**
* See {@link #sub(String, double)}
*/
public SDVariable sub(double scalar) {
return sub(null,scalar);
}
/**
* Scalar subtraction: {@code out = this - scalar}
* Output variable has the same shape as the input variable
*
* @param varName Output variable name
* @param scalar Scalar for operation
* @return Output variable
*/
public SDVariable sub(String varName, double scalar) {
val result = sameDiff.math.sub(this, scalar);
return sameDiff.updateVariableNameAndReference(result, varName);
}
/**
* See {@link #sub(String, SDVariable)}
*/
public SDVariable sub(SDVariable x) {
return sub(null,x);
}
/**
* Subtraction operation: elementwise {@code this - x}
* If this and x variables have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if this and x have different shapes and are broadcastable, the output shape is broadcast.
*
* @param name Name of the output variable
* @param x Variable to perform operation with
* @return Output (result) SDVariable
*/
public SDVariable sub(String name, SDVariable x) {
val result = sameDiff.math.sub(this,x);
return sameDiff.updateVariableNameAndReference(result,name);
}
/**
* For Kotlin operator interop
* @see #sub(String, SDVariable)
*/
public SDVariable minus(SDVariable other){
return sub(other);
}
/**
* For Kotlin operator interop
* @see #sub(String, double)
*/
public SDVariable minus(double other){
return sub(other);
}
/**
* See {@link #div(String,double)}
*/
public SDVariable div(double scalar) {
return div(null,scalar);
}
/**
* Scalar division: {@code out = this / scalar}
* Output variable has the same shape as the input variable
*
* @param varName Output variable name
* @param scalar Scalar for operation
* @return Output variable
*/
public SDVariable div(String varName, double scalar) {
val function = sameDiff.math.div(this,scalar);
return sameDiff.updateVariableNameAndReference(function,varName);
}
/**
* See {@link #div(String, SDVariable)}
*/
public SDVariable div(SDVariable x) {
return div(null,x);
}
/**
* Division operation: elementwise {@code this / x}
* If this and x variables have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if this and x have different shapes and are broadcastable, the output shape is broadcast.
*
* @param name Name of the output variable
* @param x Variable to perform operation with
* @return Output (result) SDVariable
*/
public SDVariable div(String name, SDVariable x) {
val result = sameDiff.math.div(this, x);
return sameDiff.updateVariableNameAndReference(result, name);
}
/**
* Floor division operation: elementwise {@code this // x}
* If this and x variables have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if this and x have different shapes and are broadcastable, the output shape is broadcast.
*
* @param name Name of the output variable
* @param x Variable to perform operation with
* @return Output (result) SDVariable
*/
public SDVariable fdiv(String name, SDVariable x) {
val result = sameDiff.math.floorDiv(this, x);
return sameDiff.updateVariableNameAndReference(result, name);
}
/**
* Modulo operation: elementwise {@code this / x}
* If this and x variables have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if this and x have different shapes and are broadcastable, the output shape is broadcast.
*
* @param name Name of the output variable
* @param x Variable to perform operation with
* @return Output (result) SDVariable
*/
public SDVariable mod(String name, SDVariable x) {
val result = sameDiff.math.mod(this, x);
return sameDiff.updateVariableNameAndReference(result, name);
}
/**
* See {@link #mul(String, double)}
*/
public SDVariable mul(double scalar) {
return mul(null,scalar);
}
/**
* Scalar multiplication: {@code out = this * scalar}
* Output variable has the same shape as the input variable
*
* @param varName Output variable name
* @param scalar Scalar for operation
* @return Output variable
*/
public SDVariable mul(String varName, double scalar) {
val function = sameDiff.math.mul(this, scalar);
return sameDiff.updateVariableNameAndReference(function,varName);
}
/**
* See {@link #mul(String, SDVariable)}
*/
public SDVariable mul(SDVariable x) {
return mul(null,x);
}
/**
* Multiplication operation: elementwise {@code this * x}
* If this and x variables have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if this and x have different shapes and are broadcastable, the output shape is broadcast.
*
* @param name Name of the output variable
* @param x Variable to perform operation with
* @return Output (result) SDVariable
*/
public SDVariable mul(String name, SDVariable x) {
val result = sameDiff.math.mul(this, x);
return sameDiff.updateVariableNameAndReference(result,name);
}
/**
* For Kotlin operator interop
* @see #mul(String, SDVariable)
*/
public SDVariable times(SDVariable other){
return mul(other);
}
/**
* For Kotlin operator interop
* @see #mul(String, double)
*/
public SDVariable times(double other){
return mul(other);
}
/**
* See {@link #pow(String, double)}
*/
public SDVariable pow(double scalar) {
return pow(null, scalar);
}
/**
* Scalar power operation: {@code out = this ^ scalar}
* Output variable has the same shape as the input variable
*
* @param varName Output variable name
* @param scalar Scalar for operation
* @return Output variable
*/
public SDVariable pow(String varName, double scalar) {
SDVariable ret = sameDiff.math.pow(this, scalar);
return sameDiff.updateVariableNameAndReference(ret, varName);
}
/**
* See {@link #rsub(String, double)}
*/
public SDVariable rsub(double scalar) {
return rsub(null,scalar);
}
/**
* Scalar reverse subtraction: {@code out = scalar - this}
* Output variable has the same shape as the input variable
*
* @param varName Output variable name
* @param scalar Scalar for operation
* @return Output variable
*/
public SDVariable rsub(String varName, double scalar) {
val function = sameDiff.math.rsub(this,scalar);
return sameDiff.updateVariableNameAndReference(function,varName);
}
/**
* See {@link #rsub(String, SDVariable)}
*/
public SDVariable rsub(SDVariable x) {
return rsub(null,x);
}
/**
* Reverse subtraction operation: elementwise {@code x - this}
* If this and x variables have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if this and x have different shapes and are broadcastable, the output shape is broadcast.
*
* @param name Name of the output variable
* @param x Variable to perform operation with
* @return Output (result) SDVariable
*/
public SDVariable rsub(String name, SDVariable x) {
val result = sameDiff.math.rsub(this,x);
return sameDiff.updateVariableNameAndReference(result,name);
}
/**
* See {@link #rdiv(String, double)}
*/
public SDVariable rdiv(double scalar) {
return rdiv(null,scalar);
}
/**
* Scalar reverse division: {@code out = scalar / this}
* Output variable has the same shape as the input variable
*
* @param varName Output variable name
* @param scalar Scalar for operation
* @return Output variable
*/
public SDVariable rdiv(String varName, double scalar) {
val function = sameDiff.math.rdiv(this, scalar);
return sameDiff.updateVariableNameAndReference(function, varName);
}
/**
* See {@link #rdiv(String, SDVariable)}
*/
public SDVariable rdiv(SDVariable sameDiffVariable) {
return rdiv(null,sameDiffVariable);
}
/**
* Reverse division operation: elementwise {@code x / this}
* If this and x variables have equal shape, the output shape is the same as the inputs.
* Supports broadcasting: if this and x have different shapes and are broadcastable, the output shape is broadcast.
*
* @param name Name of the output variable
* @param x Variable to perform operation with
* @return Output (result) SDVariable
*/
public SDVariable rdiv(String name, SDVariable x) {
val result = sameDiff.math.rdiv(this,x);
return sameDiff.updateVariableNameAndReference(result,name);
}
/**
* See {@link #squaredDifference(String, SDVariable)}
*/
public SDVariable squaredDifference(SDVariable x) {
return squaredDifference(null,x);
}
/**
* Squared difference operation: {@code (this - x)^2}
* @param x Other input variable
* @return squared difference between variables
*/
public SDVariable squaredDifference(String name, SDVariable x) {
val result = sameDiff.math().squaredDifference(this, x);
return sameDiff.updateVariableNameAndReference(result, name);
}
/**
* See {@link #sum(String, boolean, int...)}
*/
public SDVariable sum(int... dimensions){
return sum(null, dimensions);
}
/**
* See {@link #sum(String, boolean, int...)}
*/
public SDVariable sum(boolean keepDims, int... dimensions){
return sum(null, keepDims, dimensions);
}
/**
* See {@link #sum(String, boolean, int...)}
*/
public SDVariable sum(String name, int... dimensions){
return sum(name, false, dimensions);
}
/**
* Sum array reduction operation, optionally along specified dimensions.
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Output variable name
* @param keepDims If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensions
* @param dimensions Dimensions to reduce over. If dimensions are not specified, full array reduction is performed
* @return Output variable: reduced array of rank (input rank - num dimensions) if keepDims = false, or
* of rank (input rank) if keepdims = true
*/
public SDVariable sum(String name, boolean keepDims, int... dimensions){
return sameDiff.sum(name, this, keepDims, dimensions);
}
/**
* See {@link #mean(String, boolean, int...)}
*/
public SDVariable mean(boolean keepDims, int... dimensions){
return mean(null, keepDims, dimensions);
}
/**
* See {@link #mean(String, boolean, int...)}
*/
public SDVariable mean(String name, int... dimensions){
return mean(name, false, dimensions);
}
/**
* See {@link #mean(String, boolean, int...)}
*/
public SDVariable mean(int... dimensions){
return mean(null, false, dimensions);
}
/**
* Mean (average) array reduction operation, optionally along specified dimensions
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Output variable name
* @param keepDims If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions
* @param dimensions Dimensions to reduce over. If dimensions are not specified, full array reduction is performed
* @return Reduced array of rank (input rank - num dimensions)
*/
public SDVariable mean(String name, boolean keepDims, int... dimensions){
return sameDiff.mean(name, this, keepDims, dimensions);
}
/**
* See {@link #std(String, boolean, boolean, int...)}
*/
public SDVariable std(boolean biasCorrected, int... dimensions){
return std(null, biasCorrected, dimensions);
}
/**
* See {@link #std(String, boolean, boolean, int...)}
*/
public SDVariable std(String name, boolean biasCorrected, int... dimensions){
return sameDiff.standardDeviation(name, this, biasCorrected, dimensions);
}
/**
* Stardard deviation array reduction operation, optionally along specified dimensions
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param biasCorrected If true: divide by (N-1) (i.e., sample stdev). If false: divide by N (population stdev)
* @param keepDims If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions
* @param dimensions Dimensions to reduce over. If dimensions are not specified, full array reduction is performed
* @return Output variable: reduced array of rank (input rank - num dimensions)
*/
public SDVariable std(String name, boolean biasCorrected, boolean keepDims, int... dimensions){
return sameDiff.standardDeviation(name, this, biasCorrected, keepDims, dimensions);
}
/**
* See {@link #prod(String, boolean, int...)}
*/
public SDVariable prod(int... dimensions){
return prod(null, dimensions);
}
/**
* See {@link #prod(String, boolean, int...)}
*/
public SDVariable prod(boolean keepDims, int... dimensions){
return prod(null, keepDims, dimensions);
}
/**
* See {@link #prod(String, boolean, int...)}
*/
public SDVariable prod(String name, int... dimensions){
return sameDiff.prod(name, this, dimensions);
}
/**
* Product array reduction operation, optionally along specified dimensions
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Output variable name
* @param keepDims If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensions
* @param dimensions Dimensions to reduce over. If dimensions are not specified, full array reduction is performed
* @return Output variable: reduced array of rank (input rank - num dimensions)
*/
public SDVariable prod(String name, boolean keepDims, int... dimensions){
return sameDiff.prod(name, this, keepDims, dimensions);
}
/**
* See {@link #min(String, boolean, int...)}
*/
public SDVariable min(int... dimensions){
return min(null, dimensions);
}
/**
* See {@link #min(String, boolean, int...)}
*/
public SDVariable min(boolean keepDims, int... dimensions){
return min(null, keepDims, dimensions);
}
/**
* See {@link #min(String, boolean, int...)}
*/
public SDVariable min(String name, int... dimensions){
return min(name, false, dimensions);
}
/**
* Minimum array reduction operation, optionally along specified dimensions. out = min(in)
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Output variable name
* @param keepDims If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions
* @param dimensions Dimensions to reduce over. If dimensions are not specified, full array reduction is performed
* @return Reduced array of rank (input rank - num dimensions)
*/
public SDVariable min(String name, boolean keepDims, int... dimensions){
return sameDiff.min(name, this, keepDims, dimensions);
}
/**
* See {@link #max(String, boolean, int...)}
*/
public SDVariable max(int... dimensions) {
return max(null, dimensions);
}
/**
* See {@link #max(String, boolean, int...)}
*/
public SDVariable max(String name, int... dimensions) {
return max(name, false, dimensions);
}
/**
* See {@link #max(String, boolean, int...)}
*/
public SDVariable max(boolean keepDims, int... dimensions) {
return max(null, keepDims, dimensions);
}
/**
* Maximum array reduction operation, optionally along specified dimensions
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Output variable name
* @param keepDims If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions
* @param dimensions Dimensions to reduce over. If dimensions are not specified, full array reduction is performed
* @return Reduced array of rank (input rank - num dimensions)
*/
public SDVariable max(String name, boolean keepDims, int... dimensions) {
return sameDiff.max(name, this, keepDims, dimensions);
}
/**
* See {@link #norm1(String, boolean, int...)}
*/
public SDVariable norm1(int... dimensions){
return norm1(null, dimensions);
}
/**
* See {@link #norm1(String, boolean, int...)}
*/
public SDVariable norm1(boolean keepDims, int... dimensions){
return norm1(null, keepDims, dimensions);
}
/**
* See {@link #norm1(String, boolean, int...)}
*/
public SDVariable norm1(String name, int... dimensions){
return norm1(name, false, dimensions);
}
/**
* Norm1 (L1 norm) reduction operation: The output contains the L1 norm for each tensor/subset along the specified dimensions:
* {@code out = sum_i abs(x[i])}
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Output variable name
* @param keepDims If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions
* @param dimensions dimensions to reduce over
* @return Output variable
*/
public SDVariable norm1(String name, boolean keepDims, int... dimensions) {
return sameDiff.norm1(name, this, keepDims, dimensions);
}
/**
* See {@link #norm2(String, boolean, int...)}
*/
public SDVariable norm2(int... dimensions){
return norm2(null, dimensions);
}
/**
* See {@link #norm2(String, boolean, int...)}
*/
public SDVariable norm2(boolean keepDims, int... dimensions){
return norm2(null, keepDims, dimensions);
}
/**
* See {@link #norm2(String, boolean, int...)}
*/
public SDVariable norm2(String name, int... dimensions){
return norm2(name, false, dimensions);
}
/**
* Norm2 (L2 norm) reduction operation: The output contains the L2 norm for each tensor/subset along the specified dimensions:
* {@code out = sqrt(sum_i x[i]^2)}
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Output variable name
* @param keepDims If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions
* @param dimensions dimensions to reduce over
* @return Output variable
*/
public SDVariable norm2(String name, boolean keepDims, int... dimensions){
return sameDiff.norm2(name, this, keepDims, dimensions);
}
/**
* See {@link #normmax(String, boolean, int...)}
*/
public SDVariable normmax(int... dimensions){
return normmax(null, dimensions);
}
/**
* See {@link #normmax(String, boolean, int...)}
*/
public SDVariable normmax(boolean keepDims, int... dimensions){
return normmax(null, keepDims, dimensions);
}
/**
* See {@link #normmax(String, boolean, int...)}
*/
public SDVariable normmax(String name, int... dimensions){
return normmax(name, false, dimensions);
}
/**
* Max norm (infinity norm) reduction operation: The output contains the max norm for each tensor/subset along the
* specified dimensions:
* {@code out = max(abs(x[i]))}
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Output variable name
* @param keepDims If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions
* @param dimensions dimensions to reduce over
* @return Output variable
*/
public SDVariable normmax(String name, boolean keepDims, int... dimensions){
return sameDiff.normmax(name, this, keepDims, dimensions);
}
/**
* See {@link #argmax(String, boolean, int...)}
*/
public SDVariable argmax(int... dimensions){
return argmax(null, dimensions);
}
/**
* See {@link #argmax(String, boolean, int...)}
*/
public SDVariable argmax(String name, int... dimensions){
return sameDiff.argmax(name, this, dimensions);
}
/**
* Argmax array reduction operation, optionally along specified dimensions.
* Output values are the index of the maximum value of each slice along the specified dimension.
*
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Name of the output variable
* @param keepDims If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions
* @param dimensions Dimensions to reduce over. If dimensions are not specified, full array reduction is performed
* @return Output variable: reduced array of rank (input rank - num dimensions) if keepDims = false, or
* of rank (input rank) if keepdims = true
*/
public SDVariable argmax(String name, boolean keepDims, int... dimensions) {
return sameDiff.argmax(name, this, keepDims, dimensions);
}
/**
* See {@link #argmin(String, boolean, int...)}
*/
public SDVariable argmin(int... dimensions){
return argmin(null, dimensions);
}
/**
* See {@link #argmin(String, boolean, int...)}
*/
public SDVariable argmin(String name, int... dimensions){
return sameDiff.argmin(name, this, dimensions);
}
/**
* Argmin array reduction operation, optionally along specified dimensions.
* Output values are the index of the minimum value of each slice along the specified dimension.
*
* Note that if keepDims = true, the output variable has the same rank as the input variable,
* with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
* the mean along a dimension).
* Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
* keepDims = true: [a,1,c]
* keepDims = false: [a,c]
*
* @param name Name of the output variable
* @param keepDims If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensions
* @param dimensions Dimensions to reduce over. If dimensions are not specified, full array reduction is performed
* @return Output variable: reduced array of rank (input rank - num dimensions) if keepDims = false, or
* of rank (input rank) if keepdims = true
*/
public SDVariable argmin(String name, boolean keepDims, int... dimensions) {
return sameDiff.argmax(name, this, keepDims, dimensions);
}
/**
* Get the shape of the array as a dynamic SDVariable
* @return Shape SDVariable
*/
public SDVariable shape(){
return sameDiff.shape(this);
}
/**
* Get the rank of this variable as a dynamic SDVariable
* @return Rank SDVariable
*/
public SDVariable rank(){
return sameDiff.rank(this);
}
/**
* Reshape the current variable to the specified (dynamic) shape. The output variable will have the same values as the
* input, but with the specified shape.
* Note that prod(shape) must match length(input) == prod(input.shape)
*
* @param newShape New shape for variable
* @return Output variable
*/
public SDVariable reshape(SDVariable newShape){
return sameDiff.reshape(this, newShape);
}
/**
* Reshape the current variable to the specified shape. The output variable will have the same values as the
* input, but with the specified shape.
* Note that prod(shape) must match length(input) == prod(input.shape)
*
* @param newShape New shape for variable
* @return Output variable
*/
public SDVariable reshape(int... newShape){
return sameDiff.reshape(this, ArrayUtil.toLongArray(newShape));
}
/**
* Reshape the current variable to the specified shape. The output variable will have the same values as the
* input, but with the specified shape.
* Note that prod(shape) must match length(input) == prod(input.shape)
*
* @param newShape New shape for variable
* @return Output variable
*/
public SDVariable reshape(long... newShape){
return sameDiff.reshape(this, newShape);
}
/**
* Permute the dimensions of the current variable according to the specified permutation indices.
* Example: if the current variable has shape [a,b,c] and dimensions = [2,0,1] the output has shape [c,a,b]
*
* @param dimensions The new dimension order
* @return Output variable (permuted input)
*/
public SDVariable permute(int... dimensions){
return sameDiff.permute(this, dimensions);
}
public SDVariable permute(SDVariable dimensions){
return sameDiff.permute( this, dimensions);
}
/**
* Associate the specified array with this variable
* @param array Array to associate with this variable
* @return This variable
*/
public SDVariable setArray(INDArray array){
sameDiff.associateArrayWithVariable(array, this);
return this;
}
/**
* Evaluate the result of this variable
* @return
*/
public INDArray eval() {
Map m = sameDiff.output((Map)null, name());
return m.get(name());
}
/**
* Evaluate the result of this variable
* @return
*/
public INDArray eval(Map placeholders) {
Map m = sameDiff.output(placeholders, name());
return m.get(name());
}
@Override
public String toString() {
return "SDVariable(name=\"" + varName + "\",variableType=" + variableType + ",dtype=" + dataType +
(variableType == VariableType.PLACEHOLDER && shape != null ? ",shape=" + Arrays.toString(shape): "") + ")";
}
/**
* Add a control dependency for this variable on the specified variable.
* Control dependencies can be used to enforce the execution order.
* For example, if a control dependency X->Y exists, then Y will only be executed after X is executed - even
* if Y wouldn't normally depend on the result/values of X.
*
* @param controlDependency Control dependency to add for this variable
*/
public void addControlDependency(SDVariable controlDependency){
Variable vThis = sameDiff.getVariables().get(getVarName());
Variable vCD = sameDiff.getVariables().get(controlDependency.name());
//If possible: add control dependency on ops
if(vThis.getOutputOfOp() != null && vCD.getOutputOfOp() != null ){
//Op -> Op case
SameDiffOp oThis = sameDiff.getOps().get(vThis.getOutputOfOp());
SameDiffOp oCD = sameDiff.getOps().get(vCD.getOutputOfOp());
if(oThis.getControlDeps() == null)
oThis.setControlDeps(new ArrayList());
if(!oThis.getControlDeps().contains(oCD.getName()))
oThis.getControlDeps().add(oCD.getName());
if(oCD.getControlDepFor() == null)
oCD.setControlDepFor(new ArrayList());
if(!oCD.getControlDepFor().contains(oThis.getName()))
oCD.getControlDepFor().add(oThis.getName());
} else {
if(vThis.getOutputOfOp() != null){
//const/ph -> op case
SameDiffOp oThis = sameDiff.getOps().get(vThis.getOutputOfOp());
if(oThis.getVarControlDeps() == null)
oThis.setVarControlDeps(new ArrayList());
if(!oThis.getVarControlDeps().contains(vCD.getName()))
oThis.getVarControlDeps().add(vCD.getName());
if(vCD.getControlDepsForOp() == null)
vCD.setControlDepsForOp(new ArrayList());
if(!vCD.getControlDepsForOp().contains(oThis.getName()))
vCD.getControlDepsForOp().add(oThis.getName());
} else {
//const/ph -> const/ph case
if(vThis.getControlDeps() == null)
vThis.setControlDeps(new ArrayList());
if(!vThis.getControlDeps().contains(vCD.getName()))
vThis.getControlDeps().add(vCD.getName());
if(vCD.getControlDepsForVar() == null)
vCD.setControlDepsForVar(new ArrayList());
if(!vCD.getControlDepsForVar().contains(vThis.getName()))
vCD.getControlDepsForVar().add(vThis.getName());
}
}
}
/**
* Get a variable with content equal to a specified sub-array of this variable.
* Can be used (for example) to get rows, columns, sub-matrices, etc.
* @param indices Indices to get
* @return Sub-array variable
*/
public SDVariable get(SDIndex... indices) {
int ndims = indices.length;
long[] begin = new long[ndims];
long[] end = new long[ndims];
long[] strides = new long[ndims];
int[] begin_mask_arr = new int[ndims];
int[] end_mask_arr = new int[ndims];
int[] shrink_axis_mask_arr = new int[ndims];
for (int i = 0; i < ndims; i++) {
strides[i] = 1;
SDIndex index = indices[i];
SDIndex.IndexType indexType = index.getIndexType();
if (indexType == SDIndex.IndexType.ALL) {
begin_mask_arr[i] = 1;
end_mask_arr[i] = 1;
} else if (indexType == SDIndex.IndexType.POINT) {
long pointIndex = index.getPointIndex();
begin[i] = pointIndex;
end[i] = pointIndex + 1;
if(!index.isPointKeepDim()) {
shrink_axis_mask_arr[i] = 1;
}
} else if (indexType == SDIndex.IndexType.INTERVAL) {
if (index.getIntervalBegin() == null) {
begin_mask_arr[i] = 1;
} else {
begin[i] = index.getIntervalBegin();
}
if (index.getIntervalEnd() == null) {
end_mask_arr[i] = 1;
} else {
end[i] = index.getIntervalEnd();
}
if (index.getIntervalStrides() == null) {
strides[i] = 1;
} else {
strides[i] = index.getIntervalStrides();
}
}
}
// convert binary int[] to int
int begin_mask = binArrToInt(begin_mask_arr);
int end_mask = binArrToInt(end_mask_arr);
int shrink_axis = binArrToInt(shrink_axis_mask_arr);
return this.sameDiff.stridedSlice(this, begin, end, strides,
begin_mask, end_mask, 0, 0, shrink_axis);
}
/**
* Convert this variable to a constant. This is equivalent to "freezing" a variable so that it's value
* won't be changed by further training.
* This can only be done for variables and placeholders, not ARRAY type variables (which are usually network activations).
* As a constant, this variable will no longer be modified by any subsequent training.
*
* @return This variable (now a constant)
*/
public SDVariable convertToConstant(){
return sameDiff.convertToConstant(this);
}
/**
* Convert this variable to a VARIABLE type SDVariable.
* This can only be done for constants and placeholders, not ARRAY type variables (which are usually network activations).
* As a variable, this variable will modified during any subsequent training.
*
* @return This variable (now a variable type SDVariable)
*/
public SDVariable convertToVariable(){
return sameDiff.convertToVariable(this);
}
/**
* Rename this variable to a new name. Equivalent to {@link SameDiff#renameVariable(String, String)}
*
* @param newName The new name for the variable - no variable with this name must already exist
* @return The current variable (same object)
*/
public SDVariable rename(String newName) {
sameDiff.renameVariable(getVarName(), newName);
return this;
}
/**
* Mark this variable as a loss function variable. This means that this variable will be minimized via backprop during training.
* This will add the variable as a loss to any others - i.e., if multiple variables are marked as losses, their values will be summed
* to give the total network loss.
* Note that only floating point (Float16/32/64) variables may be marked as a loss.
* Note also that only ARRAY type SDVariables can be marked as losses to be minimized. That is, we cannot mark the value
* of a constant, variable or placeholder to be minimized as doing so would not make sense.
* This is equivalent to {@link SameDiff#addLossVariable(String)}
*/
public void markAsLoss(){
sameDiff.addLossVariable(getVarName());
}
/**
* Determine if this variable has a gradient with respect to the current loss. Note that:
* (a) Non-floating-point variables (integer, string, etc) will never have gradients
* (b) This method will return false if no gradient function has been created yet. See {@link SameDiff#createGradFunction()}
* and {@link SameDiff#setLossVariables(String...)}
* (c) Floating point variables may not have any gradient if the current loss does not depend on the variable at all
* @return True if a gradient variable exists for the specified variable, for the current loss
*/
public boolean hasGradient(){
return sameDiff.variableHasGradient(getVarName());
}
private static int binArrToInt(int[] arr) {
int x = 0;
int m = 1;
for (int i = 0; i < arr.length; i++) {
if (arr[i] == 1) {
x += m;
}
m *= 2;
}
return x;
}
@Override
public int hashCode() {
int result = super.hashCode();
result = 31 * result + (varName != null ? varName.hashCode() : 0);
result = 31 * result + (variableType != null ? variableType.hashCode() : 0);
result = 31 * result + (dataType != null ? dataType.hashCode() : 0);
return result;
}
public SDVariable clone(SameDiff sd){
SDVariable v = new SDVariable();
v.varName = varName;
v.variableType = variableType;
v.shape = shape == null ? null : shape.clone();
v.dataType = dataType;
v.sameDiff = sd;
return v;
}
@Override
public boolean equals(Object o){
if(o == this) return true;
if(!(o instanceof SDVariable))
return false;
SDVariable s = (SDVariable)o;
if(!varName.equals(s.varName))
return false;
if(variableType != s.variableType)
return false;
if(dataType != s.dataType)
return false;
if(variableType == VariableType.VARIABLE || variableType == VariableType.CONSTANT){
INDArray a1 = getArr();
INDArray a2 = s.getArr();
return a1.equals(a2);
}
return true;
}
}
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