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/*******************************************************************************
* 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 java.util.Objects;
import lombok.*;
import lombok.extern.slf4j.Slf4j;
import onnx.Onnx;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.internal.Variable;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.blas.params.MMulTranspose;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.*;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.util.ArrayUtil;
import org.nd4j.weightinit.WeightInitScheme;
import org.nd4j.weightinit.impl.ZeroInitScheme;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
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;
@Getter
@Setter
protected WeightInitScheme weightInitScheme;
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, WeightInitScheme weightInitScheme){
Preconditions.checkState(weightInitScheme == null || varType == VariableType.VARIABLE, "Weight initalization schemes can only be applied to VARIABLE type" +
" SDVariables - variable \"%s\" is of type %s but was provided a weight initialization scheme %s", varName, varType, weightInitScheme);
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.weightInitScheme = weightInitScheme;
this.shape = shape;
}
/**
* Returns true if this variable is a place holder
* @return
*/
public boolean isPlaceHolder() {
return variableType == VariableType.PLACEHOLDER;
}
public boolean isConstant(){
return variableType == VariableType.CONSTANT;
}
/**
* Allocate and return a new array
* based on the vertex id and weight initialization.
* @return the allocated array
*/
public INDArray storeAndAllocateNewArray() {
Preconditions.checkState(variableType == VariableType.VARIABLE, "Unable to allocate and store array for variable of type %s: only" +
" VARIABLE type variables can be initialized using this method", variableType);
if(!sameDiff.arrayAlreadyExistsForVarName(varName)){
long[] shape = getShape();
INDArray arr = getWeightInitScheme().create(dataType(), shape);
sameDiff.associateArrayWithVariable(arr, this);
if(log.isTraceEnabled()){
log.trace("Generated and stored new array for variable \"{}\": shape {}", getVarName(), Arrays.toString(arr.shape()));
}
return arr;
}
//Variable type SDVariables: shape should never change (i.e., these are params in the net!)
INDArray ret = getArr();
return ret;
}
/**
* 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());
//initialize value if it's actually a scalar constant (zero or 1 typically...)
if(variableType == VariableType.VARIABLE && weightInitScheme != null && shape != null){
INDArray arr = weightInitScheme.create(dataType, shape);
sameDiff.associateArrayWithVariable(arr, this);
if(log.isTraceEnabled()){
log.trace("getArr() for variable \"{}\" allocated new array: shape {}", getVarName(), Arrays.toString(getShape()));
}
return arr;
} else if(sameDiff.getShapeForVarName(getVarName()) == null) {
if (enforceExistence) {
throw new IllegalStateException("Cannot get array for SDVariable \"" + getVarName() + "\": no array has" +
" been defined, and array shape cannot be calculated");
}
if(log.isTraceEnabled()){
log.trace("SDVariable.getArr(): could not get array for variable {}: shape is null", getVarName());
}
return null;
}
return sameDiff.getArrForVarName(getVarName());
}
/**
* 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 variable \"%s\": only floating" +
" point variables have gradients", getVarName(), dataType());
return sameDiff.getGradForVariable(getVarName());
}
/**
* Returns the shape of this variable
* @return Shape of the variable
*/
public long[] getShape() {
if (variableType == VariableType.PLACEHOLDER && getArr() == null) {
if (shape != null)
return shape;
else
return new long[0];
}
long[] initialShape = sameDiff.getShapeForVarName(getVarName());
if(initialShape == null) {
val arr = getArr();
if(arr != null)
return arr.shape();
}
return initialShape;
}
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(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);
}
/**
* Negate op - returns a new variable with the values of the current variable negated
* @return Negated variable
*/
public SDVariable neg(){
return sameDiff.f().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);
}
/**
* 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.f().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.f().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.f().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.f().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.f().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.f().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.f().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.f().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.f().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.f().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.f().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.f().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.f().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.f().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.f().rdiv(this,x);
return sameDiff.updateVariableNameAndReference(result,name);
}
/**
*
* @param sameDiffVariable
* @return
*/
public SDVariable truncatedDiv(SDVariable sameDiffVariable) {
return truncatedDiv(null,sameDiffVariable);
}
/**
*
* @param sameDiffVariable
* @return
*/
public SDVariable truncatedDiv(String varName, SDVariable sameDiffVariable) {
val function = sameDiff.f().truncatedDiv(this, sameDiffVariable);
return sameDiff.updateVariableNameAndReference(function,varName);
}
/**
* 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.f().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, 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(null, 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() {
sameDiff.exec(null, getVarName());
return getArr();
}
/**
* Evaluate the result of this variable
* @return
*/
public INDArray eval(Map placeholders) {
sameDiff.exec(placeholders, getVarName());
return getArr();
}
@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 depnedencies 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){
String cdN = controlDependency.getVarName();
String n = this.getVarName();
Variable v = sameDiff.getVariables().get(n);
if(v.getControlDeps() == null)
v.setControlDeps(new ArrayList());
if(!v.getControlDeps().contains(cdN))
v.getControlDeps().add(cdN);
Variable v2 = sameDiff.getVariables().get(cdN);
if(v2.getControlDepsForVar() == null)
v2.setControlDepsForVar(new ArrayList());
if(!v2.getControlDepsForVar().contains(n))
v2.getControlDepsForVar().add(n);
}
/**
* 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 boolean equals(Object o) {
if (this == o) {
return true;
}
if (!(o instanceof SDVariable)) {
return false;
}
SDVariable that = (SDVariable) o;
if (!Objects.equals(varName, that.varName)) {
return false;
}
if (variableType != that.variableType) {
return false;
}
if(sameDiff != that.sameDiff){
return false;
}
return dataType == that.dataType;
}
@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.weightInitScheme = weightInitScheme;
v.shape = shape == null ? null : shape.clone();
v.dataType = dataType;
v.sameDiff = sd;
return v;
}
}