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+# ND4J Op Definitions and Code Generation
+This project contains the ND4J Op definitions, the DSL (Domain Specific Language) that is used for those definitions and
+code generators that use those definitions to create the actual Java code that is used to use the defined operations.
+
+
+## Why define ops externally?
+As we started to support SameDiff, we also started to introduce inconsistencies between SameDiff and ND4J. Even though
+both of those libraries use the same underlying implementations for operations, there are both small and large
+differences in the API that we provide for them. Sometimes, we have provided an official API only for one usage, and not
+the other. And very often the documentation for a single op is in many different places.
+
+In the future we want to support other programming languages with libnd4j, and provide more ways to use our C++ backend.
+This would only increase the aforementioned problems.
+
+The root of all of those problems, is that Ops are used across different environments, and there is no single way of
+defining them with an enforced interface.
+
+
+## How does this project help with enforcing a single consistent interface for ops?
+The solution we propose, is to define the operations separately, and then generate the necessary API code for them. All
+of the generated code is to be considered untouchable, editing it will result in the changes being overwritten sooner
+rather than later.
+
+The combination of external op definition and code generation, opens up many opportunities for us. The first one being
+that we can easily create consistent APIs for both ND4J and SameDiff in Java. But, looking into the future, we can also
+create those APIs for other programming languages like Python, Swift, or even C#. We can even go beyond programming
+languages, and use the op definitions to create better documentation than what JavaDoc or similar might support out of
+the box.
+
+## Maintenance
+This project is currently maintained by Paul Dubs, with feedback often collected from raver119 and Alex Black.
+
+## Current Status
+At the moment we still focus on nailing down an easily readable and contribution friendly DSL for op definition and code
+generation that can replace namespace definitions. This means that at the moment we still rely on the pre-existing Op
+definition classes that already exist in ND4J.
+
+## Roadmap
+* Replace Bitwise and Random namespaces with autogenerated code – In progress.
+* Implement a convenient CLI tool.
+* Define all Ops using the DSL.
+* Automatically generate derivative op declarations from existing ops
+* Replace all namespace definitions in ND4J / SameDiff with automatically generated ones
+* Replace all Op classes with automatically generated ones.
+
+# Usage
+Pre-requisites:
+* JDK 8 or higher
+* Maven 3.3 or higher
+
+TODO: Show usage output of the project itself
+
+TODO: Show how to use from mvn
+
+
+## Generating Code - ND4J Namespaces
+
+A script - `generate.sh` - is provided in the project root. This can be used (at present) to generate ND4J namespace classes.
+It is assumed that the deeplearning4j mono repo and the dl4j-dev-tools repo both exist and have a common parent directory
+i.e., `somedir/deeplearning4j` and `somedir/dl4j-dev-tools` both exist.
+
+The script takes as argument the name (or names) of the ND4J namespaces to generate (not case sensitive) and projects (supported
+projects are nd4j, sd and both by default).
+
+As of 26/11, namespaces names (and hence valid args) include: `bitwise`, `neuralnetwork`, `random`, and `math`
+Note also that `all` may be passed to the script to generate all namespaces.
+
+For example, to generate both bitwise and random namespaces for both nd4j and SameDiff:
+```
+./generate.sh bitwise,random
+```
+Or to generate all namespaces for both nd4j and SameDiff, use:
+```
+./generate.sh all
+```
+To generate namespaces for one project only, use:
+```
+./generate.sh linalg -projects sd
+```
+or:
+```
+./generate.sh linalg -projects nd4j
+```
+The script will first compile the project, before running.
+Internally, the `org.nd4j.codegen.cli.CLI` class is used.
+Classes are written to `deeplearning4j/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/`
+
+## Generating documentation.
+It is possible to use generate.sh for generation of code only, docs in markdown format only, or both docs and code.
+To generate docs only and store them to new folder "docs" for all namespaces:
+```
+./generate.sh all -docsdir ../../docs
+```
+Generation for selected namespaces works in the same way as for code:
+```
+./generate.sh -docsdir ../../docs bitwise,linalg
+```
+
+# Code structure
+The project is implemented using a mix of Java and Kotlin. The DSL definition and the accompanying data structures are
+implemented in Kotlin. At the moment the code generators are implemented in Java, in order to allow people who are not
+fluent in Kotlin, but know Java to be able to contribute to the code generators.
+
+The source code for this project is structured a bit different that what you would typically see in a Java or Kotlin
+project. When you take a look inside the `src/main` directory, you will find 4 sub-directories.
+
+The `java` and `kotlin` directories contain Java and Kotlin code respectively.
+
+In order to not confuse op definitions with the machinery that allows them to be defined in that way, ops are kept in a
+separate folder called `ops`.
+
+Because we use JavaPoet for Java code generator implementation, we also have yet another folder called `stubs`. That
+folder contains stub classes, that are used to reference other classes available in ND4J. These stub classes are
+intentionally left empty, as JavaPoet only requires them for naming and automatically creating proper imports. We use
+stub classes instead of depending on the actual nd4j API in order to break a cyclic dependency that would otherwise be
+created (i.e. in order to be able to generate code for ND4J, we would need an already compiled nd4j to be available).
+**Note:** If something is stubbed here and is moved in ND4J, then it also has to be moved to the appropriate place here,
+otherwise the generated code will be wrong.
+
+The `adr` folder contains "Architecture Decision Records". These files give you more insight into the "why" of some of
+the bigger decisions within this project.
+
+# DSL for Op Definition
+Ops are defined using a DSL that is implemented in Kotlin. This means that other than the DSL, as defined in the
+following, you can also use all of Kotlin when defining Ops. However, doing things the obvious and clearly
+understandable way is better than coming up with a clever way, so prefer to use the DSL as described if unsure.
+
+```kotlin
+val mathNs = Namespace("math") {
+ Op("add") {
+ javaPackage = "org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic"
+
+ Input(NUMERIC, "x") { description = "First input to add" }
+ Input(NUMERIC,"y") { count = AtLeast(1); description = "Second input to add" }
+ Arg(INT,"shape") { count = AtLeast(1); description = "shape" }
+
+
+ Output(NUMERIC, "z") { description = "Output (x+y)" }
+
+ Doc(Language.ANY, DocScope.ALL) {
+ """
+ (From AddOp) Add op doc text that will appear everywhere - classes, constructors, op creators
+ """.trimIndent()
+ }
+ Doc(Language.ANY, DocScope.CLASS_DOC_ONLY) {
+ "Add op doc text that will appear in all class docs (javadoc etc)"
+ }
+ Doc(Language.ANY, DocScope.CONSTRUCTORS_ONLY) {
+ "Add op doc text for constructors only"
+ }
+
+ }
+}
+```
+
+This example shows how a namespace is defined. Namespaces are at the top layer, and ops can only be defined within the
+context of a namespace. This example namespace contains only a single op, called "add". If we wanted to add another op,
+we would simply add it below the first.
+
+As you can see, every op has to have a name, if you try to create one without a name, you will get a compile error.
+Within the context of the op, we first set in which java package the op class can be found in, then define its inputs,
+arguments and outputs and finally add some free form documentation about what that op is doing.
+
+Like with the op itself, the inputs, arguments and outputs all have to have a name, but unlike the op, they also require
+a type. Within their context, you can set a description and a count of how many parameters they can take respectively.
+
+If an input, argument or output take anything else than exactly 1, they will be treated as arrays. Typically you would
+use this to define ops like `concat` which can take multiple input tensors or ops that might take shape arguments.
+
+## Examples
+The following shows how a typical op definition looks like and how the generated Java code may look.
+
+An op might be defined like this:
+
+```kotlin
+Op("binomial") {
+ javaPackage = "org.nd4j.linalg.api.ops.random.custom"
+ Arg(INT, "nTrials") { description = "Number of trials parameter for the binomial distribution" }
+ Arg(FLOATING_POINT, "p") { description = "Probability of success for each trial" }
+ Arg(INT, "shape") { count = AtLeast(1); description = "Shape of the new random SDVariable, as a 1D array" }
+
+ Output(NUMERIC, "output") { description = "new random SDVariable, where values are randomly sampled according to a Binomial distribution" }
+
+ Doc(Language.ANY, DocScope.ALL) {
+ """
+ Generate a new random SDVariable, where values are randomly sampled according to a Binomial distribution,
+ with the specified number of trials and probability.
+ """.trimIndent()
+ }
+}
+```
+
+The java code generator will create a method like the following for it:
+```java
+ /**
+ * Generate a new random SDVariable, where values are randomly sampled according to a Binomial distribution,
+ * with the specified number of trials and probability.
+ *
+ * @param nTrials Number of trials parameter for the binomial distribution
+ * @param p Probability of success for each trial
+ * @param shape Shape of the new random SDVariable, as a 1D array (Size: AtLeast(min=1))
+ * @return output new random SDVariable, where values are randomly sampled according to a Binomial distribution (NUMERIC type)
+ */
+ public static INDArray binomial(long nTrials, double p, long... shape) {
+ Preconditions.checkArgument(shape.length >= 1, "shape has incorrect count. Expected: AtLeast(min=1)");
+ return Nd4j.exec(new org.nd4j.linalg.api.ops.random.custom.BinomialOp(nTrials, p, shape))[0];
+ }
+```
+
+Or an op with some more constraints:
+
+```kotlin
+Op("and") {
+ javaPackage = "org.nd4j.linalg.api.ops.impl.transforms.custom"
+ val x = Input(INT, "x") { description = "First input array" }
+ val y = Input(INT, "y") { description = "Second input array" }
+ Constraint("Must be same types"){ sameType(x, y) }
+ Constraint("Must have broadcastable shapes"){ broadcastableShapes(x, y) }
+
+ Output(INT, "output"){ description = "Bitwise AND array" }
+
+ Doc(Language.ANY, DocScope.ALL){
+ """
+ Bitwise AND operation. Supports broadcasting.
+ """.trimIndent()
+ }
+}
+```
+
+will be converted to java like this:
+
+```java
+ /**
+ * Bitwise AND operation. Supports broadcasting.
+ *
+ * Inputs must satisfy the following constraints:
+ * Must be same types: isSameType(x, y)
+ * Must have broadcastable shapes: isBroadcastableShapes(x, y)
+ *
+ * @param x First input array (INT type)
+ * @param y Second input array (INT type)
+ * @return output Bitwise AND array (INT type)
+ */
+ public static INDArray and(INDArray x, INDArray y) {
+ NDValidation.validateInteger("and", x);
+ NDValidation.validateInteger("and", y);
+ Preconditions.checkArgument(isSameType(x, y), "Must be same types");
+ Preconditions.checkArgument(isBroadcastableShapes(x, y), "Must have broadcastable shapes");
+ return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.transforms.custom.AndOp(x, y))[0];
+ }
+```
+
+# Full DSL Description
+## Namespace
+
+ fun NamespaceName() = Namespace("name"){ /* Op definitions in namespace context */}
+
+Defines a namespace.
+
+## Op
+Only available within a namespace context
+
+ Op("opName") { /* op properties in op context */ }
+ Op("anotherOp", mixin) { /* op properties in op context */ }
+ Op("anotherOp2", mixin, keepInputs=false) { /* op properties in op context */ }
+
+Every op requires a namespace unique op name.
+
+When defining an op, you can also pass a mixin that it should inherit initial properties from. This has the same effect
+as using `useMixin(mixin)` as the very first thing in the op definition. If you don't want to inherit all of the
+parameters of the mixin, you can pass the same additional configuration as you would pass to
+`useMixin(mixin, ...options..)`. See [Mixin](#mixin) for more information.
+
+### Op properties
+* `javaPackage` (String): Package where the op is to be found in the java implementation.
+* `javaOpClass` (String): Name of java op class if inconsistent with opName. Default: same as opName
+* `libnd4jName` (String): The name the op has in libnd4j. Default: same as opName
+
+
+## Mixin
+Available in global context.
+
+ val mixin = Mixin("name"){ /* op properties in op context */ }
+ // within an op context:
+ useMixin(mixin)
+ useMixin(mixin, ...options...)
+
+ // When needing to access something from within the mixin
+ mixin.input("name")
+ mixin.arg("name")
+ mixin.config("name")
+ mixin.output("name")
+
+Mixins provide the facility to share commonalities between Ops. You can think of it like inheritance, especially when
+you declare the use of a mixin on Op definition. In contrast to normal (single) inheritance where only a single super
+class is possible, the mixin mechanism allows to "inherit" from multiple sources.
+
+You can define almost all the same things within a mixin that you can within an Op. The only things that *can not* be
+configured within a mixin are Op `name`, `libnd4jName` and `javaOpClass`.
+
+As mixins can be configured within the global context, you can share them across namespaces by defining them in their
+own file. If a mixin is namespace specific, you can also define it within the namespace context.
+
+Mixins are used either on definition as a parameter `Op("opname", mixin){...}`, or with `useMixin(mixin)` within the op
+definition. While the former version only supports a single mixin, the latter version allows you to use as many mixins
+as are required.
+
+You can also build up mixins by using `useMixin(mixin)` inside a Mixin itself.
+
+`useMixin(mixin, ...options...)` supports a few additional options: `keepInputs`, `keepArgs`, `keepConfigs`,
+`keepOutputs`, `keepSignatures`, `keepDoc`, `keepConstraints`. They default to `true`. If you want to skip including
+some of them, you simply set the parameter for it to `false`, e.g. `useMixin(mixin, keepDoc=false)`.
+
+When using `useMixin(mixin)`, all definitions within the mixin are applied as if this invocation was replaced with the
+content of the mixin itself. This means, that if you have already defined anything prior to using a mixin, the mixin's
+definitions will be **after** the previously defined things. This can be very useful if the commonality between ops is
+that they have a few trailing options.
+
+If a named property or section is defined in both a mixin (or multiple mixins) and the op, then the **last** to define it will
+win. Named properties are `legacy`, `javaPackage`, named sections are `Input`, `Arg`, `Output`, `Config`.
+
+For example, assume you have `javaPackage` defined in both an op and a mixin. Then you can have the following two
+cases:
+
+First case:
+```kotlin
+ Op("foo"){
+ useMixin(exampleMixin)
+ javaPackage = "some.example.package"
+ }
+```
+
+Second case:
+```kotlin
+ Op("foo"){
+ javaPackage = "some.example.package"
+ useMixin(exampleMixin)
+ }
+```
+
+In the first case, the op will have the `javaPackage` value that is defined within the op. In the second case it will
+have the `javaPackage` value defined in the mixin.
+
+For inputs, args, outputs, it works similarly. Assume you have `Input(dataType, "a")` defined in both the mixin and the
+op. Again you can have two cases:
+
+First case:
+```kotlin
+ Op("foo"){
+ useMixin(exampleMixin)
+ Input(NUMERIC, "a")
+ }
+```
+
+Second case:
+```kotlin
+ Op("foo"){
+ Input(NUMERIC, "a")
+ useMixin(exampleMixin)
+ }
+```
+
+In the first case, it will overwrite the input from the mixin. In the second case, the mixin will overwrite that the
+input from the op.
+
+## Config
+Only available within a namespace context
+
+ val nameConfig = Config("Name"){
+ /* input, arg, constraint, doc properties */
+ }
+
+Every config requires a namespace unique name.
+
+A config allows to define a configuration class, that can be used as a holder for complex properties of specific ops
+which will be passed to an op as a parameter.
+
+Similar to an op itself, it supports `Input`, `Arg`, `Constraint` and `Doc` definitions.
+
+in order to use the config within an op you either use `useConfig(cfg)` or `val configRef = useConfig(cfg)`. The second
+form allows you to reference the config.
+
+Referencing the config allows to you reference its inputs and args by name: `configRef.input("name")` and
+`configRef.arg("name")`. Also it allows you to use a config in a signature `Signature(a, b, c, configRef)`.
+
+When default and shorthand signatures are used, configs will be always placed at the end.
+
+If a config is defined but not used, an `IllegalStateException` will be thrown.
+
+See also [ADR 0007 "Configuration Objects"](adr/0007-configuration_objects.md).
+
+
+## Input
+Available within an op, mixin and a config context
+
+ Input(FLOATING_POINT, "b"){ /* input properties in input context */ }
+ val a = Input(INT, "a"){ /* input properties in input context */ }
+
+Inputs represent tensors. They are what the op will work on.
+
+Every input requires a data type (either `INT`, `FLOATING_POINT`, `NUMERIC` or `BOOLEAN`) and an op unique name.
+
+When defining an input, you can assign it to a variable in order to be able to reference it later on. You might want to
+do this when defining constraints.
+
+If you want an input to represent an array, you will have to set a count accordingly. If no count is set, it is assumed
+that the count is meant to be `Exactly(1)`.
+
+### Input properties
+* `description` (String): A short description what this input represents. Setting this is recommended.
+* `count` (Count): Can take one of `Exactly(n)`; `AtLeast(n)`; `AtMost(n)`; `Range(from, to)`
+* `defaultValue` (Input): use another input as the default if this isn't set explicitly. The data type of the other
+ input has to match the data type of this input. The other input may also have a default value.
+
+## Argument
+Available within an op, mixin and config context
+
+ Arg(FLOATING_POINT, "b"){ /* Arg properties in arg context */ }
+ val a = Arg(INT, "a"){ /* Arg properties in arg context */ }
+
+Args represent arguments. They modify how the op works on its inputs.
+
+Every arg requires a data type (either `INT`, `FLOATING_POINT`, `NUMERIC` or `BOOLEAN`) and an op unique name.
+
+When defining an arg, you can assign it to a variable in order to be able to reference it later on. You might want to do
+this when defining constraints.
+
+If you want an arg to represent an array, you will have to set a count accordingly. If no count is set, it is assumed
+that the count is meant to be `Exactly(1)`.
+
+Note (Java specific): If the last arg is defined to represent an array, it will be translated to a vararg parameter, e.g.
+`Arg(INT, "a"){ count = AtLeast(1); description = "..." }` will be turned into `long... a`.
+
+### Argument properties
+* `description` (String): A short description what this argument represents. Setting this is recommended.
+* `count` (Count): Can take one of `Exactly(n)`; `AtLeast(n)`; `AtMost(n)`; `Range(from, to)`
+* `defaultValue` (null|Number|Boolean|int[]|double[]|boolean[]|Arg|TensorShapeValue|TensorDataTypeValue|String):
+ Use given value as default value, if this isn't explicitly set. Can refer to *inputs* and *outputs* using `x.shape()`
+ and `x.dataType()`. The given default values has to match the data type for this argument. May also refer to another
+ Arg, and that Arg may also have a default value. Default values based on outputs are treated like without a default
+ in SameDiff mode.
+* `possibleValues` (String[]): only available when ENUM data type is used for the argument. Takes a list of possible
+ values for the Enum. If used in in abstract base op, the enum will only be created once. See also
+ [ADR 0006 "Op specific enums"](adr/0006-op_specific_enums.md).
+
+
+## Output
+Only available within an op and mixin context
+
+ Output(FLOATING_POINT, "b"){ /* Arg properties in arg context */ }
+
+Every output requires a data type (either `INT`, `FLOATING_POINT`, `NUMERIC` or `BOOLEAN`) and an op unique name.
+
+While outputs can be assigned to a variable, there is no intended use-case for it. In contrast to inputs and args,
+outputs can not be used in constraints.
+
+### Output properties
+* `description` (String): A short description what this argument represents. Setting this is recommended.
+
+
+## Signature
+Only available within an op and mixin context
+
+ Signature(a,b,c)
+ Signature(a,b,c) { "Some Documentation" }
+ AllParamSignature()
+ AllDefaultParamSignature()
+
+For some ops only specific signatures make sense, as for example some optional parameters may become required in the
+presence of other optional parameters. This feature is mainly meant to help with the fact that not all programming
+languages (e.g. Java) support default parameters. Each signature is meant to describe one overload in those languages.
+
+See also [ADR 0005 "Optional parameters and signatures"](adr/0005-optional_parameters_and_signatures.md).
+
+Signatures can also reference the output(s) of an op. Those signatures are only relevant in NDArray programming mode.
+They are not to be generated in SameDiff mode.
+
+`AllParamSignature()` and `AllDefaultParamSignature()` are short hands for `Signature(...all parameters...)` and
+`Signature(...only parameters with no default values...)`. Their parameters include references to outputs unless
+disabled using `withOutput=false` (e.g. `AllParamSignature(withOutput=false)`).
+
+If no signature is specified for an op, it is treated as if `AllParamSignature()` and `AllDefaultParamSignature()` are
+both specified.
+
+Each signature must satisfy the condition, that all required parameters are listed there. If this condition is not
+satisfied, an `IllegalStateException` will be thrown on construction.
+
+
+## Documentation
+Only available within an op and mixin context
+
+ Doc(Language.ANY, DocScope.ALL){
+ """ Some documentation
+ It can be multiline. And indented.
+ """.trimIndent()
+ }
+
+Documentation can be language specific, and can be set to be only given at specific places. The documentation itself is
+given as a string. Because Kotlin supports multiline strings along with proper indentation, we are using them directly
+here.
+
+Note: At the moment we are only creating java code, so the documentation can use JavaDoc syntax.
+
+You can have multiple Doc definitions; they are treated as additive.
+
+Any instances of the following values will be replaced when generating code:
+
+* `%OPNAME%` -> operation name ("Add", "Sub", etc)
+* `%LIBND4J_OPNAME%` -> libnd4j op name ("add", "sub", etc)
+* `%INPUT_TYPE%` -> input / output type depending on the generated api, i.e. `SDVariable` for SameDiff and `INDArray`
+ for ND4J
+
+See `DocTokens` class for more details.
+
+## Constraints
+Available within an op, mixin and a config context.
+
+ Constraint("Error Message if constraint isn't satisfied"){ /* constraint definition */ }
+ BackendConstraint("Error Message if constraint isn't satisfied"){ /* constraint definition */ }
+
+Many ops expect their inputs and arguments to satisfy some specific rules. Those rules can be expressed with the
+constraint system.
+
+Constraints are to be enforced within the frontend language, while BackendConstraints are currently only to be used as
+a part of the documentation. They will be enforced within the C++ backend, so there is no point in double checking them.
+
+There is a system in place to define even complex constraints for inputs and arguments.
+
+In a constraint definition, you can reference inputs and arguments directly, if they are previously assigned to
+a variable using `val name = Input(...)`. Inside the Constraint block, you can use the following operations:
+
+* `eq`: Compare equality (applicable to numbers and booleans), e.g. `x eq 7`, `x eq true`
+* `neq`: Compare inequality (applicable to numbers and booleans), e.g. `x neq 3`, `x neq true`
+* `lt`, `lte`: less than, less than equal (applicable to numbers), e.g. `x lt 3`, `x lte 4`
+* `gt`, `gte`: greater than, grater than equal (applicable to numbers), e.g. `x gt 5`, `x gte 6`
+* `and`: combine two comparisons where both have to be true, e.g. `(x eq 8) and (y lt 3)`
+* `or`: combine two comparisons where one has to be true, e.g. `(x eq 8) or (y eq true)`
+* `all`: combine N comparisons where all have to be true, e.g. `all(x eq 8, y lt 3, z eq true)`
+* `some`: combine N comparisons where at least one has to be true, e.g. `some(x eq 8, y lt 3, z eq true)`
+* `not`: negates a comparison, e.g. `not(x eq 3)`
+
+In addition to those operations, you also get access to some more complex constraints:
+* `sameType(...)`: true if all given inputs are the same type, e.g. `sameType(x,y,z)`
+* `sameShape(...)`: true if all given inputs have the same shape, e.g. `sameShape(x,y,z)`
+* `broadcastableShapes(...)`: true if all given inputs have broadcast compatible shapes, e.g. `broadcastableShapes(x,y,z)`
+
+Inputs also get some additional methods on them to define useful constraints:
+* `input.rank()`: Rank of the given input
+* `input.sizeAt(i)`: size of the given input at the i-th dimension
+* `input.isScalar()`: Short hand for `x.rank() == 1`
+
+### Examples
+Some examples of constraints, and what they evaluate to. The example code contains a little bit of context.
+
+```kotlin
+val x = Input(INT, "x") { description = "First input array" }
+val y = Input(INT, "y") { description = "Second input array" }
+Constraint("foo bar"){
+ x.sizeAt(7) eq 7 and y.isScalar()
+}
+```
+
+will evaluate to:
+```java
+ Preconditions.checkArgument((x.sizeAt(7) == 7) && (y.rank() == 1), "foo bar");
+```
+
+More examples (only the constraint itself, without context code):
+
+#### Some
+```kotlin
+some(input.rank() eq 3, input.sizeAt(2) gte 7, input.sizeAt(4) lt 5)
+```
+turns to:
+```java
+((x.rank() == 3) || (x.sizeAt(2) >= 7)) || (x.sizeAt(4) < 5)
+```
+
+# Contributing to this project
+If you want to contribute to this project other than by adding or improving op definitions, the following sections might
+be of special interest to you.
+
+## Extending the DSL
+The DSL is implemented using Kotlin’s type-safe builders feature
+(see https://kotlinlang.org/docs/reference/type-safe-builders.html). The basic principle is that functions calls can
+receive blocks that can be executed in a specified context. When combined with the fact that we are just looking to
+create an object graph that is then going to be used as input to the code generators, this allows us to create a very
+feature rich DSL without actually having to write a lot of code to support it.
+
+Most of the DSL specific code can be found in `src/kotlin/org/nd4j/codegen/dsl/OpBuilder.kt`. The actual class
+definitions for the object graph we are building, can be found in `src/kotlin/org/nd4j/codegen/api`.
+
+If you want to add just a simple field to one of the objects, it is usually enough to directly add it to the particular
+class.
+
+If you want to add a specific section to the op definition, i.e. a section like Input or Doc, you will have to add both
+the class for the object that it is going to be creating, as well as a function within OpBuilder.kt to create and
+register that section within the op.
+
+**Note:** When you extend the DSL you will most likely also have to update all code generators to support the feature
+you have added.
+
+## Adding / extending code generators
+Code generators can be written in either Java or Kotlin. Java has the advantage that more people will have experience in
+using it. Kotlin has the advantage of more convenient syntax, especially for plain string manipulation and when dealing
+with Enums and fixed sets of subclasses (called sealed classes in Kotlin).
+
+All generators have to implement the `org.nd4j.codegen.api.generator.Generator` interface. For automatic detection by
+the CLI tool, they should also be within the `org.nd4j.codegen.impl.LANGUAGE` package, where `LANGUAGE` is the actual
+language that they generate.
+
+Code generators can also use an auxiliary generator for constraint generation. Those auxiliary generators, have to
+implement ` org.nd4j.codegen.api.generator.ConstraintCodeGenerator` interface.
+
diff --git a/contrib/codegen-tools/codegen/generate.sh b/contrib/codegen-tools/codegen/generate.sh
new file mode 100644
index 000000000..a398af633
--- /dev/null
+++ b/contrib/codegen-tools/codegen/generate.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+if test "$#" -eq 0; then
+ echo "No namespaces were specified. One or more namespaces must be provided as an argument"
+ echo "Usage example 1 (single namespace): ./generate.sh math"
+ echo "Usage example 2 (multiple namespaces): ./generate.sh math,random"
+ echo "Usage example 2 (all namespaces): ./generate.sh all"
+else
+ mvn clean package -DskipTests
+ java -cp target/codegen-1.0.0-SNAPSHOT-shaded.jar org.nd4j.codegen.cli.CLI -dir ../../ -namespaces "$@"
+fi
\ No newline at end of file
diff --git a/contrib/codegen-tools/codegen/pom.xml b/contrib/codegen-tools/codegen/pom.xml
new file mode 100644
index 000000000..5272b37d9
--- /dev/null
+++ b/contrib/codegen-tools/codegen/pom.xml
@@ -0,0 +1,258 @@
+
+
+ 4.0.0
+
+ org.nd4j
+ codegen
+ 1.0.0-SNAPSHOT
+
+
+ UTF-8
+ 2.5
+ 3.12.0
+ 1.7
+ 1.18.8
+ 1.1.7
+ 4.12
+ 5.4.2
+ 1.8
+ 3.1.1
+ 1.3.50
+ 1.8
+ true
+
+
+
+
+ org.slf4j
+ slf4j-api
+ 1.7.28
+
+
+
+ ch.qos.logback
+ logback-classic
+ ${logback.version}
+
+
+
+ commons-io
+ commons-io
+ ${commonsio.version}
+
+
+
+ org.projectlombok
+ lombok
+ ${lombok.version}
+
+
+
+ org.apache.commons
+ commons-lang3
+ ${commons.lang.version}
+
+
+
+ com.squareup
+ javapoet
+ 1.11.1
+
+
+
+
+
+ org.junit.jupiter
+ junit-jupiter-api
+ ${junit-jupiter.version}
+ test
+
+
+ org.junit.jupiter
+ junit-jupiter-engine
+ ${junit-jupiter.version}
+ test
+
+
+
+ org.jetbrains.kotlin
+ kotlin-stdlib-jdk8
+ ${kotlin.version}
+
+
+ org.jetbrains.kotlin
+ kotlin-test
+ ${kotlin.version}
+ test
+
+
+
+ com.fasterxml.jackson.module
+ jackson-module-kotlin
+ 2.9.9
+
+
+
+ com.beust
+ jcommander
+ 1.78
+
+
+ org.nd4j
+ nd4j-api
+ 1.0.0-SNAPSHOT
+
+
+
+
+
+
+
+ org.codehaus.mojo
+ build-helper-maven-plugin
+ 3.0.0
+
+
+ add-source
+ generate-sources
+ add-source
+
+
+ src/main/stubs
+
+
+
+
+
+
+
+ org.apache.maven.plugins
+ maven-shade-plugin
+ ${maven-shade-plugin.version}
+
+ true
+ false
+
+
+ *:*
+
+ org/datanucleus/**
+ META-INF/*.SF
+ META-INF/*.DSA
+ META-INF/*.RSA
+
+
+
+
+
+
+
+
+ package
+
+ shade
+
+
+
+
+ reference.conf
+
+
+
+
+
+
+
+
+
+
+ org.jetbrains.kotlin
+ kotlin-maven-plugin
+ ${kotlin.version}
+
+
+ -Xjsr305=strict
+
+
+ spring
+ jpa
+
+
+
+
+ org.jetbrains.kotlin
+ kotlin-maven-allopen
+ ${kotlin.version}
+
+
+ org.jetbrains.kotlin
+ kotlin-maven-noarg
+ ${kotlin.version}
+
+
+
+
+ compile
+ compile
+
+
+ ${project.basedir}/src/main/stubs
+ ${project.basedir}/src/main/kotlin
+ ${project.basedir}/src/main/java
+ ${project.basedir}/src/main/ops
+
+
+
+
+ test-compile
+ test-compile
+
+
+ ${project.basedir}/src/test/stubs
+ ${project.basedir}/src/test/kotlin
+ ${project.basedir}/src/test/java
+ ${project.basedir}/src/test/ops
+
+
+
+
+
+
+
+
+ org.apache.maven.plugins
+ maven-compiler-plugin
+ 3.5.1
+
+
+
+ default-compile
+ none
+
+
+
+ default-testCompile
+ none
+
+
+ java-compile
+ compile
+ compile
+
+
+ java-test-compile
+ test-compile
+ testCompile
+
+
+
+ ${java.version}
+ ${java.version}
+
+
+
+
+
+
\ No newline at end of file
diff --git a/contrib/codegen-tools/codegen/src/main/java/org/nd4j/codegen/impl/java/JavaPoetGenerator.java b/contrib/codegen-tools/codegen/src/main/java/org/nd4j/codegen/impl/java/JavaPoetGenerator.java
index 7f445ed4b..e50ef4651 100644
--- a/contrib/codegen-tools/codegen/src/main/java/org/nd4j/codegen/impl/java/JavaPoetGenerator.java
+++ b/contrib/codegen-tools/codegen/src/main/java/org/nd4j/codegen/impl/java/JavaPoetGenerator.java
@@ -19,17 +19,13 @@
*/
package org.nd4j.codegen.impl.java;
-
import org.apache.commons.lang3.StringUtils;
import org.nd4j.codegen.api.Language;
-import org.nd4j.codegen.api.Namespace;
import org.nd4j.codegen.api.NamespaceOps;
-import org.nd4j.codegen.api.Op;
import org.nd4j.codegen.api.generator.Generator;
import org.nd4j.codegen.api.generator.GeneratorConfig;
import java.io.File;
-import java.io.IOException;
public class JavaPoetGenerator implements Generator {
@@ -40,12 +36,12 @@ public class JavaPoetGenerator implements Generator {
}
@Override
- public void generateNamespaceNd4j(NamespaceOps namespace, GeneratorConfig config, File directory, String className) throws IOException {
+ public void generateNamespaceNd4j(NamespaceOps namespace, GeneratorConfig config, File directory, String className) throws java.io.IOException {
Nd4jNamespaceGenerator.generate(namespace, config, directory, className, "org.nd4j.linalg.factory", StringUtils.EMPTY);
}
@Override
- public void generateNamespaceSameDiff(NamespaceOps namespace, GeneratorConfig config, File directory, String className) throws IOException {
+ public void generateNamespaceSameDiff(NamespaceOps namespace, GeneratorConfig config, File directory, String className) throws java.io.IOException {
//throw new UnsupportedOperationException("Not yet implemented");
Nd4jNamespaceGenerator.generate(namespace, config, directory, className, "org.nd4j.autodiff.samediff", StringUtils.EMPTY);
}
diff --git a/contrib/codegen-tools/codegen/src/main/ops/org/nd4j/codegen/ops/SDLoss.kt b/contrib/codegen-tools/codegen/src/main/ops/org/nd4j/codegen/ops/SDLoss.kt
index d8706bd02..b6db6f887 100644
--- a/contrib/codegen-tools/codegen/src/main/ops/org/nd4j/codegen/ops/SDLoss.kt
+++ b/contrib/codegen-tools/codegen/src/main/ops/org/nd4j/codegen/ops/SDLoss.kt
@@ -47,6 +47,23 @@ fun SDLoss() = Namespace("Loss"){
}
}
+
+ Op("ctcLoss") {
+ javaPackage = "org.nd4j.linalg.api.ops.impl.loss"
+ javaOpClass = "CtcLoss"
+ Input(NUMERIC, "targetLabels") { description = "Label array" }
+ Input(NUMERIC, "logitInput") { description = "Inputs" }
+ Input(NUMERIC, "targetLabelLengths") { description = "Length of the target label" }
+ Input(NUMERIC, "logitInputLengths") { description = "Length of the input"}
+ Output(NUMERIC, "output"){ description = "Ctc loss " }
+ Doc(Language.ANY, DocScope.ALL){
+ """
+ CTC Loss: Connectionist Temporal Classification Loss. See:
+ https://dl.acm.org/citation.cfm?id=1143891
+ """.trimIndent()
+ }
+ }
+
Op("cosineDistance") {
javaPackage = "org.nd4j.linalg.api.ops.impl.loss"
javaOpClass = "CosineDistanceLoss"
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBaseOps.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBaseOps.java
index 8e2641b47..8190c4849 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBaseOps.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBaseOps.java
@@ -1,32 +1,26 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
import java.lang.String;
-import java.util.Arrays;
-import java.util.concurrent.atomic.AtomicInteger;
-import java.util.stream.Collectors;
-
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
@@ -307,7 +301,7 @@ public class SDBaseOps {
* @param transposeB Whether to transpose B arrays or not
*/
public SDVariable[] batchMmul(SDVariable[] inputsA, SDVariable[] inputsB, boolean transposeA,
- boolean transposeB) {
+ boolean transposeB) {
SDValidation.validateNumerical("batchMmul", "inputsA", inputsA);
Preconditions.checkArgument(inputsA.length >= 1, "inputsA has incorrect size/length. Expected: inputsA.length >= 1, got %s", inputsA.length);
SDValidation.validateNumerical("batchMmul", "inputsB", inputsB);
@@ -331,7 +325,7 @@ public class SDBaseOps {
* @param transposeB Whether to transpose B arrays or not
*/
public SDVariable[] batchMmul(String[] names, SDVariable[] inputsA, SDVariable[] inputsB,
- boolean transposeA, boolean transposeB) {
+ boolean transposeA, boolean transposeB) {
SDValidation.validateNumerical("batchMmul", "inputsA", inputsA);
Preconditions.checkArgument(inputsA.length >= 1, "inputsA has incorrect size/length. Expected: inputsA.length >= 1, got %s", inputsA.length);
SDValidation.validateNumerical("batchMmul", "inputsB", inputsB);
@@ -482,7 +476,7 @@ public class SDBaseOps {
* @return output Output variable (NUMERIC type)
*/
public SDVariable cumprod(String name, SDVariable in, boolean exclusive, boolean reverse,
- int... axis) {
+ int... axis) {
SDValidation.validateNumerical("cumprod", "in", in);
Preconditions.checkArgument(axis.length >= 1, "axis has incorrect size/length. Expected: axis.length >= 1, got %s", axis.length);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.custom.CumProd(sd,in, exclusive, reverse, axis).outputVariable();
@@ -563,7 +557,7 @@ public class SDBaseOps {
* @return output (NUMERIC type)
*/
public SDVariable cumsum(String name, SDVariable in, boolean exclusive, boolean reverse,
- int... axis) {
+ int... axis) {
SDValidation.validateNumerical("cumsum", "in", in);
Preconditions.checkArgument(axis.length >= 1, "axis has incorrect size/length. Expected: axis.length >= 1, got %s", axis.length);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.custom.CumSum(sd,in, exclusive, reverse, axis).outputVariable();
@@ -680,7 +674,7 @@ public class SDBaseOps {
* @param numPartitions Number of partitions, >= 1
*/
public SDVariable[] dynamicPartition(String[] names, SDVariable x, SDVariable partitions,
- int numPartitions) {
+ int numPartitions) {
SDValidation.validateNumerical("dynamicPartition", "x", x);
SDValidation.validateInteger("dynamicPartition", "partitions", partitions);
SDVariable[] out = new org.nd4j.linalg.api.ops.impl.transforms.custom.DynamicPartition(sd,x, partitions, numPartitions).outputVariables();
@@ -1189,7 +1183,7 @@ public class SDBaseOps {
* @return output INDArray with linearly spaced elements (NUMERIC type)
*/
public SDVariable linspace(String name, DataType dataType, double start, double stop,
- long number) {
+ long number) {
SDVariable out = new org.nd4j.linalg.api.ops.impl.shape.Linspace(sd,dataType, start, stop, number).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
@@ -1205,7 +1199,7 @@ public class SDBaseOps {
* @return output INDArray with linearly spaced elements (NUMERIC type)
*/
public SDVariable linspace(SDVariable start, SDVariable stop, SDVariable number,
- DataType dataType) {
+ DataType dataType) {
SDValidation.validateNumerical("linspace", "start", start);
SDValidation.validateNumerical("linspace", "stop", stop);
SDValidation.validateInteger("linspace", "number", number);
@@ -1224,7 +1218,7 @@ public class SDBaseOps {
* @return output INDArray with linearly spaced elements (NUMERIC type)
*/
public SDVariable linspace(String name, SDVariable start, SDVariable stop, SDVariable number,
- DataType dataType) {
+ DataType dataType) {
SDValidation.validateNumerical("linspace", "start", start);
SDValidation.validateNumerical("linspace", "stop", stop);
SDValidation.validateInteger("linspace", "number", number);
@@ -1445,7 +1439,7 @@ public class SDBaseOps {
* @return output Number of elements that the condition is satisfied for (NUMERIC type)
*/
public SDVariable matchConditionCount(SDVariable in, Condition condition, boolean keepDim,
- int... dimensions) {
+ int... dimensions) {
SDValidation.validateNumerical("matchConditionCount", "in", in);
Preconditions.checkArgument(dimensions.length >= 0, "dimensions has incorrect size/length. Expected: dimensions.length >= 0, got %s", dimensions.length);
return new org.nd4j.linalg.api.ops.impl.reduce.longer.MatchCondition(sd,in, condition, keepDim, dimensions).outputVariable();
@@ -1469,7 +1463,7 @@ public class SDBaseOps {
* @return output Number of elements that the condition is satisfied for (NUMERIC type)
*/
public SDVariable matchConditionCount(String name, SDVariable in, Condition condition,
- boolean keepDim, int... dimensions) {
+ boolean keepDim, int... dimensions) {
SDValidation.validateNumerical("matchConditionCount", "in", in);
Preconditions.checkArgument(dimensions.length >= 0, "dimensions has incorrect size/length. Expected: dimensions.length >= 0, got %s", dimensions.length);
SDVariable out = new org.nd4j.linalg.api.ops.impl.reduce.longer.MatchCondition(sd,in, condition, keepDim, dimensions).outputVariable();
@@ -1514,7 +1508,7 @@ public class SDBaseOps {
* @return output Number of elements that the condition is satisfied for (NUMERIC type)
*/
public SDVariable matchConditionCount(String name, SDVariable in, Condition condition,
- int... dimensions) {
+ int... dimensions) {
SDValidation.validateNumerical("matchConditionCount", "in", in);
Preconditions.checkArgument(dimensions.length >= 0, "dimensions has incorrect size/length. Expected: dimensions.length >= 0, got %s", dimensions.length);
SDVariable out = new org.nd4j.linalg.api.ops.impl.reduce.longer.MatchCondition(sd,in, condition, false, dimensions).outputVariable();
@@ -1895,7 +1889,7 @@ public class SDBaseOps {
* @return output (NUMERIC type)
*/
public SDVariable mmul(SDVariable x, SDVariable y, boolean transposeX, boolean transposeY,
- boolean transposeZ) {
+ boolean transposeZ) {
SDValidation.validateNumerical("mmul", "x", x);
SDValidation.validateNumerical("mmul", "y", y);
return new org.nd4j.linalg.api.ops.impl.reduce.Mmul(sd,x, y, transposeX, transposeY, transposeZ).outputVariable();
@@ -1914,7 +1908,7 @@ public class SDBaseOps {
* @return output (NUMERIC type)
*/
public SDVariable mmul(String name, SDVariable x, SDVariable y, boolean transposeX,
- boolean transposeY, boolean transposeZ) {
+ boolean transposeY, boolean transposeZ) {
SDValidation.validateNumerical("mmul", "x", x);
SDValidation.validateNumerical("mmul", "y", y);
SDVariable out = new org.nd4j.linalg.api.ops.impl.reduce.Mmul(sd,x, y, transposeX, transposeY, transposeZ).outputVariable();
@@ -2304,14 +2298,14 @@ public class SDBaseOps {
*
* @param indices Indices - value 0 to depth-1 (NUMERIC type)
* @param depth Number of classes
- * @param axis
- * @param on
- * @param off
+ * @param axis
+ * @param on
+ * @param off
* @param dataType Output data type
* @return output Output variable (NUMERIC type)
*/
public SDVariable oneHot(SDVariable indices, int depth, int axis, double on, double off,
- DataType dataType) {
+ DataType dataType) {
SDValidation.validateNumerical("oneHot", "indices", indices);
return new org.nd4j.linalg.api.ops.impl.shape.OneHot(sd,indices, depth, axis, on, off, dataType).outputVariable();
}
@@ -2324,14 +2318,14 @@ public class SDBaseOps {
* @param name name May be null. Name for the output variable
* @param indices Indices - value 0 to depth-1 (NUMERIC type)
* @param depth Number of classes
- * @param axis
- * @param on
- * @param off
+ * @param axis
+ * @param on
+ * @param off
* @param dataType Output data type
* @return output Output variable (NUMERIC type)
*/
public SDVariable oneHot(String name, SDVariable indices, int depth, int axis, double on,
- double off, DataType dataType) {
+ double off, DataType dataType) {
SDValidation.validateNumerical("oneHot", "indices", indices);
SDVariable out = new org.nd4j.linalg.api.ops.impl.shape.OneHot(sd,indices, depth, axis, on, off, dataType).outputVariable();
return sd.updateVariableNameAndReference(out, name);
@@ -2344,9 +2338,9 @@ public class SDBaseOps {
*
* @param indices Indices - value 0 to depth-1 (NUMERIC type)
* @param depth Number of classes
- * @param axis
- * @param on
- * @param off
+ * @param axis
+ * @param on
+ * @param off
* @return output Output variable (NUMERIC type)
*/
public SDVariable oneHot(SDVariable indices, int depth, int axis, double on, double off) {
@@ -2362,13 +2356,13 @@ public class SDBaseOps {
* @param name name May be null. Name for the output variable
* @param indices Indices - value 0 to depth-1 (NUMERIC type)
* @param depth Number of classes
- * @param axis
- * @param on
- * @param off
+ * @param axis
+ * @param on
+ * @param off
* @return output Output variable (NUMERIC type)
*/
public SDVariable oneHot(String name, SDVariable indices, int depth, int axis, double on,
- double off) {
+ double off) {
SDValidation.validateNumerical("oneHot", "indices", indices);
SDVariable out = new org.nd4j.linalg.api.ops.impl.shape.OneHot(sd,indices, depth, axis, on, off, DataType.FLOAT).outputVariable();
return sd.updateVariableNameAndReference(out, name);
@@ -2436,7 +2430,7 @@ public class SDBaseOps {
* As per onesLike(String, SDVariable) but the output datatype may be specified
*
* @param input (NUMERIC type)
- * @param dataType
+ * @param dataType
* @return output (NUMERIC type)
*/
public SDVariable onesLike(SDVariable input, DataType dataType) {
@@ -2449,7 +2443,7 @@ public class SDBaseOps {
*
* @param name name May be null. Name for the output variable
* @param input (NUMERIC type)
- * @param dataType
+ * @param dataType
* @return output (NUMERIC type)
*/
public SDVariable onesLike(String name, SDVariable input, DataType dataType) {
@@ -2612,7 +2606,7 @@ public class SDBaseOps {
* @param from Initial/smallest value
* @param to Largest value (exclusive)
* @param step Step size
- * @param dataType
+ * @param dataType
* @return output INDArray with the specified values (NUMERIC type)
*/
public SDVariable range(double from, double to, double step, DataType dataType) {
@@ -2628,7 +2622,7 @@ public class SDBaseOps {
* @param from Initial/smallest value
* @param to Largest value (exclusive)
* @param step Step size
- * @param dataType
+ * @param dataType
* @return output INDArray with the specified values (NUMERIC type)
*/
public SDVariable range(String name, double from, double to, double step, DataType dataType) {
@@ -2644,7 +2638,7 @@ public class SDBaseOps {
* @param from Initial/smallest value (NUMERIC type)
* @param to Largest value (exclusive) (NUMERIC type)
* @param step Step size (NUMERIC type)
- * @param dataType
+ * @param dataType
* @return output INDArray with the specified values (NUMERIC type)
*/
public SDVariable range(SDVariable from, SDVariable to, SDVariable step, DataType dataType) {
@@ -2663,11 +2657,11 @@ public class SDBaseOps {
* @param from Initial/smallest value (NUMERIC type)
* @param to Largest value (exclusive) (NUMERIC type)
* @param step Step size (NUMERIC type)
- * @param dataType
+ * @param dataType
* @return output INDArray with the specified values (NUMERIC type)
*/
public SDVariable range(String name, SDVariable from, SDVariable to, SDVariable step,
- DataType dataType) {
+ DataType dataType) {
SDValidation.validateNumerical("range", "from", from);
SDValidation.validateNumerical("range", "to", to);
SDValidation.validateNumerical("range", "step", step);
@@ -2727,7 +2721,7 @@ public class SDBaseOps {
* @return output New array with values replaced where condition is satisfied (NUMERIC type)
*/
public SDVariable replaceWhere(String name, SDVariable update, SDVariable from,
- Condition condition) {
+ Condition condition) {
SDValidation.validateNumerical("replaceWhere", "update", update);
SDValidation.validateNumerical("replaceWhere", "from", from);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndReplace(sd,update, from, condition).outputVariable();
@@ -2761,7 +2755,7 @@ public class SDBaseOps {
* @return output New array with values replaced where condition is satisfied (NUMERIC type)
*/
public SDVariable replaceWhere(String name, SDVariable update, double value,
- Condition condition) {
+ Condition condition) {
SDValidation.validateNumerical("replaceWhere", "update", update);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndSet(sd,update, value, condition).outputVariable();
return sd.updateVariableNameAndReference(out, name);
@@ -2799,47 +2793,6 @@ public class SDBaseOps {
return sd.updateVariableNameAndReference(out, name);
}
-
- /**
- * Split the input in to a list of sub tensors
- * @param input the input to split
- * @param numSizeSplits the number of splits
- * @param splitDim the dimension to split along
- * @return the set of output variables
- */
- public SDVariable[] split(SDVariable input,int numSizeSplits,int splitDim) {
- SDValidation.validateNumerical("split",input);
- SDVariable[] out = new org.nd4j.linalg.api.ops.impl.shape.Split(sd,input,numSizeSplits,splitDim).outputVariables();
- return out;
- }
-
- /**
- * Split the input in to a list of sub tensors
- * @param name the potential name of the input
- * @param input the input to split
- * @param numSizeSplits the number of splits
- * @param splitDim the dimension to split along
- * @return the set of output variables
- */
- public SDVariable[] split(String name,SDVariable input,int numSizeSplits,int splitDim) {
- SDValidation.validateNumerical("split",input);
- SDVariable[] out = new org.nd4j.linalg.api.ops.impl.shape.Split(sd,input,numSizeSplits,splitDim).outputVariables();
- SDVariable[] ret = new SDVariable[out.length];
- AtomicInteger index = new AtomicInteger(0);
- Arrays.stream(out).forEach(output -> {
- if(index.get() < 1) {
- ret[index.get()] = sd.updateVariableNameAndReference(output,name);
- index.incrementAndGet();
- }
- else {
- ret[index.get()] = sd.updateVariableNameAndReference(output,name + ":" + index.get());
- index.incrementAndGet();
- }
- });
-
- return ret;
- }
-
/**
* Reshape the input variable to the specified (fixed) shape. The output variable will have the same values as the
* input, but with the specified shape.
@@ -2930,7 +2883,7 @@ public class SDBaseOps {
* @return output Reversed sequences (NUMERIC type)
*/
public SDVariable reverseSequence(SDVariable x, SDVariable seq_lengths, int seqDim,
- int batchDim) {
+ int batchDim) {
SDValidation.validateNumerical("reverseSequence", "x", x);
SDValidation.validateInteger("reverseSequence", "seq_lengths", seq_lengths);
return new org.nd4j.linalg.api.ops.impl.transforms.custom.ReverseSequence(sd,x, seq_lengths, seqDim, batchDim).outputVariable();
@@ -2947,7 +2900,7 @@ public class SDBaseOps {
* @return output Reversed sequences (NUMERIC type)
*/
public SDVariable reverseSequence(String name, SDVariable x, SDVariable seq_lengths, int seqDim,
- int batchDim) {
+ int batchDim) {
SDValidation.validateNumerical("reverseSequence", "x", x);
SDValidation.validateInteger("reverseSequence", "seq_lengths", seq_lengths);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.custom.ReverseSequence(sd,x, seq_lengths, seqDim, batchDim).outputVariable();
@@ -3123,7 +3076,7 @@ public class SDBaseOps {
* @return output The updated variable (NUMERIC type)
*/
public SDVariable scatterAdd(String name, SDVariable ref, SDVariable indices,
- SDVariable updates) {
+ SDVariable updates) {
SDValidation.validateNumerical("scatterAdd", "ref", ref);
SDValidation.validateNumerical("scatterAdd", "indices", indices);
SDValidation.validateNumerical("scatterAdd", "updates", updates);
@@ -3166,7 +3119,7 @@ public class SDBaseOps {
* @return output The updated variable (NUMERIC type)
*/
public SDVariable scatterDiv(String name, SDVariable ref, SDVariable indices,
- SDVariable updates) {
+ SDVariable updates) {
SDValidation.validateNumerical("scatterDiv", "ref", ref);
SDValidation.validateNumerical("scatterDiv", "indices", indices);
SDValidation.validateNumerical("scatterDiv", "updates", updates);
@@ -3209,7 +3162,7 @@ public class SDBaseOps {
* @return output The updated variable (NUMERIC type)
*/
public SDVariable scatterMax(String name, SDVariable ref, SDVariable indices,
- SDVariable updates) {
+ SDVariable updates) {
SDValidation.validateNumerical("scatterMax", "ref", ref);
SDValidation.validateNumerical("scatterMax", "indices", indices);
SDValidation.validateNumerical("scatterMax", "updates", updates);
@@ -3252,7 +3205,7 @@ public class SDBaseOps {
* @return output The updated variable (NUMERIC type)
*/
public SDVariable scatterMin(String name, SDVariable ref, SDVariable indices,
- SDVariable updates) {
+ SDVariable updates) {
SDValidation.validateNumerical("scatterMin", "ref", ref);
SDValidation.validateNumerical("scatterMin", "indices", indices);
SDValidation.validateNumerical("scatterMin", "updates", updates);
@@ -3295,7 +3248,7 @@ public class SDBaseOps {
* @return output The updated variable (NUMERIC type)
*/
public SDVariable scatterMul(String name, SDVariable ref, SDVariable indices,
- SDVariable updates) {
+ SDVariable updates) {
SDValidation.validateNumerical("scatterMul", "ref", ref);
SDValidation.validateNumerical("scatterMul", "indices", indices);
SDValidation.validateNumerical("scatterMul", "updates", updates);
@@ -3338,7 +3291,7 @@ public class SDBaseOps {
* @return output The updated variable (NUMERIC type)
*/
public SDVariable scatterSub(String name, SDVariable ref, SDVariable indices,
- SDVariable updates) {
+ SDVariable updates) {
SDValidation.validateNumerical("scatterSub", "ref", ref);
SDValidation.validateNumerical("scatterSub", "indices", indices);
SDValidation.validateNumerical("scatterSub", "updates", updates);
@@ -3381,7 +3334,7 @@ public class SDBaseOps {
* @return output The updated variable (NUMERIC type)
*/
public SDVariable scatterUpdate(String name, SDVariable ref, SDVariable indices,
- SDVariable updates) {
+ SDVariable updates) {
SDValidation.validateNumerical("scatterUpdate", "ref", ref);
SDValidation.validateNumerical("scatterUpdate", "indices", indices);
SDValidation.validateNumerical("scatterUpdate", "updates", updates);
@@ -3595,7 +3548,7 @@ public class SDBaseOps {
*
* @param lengths Lengths of the sequences (NUMERIC type)
* @param maxLen Maximum sequence length
- * @param dataType
+ * @param dataType
* @return output Output variable (NUMERIC type)
*/
public SDVariable sequenceMask(SDVariable lengths, int maxLen, DataType dataType) {
@@ -3610,7 +3563,7 @@ public class SDBaseOps {
* @param name name May be null. Name for the output variable
* @param lengths Lengths of the sequences (NUMERIC type)
* @param maxLen Maximum sequence length
- * @param dataType
+ * @param dataType
* @return output Output variable (NUMERIC type)
*/
public SDVariable sequenceMask(String name, SDVariable lengths, int maxLen, DataType dataType) {
@@ -3625,7 +3578,7 @@ public class SDBaseOps {
*
* @param lengths Lengths of the sequences (NUMERIC type)
* @param maxLen Maximum sequence length (INT type)
- * @param dataType
+ * @param dataType
* @return output Output variable (NUMERIC type)
*/
public SDVariable sequenceMask(SDVariable lengths, SDVariable maxLen, DataType dataType) {
@@ -3641,11 +3594,11 @@ public class SDBaseOps {
* @param name name May be null. Name for the output variable
* @param lengths Lengths of the sequences (NUMERIC type)
* @param maxLen Maximum sequence length (INT type)
- * @param dataType
+ * @param dataType
* @return output Output variable (NUMERIC type)
*/
public SDVariable sequenceMask(String name, SDVariable lengths, SDVariable maxLen,
- DataType dataType) {
+ DataType dataType) {
SDValidation.validateNumerical("sequenceMask", "lengths", lengths);
SDValidation.validateInteger("sequenceMask", "maxLen", maxLen);
SDVariable out = new org.nd4j.linalg.api.ops.impl.shape.SequenceMask(sd,lengths, maxLen, dataType).outputVariable();
@@ -3656,7 +3609,7 @@ public class SDBaseOps {
* see sequenceMask(String, SDVariable, SDVariable, DataType)
*
* @param lengths (NUMERIC type)
- * @param dataType
+ * @param dataType
* @return output (NUMERIC type)
*/
public SDVariable sequenceMask(SDVariable lengths, DataType dataType) {
@@ -3669,7 +3622,7 @@ public class SDBaseOps {
*
* @param name name May be null. Name for the output variable
* @param lengths (NUMERIC type)
- * @param dataType
+ * @param dataType
* @return output (NUMERIC type)
*/
public SDVariable sequenceMask(String name, SDVariable lengths, DataType dataType) {
@@ -3857,7 +3810,7 @@ public class SDBaseOps {
* keepDims = false: [a,c]
*
* @param x (NUMERIC type)
- * @param keepDims
+ * @param keepDims
* @param dimensions (Size: AtLeast(min=0))
* @return output (NUMERIC type)
*/
@@ -3879,7 +3832,7 @@ public class SDBaseOps {
*
* @param name name May be null. Name for the output variable
* @param x (NUMERIC type)
- * @param keepDims
+ * @param keepDims
* @param dimensions (Size: AtLeast(min=0))
* @return output (NUMERIC type)
*/
@@ -4015,7 +3968,7 @@ public class SDBaseOps {
* @return output reduced array of rank (input rank - num dimensions) (NUMERIC type)
*/
public SDVariable standardDeviation(SDVariable x, boolean biasCorrected, boolean keepDims,
- int... dimensions) {
+ int... dimensions) {
SDValidation.validateNumerical("standardDeviation", "x", x);
Preconditions.checkArgument(dimensions.length >= 0, "dimensions has incorrect size/length. Expected: dimensions.length >= 0, got %s", dimensions.length);
return new org.nd4j.linalg.api.ops.impl.summarystats.StandardDeviation(sd,x, biasCorrected, keepDims, dimensions).outputVariable();
@@ -4039,7 +3992,7 @@ public class SDBaseOps {
* @return output reduced array of rank (input rank - num dimensions) (NUMERIC type)
*/
public SDVariable standardDeviation(String name, SDVariable x, boolean biasCorrected,
- boolean keepDims, int... dimensions) {
+ boolean keepDims, int... dimensions) {
SDValidation.validateNumerical("standardDeviation", "x", x);
Preconditions.checkArgument(dimensions.length >= 0, "dimensions has incorrect size/length. Expected: dimensions.length >= 0, got %s", dimensions.length);
SDVariable out = new org.nd4j.linalg.api.ops.impl.summarystats.StandardDeviation(sd,x, biasCorrected, keepDims, dimensions).outputVariable();
@@ -4084,7 +4037,7 @@ public class SDBaseOps {
* @return output reduced array of rank (input rank - num dimensions) (NUMERIC type)
*/
public SDVariable standardDeviation(String name, SDVariable x, boolean biasCorrected,
- int... dimensions) {
+ int... dimensions) {
SDValidation.validateNumerical("standardDeviation", "x", x);
Preconditions.checkArgument(dimensions.length >= 0, "dimensions has incorrect size/length. Expected: dimensions.length >= 0, got %s", dimensions.length);
SDVariable out = new org.nd4j.linalg.api.ops.impl.summarystats.StandardDeviation(sd,x, biasCorrected, false, dimensions).outputVariable();
@@ -4113,7 +4066,7 @@ public class SDBaseOps {
* @return output A subset of the input array (NUMERIC type)
*/
public SDVariable stridedSlice(SDVariable in, long[] begin, long[] end, long[] strides,
- int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask) {
+ int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask) {
SDValidation.validateNumerical("stridedSlice", "in", in);
Preconditions.checkArgument(begin.length >= 1, "begin has incorrect size/length. Expected: begin.length >= 1, got %s", begin.length);
Preconditions.checkArgument(end.length >= 1, "end has incorrect size/length. Expected: end.length >= 1, got %s", end.length);
@@ -4144,8 +4097,8 @@ public class SDBaseOps {
* @return output A subset of the input array (NUMERIC type)
*/
public SDVariable stridedSlice(String name, SDVariable in, long[] begin, long[] end,
- long[] strides, int beginMask, int endMask, int ellipsisMask, int newAxisMask,
- int shrinkAxisMask) {
+ long[] strides, int beginMask, int endMask, int ellipsisMask, int newAxisMask,
+ int shrinkAxisMask) {
SDValidation.validateNumerical("stridedSlice", "in", in);
Preconditions.checkArgument(begin.length >= 1, "begin has incorrect size/length. Expected: begin.length >= 1, got %s", begin.length);
Preconditions.checkArgument(end.length >= 1, "end has incorrect size/length. Expected: end.length >= 1, got %s", end.length);
@@ -4196,7 +4149,7 @@ public class SDBaseOps {
* @return output A subset of the input array (NUMERIC type)
*/
public SDVariable stridedSlice(String name, SDVariable in, long[] begin, long[] end,
- long... strides) {
+ long... strides) {
SDValidation.validateNumerical("stridedSlice", "in", in);
Preconditions.checkArgument(begin.length >= 1, "begin has incorrect size/length. Expected: begin.length >= 1, got %s", begin.length);
Preconditions.checkArgument(end.length >= 1, "end has incorrect size/length. Expected: end.length >= 1, got %s", end.length);
@@ -4330,7 +4283,7 @@ public class SDBaseOps {
* @return output Output variable (NUMERIC type)
*/
public SDVariable tensorMmul(SDVariable x, SDVariable y, int[] dimensionsX, int[] dimensionsY,
- boolean transposeX, boolean transposeY, boolean transposeZ) {
+ boolean transposeX, boolean transposeY, boolean transposeZ) {
SDValidation.validateNumerical("tensorMmul", "x", x);
SDValidation.validateNumerical("tensorMmul", "y", y);
Preconditions.checkArgument(dimensionsX.length >= 1, "dimensionsX has incorrect size/length. Expected: dimensionsX.length >= 1, got %s", dimensionsX.length);
@@ -4352,7 +4305,7 @@ public class SDBaseOps {
* @return output Output variable (NUMERIC type)
*/
public SDVariable tensorMmul(String name, SDVariable x, SDVariable y, int[] dimensionsX,
- int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ) {
+ int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ) {
SDValidation.validateNumerical("tensorMmul", "x", x);
SDValidation.validateNumerical("tensorMmul", "y", y);
Preconditions.checkArgument(dimensionsX.length >= 1, "dimensionsX has incorrect size/length. Expected: dimensionsX.length >= 1, got %s", dimensionsX.length);
@@ -4389,7 +4342,7 @@ public class SDBaseOps {
* @return output Output variable (NUMERIC type)
*/
public SDVariable tensorMmul(String name, SDVariable x, SDVariable y, int[] dimensionsX,
- int... dimensionsY) {
+ int... dimensionsY) {
SDValidation.validateNumerical("tensorMmul", "x", x);
SDValidation.validateNumerical("tensorMmul", "y", y);
Preconditions.checkArgument(dimensionsX.length >= 1, "dimensionsX has incorrect size/length. Expected: dimensionsX.length >= 1, got %s", dimensionsX.length);
@@ -4522,7 +4475,7 @@ public class SDBaseOps {
* @return output Unsorted segment output (NUMERIC type)
*/
public SDVariable unsortedSegmentMax(String name, SDVariable data, SDVariable segmentIds,
- int numSegments) {
+ int numSegments) {
SDValidation.validateNumerical("unsortedSegmentMax", "data", data);
SDValidation.validateNumerical("unsortedSegmentMax", "segmentIds", segmentIds);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.segment.UnsortedSegmentMax(sd,data, segmentIds, numSegments).outputVariable();
@@ -4561,7 +4514,7 @@ public class SDBaseOps {
* @return output Unsorted segment output (NUMERIC type)
*/
public SDVariable unsortedSegmentMean(String name, SDVariable data, SDVariable segmentIds,
- int numSegments) {
+ int numSegments) {
SDValidation.validateNumerical("unsortedSegmentMean", "data", data);
SDValidation.validateNumerical("unsortedSegmentMean", "segmentIds", segmentIds);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.segment.UnsortedSegmentMean(sd,data, segmentIds, numSegments).outputVariable();
@@ -4600,7 +4553,7 @@ public class SDBaseOps {
* @return output Unsorted segment output (NUMERIC type)
*/
public SDVariable unsortedSegmentMin(String name, SDVariable data, SDVariable segmentIds,
- int numSegments) {
+ int numSegments) {
SDValidation.validateNumerical("unsortedSegmentMin", "data", data);
SDValidation.validateNumerical("unsortedSegmentMin", "segmentIds", segmentIds);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.segment.UnsortedSegmentMin(sd,data, segmentIds, numSegments).outputVariable();
@@ -4639,7 +4592,7 @@ public class SDBaseOps {
* @return output Unsorted segment output (NUMERIC type)
*/
public SDVariable unsortedSegmentProd(String name, SDVariable data, SDVariable segmentIds,
- int numSegments) {
+ int numSegments) {
SDValidation.validateNumerical("unsortedSegmentProd", "data", data);
SDValidation.validateNumerical("unsortedSegmentProd", "segmentIds", segmentIds);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.segment.UnsortedSegmentProd(sd,data, segmentIds, numSegments).outputVariable();
@@ -4676,7 +4629,7 @@ public class SDBaseOps {
* @return output Unsorted segment output (NUMERIC type)
*/
public SDVariable unsortedSegmentSqrtN(String name, SDVariable data, SDVariable segmentIds,
- int numSegments) {
+ int numSegments) {
SDValidation.validateNumerical("unsortedSegmentSqrtN", "data", data);
SDValidation.validateNumerical("unsortedSegmentSqrtN", "segmentIds", segmentIds);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.segment.UnsortedSegmentSqrtN(sd,data, segmentIds, numSegments).outputVariable();
@@ -4715,7 +4668,7 @@ public class SDBaseOps {
* @return output Unsorted segment output (NUMERIC type)
*/
public SDVariable unsortedSegmentSum(String name, SDVariable data, SDVariable segmentIds,
- int numSegments) {
+ int numSegments) {
SDValidation.validateNumerical("unsortedSegmentSum", "data", data);
SDValidation.validateNumerical("unsortedSegmentSum", "segmentIds", segmentIds);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.segment.UnsortedSegmentSum(sd,data, segmentIds, numSegments).outputVariable();
@@ -4771,7 +4724,7 @@ public class SDBaseOps {
* @return output reduced array of rank (input rank - num dimensions) (NUMERIC type)
*/
public SDVariable variance(SDVariable x, boolean biasCorrected, boolean keepDims,
- int... dimensions) {
+ int... dimensions) {
SDValidation.validateNumerical("variance", "x", x);
Preconditions.checkArgument(dimensions.length >= 0, "dimensions has incorrect size/length. Expected: dimensions.length >= 0, got %s", dimensions.length);
return new org.nd4j.linalg.api.ops.impl.summarystats.Variance(sd,x, biasCorrected, keepDims, dimensions).outputVariable();
@@ -4795,7 +4748,7 @@ public class SDBaseOps {
* @return output reduced array of rank (input rank - num dimensions) (NUMERIC type)
*/
public SDVariable variance(String name, SDVariable x, boolean biasCorrected, boolean keepDims,
- int... dimensions) {
+ int... dimensions) {
SDValidation.validateNumerical("variance", "x", x);
Preconditions.checkArgument(dimensions.length >= 0, "dimensions has incorrect size/length. Expected: dimensions.length >= 0, got %s", dimensions.length);
SDVariable out = new org.nd4j.linalg.api.ops.impl.summarystats.Variance(sd,x, biasCorrected, keepDims, dimensions).outputVariable();
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBitwise.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBitwise.java
index 00102c498..299330359 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBitwise.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBitwise.java
@@ -1,22 +1,20 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
@@ -250,7 +248,7 @@ public class SDBitwise extends SDOps {
/**
* Bitwise left cyclical shift operation. Supports broadcasting.
- * Unlike {@link #leftShift(INDArray, INDArray)} the bits will "wrap around":
+ * Unlike #leftShift(INDArray, INDArray) the bits will "wrap around":
* {@code leftShiftCyclic(01110000, 2) -> 11000001}
*
* @param x Input to be bit shifted (INT type)
@@ -265,7 +263,7 @@ public class SDBitwise extends SDOps {
/**
* Bitwise left cyclical shift operation. Supports broadcasting.
- * Unlike {@link #leftShift(INDArray, INDArray)} the bits will "wrap around":
+ * Unlike #leftShift(INDArray, INDArray) the bits will "wrap around":
* {@code leftShiftCyclic(01110000, 2) -> 11000001}
*
* @param name name May be null. Name for the output variable
@@ -348,7 +346,7 @@ public class SDBitwise extends SDOps {
/**
* Bitwise right cyclical shift operation. Supports broadcasting.
- * Unlike {@link #rightShift(INDArray, INDArray)} the bits will "wrap around":
+ * Unlike rightShift(INDArray, INDArray) the bits will "wrap around":
* {@code rightShiftCyclic(00001110, 2) -> 10000011}
*
* @param x Input to be bit shifted (INT type)
@@ -363,7 +361,7 @@ public class SDBitwise extends SDOps {
/**
* Bitwise right cyclical shift operation. Supports broadcasting.
- * Unlike {@link #rightShift(INDArray, INDArray)} the bits will "wrap around":
+ * Unlike rightShift(INDArray, INDArray) the bits will "wrap around":
* {@code rightShiftCyclic(00001110, 2) -> 10000011}
*
* @param name name May be null. Name for the output variable
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDCNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDCNN.java
index b89e4ec3c..066f9b150 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDCNN.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDCNN.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
import java.lang.String;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
@@ -42,8 +42,7 @@ public class SDCNN extends SDOps {
/**
* 2D Convolution layer operation - average pooling 2d
*
- * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param Pooling2DConfig Configuration Object
* @return output Result after applying average pooling on the input (NUMERIC type)
*/
@@ -56,8 +55,7 @@ public class SDCNN extends SDOps {
* 2D Convolution layer operation - average pooling 2d
*
* @param name name May be null. Name for the output variable
- * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param Pooling2DConfig Configuration Object
* @return output Result after applying average pooling on the input (NUMERIC type)
*/
@@ -70,9 +68,7 @@ public class SDCNN extends SDOps {
/**
* 3D convolution layer operation - average pooling 3d
*
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param Pooling3DConfig Configuration Object
* @return output after applying average pooling on the input (NUMERIC type)
*/
@@ -85,9 +81,7 @@ public class SDCNN extends SDOps {
* 3D convolution layer operation - average pooling 3d
*
* @param name name May be null. Name for the output variable
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param Pooling3DConfig Configuration Object
* @return output after applying average pooling on the input (NUMERIC type)
*/
@@ -302,9 +296,7 @@ public class SDCNN extends SDOps {
/**
* Convolution 3D operation with optional bias
*
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param weights Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
* @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
* @param Conv3DConfig Configuration Object
@@ -322,9 +314,7 @@ public class SDCNN extends SDOps {
* Convolution 3D operation with optional bias
*
* @param name name May be null. Name for the output variable
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param weights Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
* @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
* @param Conv3DConfig Configuration Object
@@ -342,9 +332,7 @@ public class SDCNN extends SDOps {
/**
* Convolution 3D operation with optional bias
*
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param weights Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
* @param Conv3DConfig Configuration Object
* @return output Conv3d output variable (NUMERIC type)
@@ -359,9 +347,7 @@ public class SDCNN extends SDOps {
* Convolution 3D operation with optional bias
*
* @param name name May be null. Name for the output variable
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param weights Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
* @param Conv3DConfig Configuration Object
* @return output Conv3d output variable (NUMERIC type)
@@ -377,8 +363,7 @@ public class SDCNN extends SDOps {
/**
* 2D deconvolution operation with optional bias
*
- * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
* @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
* @param DeConv2DConfig Configuration Object
@@ -396,8 +381,7 @@ public class SDCNN extends SDOps {
* 2D deconvolution operation with optional bias
*
* @param name name May be null. Name for the output variable
- * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
* @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
* @param DeConv2DConfig Configuration Object
@@ -415,8 +399,7 @@ public class SDCNN extends SDOps {
/**
* 2D deconvolution operation with optional bias
*
- * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
* @param DeConv2DConfig Configuration Object
* @return output result of deconv2d op (NUMERIC type)
@@ -432,8 +415,7 @@ public class SDCNN extends SDOps {
* 2D deconvolution operation with optional bias
*
* @param name name May be null. Name for the output variable
- * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
* @param DeConv2DConfig Configuration Object
* @return output result of deconv2d op (NUMERIC type)
@@ -519,8 +501,7 @@ public class SDCNN extends SDOps {
* Example: if input has shape [mb, 8, 2, 2] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
* = [mb, 2, 4, 4]
*
- * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param blockSize Block size, in the height/width dimension
* @param dataFormat Data format: "NCHW" or "NHWC"
* @return output Output variable (NUMERIC type)
@@ -537,8 +518,7 @@ public class SDCNN extends SDOps {
* = [mb, 2, 4, 4]
*
* @param name name May be null. Name for the output variable
- * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param blockSize Block size, in the height/width dimension
* @param dataFormat Data format: "NCHW" or "NHWC"
* @return output Output variable (NUMERIC type)
@@ -756,8 +736,7 @@ public class SDCNN extends SDOps {
/**
* 2D Convolution layer operation - Max pooling on the input and outputs both max values and indices
*
- * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param Pooling2DConfig Configuration Object
*/
public SDVariable[] maxPoolWithArgmax(SDVariable input, Pooling2DConfig Pooling2DConfig) {
@@ -769,8 +748,7 @@ public class SDCNN extends SDOps {
* 2D Convolution layer operation - Max pooling on the input and outputs both max values and indices
*
* @param names names May be null. Arrays of names for the output variables.
- * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param Pooling2DConfig Configuration Object
*/
public SDVariable[] maxPoolWithArgmax(String[] names, SDVariable input,
@@ -783,8 +761,7 @@ public class SDCNN extends SDOps {
/**
* 2D Convolution layer operation - max pooling 2d
*
- * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param Pooling2DConfig Configuration Object
* @return output Result after applying max pooling on the input (NUMERIC type)
*/
@@ -797,8 +774,7 @@ public class SDCNN extends SDOps {
* 2D Convolution layer operation - max pooling 2d
*
* @param name name May be null. Name for the output variable
- * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param Pooling2DConfig Configuration Object
* @return output Result after applying max pooling on the input (NUMERIC type)
*/
@@ -811,9 +787,7 @@ public class SDCNN extends SDOps {
/**
* 3D convolution layer operation - max pooling 3d operation.
*
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param Pooling3DConfig Configuration Object
* @return output Result after applying max pooling on the input (NUMERIC type)
*/
@@ -826,9 +800,7 @@ public class SDCNN extends SDOps {
* 3D convolution layer operation - max pooling 3d operation.
*
* @param name name May be null. Name for the output variable
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param Pooling3DConfig Configuration Object
* @return output Result after applying max pooling on the input (NUMERIC type)
*/
@@ -841,8 +813,7 @@ public class SDCNN extends SDOps {
/**
* Separable 2D convolution operation with optional bias
*
- * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
* @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
* @param bias Optional bias, rank 1 with shape [outputChannels]. May be null. (NUMERIC type)
@@ -862,8 +833,7 @@ public class SDCNN extends SDOps {
* Separable 2D convolution operation with optional bias
*
* @param name name May be null. Name for the output variable
- * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
* @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
* @param bias Optional bias, rank 1 with shape [outputChannels]. May be null. (NUMERIC type)
@@ -883,8 +853,7 @@ public class SDCNN extends SDOps {
/**
* Separable 2D convolution operation with optional bias
*
- * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
* @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
* @param Conv2DConfig Configuration Object
@@ -902,8 +871,7 @@ public class SDCNN extends SDOps {
* Separable 2D convolution operation with optional bias
*
* @param name name May be null. Name for the output variable
- * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
* @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
* @param Conv2DConfig Configuration Object
@@ -964,8 +932,7 @@ public class SDCNN extends SDOps {
* Example: if input has shape [mb, 2, 4, 4] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
* = [mb, 2, 4, 4]
*
- * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param blockSize Block size, in the height/width dimension
* @param dataFormat Data format: "NCHW" or "NHWC"
* @return output Output variable (NUMERIC type)
@@ -982,8 +949,7 @@ public class SDCNN extends SDOps {
* = [mb, 2, 4, 4]
*
* @param name name May be null. Name for the output variable
- * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param blockSize Block size, in the height/width dimension
* @param dataFormat Data format: "NCHW" or "NHWC"
* @return output Output variable (NUMERIC type)
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDImage.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDImage.java
index 6317e0941..907a8e48e 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDImage.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDImage.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
import java.lang.String;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLinalg.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLinalg.java
index 2ddba4fcb..ae97b2c4a 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLinalg.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLinalg.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
import java.lang.String;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLoss.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLoss.java
index c6fef378e..533d22783 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLoss.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLoss.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
import java.lang.String;
import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
@@ -36,7 +36,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output loss variable (NUMERIC type)
*/
public SDVariable absoluteDifference(SDVariable label, SDVariable predictions, SDVariable weights,
@@ -56,7 +56,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output loss variable (NUMERIC type)
*/
public SDVariable absoluteDifference(String name, SDVariable label, SDVariable predictions,
@@ -116,7 +116,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param dimension Dimension to perform the cosine distance over
* @return output Cosine distance loss (NUMERIC type)
*/
@@ -141,7 +141,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param dimension Dimension to perform the cosine distance over
* @return output Cosine distance loss (NUMERIC type)
*/
@@ -202,6 +202,49 @@ public class SDLoss extends SDOps {
return sd.updateVariableNameAndReference(out, name);
}
+ /**
+ * CTC Loss: Connectionist Temporal Classification Loss. See:
+ * https://dl.acm.org/citation.cfm?id=1143891
+ *
+ * @param targetLabels Label array (NUMERIC type)
+ * @param logitInput Inputs (NUMERIC type)
+ * @param targetLabelLengths Length of the target label (NUMERIC type)
+ * @param logitInputLengths Length of the input (NUMERIC type)
+ * @return output Ctc loss (NUMERIC type)
+ */
+ public SDVariable ctcLoss(SDVariable targetLabels, SDVariable logitInput,
+ SDVariable targetLabelLengths, SDVariable logitInputLengths) {
+ SDValidation.validateNumerical("ctcLoss", "targetLabels", targetLabels);
+ SDValidation.validateNumerical("ctcLoss", "logitInput", logitInput);
+ SDValidation.validateNumerical("ctcLoss", "targetLabelLengths", targetLabelLengths);
+ SDValidation.validateNumerical("ctcLoss", "logitInputLengths", logitInputLengths);
+ SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.CtcLoss(sd,targetLabels, logitInput, targetLabelLengths, logitInputLengths).outputVariable();
+ out.markAsLoss();
+ return out;
+ }
+
+ /**
+ * CTC Loss: Connectionist Temporal Classification Loss. See:
+ * https://dl.acm.org/citation.cfm?id=1143891
+ *
+ * @param name name May be null. Name for the output variable
+ * @param targetLabels Label array (NUMERIC type)
+ * @param logitInput Inputs (NUMERIC type)
+ * @param targetLabelLengths Length of the target label (NUMERIC type)
+ * @param logitInputLengths Length of the input (NUMERIC type)
+ * @return output Ctc loss (NUMERIC type)
+ */
+ public SDVariable ctcLoss(String name, SDVariable targetLabels, SDVariable logitInput,
+ SDVariable targetLabelLengths, SDVariable logitInputLengths) {
+ SDValidation.validateNumerical("ctcLoss", "targetLabels", targetLabels);
+ SDValidation.validateNumerical("ctcLoss", "logitInput", logitInput);
+ SDValidation.validateNumerical("ctcLoss", "targetLabelLengths", targetLabelLengths);
+ SDValidation.validateNumerical("ctcLoss", "logitInputLengths", logitInputLengths);
+ SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.CtcLoss(sd,targetLabels, logitInput, targetLabelLengths, logitInputLengths).outputVariable();
+ out.markAsLoss();
+ return sd.updateVariableNameAndReference(out, name);
+ }
+
/**
* Hinge loss: a loss function used for training classifiers.
* Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}
@@ -210,7 +253,7 @@ public class SDLoss extends SDOps {
* @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public SDVariable hingeLoss(SDVariable label, SDVariable predictions, SDVariable weights,
@@ -232,7 +275,7 @@ public class SDLoss extends SDOps {
* @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public SDVariable hingeLoss(String name, SDVariable label, SDVariable predictions,
@@ -297,7 +340,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param delta Loss function delta value
* @return output Huber loss (NUMERIC type)
*/
@@ -324,7 +367,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param delta Loss function delta value
* @return output Huber loss (NUMERIC type)
*/
@@ -423,7 +466,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param epsilon epsilon
* @return output Log loss (NUMERIC type)
*/
@@ -445,7 +488,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param epsilon epsilon
* @return output Log loss (NUMERIC type)
*/
@@ -499,7 +542,7 @@ public class SDLoss extends SDOps {
* @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
* @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param full Boolean flag. true for logPoissonFull, false for logPoisson
* @return output Loss variable (NUMERIC type)
*/
@@ -521,7 +564,7 @@ public class SDLoss extends SDOps {
* @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
* @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param full Boolean flag. true for logPoissonFull, false for logPoisson
* @return output Loss variable (NUMERIC type)
*/
@@ -585,7 +628,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable, scalar output (NUMERIC type)
*/
public SDVariable meanPairwiseSquaredError(SDVariable label, SDVariable predictions,
@@ -608,7 +651,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable, scalar output (NUMERIC type)
*/
public SDVariable meanPairwiseSquaredError(String name, SDVariable label, SDVariable predictions,
@@ -666,13 +709,13 @@ public class SDLoss extends SDOps {
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
- * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))
+ * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public SDVariable meanSquaredError(SDVariable label, SDVariable predictions, SDVariable weights,
@@ -687,14 +730,14 @@ public class SDLoss extends SDOps {
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
- * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))
+ * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param name name May be null. Name for the output variable
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public SDVariable meanSquaredError(String name, SDVariable label, SDVariable predictions,
@@ -709,7 +752,7 @@ public class SDLoss extends SDOps {
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
- * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))
+ * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param label Label array (NUMERIC type)
@@ -728,7 +771,7 @@ public class SDLoss extends SDOps {
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
- * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))
+ * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param name name May be null. Name for the output variable
@@ -764,7 +807,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictionLogits Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
@@ -796,7 +839,7 @@ public class SDLoss extends SDOps {
* @param label Label array (NUMERIC type)
* @param predictionLogits Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
@@ -872,7 +915,7 @@ public class SDLoss extends SDOps {
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
- * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
+ * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
@@ -884,7 +927,7 @@ public class SDLoss extends SDOps {
* @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
* @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
@@ -901,7 +944,7 @@ public class SDLoss extends SDOps {
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
- * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
+ * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
@@ -914,7 +957,7 @@ public class SDLoss extends SDOps {
* @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
* @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
@@ -932,7 +975,7 @@ public class SDLoss extends SDOps {
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
- * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
+ * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
@@ -959,7 +1002,7 @@ public class SDLoss extends SDOps {
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
- * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
+ * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDMath.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDMath.java
index 5c3579396..15a26059f 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDMath.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDMath.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
import java.lang.String;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDNN.java
index 846291e47..016fc721d 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDNN.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDNN.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
import java.lang.String;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java
index 4322910b4..a72420888 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
import java.lang.String;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
@@ -144,22 +144,22 @@ public class SDRNN extends SDOps {
/**
* Long Short-Term Memory layer - Hochreiter 1997.
- * SUPPORTS following data formats:\n
- * for unidirectional: \n" +
- * TNS: shapes [timeLength, numExamples, inOutSize]\n
- * NST: shapes [numExamples, inOutSize, timeLength]\n
+ * SUPPORTS following data formats:
+ * for unidirectional:
+ * TNS: shapes [timeLength, numExamples, inOutSize]
+ * NST: shapes [numExamples, inOutSize, timeLength]
* NTS: shapes [numExamples, timeLength, inOutSize]
- * for bidirectional:\n
- * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n
- * SUPPORTS following direction modes:\n
+ * for bidirectional:
+ * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)
+ * SUPPORTS following direction modes:
* FWD: forward
* BWD: backward
- * BIDIR_SUM: bidirectional sum\n
- * BIDIR_CONCAT: bidirectional concat\n" +
- * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"
+ * BIDIR_SUM: bidirectional sum
+ * BIDIR_CONCAT: bidirectional concat
+ * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)
* You may use different gate configurations:
- * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n
- * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n
+ * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum
+ * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")
* Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
*
* @param x Input, with shape dependent on the data format (in config). (NUMERIC type)
@@ -180,22 +180,22 @@ public class SDRNN extends SDOps {
/**
* Long Short-Term Memory layer - Hochreiter 1997.
- * SUPPORTS following data formats:\n
- * for unidirectional: \n" +
- * TNS: shapes [timeLength, numExamples, inOutSize]\n
- * NST: shapes [numExamples, inOutSize, timeLength]\n
+ * SUPPORTS following data formats:
+ * for unidirectional:
+ * TNS: shapes [timeLength, numExamples, inOutSize]
+ * NST: shapes [numExamples, inOutSize, timeLength]
* NTS: shapes [numExamples, timeLength, inOutSize]
- * for bidirectional:\n
- * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n
- * SUPPORTS following direction modes:\n
+ * for bidirectional:
+ * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)
+ * SUPPORTS following direction modes:
* FWD: forward
* BWD: backward
- * BIDIR_SUM: bidirectional sum\n
- * BIDIR_CONCAT: bidirectional concat\n" +
- * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"
+ * BIDIR_SUM: bidirectional sum
+ * BIDIR_CONCAT: bidirectional concat
+ * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)
* You may use different gate configurations:
- * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n
- * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n
+ * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum
+ * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")
* Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
*
* @param names names May be null. Arrays of names for the output variables.
@@ -218,22 +218,22 @@ public class SDRNN extends SDOps {
/**
* Long Short-Term Memory layer - Hochreiter 1997.
- * SUPPORTS following data formats:\n
- * for unidirectional: \n" +
- * TNS: shapes [timeLength, numExamples, inOutSize]\n
- * NST: shapes [numExamples, inOutSize, timeLength]\n
+ * SUPPORTS following data formats:
+ * for unidirectional:
+ * TNS: shapes [timeLength, numExamples, inOutSize]
+ * NST: shapes [numExamples, inOutSize, timeLength]
* NTS: shapes [numExamples, timeLength, inOutSize]
- * for bidirectional:\n
- * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n
- * SUPPORTS following direction modes:\n
+ * for bidirectional:
+ * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)
+ * SUPPORTS following direction modes:
* FWD: forward
* BWD: backward
- * BIDIR_SUM: bidirectional sum\n
- * BIDIR_CONCAT: bidirectional concat\n" +
- * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"
+ * BIDIR_SUM: bidirectional sum
+ * BIDIR_CONCAT: bidirectional concat
+ * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)
* You may use different gate configurations:
- * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n
- * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n
+ * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum
+ * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")
* Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
*
* @param x Input, with shape dependent on the data format (in config). (NUMERIC type)
@@ -248,22 +248,22 @@ public class SDRNN extends SDOps {
/**
* Long Short-Term Memory layer - Hochreiter 1997.
- * SUPPORTS following data formats:\n
- * for unidirectional: \n" +
- * TNS: shapes [timeLength, numExamples, inOutSize]\n
- * NST: shapes [numExamples, inOutSize, timeLength]\n
+ * SUPPORTS following data formats:
+ * for unidirectional:
+ * TNS: shapes [timeLength, numExamples, inOutSize]
+ * NST: shapes [numExamples, inOutSize, timeLength]
* NTS: shapes [numExamples, timeLength, inOutSize]
- * for bidirectional:\n
- * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n
- * SUPPORTS following direction modes:\n
+ * for bidirectional:
+ * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)
+ * SUPPORTS following direction modes:
* FWD: forward
* BWD: backward
- * BIDIR_SUM: bidirectional sum\n
- * BIDIR_CONCAT: bidirectional concat\n" +
- * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"
+ * BIDIR_SUM: bidirectional sum
+ * BIDIR_CONCAT: bidirectional concat
+ * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)
* You may use different gate configurations:
- * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n
- * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n
+ * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum
+ * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")
* Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
*
* @param names names May be null. Arrays of names for the output variables.
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRandom.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRandom.java
index d57afe876..3bd31f3b6 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRandom.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRandom.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
import java.lang.String;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/CellAct.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/CellAct.java
index 8e6bca5b8..f7f458ffd 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/CellAct.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/CellAct.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
+/**
+ * Activations */
public enum CellAct {
TANH,
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/DataFormat.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/DataFormat.java
index 547b142aa..c42795070 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/DataFormat.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/DataFormat.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
+/**
+ * Data format: "NCHW" or "NHWC" */
public enum DataFormat {
NCHW,
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/GateAct.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/GateAct.java
index 1e38b989e..498f825fd 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/GateAct.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/GateAct.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
+/**
+ * Activations */
public enum GateAct {
TANH,
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/ImageResizeMethod.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/ImageResizeMethod.java
index ad8c82573..42043dad7 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/ImageResizeMethod.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/ImageResizeMethod.java
@@ -1,32 +1,43 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
+/**
+ * ResizeBilinear: Bilinear interpolation. If 'antialias' is true, becomes a hat/tent filter function with radius 1 when downsampling.
+ * ResizeLanczos5: Lanczos kernel with radius 5. Very-high-quality filter but may have stronger ringing.
+ * ResizeBicubic: Cubic interpolant of Keys. Equivalent to Catmull-Rom kernel. Reasonably good quality and faster than Lanczos3Kernel, particularly when upsampling.
+ * ResizeGaussian: Gaussian kernel with radius 3, sigma = 1.5 / 3.0.
+ * ResizeNearest: Nearest neighbor interpolation. 'antialias' has no effect when used with nearest neighbor interpolation.
+ * ResizeArea: Anti-aliased resampling with area interpolation. 'antialias' has no effect when used with area interpolation; it always anti-aliases.
+ * ResizeMitchelcubic: Mitchell-Netravali Cubic non-interpolating filter. For synthetic images (especially those lacking proper prefiltering), less ringing than Keys cubic kernel but less sharp. */
public enum ImageResizeMethod {
- ResizeBilinear, // as java require
- ResizeNearest,
+ ResizeBilinear,
+
ResizeBicubic,
- ResizeArea,
+
+ ResizeNearest,
+
ResizeGaussian,
- ResizeLanczos3,
+
ResizeLanczos5,
- ResizeMitchellcubic;
+
+ ResizeMitchelcubic,
+
+ ResizeArea
}
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDataFormat.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDataFormat.java
index e72d1c50f..7c741e8e4 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDataFormat.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDataFormat.java
@@ -1,25 +1,28 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
+/**
+ * for unidirectional: TNS: shape [timeLength, numExamples, inOutSize] - sometimes referred to as "time major"
+ * NST: shape [numExamples, inOutSize, timeLength]
+ * NTS: shape [numExamples, timeLength, inOutSize] - TF "time_major=false" layout for bidirectional:
+ * T2NS: 3 = [timeLength, 2, numExamples, inOutSize] (for ONNX) */
public enum LSTMDataFormat {
TNS,
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDirectionMode.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDirectionMode.java
index 488bd567a..4732fc611 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDirectionMode.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDirectionMode.java
@@ -1,25 +1,30 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
+/**
+ * direction
+ * FWD: 0 = fwd
+ * BWD: 1 = bwd
+ * BIDIR_SUM: 2 = bidirectional sum
+ * BIDIR_CONCAT: 3 = bidirectional concat
+ * BIDIR_EXTRA_DIM: 4 = bidirectional extra output dim (in conjunction with format dataFormat = 3) */
public enum LSTMDirectionMode {
FWD,
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/OutAct.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/OutAct.java
index 162227778..df034a294 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/OutAct.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/OutAct.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
+/**
+ * Activations */
public enum OutAct {
TANH,
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PadMode.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PadMode.java
index dd17c9580..4802ebdaf 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PadMode.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PadMode.java
@@ -1,22 +1,20 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PartitionMode.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PartitionMode.java
index 42f0df0b8..565ffd792 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PartitionMode.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PartitionMode.java
@@ -1,22 +1,20 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/RnnDataFormat.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/RnnDataFormat.java
index 5d5b5af4c..8b6e2fbd6 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/RnnDataFormat.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/RnnDataFormat.java
@@ -1,25 +1,28 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.enums;
+/**
+ * The data format of the input. Input shape depends on data format (in config):
+ * TNS -> [timeSteps, batchSize, inSize]
+ * NST -> [batchSize, inSize, timeSteps]
+ * NTS -> [batchSize, timeSteps, inSize] */
public enum RnnDataFormat {
TNS,
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/BaseLoss.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/BaseLoss.java
index aee494750..f76fea691 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/BaseLoss.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/BaseLoss.java
@@ -50,7 +50,7 @@ public abstract class BaseLoss extends DynamicCustomOp {
addArgs();
}
- protected static INDArray getWeights(INDArray weights, INDArray predictions){
+ protected static INDArray getWeights(INDArray weights, INDArray predictions) {
return (weights != null) ? weights : Nd4j.scalar(predictions.dataType(), 1.0);
}
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/CtcLoss.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/CtcLoss.java
index 0b21c8aa0..5edfdc363 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/CtcLoss.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/CtcLoss.java
@@ -20,29 +20,20 @@
package org.nd4j.linalg.api.ops.impl.loss;
-import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.loss.bp.AbsoluteDifferenceLossBp;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+import org.nd4j.linalg.api.ops.impl.loss.bp.CtcLossBp;
import java.util.List;
-public class CtcLoss extends BaseLoss {
+public class CtcLoss extends DynamicCustomOp {
- public CtcLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels){
- super(sameDiff, lossReduce, predictions, weights, labels);
+ public CtcLoss(SameDiff sameDiff, SDVariable targetLabels,SDVariable logitInputs,SDVariable targetLabelLengths,SDVariable logitInputLengths){
+ super(sameDiff,new SDVariable[]{targetLabels,logitInputs,targetLabelLengths,logitInputLengths});
}
- public CtcLoss(SameDiff sameDiff, SDVariable label, SDVariable predictions, SDVariable weights,
- LossReduce lossReduce) {
- this(sameDiff, lossReduce, predictions, weights, label);
- }
-
- public CtcLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce){
- super(lossReduce, predictions, weights, labels);
- }
public CtcLoss(){ }
@@ -52,9 +43,9 @@ public class CtcLoss extends BaseLoss {
}
@Override
- public List doDiff(List grad){
+ public List doDiff(List grad) {
//No external gradient
//Args are: predictions, weights, label
- return new AbsoluteDifferenceLossBp(sameDiff, lossReduce, arg(0), arg(1), arg(2)).outputs();
+ return new CtcLossBp(sameDiff, arg(0), arg(1), arg(2),arg(3)).outputs();
}
}
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/bp/CtcLossBp.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/bp/CtcLossBp.java
index bc5b8461d..3084f3ce8 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/bp/CtcLossBp.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/bp/CtcLossBp.java
@@ -20,17 +20,17 @@
package org.nd4j.linalg.api.ops.impl.loss.bp;
-import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.List;
-public class CtcLossBp extends BaseLossBp {
+public class CtcLossBp extends DynamicCustomOp {
- public CtcLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels){
- super(sameDiff, lossReduce, predictions, weights, labels);
+ public CtcLossBp(SameDiff sameDiff, SDVariable targetLabels,SDVariable logitInputs,SDVariable targetLabelLengths,SDVariable logitInputLengths){
+ super(sameDiff,new SDVariable[]{targetLabels,logitInputs,targetLabelLengths,logitInputLengths});
}
public CtcLossBp(){ }
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBase.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBase.java
index be72896aa..1b2718e2e 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBase.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBase.java
@@ -1,22 +1,20 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBitwise.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBitwise.java
index 7bab6e455..9b85a44d7 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBitwise.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBitwise.java
@@ -1,22 +1,20 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
@@ -134,7 +132,7 @@ public class NDBitwise {
/**
* Bitwise left cyclical shift operation. Supports broadcasting.
- * Unlike {@link #leftShift(INDArray, INDArray)} the bits will "wrap around":
+ * Unlike #leftShift(INDArray, INDArray) the bits will "wrap around":
* {@code leftShiftCyclic(01110000, 2) -> 11000001}
*
* @param x Input to be bit shifted (INT type)
@@ -180,7 +178,7 @@ public class NDBitwise {
/**
* Bitwise right cyclical shift operation. Supports broadcasting.
- * Unlike {@link #rightShift(INDArray, INDArray)} the bits will "wrap around":
+ * Unlike rightShift(INDArray, INDArray) the bits will "wrap around":
* {@code rightShiftCyclic(00001110, 2) -> 10000011}
*
* @param x Input to be bit shifted (INT type)
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDCNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDCNN.java
index 38bccd9a6..45b30816d 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDCNN.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDCNN.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
import org.nd4j.common.base.Preconditions;
import org.nd4j.enums.DataFormat;
import org.nd4j.linalg.api.ndarray.INDArray;
@@ -41,8 +41,7 @@ public class NDCNN {
/**
* 2D Convolution layer operation - average pooling 2d
*
- * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param Pooling2DConfig Configuration Object
* @return output Result after applying average pooling on the input (NUMERIC type)
*/
@@ -54,9 +53,7 @@ public class NDCNN {
/**
* 3D convolution layer operation - average pooling 3d
*
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param Pooling3DConfig Configuration Object
* @return output after applying average pooling on the input (NUMERIC type)
*/
@@ -161,9 +158,7 @@ public class NDCNN {
/**
* Convolution 3D operation with optional bias
*
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param weights Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
* @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
* @param Conv3DConfig Configuration Object
@@ -180,9 +175,7 @@ public class NDCNN {
/**
* Convolution 3D operation with optional bias
*
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param weights Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
* @param Conv3DConfig Configuration Object
* @return output Conv3d output variable (NUMERIC type)
@@ -196,8 +189,7 @@ public class NDCNN {
/**
* 2D deconvolution operation with optional bias
*
- * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
* @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
* @param DeConv2DConfig Configuration Object
@@ -214,8 +206,7 @@ public class NDCNN {
/**
* 2D deconvolution operation with optional bias
*
- * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
* @param DeConv2DConfig Configuration Object
* @return output result of deconv2d op (NUMERIC type)
@@ -263,8 +254,7 @@ public class NDCNN {
* Example: if input has shape [mb, 8, 2, 2] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
* = [mb, 2, 4, 4]
*
- * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param blockSize Block size, in the height/width dimension
* @param dataFormat Data format: "NCHW" or "NHWC"
* @return output Output variable (NUMERIC type)
@@ -373,8 +363,7 @@ public class NDCNN {
/**
* 2D Convolution layer operation - Max pooling on the input and outputs both max values and indices
*
- * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param Pooling2DConfig Configuration Object
*/
public INDArray[] maxPoolWithArgmax(INDArray input, Pooling2DConfig Pooling2DConfig) {
@@ -385,8 +374,7 @@ public class NDCNN {
/**
* 2D Convolution layer operation - max pooling 2d
*
- * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param Pooling2DConfig Configuration Object
* @return output Result after applying max pooling on the input (NUMERIC type)
*/
@@ -398,9 +386,7 @@ public class NDCNN {
/**
* 3D convolution layer operation - max pooling 3d operation.
*
- * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
- * (shape [minibatch, channels, depth, height, width]) or NDHWC format
- * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
+ * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
* @param Pooling3DConfig Configuration Object
* @return output Result after applying max pooling on the input (NUMERIC type)
*/
@@ -412,8 +398,7 @@ public class NDCNN {
/**
* Separable 2D convolution operation with optional bias
*
- * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
* @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
* @param bias Optional bias, rank 1 with shape [outputChannels]. May be null. (NUMERIC type)
@@ -432,8 +417,7 @@ public class NDCNN {
/**
* Separable 2D convolution operation with optional bias
*
- * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
* @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
* @param Conv2DConfig Configuration Object
@@ -471,8 +455,7 @@ public class NDCNN {
* Example: if input has shape [mb, 2, 4, 4] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
* = [mb, 2, 4, 4]
*
- * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
- * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
+ * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
* @param blockSize Block size, in the height/width dimension
* @param dataFormat Data format: "NCHW" or "NHWC"
* @return output Output variable (NUMERIC type)
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDImage.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDImage.java
index 5e6d2c947..e2443b300 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDImage.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDImage.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
import org.nd4j.common.base.Preconditions;
import org.nd4j.enums.ImageResizeMethod;
import org.nd4j.linalg.api.ndarray.INDArray;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLinalg.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLinalg.java
index 3bf70c4b0..0112515dd 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLinalg.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLinalg.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.NDValidation;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLoss.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLoss.java
index ec9f2b8b2..fd362144d 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLoss.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLoss.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.NDValidation;
@@ -35,7 +35,7 @@ public class NDLoss {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output loss variable (NUMERIC type)
*/
public INDArray absoluteDifference(INDArray label, INDArray predictions, INDArray weights,
@@ -71,7 +71,7 @@ public class NDLoss {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param dimension Dimension to perform the cosine distance over
* @return output Cosine distance loss (NUMERIC type)
*/
@@ -104,6 +104,25 @@ public class NDLoss {
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.CosineDistanceLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, dimension))[0];
}
+ /**
+ * CTC Loss: Connectionist Temporal Classification Loss. See:
+ * https://dl.acm.org/citation.cfm?id=1143891
+ *
+ * @param targetLabels Label array (NUMERIC type)
+ * @param logitInput Inputs (NUMERIC type)
+ * @param targetLabelLengths Length of the target label (NUMERIC type)
+ * @param logitInputLengths Length of the input (NUMERIC type)
+ * @return output Ctc loss (NUMERIC type)
+ */
+ public INDArray ctcLoss(INDArray targetLabels, INDArray logitInput, INDArray targetLabelLengths,
+ INDArray logitInputLengths) {
+ NDValidation.validateNumerical("ctcLoss", "targetLabels", targetLabels);
+ NDValidation.validateNumerical("ctcLoss", "logitInput", logitInput);
+ NDValidation.validateNumerical("ctcLoss", "targetLabelLengths", targetLabelLengths);
+ NDValidation.validateNumerical("ctcLoss", "logitInputLengths", logitInputLengths);
+ return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.CtcLoss(targetLabels, logitInput, targetLabelLengths, logitInputLengths))[0];
+ }
+
/**
* Hinge loss: a loss function used for training classifiers.
* Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}
@@ -112,7 +131,7 @@ public class NDLoss {
* @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public INDArray hingeLoss(INDArray label, INDArray predictions, INDArray weights,
@@ -152,7 +171,7 @@ public class NDLoss {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param delta Loss function delta value
* @return output Huber loss (NUMERIC type)
*/
@@ -204,7 +223,7 @@ public class NDLoss {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param epsilon epsilon
* @return output Log loss (NUMERIC type)
*/
@@ -237,7 +256,7 @@ public class NDLoss {
* @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
* @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param full Boolean flag. true for logPoissonFull, false for logPoisson
* @return output Loss variable (NUMERIC type)
*/
@@ -275,7 +294,7 @@ public class NDLoss {
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable, scalar output (NUMERIC type)
*/
public INDArray meanPairwiseSquaredError(INDArray label, INDArray predictions, INDArray weights,
@@ -306,13 +325,13 @@ public class NDLoss {
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
- * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))
+ * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param label Label array (NUMERIC type)
* @param predictions Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @return output Loss variable (NUMERIC type)
*/
public INDArray meanSquaredError(INDArray label, INDArray predictions, INDArray weights,
@@ -325,7 +344,7 @@ public class NDLoss {
/**
* Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
- * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))
+ * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))
* this is the mean squared error loss function.
*
* @param label Label array (NUMERIC type)
@@ -357,7 +376,7 @@ public class NDLoss {
* @param label Label array (NUMERIC type)
* @param predictionLogits Predictions array (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
@@ -398,7 +417,7 @@ public class NDLoss {
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
- * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
+ * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
@@ -410,7 +429,7 @@ public class NDLoss {
* @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
* @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
* @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
- * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+ * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
* @param labelSmoothing Label smoothing value. Default value: 0
* @return output Loss variable (NUMERIC type)
*/
@@ -425,7 +444,7 @@ public class NDLoss {
/**
* Applies the softmax activation function to the input, then implement multi-class cross entropy:
* {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
- * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
+ * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
* otherwise, the output is a scalar.
*
* When label smoothing is > 0, the following label smoothing is used:
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDMath.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDMath.java
index 2387e9177..cf03080f0 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDMath.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDMath.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
import org.nd4j.common.base.Preconditions;
import org.nd4j.enums.PartitionMode;
import org.nd4j.linalg.api.buffer.DataType;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDNN.java
index 1ae68e99d..f12b2dac6 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDNN.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDNN.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
import org.nd4j.common.base.Preconditions;
import org.nd4j.enums.PadMode;
import org.nd4j.linalg.api.ndarray.INDArray;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java
index a60e0ef9e..f9a594421 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMLayerConfig;
@@ -85,22 +85,22 @@ public class NDRNN {
/**
* Long Short-Term Memory layer - Hochreiter 1997.
- * SUPPORTS following data formats:\n
- * for unidirectional: \n" +
- * TNS: shapes [timeLength, numExamples, inOutSize]\n
- * NST: shapes [numExamples, inOutSize, timeLength]\n
+ * SUPPORTS following data formats:
+ * for unidirectional:
+ * TNS: shapes [timeLength, numExamples, inOutSize]
+ * NST: shapes [numExamples, inOutSize, timeLength]
* NTS: shapes [numExamples, timeLength, inOutSize]
- * for bidirectional:\n
- * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n
- * SUPPORTS following direction modes:\n
+ * for bidirectional:
+ * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)
+ * SUPPORTS following direction modes:
* FWD: forward
* BWD: backward
- * BIDIR_SUM: bidirectional sum\n
- * BIDIR_CONCAT: bidirectional concat\n" +
- * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"
+ * BIDIR_SUM: bidirectional sum
+ * BIDIR_CONCAT: bidirectional concat
+ * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)
* You may use different gate configurations:
- * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n
- * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n
+ * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum
+ * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")
* Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
*
* @param x Input, with shape dependent on the data format (in config). (NUMERIC type)
@@ -121,22 +121,22 @@ public class NDRNN {
/**
* Long Short-Term Memory layer - Hochreiter 1997.
- * SUPPORTS following data formats:\n
- * for unidirectional: \n" +
- * TNS: shapes [timeLength, numExamples, inOutSize]\n
- * NST: shapes [numExamples, inOutSize, timeLength]\n
+ * SUPPORTS following data formats:
+ * for unidirectional:
+ * TNS: shapes [timeLength, numExamples, inOutSize]
+ * NST: shapes [numExamples, inOutSize, timeLength]
* NTS: shapes [numExamples, timeLength, inOutSize]
- * for bidirectional:\n
- * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n
- * SUPPORTS following direction modes:\n
+ * for bidirectional:
+ * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)
+ * SUPPORTS following direction modes:
* FWD: forward
* BWD: backward
- * BIDIR_SUM: bidirectional sum\n
- * BIDIR_CONCAT: bidirectional concat\n" +
- * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"
+ * BIDIR_SUM: bidirectional sum
+ * BIDIR_CONCAT: bidirectional concat
+ * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)
* You may use different gate configurations:
- * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n
- * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n
+ * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum
+ * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")
* Also this layer supports MKLDNN (DNNL) and cuDNN acceleration
*
* @param x Input, with shape dependent on the data format (in config). (NUMERIC type)
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRandom.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRandom.java
index 76f57c52a..f40a67891 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRandom.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRandom.java
@@ -1,25 +1,25 @@
-/*
- * ******************************************************************************
- * *
- * *
- * * 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.
- * *
- * * See the NOTICE file distributed with this work for additional
- * * information regarding copyright ownership.
- * * 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
- * *****************************************************************************
- */
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;