FeedForward

Instance Constructors

new FeedForward(symbol: SymbolGenerator, ctx: Array[Context], numEpoch: Int, epochSize: Int, optimizer: Optimizer, initializer: Initializer, batchSize: Int, argParams: Map[String, NDArray], auxParams: Map[String, NDArray], allowExtraParams: Boolean, beginEpoch: Int)
new FeedForward(symbol: Symbol, ctx: Array[Context] = Array(Context.cpu()), numEpoch: Int = 1, epochSize: Int = 1, optimizer: Optimizer = new SGD(), initializer: Initializer = new Uniform(0.01f), batchSize: Int = 128, argParams: Map[String, NDArray] = null, auxParams: Map[String, NDArray] = null, allowExtraParams: Boolean = false, beginEpoch: Int = 0)

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val beginEpoch: Int

The beginning training epoch.
val bytesAllocated: Long

Off-Heap Bytes Allocated for this object
Off-Heap Bytes Allocated for this object

Definition Classes
FeedForward → NativeResource
def checkArguments(): Unit
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def close(): Unit

Definition Classes
NativeResource → AutoCloseable
val creationTrace: Option[Array[StackTraceElement]]

Attributes
protected
Definition Classes
WarnIfNotDisposed
def dispose(): Unit

Definition Classes
FeedForward → NativeResource
val epochSize: Int

Number of batches in a epoch.
Number of batches in a epoch. In default, it is set to ceil(num_train_examples / batch_size)
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected
Definition Classes
WarnIfNotDisposed → AnyRef
def fit(trainData: DataIter, evalData: DataIter, kvStore: KVStore): Unit
def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStore: KVStore): Unit
def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStore: KVStore, epochEndCallback: EpochEndCallback, batchEndCallback: BatchEndCallback): Unit
def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kv: KVStore, epochEndCallback: EpochEndCallback, batchEndCallback: BatchEndCallback, logger: Logger, workLoadList: Seq[Float]): Unit
def fit(trainData: DataIter, evalData: DataIter): Unit
def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric): Unit
def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStoreType: String): Unit
def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStoreType: String, epochEndCallback: EpochEndCallback, batchEndCallback: BatchEndCallback): Unit
def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStoreType: String, epochEndCallback: EpochEndCallback, batchEndCallback: BatchEndCallback, logger: Logger, workLoadList: Seq[Float]): Unit

Fit the model.
Fit the model.
trainData
Training data
evalData
Evaluation data
evalMetric
The evaluation metric, cannot be null
kvStoreType
A string kvstore type: 'local' : multi-devices on a single machine, will automatically choose one from 'local_update_cpu', 'local_allreduce_cpu', and 'local_allreduce_device' 'dist_sync' : multi-machines with BSP 'dist_async' : multi-machines with partical asynchronous In default uses 'local', often no need to change for single machine.
epochEndCallback
A callback that is invoked at end of each epoch. This can be used to checkpoint model each epoch.
batchEndCallback
A callback that is invoked at end of each batch For print purpose
logger
When not specified, default logger will be used.
workLoadList
The list of work load for different devices, in the same order as ctx
def getArgParams: Map[String, NDArray]
def getAuxParams: Map[String, NDArray]
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
def isDisposed: Boolean

Definition Classes
NativeResource → WarnIfNotDisposed
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def logDisposeWarning(): Unit

Attributes
protected
Definition Classes
WarnIfNotDisposed
val logger: Logger
def nativeAddress: CPtrAddress

native Address associated with this object
native Address associated with this object

Definition Classes
FeedForward → NativeResource
def nativeDeAllocator: (CPtrAddress) ⇒ Int

Function Pointer to the NativeDeAllocator of nativeAddress
Function Pointer to the NativeDeAllocator of nativeAddress

Definition Classes
FeedForward → NativeResource
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
var predExec: Executor
def predict(data: DataIter, numBatch: Int = 1): Array[NDArray]

Run the prediction, always only use one device.
Run the prediction, always only use one device.
data
eval data
numBatch
the number of batch to run. Go though all batches if set -1
returns
The predicted value of the output. Note the network may have multiple outputs, thus it return an array of NDArray
val ref: NativeResourceRef

Call NativeResource.register to get the reference
Call NativeResource.register to get the reference

Definition Classes
FeedForward → NativeResource
def register(): NativeResourceRef

Register this object for PhantomReference tracking and in ResourceScope if used inside ResourceScope.
Register this object for PhantomReference tracking and in ResourceScope if used inside ResourceScope.
returns
NativeResourceRef that tracks reachability of this object using PhantomReference

Definition Classes
NativeResource
def save(prefix: String, epoch: Int = this.numEpoch): Unit

Checkpoint the model checkpoint into file.
Checkpoint the model checkpoint into file. You can also use pickle to do the job if you only work on python. The advantage of load/save is the file is language agnostic. This means the file saved using save can be loaded by other language binding of mxnet. You also get the benefit being able to directly load/save from cloud storage(S3, HDFS)
prefix
Prefix of model name.

Note
- prefix-symbol.json will be saved for symbol. - prefix-epoch.params will be saved for parameters.
See also
FeedForward.load : the method to load the model back.
def serialize(): Array[Byte]

Serialize the model to Java byte array
Serialize the model to Java byte array
returns
serialized model bytes
def setMonitor(m: Monitor): Unit
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def tracingEnabled: Boolean

Attributes
protected
Definition Classes
WarnIfNotDisposed
def unsetMonitor(): Unit
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object FeedForward | package mxnet

class FeedForward extends NativeResource

Instance Constructors

new FeedForward(symbol: SymbolGenerator, ctx: Array[Context], numEpoch: Int, epochSize: Int, optimizer: Optimizer, initializer: Initializer, batchSize: Int, argParams: Map[String, NDArray], auxParams: Map[String, NDArray], allowExtraParams: Boolean, beginEpoch: Int)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val beginEpoch: Int

val bytesAllocated: Long

def checkArguments(): Unit

def clone(): AnyRef

def close(): Unit

val creationTrace: Option[Array[StackTraceElement]]

def dispose(): Unit

val epochSize: Int

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def fit(trainData: DataIter, evalData: DataIter, kvStore: KVStore): Unit

def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStore: KVStore): Unit

def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStore: KVStore, epochEndCallback: EpochEndCallback, batchEndCallback: BatchEndCallback): Unit

def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kv: KVStore, epochEndCallback: EpochEndCallback, batchEndCallback: BatchEndCallback, logger: Logger, workLoadList: Seq[Float]): Unit

def fit(trainData: DataIter, evalData: DataIter): Unit

def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric): Unit

def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStoreType: String): Unit

def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStoreType: String, epochEndCallback: EpochEndCallback, batchEndCallback: BatchEndCallback): Unit

def fit(trainData: DataIter, evalData: DataIter, evalMetric: EvalMetric, kvStoreType: String, epochEndCallback: EpochEndCallback, batchEndCallback: BatchEndCallback, logger: Logger, workLoadList: Seq[Float]): Unit

def getArgParams: Map[String, NDArray]

def getAuxParams: Map[String, NDArray]

final def getClass(): Class[_]

def hashCode(): Int

def isDisposed: Boolean

final def isInstanceOf[T0]: Boolean

def logDisposeWarning(): Unit

val logger: Logger

def nativeAddress: CPtrAddress

def nativeDeAllocator: (CPtrAddress) ⇒ Int

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var predExec: Executor

def predict(data: DataIter, numBatch: Int = 1): Array[NDArray]

val ref: NativeResourceRef

def register(): NativeResourceRef

def save(prefix: String, epoch: Int = this.numEpoch): Unit

def serialize(): Array[Byte]

def setMonitor(m: Monitor): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def tracingEnabled: Boolean

def unsetMonitor(): Unit

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from NativeResource

Inherited from WarnIfNotDisposed

Inherited from AutoCloseable

Inherited from AnyRef

Inherited from Any

Ungrouped