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

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

Definition Classes
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val beginEpoch: Int

The beginning training epoch.
def checkArguments(): Unit
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( ... )
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

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def equals(arg0: Any): Boolean

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def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( classOf[java.lang.Throwable] )
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[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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val logger: Logger
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

Definition Classes
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final def notifyAll(): Unit

Definition Classes
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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
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
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def toString(): String

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def unsetMonitor(): Unit
final def wait(): Unit

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

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

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@throws( ... )

Related Docs: object FeedForward | package mxnet

class FeedForward extends AnyRef

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

def checkArguments(): Unit

def clone(): AnyRef

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

final def isInstanceOf[T0]: Boolean

val logger: Logger

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]

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 unsetMonitor(): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from AnyRef

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