DeepLearning

abstract def forward(differentiable: Differentiable): Do[Tape[Data, Delta]]

Returns an asynchronous Do of forward pass, which creates a wengert list.

final def !=(arg0: Any): Boolean

final def ##(): Int

def +(other: String): String

def ->[B](y: B): (DeepLearning[Differentiable], B)

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def ensuring(cond: (DeepLearning[Differentiable]) ⇒ Boolean, msg: ⇒ Any): DeepLearning[Differentiable]

def ensuring(cond: (DeepLearning[Differentiable]) ⇒ Boolean): DeepLearning[Differentiable]

def ensuring(cond: Boolean, msg: ⇒ Any): DeepLearning[Differentiable]

def ensuring(cond: Boolean): DeepLearning[Differentiable]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def formatted(fmtstr: String): String

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def predict(differentiable: Differentiable): Future[Data]

Returns a Future of the data of the differentiable expression.

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

def toString(): String

final def train(differentiable: Differentiable)(implicit monoid: MultiplicativeMonoid[Delta]): Future[Data]

Returns a Future that updates Weight internally used by differentiable.

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def →[B](y: B): (DeepLearning[Differentiable], B)