class MXNet::NDArray::Sparse

Defined in:

Class Method Summary

Class Method Detail

def self._abs(data : MXNet::NDArray?, **kwargs) #

def self._adagrad_update(weight : MXNet::NDArray?, grad : MXNet::NDArray?, history : MXNet::NDArray?, lr, **kwargs) #

def self._adam_update(weight : MXNet::NDArray?, grad : MXNet::NDArray?, mean : MXNet::NDArray?, var : MXNet::NDArray?, lr, **kwargs) #

def self._add_n(args : Array(MXNet::NDArray), **kwargs) #

def self._arccos(data : MXNet::NDArray?, **kwargs) #

def self._arccosh(data : MXNet::NDArray?, **kwargs) #

def self._arcsin(data : MXNet::NDArray?, **kwargs) #

def self._arcsinh(data : MXNet::NDArray?, **kwargs) #

def self._arctan(data : MXNet::NDArray?, **kwargs) #

def self._arctanh(data : MXNet::NDArray?, **kwargs) #

def self._broadcast_add(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._broadcast_div(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._broadcast_minus(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._broadcast_mul(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._broadcast_plus(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._broadcast_sub(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._cast_storage(data : MXNet::NDArray?, stype, **kwargs) #

def self._cbrt(data : MXNet::NDArray?, **kwargs) #

def self._ceil(data : MXNet::NDArray?, **kwargs) #

def self._clip(data : MXNet::NDArray?, a_min, a_max, **kwargs) #

def self._concat(data : Array(MXNet::NDArray), num_args, **kwargs) #

def self._cos(data : MXNet::NDArray?, **kwargs) #

def self._cosh(data : MXNet::NDArray?, **kwargs) #

def self._degrees(data : MXNet::NDArray?, **kwargs) #

def self._dot(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._ElementWiseSum(args : Array(MXNet::NDArray), **kwargs) #

def self._elemwise_add(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._elemwise_div(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._elemwise_mul(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._elemwise_sub(lhs : MXNet::NDArray?, rhs : MXNet::NDArray?, **kwargs) #

def self._Embedding(data : MXNet::NDArray?, weight : MXNet::NDArray?, input_dim, output_dim, **kwargs) #

def self._exp(data : MXNet::NDArray?, **kwargs) #

def self._expm1(data : MXNet::NDArray?, **kwargs) #

def self._fix(data : MXNet::NDArray?, **kwargs) #

def self._floor(data : MXNet::NDArray?, **kwargs) #

def self._ftrl_update(weight : MXNet::NDArray?, grad : MXNet::NDArray?, z : MXNet::NDArray?, n : MXNet::NDArray?, lr, **kwargs) #

def self._FullyConnected(data : MXNet::NDArray?, weight : MXNet::NDArray?, bias : MXNet::NDArray?, num_hidden, **kwargs) #

def self._gamma(data : MXNet::NDArray?, **kwargs) #

def self._gammaln(data : MXNet::NDArray?, **kwargs) #

def self._LinearRegressionOutput(data : MXNet::NDArray?, label : MXNet::NDArray?, **kwargs) #

def self._log(data : MXNet::NDArray?, **kwargs) #

def self._log10(data : MXNet::NDArray?, **kwargs) #

def self._log1p(data : MXNet::NDArray?, **kwargs) #

def self._log2(data : MXNet::NDArray?, **kwargs) #

def self._LogisticRegressionOutput(data : MXNet::NDArray?, label : MXNet::NDArray?, **kwargs) #

def self._MAERegressionOutput(data : MXNet::NDArray?, label : MXNet::NDArray?, **kwargs) #

def self._make_loss(data : MXNet::NDArray?, **kwargs) #

def self._mean(data : MXNet::NDArray?, **kwargs) #

def self._negative(data : MXNet::NDArray?, **kwargs) #

def self._norm(data : MXNet::NDArray?, **kwargs) #

def self._radians(data : MXNet::NDArray?, **kwargs) #

def self._relu(data : MXNet::NDArray?, **kwargs) #

def self._retain(data : MXNet::NDArray?, indices : MXNet::NDArray?, **kwargs) #

def self._rint(data : MXNet::NDArray?, **kwargs) #

def self._round(data : MXNet::NDArray?, **kwargs) #

def self._rsqrt(data : MXNet::NDArray?, **kwargs) #

def self._sgd_mom_update(weight : MXNet::NDArray?, grad : MXNet::NDArray?, mom : MXNet::NDArray?, lr, **kwargs) #

def self._sgd_update(weight : MXNet::NDArray?, grad : MXNet::NDArray?, lr, **kwargs) #

def self._sigmoid(data : MXNet::NDArray?, **kwargs) #

def self._sign(data : MXNet::NDArray?, **kwargs) #

def self._sin(data : MXNet::NDArray?, **kwargs) #

def self._sinh(data : MXNet::NDArray?, **kwargs) #

def self._slice(data : MXNet::NDArray?, begin _begin, end _end, **kwargs) #

def self._sqrt(data : MXNet::NDArray?, **kwargs) #

def self._square(data : MXNet::NDArray?, **kwargs) #

def self._stop_gradient(data : MXNet::NDArray?, **kwargs) #

def self._sum(data : MXNet::NDArray?, **kwargs) #

def self._tan(data : MXNet::NDArray?, **kwargs) #

def self._tanh(data : MXNet::NDArray?, **kwargs) #

def self._trunc(data : MXNet::NDArray?, **kwargs) #

def self._where(condition : MXNet::NDArray?, x : MXNet::NDArray?, y : MXNet::NDArray?, **kwargs) #

def self._zeros_like(data : MXNet::NDArray?, **kwargs) #