diff --git a/scala-package/core/src/main/scala/ml/dmlc/mxnet/Symbol.scala b/scala-package/core/src/main/scala/ml/dmlc/mxnet/Symbol.scala index d11f8ec11129..7199c0527e82 100644 --- a/scala-package/core/src/main/scala/ml/dmlc/mxnet/Symbol.scala +++ b/scala-package/core/src/main/scala/ml/dmlc/mxnet/Symbol.scala @@ -852,165 +852,164 @@ object Symbol { sym } - /** TODO - * BlockGrad -Get output from a symbol and pass 0 gradient back - -Parameters ----------- -data : Symbol -Input data. - * @return + /** + * Get output from a symbol and pass 0 gradient back + * + * Parameters + * ---------- + * data : Symbol. Input data. */ + def BlockGrad(name: String = null, attr: Map[String, String] = null): SymbolCreateNamedFunc = { + createFromNamedSymbolsNoCheck("BlockGrad", name, attr) + } - /** TODO - * Crop - * Crop the 2th and 3th dim of input data, with the corresponding size of w_h or with width - * and height of the second input symbol - -Parameters ----------- -num_args : int, required -Number of inputs for crop, if equals one, then we will use the h_wfor crop heihgt and width, -else if equals two, then we will use the heightand width of the second input symbol, -we name crop_like here -offset : Shape(tuple), optional, default=(0, 0) -corp offset coordinate: (y, x) -h_w : Shape(tuple), optional, default=(0, 0) -corp height and weight: (h, w) -center_crop : boolean, optional, default=False -If set to true, then it will use be the center_crop,or it will crop using the shape of crop_like - * @return - */ - - /** TODO - Dropout -Apply dropout to input - -Parameters ----------- -data : Symbol -Input data to dropout. -p : float, optional, default=0.5 -Fraction of the input that gets dropped out at training time + /** + * Crop the 2th and 3th dim of input data, with the corresponding size of w_h or with width + * and height of the second input symbol + * + * Parameters + * ---------- + * num_args : int, required. + * Number of inputs for crop, + * if equals one, then we will use the h_w for crop height and width, + * else if equals two, + * then we will use the height and width of the second input symbol, + * we name crop_like here + * offset : Shape(tuple), optional, default=(0, 0), corp offset coordinate: (y, x) + * h_w : Shape(tuple), optional, default=(0, 0), corp height and weight: (h, w) + * center_crop : boolean, optional, default=False. + * If set to true, then it will use be the center_crop, + * or it will crop using the shape of crop_like */ + def Crop(name: String = null, attr: Map[String, String] = null)( + inputs: Array[Symbol], params: Map[String, Any] = null): Symbol = { + createFromListedSymbolsNoCheck("Crop", name, attr)(inputs, params) + } /** - * IdentityAttachKLSparseReg -Apply a sparse regularization to the output a sigmoid activation function. - -Parameters ----------- -data : Symbol -Input data. -sparseness_target : float, optional, default=0.1 -The sparseness target -penalty : float, optional, default=0.001 -The tradeoff parameter for the sparseness penalty -momentum : float, optional, default=0.9 -The momentum for running average - * @return + * Apply dropout to input + * + * Parameters + * ---------- + * data : Symbol. Input data to dropout. + * p : float, optional, default=0.5. Fraction of the input that gets dropped out at training time */ + def Dropout(name: String = null, attr: Map[String, String] = null): SymbolCreateNamedFunc = { + createFromNamedSymbolsNoCheck("Dropout", name, attr) + } /** - * LeakyReLU -Apply activation function to input. - -Parameters ----------- -data : Symbol -Input data to activation function. -act_type : {'elu', 'leaky', 'prelu', 'rrelu'},optional, default='leaky' -Activation function to be applied. -slope : float, optional, default=0.25 -Init slope for the activation. (For leaky and elu only) -lower_bound : float, optional, default=0.125 -Lower bound of random slope. (For rrelu only) -upper_bound : float, optional, default=0.334 -Upper bound of random slope. (For rrelu only) - * @return + * Apply a sparse regularization to the output a sigmoid activation function. + * + * Parameters + * ---------- + * data : Symbol. Input data. + * sparseness_target : float, optional, default=0.1. The sparseness target + * penalty : float, optional, default=0.001. The tradeoff parameter for the sparseness penalty + * momentum : float, optional, default=0.9. The momentum for running average */ + def IdentityAttachKLSparseReg(name: String = null, + attr: Map[String, String] = null): SymbolCreateNamedFunc = { + createFromNamedSymbolsNoCheck("IdentityAttachKLSparseReg", name, attr) + } /** - * LRN -Apply convolution to input then add a bias. - -Parameters ----------- -data : Symbol -Input data to the ConvolutionOp. -alpha : float, optional, default=0.0001 -value of the alpha variance scaling parameter in the normalization formula -beta : float, optional, default=0.75 -value of the beta power parameter in the normalization formula -knorm : float, optional, default=2 -value of the k parameter in normalization formula -nsize : int (non-negative), required -normalization window width in elements. - * @return + * Apply activation function to input. + * + * Parameters + * ---------- + * data : Symbol. Input data to activation function. + * act_type : {'elu', 'leaky', 'prelu', 'rrelu'},optional, default='leaky' + * Activation function to be applied. + * slope : float, optional, default=0.25. Init slope for the activation. (For leaky and elu only) + * lower_bound : float, optional, default=0.125. Lower bound of random slope. (For rrelu only) + * upper_bound : float, optional, default=0.334. Upper bound of random slope. (For rrelu only) */ + def LeakyReLU(name: String = null, attr: Map[String, String] = null): SymbolCreateNamedFunc = { + createFromNamedSymbolsNoCheck("LeakyReLU", name, attr) + } /** - * MAERegressionOutput -Use mean absolute error regression for final output, this is used on final output of a net. - -Parameters ----------- -data : Symbol -Input data to function. -label : Symbol -Input label to function. -grad_scale : float, optional, default=1 -Scale the gradient by a float factor - * @return + * Apply convolution to input then add a bias. + * + * Parameters + * ---------- + * data : Symbol. Input data to the ConvolutionOp. + * alpha : float, optional, default=0.0001, + * value of the alpha variance scaling parameter in the normalization formula + * beta : float, optional, default=0.75, + * value of the beta power parameter in the normalization formula + * knorm : float, optional, default=2, value of the k parameter in normalization formula + * nsize : int (non-negative), required, normalization window width in elements. */ + def LRN(name: String = null, attr: Map[String, String] = null): SymbolCreateNamedFunc = { + createFromNamedSymbolsNoCheck("LRN", name, attr) + } /** - * Reshape -Reshape input to target shape - -Parameters ----------- -data : Symbol -Input data to reshape. -target_shape : Shape(tuple), required -Target new shape. One and only one dim can be 0, -in which case it will be infered from the rest of dims - * @return + * Use mean absolute error regression for final output, this is used on final output of a net. + * + * Parameters + * ---------- + * data : Symbol. Input data to function. + * label : Symbol. Input label to function. + * grad_scale : float, optional, default=1. Scale the gradient by a float factor */ + def MAERegressionOutput(name: String = null, + attr: Map[String, String] = null): SymbolCreateNamedFunc = { + createFromNamedSymbolsNoCheck("MAERegressionOutput", name, attr) + } /** - * SliceChannel -Slice channel into many outputs with equally divided channel + * Reshape input to target shape + * + * Parameters + * ---------- + * data : Symbol. Input data to reshape. + * target_shape : Shape(tuple), required. Target new shape. One and only one dim can be 0, + * in which case it will be infered from the rest of dims + */ + def Reshape(name: String = null, attr: Map[String, String] = null): SymbolCreateNamedFunc = { + createFromNamedSymbolsNoCheck("Reshape", name, attr) + } -Parameters ----------- -num_outputs : int, required -Number of outputs to be sliced. - * @return + /** + * Slice channel into many outputs with equally divided channel + * + * Parameters + * ---------- + * num_outputs : int, required. Number of outputs to be sliced. */ + def SliceChannel(name: String = null, attr: Map[String, String] = null)( + inputs: Array[Symbol], params: Map[String, Any] = null): Symbol = { + createFromListedSymbolsNoCheck("SliceChannel", name, attr)(inputs, params) + } /** - * SoftmaxActivation -Apply softmax activation to input. This is intended for internal layers. For output (loss layer) -please use SoftmaxOutput. If type=instance, -this operator will compute a softmax for each instance in the batch; -this is the default mode. If type=channel, -this operator will compute a num_channel-class softmax at each position of each instance; -this can be used for fully convolutional network, image segmentation, etc. - -Parameters ----------- -data : Symbol -Input data to activation function. -type : {'channel', 'instance'},optional, default='instance' -Softmax Mode. If set to instance, -this operator will compute a softmax for each instance in the batch; this is the default mode. -If set to channel, this operator will compute a num_channel-class softmax -at each position of each instance; this can be used for fully convolutional network, -image segmentation, etc. - * @return + * Apply softmax activation to input. + * This is intended for internal layers. For output (loss layer) please use SoftmaxOutput. + * If type=instance, + * this operator will compute a softmax for each instance in the batch; this is the default mode. + * If type=channel, + * this operator will compute a num_channel-class softmax at each position of each instance; + * this can be used for fully convolutional network, image segmentation, etc. + * + * Parameters + * ---------- + * data : Symbol. Input data to activation function. + * type : {'channel', 'instance'},optional, default='instance'. Softmax Mode. + * If set to instance, + * this operator will compute a softmax for each instance in the batch; + * this is the default mode. + * If set to channel, + * this operator will compute a num_channel-class softmax + * at each position of each instance; + * this can be used for fully convolutional network, image segmentation, etc. */ + def SoftmaxActivation(name: String = null, + attr: Map[String, String] = null): SymbolCreateNamedFunc = { + createFromNamedSymbolsNoCheck("SoftmaxActivation", name, attr) + } /** * Apply matrix multiplication to input then add a bias.