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add BlockGrad, Crop, ... operators for Symbol
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@yzhliu
yzhliu committed Feb 18, 2016
1 parent 37fa56b commit ebb43f8
Showing 1 changed file with 133 additions and 134 deletions.
267 changes: 133 additions & 134 deletions scala-package/core/src/main/scala/ml/dmlc/mxnet/Symbol.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -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.
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