Math - Deeplearning4j

ClipByAvgNorm
INDArray ClipByAvgNorm(INDArray x, double clipValue, int[] dimensions)
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SDVariable ClipByAvgNorm(SDVariable x, double clipValue, int[] dimensions)
SDVariable ClipByAvgNorm(String name, SDVariable x, double clipValue, int[] dimensions)
Clips tensor values to a maximum average L2-norm.
x  (NUMERIC) - Input variable
clipValue - Value for clipping
dimensions - Dimensions to reduce over (Size: AtLeast(min=0))
EmbeddingLookup
INDArray EmbeddingLookup(INDArray x, INDArray indices, PartitionMode PartitionMode)
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SDVariable EmbeddingLookup(SDVariable x, SDVariable indices, PartitionMode PartitionMode)
SDVariable EmbeddingLookup(String name, SDVariable x, SDVariable indices, PartitionMode PartitionMode)
Looks up ids in a list of embedding tensors.
x  (NUMERIC) - Input tensor
indices  (INT) - A Tensor containing the ids to be looked up.
PartitionMode - partition_mode == 0 - i.e. 'mod' , 1 - 'div'
MergeMaxIndex
INDArray MergeMaxIndex(INDArray x, DataType dataType)
INDArray MergeMaxIndex(INDArray x)
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SDVariable MergeMaxIndex(SDVariable x, DataType dataType)
SDVariable MergeMaxIndex(SDVariable x)
SDVariable MergeMaxIndex(String name, SDVariable x, DataType dataType)
SDVariable MergeMaxIndex(String name, SDVariable x)
Return array of max elements indices with along tensor dimensions
x  (NUMERIC) - Input tensor
dataType - Data type - default = DataType.INT
abs
INDArray abs(INDArray x)
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SDVariable abs(SDVariable x)
SDVariable abs(String name, SDVariable x)
Elementwise absolute value operation: out = abs(x)
x  (NUMERIC) - Input variable
acos
INDArray acos(INDArray x)
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SDVariable acos(SDVariable x)
SDVariable acos(String name, SDVariable x)
Elementwise acos (arccosine, inverse cosine) operation: out = arccos(x)
x  (NUMERIC) - Input variable
acosh
INDArray acosh(INDArray x)
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SDVariable acosh(SDVariable x)
SDVariable acosh(String name, SDVariable x)
Elementwise acosh (inverse hyperbolic cosine) function: out = acosh(x)
x  (NUMERIC) - Input variable
add
INDArray add(INDArray x, INDArray y)
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SDVariable add(SDVariable x, SDVariable y)
SDVariable add(String name, SDVariable x, SDVariable y)
Pairwise addition operation, out = x + y
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy: https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html​
x  (NUMERIC) - Input variable
y  (NUMERIC) - Input variable
add
INDArray add(INDArray x, double value)
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SDVariable add(SDVariable x, double value)
SDVariable add(String name, SDVariable x, double value)
Scalar add operation, out = in + scalar
x  (NUMERIC) - Input variable
value - Scalar value for op
amax
INDArray amax(INDArray in, int[] dimensions)
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SDVariable amax(SDVariable in, int[] dimensions)
SDVariable amax(String name, SDVariable in, int[] dimensions)
Absolute max array reduction operation, optionally along specified dimensions: out = max(abs(x))
in  (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
amean
INDArray amean(INDArray in, int[] dimensions)
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SDVariable amean(SDVariable in, int[] dimensions)
SDVariable amean(String name, SDVariable in, int[] dimensions)
Absolute mean array reduction operation, optionally along specified dimensions: out = mean(abs(x))
in  (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
amin
INDArray amin(INDArray in, int[] dimensions)
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SDVariable amin(SDVariable in, int[] dimensions)
SDVariable amin(String name, SDVariable in, int[] dimensions)
Absolute min array reduction operation, optionally along specified dimensions: out = min(abs(x))
in  (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
and
INDArray and(INDArray x, INDArray y)
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SDVariable and(SDVariable x, SDVariable y)
SDVariable and(String name, SDVariable x, SDVariable y)
Boolean AND operation: elementwise (x != 0) && (y != 0)
If x and y arrays have equal shape, the output shape is the same as these inputs.
Note: supports broadcasting if x and y have different shapes and are broadcastable.
Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
x  (BOOL) - Input 1
y  (BOOL) - Input 2
asin
INDArray asin(INDArray x)
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SDVariable asin(SDVariable x)
SDVariable asin(String name, SDVariable x)
Elementwise asin (arcsin, inverse sine) operation: out = arcsin(x)
x  (NUMERIC) - Input variable
asinh
INDArray asinh(INDArray x)
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SDVariable asinh(SDVariable x)
SDVariable asinh(String name, SDVariable x)
Elementwise asinh (inverse hyperbolic sine) function: out = asinh(x)
x  (NUMERIC) - Input variable
asum
INDArray asum(INDArray in, int[] dimensions)
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SDVariable asum(SDVariable in, int[] dimensions)
SDVariable asum(String name, SDVariable in, int[] dimensions)
Absolute sum array reduction operation, optionally along specified dimensions: out = sum(abs(x))
in  (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
atan
INDArray atan(INDArray x)
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SDVariable atan(SDVariable x)
SDVariable atan(String name, SDVariable x)
Elementwise atan (arctangent, inverse tangent) operation: out = arctangent(x)
x  (NUMERIC) - Input variable
atan2
INDArray atan2(INDArray y, INDArray x)
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SDVariable atan2(SDVariable y, SDVariable x)
SDVariable atan2(String name, SDVariable y, SDVariable x)
Elementwise atan (arctangent, inverse tangent) operation: out = atan2(x,y).
Similar to atan(y/x) but sigts of x and y are used to determine the location of the result
y  (NUMERIC) - Input Y variable
x  (NUMERIC) - Input X variable
atanh
INDArray atanh(INDArray x)
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SDVariable atanh(SDVariable x)
SDVariable atanh(String name, SDVariable x)
Elementwise atanh (inverse hyperbolic tangent) function: out = atanh(x)
x  (NUMERIC) - Input variable
bitShift
INDArray bitShift(INDArray x, INDArray shift)
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SDVariable bitShift(SDVariable x, SDVariable shift)
SDVariable bitShift(String name, SDVariable x, SDVariable shift)
Bit shift operation
x  (NUMERIC) - input
shift  (NUMERIC) - shift value
bitShiftRight
INDArray bitShiftRight(INDArray x, INDArray shift)
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SDVariable bitShiftRight(SDVariable x, SDVariable shift)
SDVariable bitShiftRight(String name, SDVariable x, SDVariable shift)
Right bit shift operation
x  (NUMERIC) - Input tensor
shift  (NUMERIC) - shift argument
bitShiftRotl
INDArray bitShiftRotl(INDArray x, INDArray shift)
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SDVariable bitShiftRotl(SDVariable x, SDVariable shift)
SDVariable bitShiftRotl(String name, SDVariable x, SDVariable shift)
Cyclic bit shift operation
x  (NUMERIC) - Input tensor
shift  (NUMERIC) - shift argy=ument
bitShiftRotr
INDArray bitShiftRotr(INDArray x, INDArray shift)
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SDVariable bitShiftRotr(SDVariable x, SDVariable shift)
SDVariable bitShiftRotr(String name, SDVariable x, SDVariable shift)
Cyclic right shift operation
x  (NUMERIC) - Input tensor
shift  (NUMERIC) - Shift argument
ceil
INDArray ceil(INDArray x)
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SDVariable ceil(SDVariable x)
SDVariable ceil(String name, SDVariable x)
Element-wise ceiling function: out = ceil(x).
Rounds each value up to the nearest integer value (if not already an integer)
x  (NUMERIC) - Input variable
clipByNorm
INDArray clipByNorm(INDArray x, double clipValue, int[] dimensions)
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SDVariable clipByNorm(SDVariable x, double clipValue, int[] dimensions)
SDVariable clipByNorm(String name, SDVariable x, double clipValue, int[] dimensions)
Clipping by L2 norm, optionally along dimension(s)
if l2Norm(x,dimension) < clipValue, then input is returned unmodifed
Otherwise, out[i] = in[i] * clipValue / l2Norm(in, dimensions) where each value is clipped according
to the corresponding l2Norm along the specified dimensions
x  (NUMERIC) - Input variable
clipValue - Clipping value (maximum l2 norm)
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
clipByValue
INDArray clipByValue(INDArray x, double clipValueMin, double clipValueMax)
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SDVariable clipByValue(SDVariable x, double clipValueMin, double clipValueMax)
SDVariable clipByValue(String name, SDVariable x, double clipValueMin, double clipValueMax)
Element-wise clipping function:
out[i] = in[i] if in[i] >= clipValueMin and in[i] <= clipValueMax
out[i] = clipValueMin if in[i] < clipValueMin
out[i] = clipValueMax if in[i] > clipValueMax
x  (NUMERIC) - Input variable
clipValueMin - Minimum value for clipping
clipValueMax - Maximum value for clipping
confusionMatrix
INDArray confusionMatrix(INDArray labels, INDArray pred, DataType dataType)
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SDVariable confusionMatrix(SDVariable labels, SDVariable pred, DataType dataType)
SDVariable confusionMatrix(String name, SDVariable labels, SDVariable pred, DataType dataType)
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values. This version assumes the number of classes is 1 + max(max(labels), max(pred))
For example, if labels = [0, 1, 1] and predicted = [0, 2, 1] then output is:
[1, 0, 0]
[0, 1, 1]
[0, 0, 0]
labels  (NUMERIC) - Labels - 1D array of integer values representing label values
pred  (NUMERIC) - Predictions - 1D array of integer values representing predictions. Same length as labels
dataType - Data type
confusionMatrix
INDArray confusionMatrix(INDArray labels, INDArray pred, int numClasses)
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SDVariable confusionMatrix(SDVariable labels, SDVariable pred, int numClasses)
SDVariable confusionMatrix(String name, SDVariable labels, SDVariable pred, int numClasses)
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values.
For example, if labels = [0, 1, 1], predicted = [0, 2, 1], and numClasses=4 then output is:
[1, 0, 0, 0]
[0, 1, 1, 0]
[0, 0, 0, 0]
[0, 0, 0, 0]
labels  (NUMERIC) - Labels - 1D array of integer values representing label values
pred  (NUMERIC) - Predictions - 1D array of integer values representing predictions. Same length as labels
numClasses - Number of classes
confusionMatrix
INDArray confusionMatrix(INDArray labels, INDArray pred, INDArray weights)
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SDVariable confusionMatrix(SDVariable labels, SDVariable pred, SDVariable weights)
SDVariable confusionMatrix(String name, SDVariable labels, SDVariable pred, SDVariable weights)
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values. This version assumes the number of classes is 1 + max(max(labels), max(pred))
For example, if labels = [0, 1, 1], predicted = [0, 2, 1] and weights = [1, 2, 3]
[1, 0, 0]
[0, 3, 2]
[0, 0, 0]
labels  (NUMERIC) - Labels - 1D array of integer values representing label values
pred  (NUMERIC) - Predictions - 1D array of integer values representing predictions. Same length as labels
weights  (NUMERIC) - Weights - 1D array of values (may be real/decimal) representing the weight/contribution of each prediction. Must be same length as both labels and predictions arrays
confusionMatrix
INDArray confusionMatrix(INDArray labels, INDArray pred, INDArray weights, int numClasses)
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SDVariable confusionMatrix(SDVariable labels, SDVariable pred, SDVariable weights, int numClasses)
SDVariable confusionMatrix(String name, SDVariable labels, SDVariable pred, SDVariable weights, int numClasses)
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values.
For example, if labels = [0, 1, 1], predicted = [0, 2, 1], numClasses = 4, and weights = [1, 2, 3]
[1, 0, 0, 0]
[0, 3, 2, 0]
[0, 0, 0, 0]
[0, 0, 0, 0]
labels  (NUMERIC) - Labels - 1D array of integer values representing label values
pred  (NUMERIC) - Predictions - 1D array of integer values representing predictions. Same length as labels
weights  (NUMERIC) - Weights - 1D array of values (may be real/decimal) representing the weight/contribution of each prediction. Must be same length as both labels and predictions arrays
numClasses - 
cos
INDArray cos(INDArray x)
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SDVariable cos(SDVariable x)
SDVariable cos(String name, SDVariable x)
Elementwise cosine operation: out = cos(x)
x  (NUMERIC) - Input variable
cosh
INDArray cosh(INDArray x)
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SDVariable cosh(SDVariable x)
SDVariable cosh(String name, SDVariable x)
Elementwise cosh (hyperbolic cosine) operation: out = cosh(x)
x  (NUMERIC) - Input variable
cosineDistance
INDArray cosineDistance(INDArray x, INDArray y, int[] dimensions)
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SDVariable cosineDistance(SDVariable x, SDVariable y, int[] dimensions)
SDVariable cosineDistance(String name, SDVariable x, SDVariable y, int[] dimensions)
Cosine distance reduction operation. The output contains the cosine distance for each
tensor/subset along the specified dimensions:
out = 1.0 - cosineSimilarity(x,y)
x  (NUMERIC) - Input variable x
y  (NUMERIC) - Input variable y
dimensions - Dimensions to calculate cosineDistance over (Size: AtLeast(min=0))
cosineSimilarity
INDArray cosineSimilarity(INDArray x, INDArray y, int[] dimensions)
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SDVariable cosineSimilarity(SDVariable x, SDVariable y, int[] dimensions)
SDVariable cosineSimilarity(String name, SDVariable x, SDVariable y, int[] dimensions)
Cosine similarity pairwise reduction operation. The output contains the cosine similarity for each tensor/subset
along the specified dimensions:
out = (sum_i x[i] y[i]) / ( sqrt(sum_i x[i]^2) sqrt(sum_i y[i]^2)
x  (NUMERIC) - Input variable x
y  (NUMERIC) - Input variable y
dimensions - Dimensions to calculate cosineSimilarity over (Size: AtLeast(min=0))
countNonZero
INDArray countNonZero(INDArray in, int[] dimensions)
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SDVariable countNonZero(SDVariable in, int[] dimensions)
SDVariable countNonZero(String name, SDVariable in, int[] dimensions)
Count non zero array reduction operation, optionally along specified dimensions: out = count(x != 0)
in  (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
countZero
INDArray countZero(INDArray in, int[] dimensions)
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SDVariable countZero(SDVariable in, int[] dimensions)
SDVariable countZero(String name, SDVariable in, int[] dimensions)
Count zero array reduction operation, optionally along specified dimensions: out = count(x == 0)
in  (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
cross
INDArray cross(INDArray a, INDArray b)
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SDVariable cross(SDVariable a, SDVariable b)
SDVariable cross(String name, SDVariable a, SDVariable b)
Returns the pair-wise cross product of equal size arrays a and b: a x b = ||a||x||b|| sin(theta).
Can take rank 1 or above inputs (of equal shapes), but note that the last dimension must have dimension 3
a  (NUMERIC) - First input
b  (NUMERIC) - Second input
cube
INDArray cube(INDArray x)
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SDVariable cube(SDVariable x)
SDVariable cube(String name, SDVariable x)
Element-wise cube function: out = x^3
x  (NUMERIC) - Input variable
diag
INDArray diag(INDArray x)
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SDVariable diag(SDVariable x)
SDVariable diag(String name, SDVariable x)
Returns an output variable with diagonal values equal to the specified values; off-diagonal values will be set to 0
For example, if input = [1,2,3], then output is given by:
[ 1, 0, 0]
[ 0, 2, 0]
[ 0, 0, 3]
Higher input ranks are also supported: if input has shape [a,...,R-1] then output[i,...,k,i,...,k] = input[i,...,k].
i.e., for input rank R, output has rank 2R
x  (NUMERIC) - Input variable
diagPart
INDArray diagPart(INDArray x)
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SDVariable diagPart(SDVariable x)
SDVariable diagPart(String name, SDVariable x)
Extract the diagonal part from the input array.
If input is
[ 1, 0, 0]
[ 0, 2, 0]
[ 0, 0, 3]
then output is [1, 2, 3].
Supports higher dimensions: in general, out[i,...,k] = in[i,...,k,i,...,k]
x  (NUMERIC) - Input variable
div
INDArray div(INDArray x, INDArray y)
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SDVariable div(SDVariable x, SDVariable y)
SDVariable div(String name, SDVariable x, SDVariable y)
Pairwise division operation, out = x / y
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy: https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html​
x  (NUMERIC) - Input variable
y  (NUMERIC) - Input variable
div
INDArray div(INDArray x, double value)
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SDVariable div(SDVariable x, double value)
SDVariable div(String name, SDVariable x, double value)
Scalar division operation, out = in / scalar
x  (NUMERIC) - Input variable
value - Scalar value for op
entropy
INDArray entropy(INDArray in, int[] dimensions)
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SDVariable entropy(SDVariable in, int[] dimensions)
SDVariable entropy(String name, SDVariable in, int[] dimensions)
Entropy reduction: -sum(x * log(x))
in  (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
erf
INDArray erf(INDArray x)
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SDVariable erf(SDVariable x)
SDVariable erf(String name, SDVariable x)
Element-wise Gaussian error function - out = erf(in)
x  (NUMERIC) - Input variable
erfc
INDArray erfc(INDArray x)
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SDVariable erfc(SDVariable x)
SDVariable erfc(String name, SDVariable x)
Element-wise complementary Gaussian error function - out = erfc(in) = 1 - erf(in)
x  (NUMERIC) - Input variable
euclideanDistance
INDArray euclideanDistance(INDArray x, INDArray y, int[] dimensions)
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SDVariable euclideanDistance(SDVariable x, SDVariable y, int[] dimensions)
SDVariable euclideanDistance(String name, SDVariable x, SDVariable y, int[] dimensions)
Euclidean distance (l2 norm, l2 distance) reduction operation. The output contains the Euclidean distance for each
tensor/subset along the specified dimensions:
out = sqrt( sum_i (x[i] - y[i])^2 )
x  (NUMERIC) - Input variable x
y  (NUMERIC) - Input variable y
dimensions - Dimensions to calculate euclideanDistance over (Size: AtLeast(min=0))
exp
INDArray exp(INDArray x)
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SDVariable exp(SDVariable x)
SDVariable exp(String name, SDVariable x)
Elementwise exponent function: out = exp(x) = 2.71828...^x
x  (NUMERIC) - Input variable
expm1
INDArray expm1(INDArray x)
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SDVariable expm1(SDVariable x)
SDVariable expm1(String name, SDVariable x)
Elementwise 1.0 - exponent function: out = 1.0 - exp(x) = 1.0 - 2.71828...^x
x  (NUMERIC) - Input variable
eye
INDArray eye(int rows)
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SDVariable eye(int rows)
SDVariable eye(String name, int rows)
Generate an identity matrix with the specified number of rows and columns.
rows - Number of rows
eye
INDArray eye(int rows, int cols)
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SDVariable eye(int rows, int cols)
SDVariable eye(String name, int rows, int cols)
As per eye(String, int, int, DataType) but with the default datatype, Eye.DEFAULT_DTYPE
rows - Number of rows
cols - Number of columns
eye
INDArray eye(int rows, int cols, DataType dataType, int[] dimensions)
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SDVariable eye(int rows, int cols, DataType dataType, int[] dimensions)
SDVariable eye(String name, int rows, int cols, DataType dataType, int[] dimensions)
Generate an identity matrix with the specified number of rows and columns
Example:
`INDArray eye = eye(3,2)
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eye:
​
[ 1, 0]
​
[ 0, 1]
​
[ 0, 0]`
rows - Number of rows
cols - Number of columns
dataType - Data type
dimensions -  (Size: AtLeast(min=0))
eye
INDArray eye(INDArray rows, INDArray cols)
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SDVariable eye(SDVariable rows, SDVariable cols)
SDVariable eye(String name, SDVariable rows, SDVariable cols)
As per eye(int, int) bit with the number of rows/columns specified as scalar INDArrays
rows  (INT) - Number of rows
cols  (INT) - Number of columns
eye
INDArray eye(INDArray rows)
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SDVariable eye(SDVariable rows)
SDVariable eye(String name, SDVariable rows)
As per eye(String, int) but with the number of rows specified as a scalar INDArray
rows  (INT) - Number of rows
firstIndex
INDArray firstIndex(INDArray in, Condition condition, int[] dimensions)
INDArray firstIndex(INDArray in, Condition condition, boolean keepDims, int[] dimensions)
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SDVariable firstIndex(SDVariable in, Condition condition, int[] dimensions)
SDVariable firstIndex(SDVariable in, Condition condition, boolean keepDims, int[] dimensions)
SDVariable firstIndex(String name, SDVariable in, Condition condition, int[] dimensions)
SDVariable firstIndex(String name, SDVariable in, Condition condition, boolean keepDims, int[] dimensions)
First index reduction operation.
Returns a variable that contains the index of the first element that matches the specified condition (for each
slice along the specified dimensions)
Note that if keepDims = true, the output variable has the same rank as the input variable,
with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
the mean along a dimension).
Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
keepDims = true: [a,1,c]
keepDims = false: [a,c]
in  (NUMERIC) - Input variable
condition - Condition to check on input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=1))
keepDims - If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensions - default = false
floor
INDArray floor(INDArray x)
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SDVariable floor(SDVariable x)
SDVariable floor(String name, SDVariable x)
Element-wise floor function: out = floor(x).
Rounds each value down to the nearest integer value (if not already an integer)
x  (NUMERIC) - Input variable
floorDiv
INDArray floorDiv(INDArray x, INDArray y)
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SDVariable floorDiv(SDVariable x, SDVariable y)
SDVariable floorDiv(String name, SDVariable x, SDVariable y)
Pairwise floor division operation, out = floor(x / y)
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy: https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html​
x  (NUMERIC) - Input variable
y  (NUMERIC) - Input variable
floorMod
INDArray floorMod(INDArray x, INDArray y)
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SDVariable floorMod(SDVariable x, SDVariable y)
SDVariable floorMod(String name, SDVariable x, SDVariable y)
Pairwise Modulus division operation
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy: https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html​
x  (NUMERIC) - Input variable
y  (NUMERIC) - Input variable
floorMod
INDArray floorMod(INDArray x, double value)
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SDVariable floorMod(SDVariable x, double value)
SDVariable floorMod(String name, SDVariable x, double value)
Scalar floor modulus operation
x  (NUMERIC) - Input variable
value - Scalar value for op
hammingDistance