Base Operations

These ops are generally available directly on SameDiff instances. Due to an oversight before the release, this ops aren't also available on Nd4j. To use the INDArray variants of these operations, you will have to instantiate a NDBase instance.
all
INDArray all(INDArray x, int[] dimensions)
​
SDVariable all(SDVariable x, int[] dimensions)
SDVariable all(String name, SDVariable x, int[] dimensions)
Boolean and array reduction operation, optionally along specified dimensions
x  (NDARRAY) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
any
INDArray any(INDArray x, int[] dimensions)
​
SDVariable any(SDVariable x, int[] dimensions)
SDVariable any(String name, SDVariable x, int[] dimensions)
Boolean or array reduction operation, optionally along specified dimensions
x  (NDARRAY) -  Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
argmax
INDArray argmax(INDArray in, boolean keepDims, int[] dimensions)
INDArray argmax(INDArray in, int[] dimensions)
​
SDVariable argmax(SDVariable in, boolean keepDims, int[] dimensions)
SDVariable argmax(SDVariable in, int[] dimensions)
SDVariable argmax(String name, SDVariable in, boolean keepDims, int[] dimensions)
SDVariable argmax(String name, SDVariable in, int[] dimensions)
Argmax array reduction operation, optionally along specified dimensions.
Output values are the index of the maximum value of each slice along the specified dimension.
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
keepDims - If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions - default = false
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
argmin
INDArray argmin(INDArray in, boolean keepDims, int[] dimensions)
INDArray argmin(INDArray in, int[] dimensions)
​
SDVariable argmin(SDVariable in, boolean keepDims, int[] dimensions)
SDVariable argmin(SDVariable in, int[] dimensions)
SDVariable argmin(String name, SDVariable in, boolean keepDims, int[] dimensions)
SDVariable argmin(String name, SDVariable in, int[] dimensions)
Argmin array reduction operation, optionally along specified dimensions.
Output values are the index of the minimum value of each slice along the specified dimension.
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]
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​
in  (NUMERIC) - Input variable
keepDims - If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions - default = false
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
batchMmul
INDArray batchMmul(INDArray inputsA, INDArray inputsB, boolean transposeA, boolean transposeB)
INDArray batchMmul(INDArray inputsA, INDArray inputsB)
​
SDVariable batchMmul(SDVariable inputsA, SDVariable inputsB, boolean transposeA, boolean transposeB)
SDVariable batchMmul(SDVariable inputsA, SDVariable inputsB)
SDVariable batchMmul(String name, SDVariable inputsA, SDVariable inputsB, boolean transposeA, boolean transposeB)
SDVariable batchMmul(String name, SDVariable inputsA, SDVariable inputsB)
Matrix multiply a batch of matrices. matricesA and matricesB have to be arrays of same
length and each pair taken from these sets has to have dimensions (M, N) and (N, K),
respectively. If transposeA is true, matrices from matricesA will have shape (N, M) instead.
Likewise, if transposeB is true, matrices from matricesB will have shape (K, N).
The result of this operation will be a batch of multiplied matrices. The
result has the same length as both input batches and each output matrix is of shape (M, K).
inputsA  (NUMERIC) - First array of input matrices, all of shape (M, N) or (N, M)
inputsB  (NUMERIC) -  Second array of input matrices, all of shape (N, K) or (K, N)
transposeA - Whether to transpose A arrays or not - default = false
transposeB - Whether to transpose B arrays or not - default = false
castTo
INDArray castTo(INDArray arg, DataType datatype)
​
SDVariable castTo(SDVariable arg, DataType datatype)
SDVariable castTo(String name, SDVariable arg, DataType datatype)
Cast the array to a new datatype - for example, Integer -> Float
arg  (NDARRAY) - Input variable to cast
datatype - Datatype to cast to
concat
INDArray concat(INDArray inputs, int dimension)
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SDVariable concat(SDVariable inputs, int dimension)
SDVariable concat(String name, SDVariable inputs, int dimension)
Concatenate a set of inputs along the specified dimension.
Note that inputs must have identical rank and identical dimensions, other than the dimension to stack on.
For example, if 2 inputs have shape [a, x, c] and [a, y, c] and dimension = 1, then the output has shape [a, x+y, c]
inputs  (NUMERIC) - Input variables
dimension - Dimension to concatenate on
cumprod
INDArray cumprod(INDArray in, boolean exclusive, boolean reverse, int[] axis)
INDArray cumprod(INDArray in, int[] axis)
​
SDVariable cumprod(SDVariable in, boolean exclusive, boolean reverse, int[] axis)
SDVariable cumprod(SDVariable in, int[] axis)
SDVariable cumprod(String name, SDVariable in, boolean exclusive, boolean reverse, int[] axis)
SDVariable cumprod(String name, SDVariable in, int[] axis)
Cumulative product operation.
For input: [ a, b, c], output is:
exclusive=false, reverse=false: [a, a_b, a_b*c]
exclusive=true, reverse=false, [0, a, a*b]
exclusive=false, reverse=true: [a_b_c, b*c, c]
exclusive=true, reverse=true: [b*c, c, 0]
in  (NUMERIC) - Input variable
exclusive - If true: exclude the first value - default = false
reverse - If true: reverse the direction of the accumulation - default = false
axis - Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))
cumsum
INDArray cumsum(INDArray in, boolean exclusive, boolean reverse, int[] axis)
INDArray cumsum(INDArray in, int[] axis)
​
SDVariable cumsum(SDVariable in, boolean exclusive, boolean reverse, int[] axis)
SDVariable cumsum(SDVariable in, int[] axis)
SDVariable cumsum(String name, SDVariable in, boolean exclusive, boolean reverse, int[] axis)
SDVariable cumsum(String name, SDVariable in, int[] axis)
Cumulative sum operation.
For input: [ a, b, c], output is:
exclusive=false, reverse=false: [a, a+b, a+b+c]
exclusive=true, reverse=false, [0, a, a+b]
exclusive=false, reverse=true: [a+b+c, b+c, c]
exclusive=true, reverse=true: [b+c, c, 0]
in  (NUMERIC) - Input variable
exclusive - If true: exclude the first value - default = false
reverse - If true: reverse the direction of the accumulation - default = false
axis - Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))
dot
INDArray dot(INDArray x, INDArray y, int[] dimensions)
​
SDVariable dot(SDVariable x, SDVariable y, int[] dimensions)
SDVariable dot(String name, SDVariable x, SDVariable y, int[] dimensions)
Pairwise dot product reduction along dimension
output = sum(i=0 ... size(dim)-1) x[i] * y[i]
x  (NUMERIC) - first input
y  (NUMERIC) - second input
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
dynamicPartition
INDArray dynamicPartition(INDArray x, INDArray partitions, int numPartitions)
​
SDVariable dynamicPartition(SDVariable x, SDVariable partitions, int numPartitions)
SDVariable dynamicPartition(String name, SDVariable x, SDVariable partitions, int numPartitions)
Dynamically partition the input variable values into the specified number of paritions, using the indices.
Example:
input = [1,2,3,4,5]
​
numPartitions = 2
​
partitions = [1,0,0,1,0]
​
out[0] = [2,3,5]
​
out[1] = [1,4] `
x  (NUMERIC) - Input variable
partitions  (INT) - 1D input with values 0 to numPartitions-1
numPartitions - Number of partitions, >= 1
dynamicStitch
INDArray dynamicStitch(INDArray indices, INDArray x)
​
SDVariable dynamicStitch(SDVariable indices, SDVariable x)
SDVariable dynamicStitch(String name, SDVariable indices, SDVariable x)
Dynamically merge the specified input arrays into a single array, using the specified indices
indices  (INT) - Indices to use when merging. Must be >= 1, same length as input variables
x  (NUMERIC) - Input variables.
eq
INDArray eq(INDArray x, double y)
​
SDVariable eq(SDVariable x, double y)
SDVariable eq(String name, SDVariable x, double y)
Equals operation: elementwise x == y
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input array
y - Double value argument to use in operation
eq
INDArray eq(INDArray x, INDArray y)
​
SDVariable eq(SDVariable x, SDVariable y)
SDVariable eq(String name, SDVariable x, SDVariable y)
Equal to operation: elementwise x == y
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.
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​
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input 1
y  (NUMERIC) - Input 2
expandDims
INDArray expandDims(INDArray x, int axis)
​
SDVariable expandDims(SDVariable x, int axis)
SDVariable expandDims(String name, SDVariable x, int axis)
Reshape the input by adding a 1 at the specified location.
For example, if input has shape [a, b], then output shape is:
axis = 0: [1, a, b]
axis = 1: [a, 1, b]
axis = 2: [a, b, 1]
x  (NDARRAY) - Input variable
axis - Axis to expand
fill
INDArray fill(INDArray shape, DataType dataType, double value)
​
SDVariable fill(SDVariable shape, DataType dataType, double value)
SDVariable fill(String name, SDVariable shape, DataType dataType, double value)
Generate an output variable with the specified (dynamic) shape with all elements set to the specified value
shape  (INT) - Shape: must be a 1D array/variable
dataType - Datatype of the output array
value - Value to set all elements to
gather
INDArray gather(INDArray df, int[] indices, int axis)
​
SDVariable gather(SDVariable df, int[] indices, int axis)
SDVariable gather(String name, SDVariable df, int[] indices, int axis)
Gather slices from the input variable where the indices are specified as fixed int[] values.
Output shape is same as input shape, except for axis dimension, which has size equal to indices.length.
df  (NUMERIC) - Input variable
indices - Indices to get (Size: AtLeast(min=1))
axis - Axis that the indices refer to
gather
INDArray gather(INDArray df, INDArray indices, int axis)
​
SDVariable gather(SDVariable df, SDVariable indices, int axis)
SDVariable gather(String name, SDVariable df, SDVariable indices, int axis)
Gather slices from the input variable where the indices are specified as dynamic array values.
Output shape is same as input shape, except for axis dimension, which has size equal to indices.length.
df  (NUMERIC) - Input variable
indices  (INT) - Indices to get slices for. Rank 0 or 1 input
axis - Axis that the indices refer to
gatherNd
INDArray gatherNd(INDArray df, INDArray indices)
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SDVariable gatherNd(SDVariable df, SDVariable indices)
SDVariable gatherNd(String name, SDVariable df, SDVariable indices)
Gather slices from df with shape specified by indices.
df  (NUMERIC) - 
indices  (NUMERIC) - 
gt
INDArray gt(INDArray x, double y)
​
SDVariable gt(SDVariable x, double y)
SDVariable gt(String name, SDVariable x, double y)
Greater than operation: elementwise x > y
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input array
y - Double value argument to use in operation
gt
INDArray gt(INDArray x, INDArray y)
​
SDVariable gt(SDVariable x, SDVariable y)
SDVariable gt(String name, SDVariable x, SDVariable y)
Greater than operation: elementwise x > y
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.
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​
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input 1
y  (NUMERIC) - Input 2
gte
INDArray gte(INDArray x, double y)
​
SDVariable gte(SDVariable x, double y)
SDVariable gte(String name, SDVariable x, double y)
Greater than or equals operation: elementwise x >= y
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input array
y - Double value argument to use in operation
gte
INDArray gte(INDArray x, INDArray y)
​
SDVariable gte(SDVariable x, SDVariable y)
SDVariable gte(String name, SDVariable x, SDVariable y)
Greater than or equal to operation: elementwise x >= y
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.
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​
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input 1
y  (NUMERIC) - Input 2
identity
INDArray identity(INDArray input)
​
SDVariable identity(SDVariable input)
SDVariable identity(String name, SDVariable input)
Elementwise identity operation: out = x
input  (NUMERIC) - Input variable
invertPermutation
INDArray invertPermutation(INDArray input)
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SDVariable invertPermutation(SDVariable input)
SDVariable invertPermutation(String name, SDVariable input)
Compute the inverse permutation indices for a permutation operation
Example: if input is [2, 0, 1] then output is [1, 2, 0]
The idea is that x.permute(input).permute(invertPermutation(input)) == x
input  (INT) - 1D indices for permutation
isNumericTensor
INDArray isNumericTensor(INDArray x)
​
SDVariable isNumericTensor(SDVariable x)
SDVariable isNumericTensor(String name, SDVariable x)
Is the director a numeric tensor? In the current version of ND4J/SameDiff, this always returns true/1
x  (NUMERIC) - Input variable
linspace
INDArray linspace(DataType dataType, double start, double stop, long number)
​
SDVariable linspace(DataType dataType, double start, double stop, long number)
SDVariable linspace(String name, DataType dataType, double start, double stop, long number)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0]
dataType - Data type of the output array
start - Start value
stop - Stop value
number - Number of values to generate
linspace
INDArray linspace(INDArray start, INDArray stop, INDArray number, DataType dataType)
​
SDVariable linspace(SDVariable start, SDVariable stop, SDVariable number, DataType dataType)
SDVariable linspace(String name, SDVariable start, SDVariable stop, SDVariable number, DataType dataType)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0]
start  (NUMERIC) - Start value
stop  (NUMERIC) - Stop value
number  (LONG) - Number of values to generate
dataType - Data type of the output array
lt
INDArray lt(INDArray x, double y)
​
SDVariable lt(SDVariable x, double y)
SDVariable lt(String name, SDVariable x, double y)
Less than operation: elementwise x < y
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input array
y - Double value argument to use in operation
lt
INDArray lt(INDArray x, INDArray y)
​
SDVariable lt(SDVariable x, SDVariable y)
SDVariable lt(String name, SDVariable x, SDVariable y)
Less than operation: elementwise x < y
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.
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​
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input 1
y  (NUMERIC) - Input 2
lte
INDArray lte(INDArray x, double y)
​
SDVariable lte(SDVariable x, double y)
SDVariable lte(String name, SDVariable x, double y)
Less than or equals operation: elementwise x <= y
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input array
y - Double value argument to use in operation
lte
INDArray lte(INDArray x, INDArray y)
​
SDVariable lte(SDVariable x, SDVariable y)
SDVariable lte(String name, SDVariable x, SDVariable y)
Less than or equal to operation: elementwise x <= y
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.
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​
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input 1
y  (NUMERIC) - Input 2
matchCondition
INDArray matchCondition(INDArray in, Condition condition)
​
SDVariable matchCondition(SDVariable in, Condition condition)
SDVariable matchCondition(String name, SDVariable in, Condition condition)
Returns a boolean mask of equal shape to the input, where the condition is satisfied - value 1 where satisfied, 0 otherwise
in  (NUMERIC) - Input
condition - Condition
matchConditionCount
INDArray matchConditionCount(INDArray in, Condition condition)
​
SDVariable matchConditionCount(SDVariable in, Condition condition)
SDVariable matchConditionCount(String name, SDVariable in, Condition condition)
Returns a count of the number of elements that satisfy the condition
in  (NUMERIC) - Input
condition - Condition
matchConditionCount
INDArray matchConditionCount(INDArray in, Condition condition, boolean keepDim, int[] dimensions)
INDArray matchConditionCount(INDArray in, Condition condition, int[] dimensions)
​
SDVariable matchConditionCount(SDVariable in, Condition condition, boolean keepDim, int[] dimensions)
SDVariable matchConditionCount(SDVariable in, Condition condition, int[] dimensions)
SDVariable matchConditionCount(String name, SDVariable in, Condition condition, boolean keepDim, int[] dimensions)
SDVariable matchConditionCount(String name, SDVariable in, Condition condition, int[] dimensions)
Returns a count of the number of elements that satisfy the 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
keepDim - If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions - default = false
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
max
INDArray max(INDArray x, boolean keepDims, int[] dimensions)
INDArray max(INDArray x, int[] dimensions)
​
SDVariable max(SDVariable x, boolean keepDims, int[] dimensions)
SDVariable max(SDVariable x, int[] dimensions)
SDVariable max(String name, SDVariable x, boolean keepDims, int[] dimensions)
SDVariable max(String name, SDVariable x, int[] dimensions)
Max array reduction operation, optionally along 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]
x  (NUMERIC) - Input variable
keepDims - If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions - default = false
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
max
INDArray max(INDArray first, INDArray second)
​
SDVariable max(SDVariable first, SDVariable second)
SDVariable max(String name, SDVariable first, SDVariable second)
Element-wise maximum operation: out[i] = max(first[i], second[i])
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​
first  (NUMERIC) - First input array
second  (NUMERIC) - Second input array
mean
INDArray mean(INDArray x, boolean keepDims, int[] dimensions)
INDArray mean(INDArray x, int[] dimensions)
​
SDVariable mean(SDVariable x, boolean keepDims, int[] dimensions)
SDVariable mean(SDVariable x, int[] dimensions)
SDVariable mean(String name, SDVariable x, boolean keepDims, int[] dimensions)
SDVariable mean(String name, SDVariable x, int[] dimensions)
Mean (average) array reduction operation, optionally along 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]
x  (NUMERIC) - Input variable
keepDims - If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions - default = false
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
merge
INDArray merge(INDArray x, INDArray y)
​
SDVariable merge(SDVariable x, SDVariable y)
SDVariable merge(String name, SDVariable x, SDVariable y)
The merge operation is a control operation that forwards the either of the inputs to the output, when
the first of them becomes available. If both are available, the output is undefined (either input could
be forwarded to the output)
x  (NUMERIC) - Input variable
y  (NUMERIC) - Input variable
min
INDArray min(INDArray x, boolean keepDims, int[] dimensions)
INDArray min(INDArray x, int[] dimensions)
​
SDVariable min(SDVariable x, boolean keepDims, int[] dimensions)
SDVariable min(SDVariable x, int[] dimensions)
SDVariable min(String name, SDVariable x, boolean keepDims, int[] dimensions)
SDVariable min(String name, SDVariable x, int[] dimensions)
Minimum array reduction operation, optionally along specified dimensions. out = min(in)
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]
x  (NUMERIC) - Input variable
keepDims - If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions - default = false
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
min
INDArray min(INDArray first, INDArray second)
​
SDVariable min(SDVariable first, SDVariable second)
SDVariable min(String name, SDVariable first, SDVariable second)
Element-wise minimum operation: out[i] = min(first[i], second[i])
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​
first  (NUMERIC) - First input array
second  (NUMERIC) - Second input array
mmul
INDArray mmul(INDArray x, INDArray y, boolean transposeX, boolean transposeY, boolean transposeZ)
INDArray mmul(INDArray x, INDArray y)
​
SDVariable mmul(SDVariable x, SDVariable y, boolean transposeX, boolean transposeY, boolean transposeZ)
SDVariable mmul(SDVariable x, SDVariable y)
SDVariable mmul(String name, SDVariable x, SDVariable y, boolean transposeX, boolean transposeY, boolean transposeZ)
SDVariable mmul(String name, SDVariable x, SDVariable y)
Matrix multiplication: out = mmul(x,y)
Supports specifying transpose argument to perform operation such as mmul(a^T, b), etc.
x  (NUMERIC) - First input variable
y  (NUMERIC) - Second input variable
transposeX - Transpose x (first argument) - default = false
transposeY - Transpose y (second argument) - default = false
transposeZ - Transpose result array - default = false
neq
INDArray neq(INDArray x, double y)
​
SDVariable neq(SDVariable x, double y)
SDVariable neq(String name, SDVariable x, double y)
Not equals operation: elementwise x != y
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input array
y - Double value argument to use in operation
neq
INDArray neq(INDArray x, INDArray y)
​
SDVariable neq(SDVariable x, SDVariable y)
SDVariable neq(String name, SDVariable x, SDVariable y)
Not equal to operation: elementwise x != y
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.
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​
Return boolean array with values true where satisfied, or false otherwise.
x  (NUMERIC) - Input 1
y  (NUMERIC) - Input 2
norm1
INDArray norm1(INDArray x, boolean keepDims, int[] dimensions)
INDArray norm1(INDArray x, int[] dimensions)
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SDVariable norm1(SDVariable x, boolean keepDims, int[] dimensions)
SDVariable norm1(SDVariable x, int[] dimensions)
SDVariable norm1(String name, SDVariable x, boolean keepDims, int[] dimensions)
SDVariable norm1(String name, SDVariable x, 