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))
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 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 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:
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))
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
Cast the array to a new datatype - for example, Integer -> Float
arg (NDARRAY) - Input variable to cast
datatype - Datatype to cast to
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
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
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
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))
Dynamically partition the input variable values into the specified number of paritions, using the indices.
Example:
x (NUMERIC) - Input variable
partitions (INT) - 1D input with values 0 to numPartitions-1
numPartitions - Number of partitions, >= 1
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.
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
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:
Return boolean array with values true where satisfied, or false otherwise.
x (NUMERIC) - Input 1
y (NUMERIC) - Input 2
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
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 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 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
Gather slices from df with shape specified by indices.
df (NUMERIC) -
indices (NUMERIC) -
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
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:
Return boolean array with values true where satisfied, or false otherwise.
x (NUMERIC) - Input 1
y (NUMERIC) - Input 2
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
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:
Return boolean array with values true where satisfied, or false otherwise.
x (NUMERIC) - Input 1
y (NUMERIC) - Input 2
Elementwise identity operation: out = x
input (NUMERIC) - Input variable
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
Is the director a numeric tensor? In the current version of ND4J/SameDiff, this always returns true/1
x (NUMERIC) - Input variable
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
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
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
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:
Return boolean array with values true where satisfied, or false otherwise.
x (NUMERIC) - Input 1
y (NUMERIC) - Input 2
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
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:
Return boolean array with values true where satisfied, or false otherwise.
x (NUMERIC) - Input 1
y (NUMERIC) - Input 2
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
Returns a count of the number of elements that satisfy the condition
in (NUMERIC) - Input
condition - Condition
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
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))
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:
first (NUMERIC) - First input array
second (NUMERIC) - Second input array
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))
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
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))
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:
first (NUMERIC) - First input array
second (NUMERIC) - Second input array
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
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
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:
Return boolean array with values true where satisfied, or false otherwise.
x (NUMERIC) - Input 1
y (NUMERIC) - Input 2
Norm1 (L1 norm) reduction operation: The output contains the L1 norm for each tensor/subset along the specified dimensions:
out = sum_i abs(x[i])
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 (Size: AtLeast(min=0))
Norm2 (L2 norm) reduction operation: The output contains the L2 norm for each tensor/subset along the specified dimensions:
out = sqrt(sum_i x[i]^2)
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 dimensions to reduce over (Size: AtLeast(min=0))
Max norm (infinity norm) reduction operation: The output contains the max norm for each tensor/subset along the
specified dimensions:
out = max(abs(x[i]))
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 (Size: AtLeast(min=0))
Convert the array to a one-hot array with walues and for each entry
If input has shape [ a, ..., n] then output has shape [ a, ..., n, depth],
with {out[i, ..., j, in[i,...,j]] with other values being set to
indices (NUMERIC) - Indices - value 0 to depth-1
depth - Number of classes
axis -
on -
Convert the array to a one-hot array with walues 0 and 1 for each entry
If input has shape [ a, ..., n] then output has shape [ a, ..., n, depth],
with out[i, ..., j, in[i,...,j]] = 1 with other values being set to 0
see oneHot(SDVariable, int, int, double, double)
indices (NUMERIC) - Indices - value 0 to depth-1
depth - Number of classes
Return a variable of all 1s, with the same shape as the input variable. Note that this is dynamic:
if the input shape changes in later execution, the returned variable's shape will also be updated
input (NUMERIC) - Input INDArray
As per onesLike(String, SDVariable) but the output datatype may be specified
input (NUMERIC) -
dataType -
Array permutation operation: permute the dimensions according to the specified permutation indices.
Example: if input has shape [a,b,c] and dimensions = [2,0,1] the output has shape [c,a,b]
x (NUMERIC) - Input variable
dimensions (INT) - Permute dimensions
Array permutation operation: permute the dimensions according to the specified permutation indices.
Example: if input has shape [a,b,c] and dimensions = [2,0,1] the output has shape [c,a,b]
x (NUMERIC) - Input variable
dimensions - (Size: AtLeast(min=0))
Product 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))
Create a new variable with a 1d array, where the values start at from and increment by step
up to (but not including) limit.
For example, range(1.0, 3.0, 0.5) will return [1.0, 1.5, 2.0, 2.5]
from - Initial/smallest value
to - Largest value (exclusive)
step - Step size
dataType -
Create a new variable with a 1d array, where the values start at from and increment by step
up to (but not including) limit.
For example, range(1.0, 3.0, 0.5) will return [1.0, 1.5, 2.0, 2.5]
from (NUMERIC) - Initial/smallest value
to (NUMERIC) - Largest value (exclusive)
step (NUMERIC) - Step size
dataType -
Returns the rank (number of dimensions, i.e., length(shape)) of the specified INDArray as a 0D scalar variable
in (NUMERIC) - Input variable
Element-wise replace where condition:
out[i] = from[i] if condition(update[i]) is satisfied, or
out[i] = update[i] if condition(update[i]) is NOT satisfied
update (NUMERIC) - Source array
from (NUMERIC) - Replacement values array (used conditionally). Must be same shape as 'update' array
condition - Condition to check on update array elements
Element-wise replace where condition:
out[i] = value if condition(update[i]) is satisfied, or
out[i] = update[i] if condition(update[i]) is NOT satisfied
update (NUMERIC) - Source array
value - Value to set at the output, if the condition is satisfied
condition - Condition to check on update array elements
Reshape the input variable to the specified (fixed) shape. The output variable will have the same values as the
input, but with the specified shape.
Note that prod(shape) must match length(input) == prod(input.shape)
x (NUMERIC) - Input variable
shape (NUMERIC) - New shape for variable
Reshape the input variable to the specified (fixed) shape. The output variable will have the same values as the
input, but with the specified shape.
Note that prod(shape) must match length(input) == prod(input.shape)
x (NUMERIC) - Input variable
shape - New shape for variable (Size: AtLeast(min=0))
Reverse the values of an array for the specified dimensions
If input is:
[ 1, 2, 3]
[ 4, 5, 6]
then
reverse(in, 0):
[3, 2, 1]
[6, 5, 4]
reverse(in, 1):
[4, 5, 6]
[1, 2 3]
x (NUMERIC) - Input variable
dimensions - Input variable (Size: AtLeast(min=0))
Reverse sequence op: for each slice along dimension seqDimension, the first seqLength values are reversed
x (NUMERIC) - Input variable
seq_lengths (INT) - Length of the sequences
seqDim - Sequence dimension - default = -1
batchDim - Batch dimension - default = 0
Element-wise scalar floor modulus operation: out = floorMod(in, value).
i.e., returns the remainder after division by 'value'
in (NUMERIC) - Input variable
value - Scalar value to compare
Element-wise scalar maximum operation: out = max(in, value)
in (NUMERIC) - Input variable
value - Scalar value to compare
Element-wise scalar minimum operation: out = min(in, value)
in (NUMERIC) - Input variable
value - Scalar value to compare
Return a variable with equal shape to the input, but all elements set to value 'set'
in (NUMERIC) - Input variable
set - Value to set
Scatter addition operation.
If indices is rank 0 (a scalar), then out[index, ...] = out[index, ...] + op(updates[...])
If indices is rank 1 (a vector), then for each position i, out[indices[i], ...] = out[indices[i], ...] + op(updates[i, ...])
If indices is rank 2+, then for each position (i,...,k), out[indices[i], ..., indices[k], ...] = out[indices[i], ..., indices[k], ...] + op(updates[i, ..., k, ...])
Note that if multiple indices refer to the same location, the contributions from each is handled correctly.
ref (NUMERIC) - Initial/source variable
indices (NUMERIC) - Indices array
updates (NUMERIC) - Updates to add to the initial/source array
Scatter division operation.
If indices is rank 0 (a scalar), then out[index, ...] = out[index, ...] + op(updates[...])
If indices is rank 1 (a vector), then for each position i, out[indices[i], ...] = out[indices[i], ...] + op(updates[i, ...])
If indices is rank 2+, then for each position (i,...,k), out[indices[i], ..., indices[k], ...] = out[indices[i], ..., indices[k], ...] + op(updates[i, ..., k, ...])
Note that if multiple indices refer to the same location, the contributions from each is handled correctly.
ref (NUMERIC) - Initial/source variable
indices (NUMERIC) - Indices array
updates (NUMERIC) - Updates to add to the initial/source array
Scatter max operation.
If indices is rank 0 (a scalar), then out[index, ...] = out[index, ...] + op(updates[...])
If indices is rank 1 (a vector), then for each position i, out[indices[i], ...] = out[indices[i], ...] + op(updates[i, ...])
If indices is rank 2+, then for each position (i,...,k), out[indices[i], ..., indices[k], ...] = out[indices[i], ..., indices[k], ...] + op(updates[i, ..., k, ...])
Note that if multiple indices refer to the same location, the contributions from each is handled correctly.
ref (NUMERIC) - Initial/source variable
indices (NUMERIC) - Indices array
updates (NUMERIC) - Updates to add to the initial/source array
Scatter min operation.
If indices is rank 0 (a scalar), then out[index, ...] = out[index, ...] + op(updates[...])
If indices is rank 1 (a vector), then for each position i, out[indices[i], ...] = out[indices[i], ...] + op(updates[i, ...])
If indices is rank 2+, then for each position (i,...,k), out[indices[i], ..., indices[k], ...] = out[indices[i], ..., indices[k], ...] + op(updates[i, ..., k, ...])
Note that if multiple indices refer to the same location, the contributions from each is handled correctly.
ref (NUMERIC) - Initial/source variable
indices (NUMERIC) - Indices array
updates (NUMERIC) - Updates to add to the initial/source array
Scatter multiplication operation.
If indices is rank 0 (a scalar), then out[index, ...] = out[index, ...] + op(updates[...])
If indices is rank 1 (a vector), then for each position i, out[indices[i], ...] = out[indices[i], ...] + op(updates[i, ...])
If indices is rank 2+, then for each position (i,...,k), out[indices[i], ..., indices[k], ...] = out[indices[i], ..., indices[k], ...] + op(updates[i, ..., k, ...])
Note that if multiple indices refer to the same location, the contributions from each is handled correctly.
ref (NUMERIC) - Initial/source variable
indices (NUMERIC) - Indices array
updates (NUMERIC) - Updates to add to the initial/source array
Scatter subtraction operation.
If indices is rank 0 (a scalar), then out[index, ...] = out[index, ...] + op(updates[...])
If indices is rank 1 (a vector), then for each position i, out[indices[i], ...] = out[indices[i], ...] + op(updates[i, ...])
If indices is rank 2+, then for each position (i,...,k), out[indices[i], ..., indices[k], ...] = out[indices[i], ..., indices[k], ...] + op(updates[i, ..., k, ...])
Note that if multiple indices refer to the same location, the contributions from each is handled correctly.
ref (NUMERIC) - Initial/source variable
indices (NUMERIC) - Indices array
updates (NUMERIC) - Updates to add to the initial/source array
Scatter update operation.
If indices is rank 0 (a scalar), then out[index, ...] = out[index, ...] + op(updates[...])
If indices is rank 1 (a vector), then for each position i, out[indices[i], ...] = out[indices[i], ...] + op(updates[i, ...])
If indices is rank 2+, then for each position (i,...,k), out[indices[i], ..., indices[k], ...] = out[indices[i], ..., indices[k], ...] + op(updates[i, ..., k, ...])
Note that if multiple indices refer to the same location, the contributions from each is handled correctly.
ref (NUMERIC) - Initial/source variable
indices (NUMERIC) - Indices array
updates (NUMERIC) - Updates to add to the initial/source array
Segment max operation.
If data = [3, 6, 1, 4, 9, 2, 8]
segmentIds = [0, 0, 1, 1, 1, 2, 2]
then output = [6, 9, 8] = [op(3,6), op(1,4,9), op(2,8)]
Note that the segment IDs must be sorted from smallest to largest segment.
See {unsortedSegment (String, SDVariable, SDVariable, int) ops
for the same op without this sorted requirement
data (NDARRAY) - Data to perform segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
Segment mean operation.
If data = [3, 6, 1, 4, 9, 2, 8]
segmentIds = [0, 0, 1, 1, 1, 2, 2]
then output = [6, 9, 8] = [op(3,6), op(1,4,9), op(2,8)]
Note that the segment IDs must be sorted from smallest to largest segment.
See {unsortedSegment (String, SDVariable, SDVariable, int) ops
for the same op without this sorted requirement
data (NDARRAY) - Data to perform segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
Segment min operation.
If data = [3, 6, 1, 4, 9, 2, 8]
segmentIds = [0, 0, 1, 1, 1, 2, 2]
then output = [6, 9, 8] = [op(3,6), op(1,4,9), op(2,8)]
Note that the segment IDs must be sorted from smallest to largest segment.
See {unsortedSegment (String, SDVariable, SDVariable, int) ops
for the same op without this sorted requirement
data (NDARRAY) - Data to perform segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
Segment product operation.
If data = [3, 6, 1, 4, 9, 2, 8]
segmentIds = [0, 0, 1, 1, 1, 2, 2]
then output = [6, 9, 8] = [op(3,6), op(1,4,9), op(2,8)]
Note that the segment IDs must be sorted from smallest to largest segment.
See {unsortedSegment (String, SDVariable, SDVariable, int) ops
for the same op without this sorted requirement
data (NDARRAY) - Data to perform segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
Segment sum operation.
If data = [3, 6, 1, 4, 9, 2, 8]
segmentIds = [0, 0, 1, 1, 1, 2, 2]
then output = [6, 9, 8] = [op(3,6), op(1,4,9), op(2,8)]
Note that the segment IDs must be sorted from smallest to largest segment.
See {unsortedSegment (String, SDVariable, SDVariable, int) ops
for the same op without this sorted requirement
data (NDARRAY) - Data to perform segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? 1.0 : 0.0)
lengths (NUMERIC) - Lengths of the sequences
maxLen - Maximum sequence length
dataType -
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? 1.0 : 0.0)
lengths (NUMERIC) - Lengths of the sequences
maxLen (INT) - Maximum sequence length
dataType -
see sequenceMask(String, SDVariable, SDVariable, DataType)
lengths (NUMERIC) -
dataType -
Returns the shape of the specified INDArray as a 1D INDArray
input (NUMERIC) - Input variable
Returns the size (number of elements, i.e., prod(shape)) of the specified INDArray as a 0D scalar variable
in (NUMERIC) - Input variable
Returns a rank 0 (scalar) variable for the size of the specified dimension.
For example, if X has shape [10,20,30] then sizeAt(X,1)=20. Similarly, sizeAt(X,-1)=30
in (NUMERIC) - Input variable
dimension - Dimension to get size of
Get a subset of the specified input, by specifying the first element and the size of the array.
For example, if input is:
[a, b, c]
[d, e, f]
then slice(input, begin=[0,1], size=[2,1] will return:
[b]
[e]
Note that for each dimension i, begin[i] + size[i] <= input.size(i)
input (NUMERIC) - input Variable to get subset of
begin - Beginning index. Must be same length as rank of input array (Size: AtLeast(min=1))
size - Size of the output array. Must be same length as rank of input array (Size: AtLeast(min=1))
Get a subset of the specified input, by specifying the first element and the size of the array.
For example, if input is:
[a, b, c]
[d, e, f]
then slice(input, begin=[0,1], size=[2,1] will return:
[b]
[e]
Note that for each dimension i, begin[i] + size[i] <= input.size(i)
input (NUMERIC) - input Variable to get subset of
begin (INT) - Beginning index. Must be same length as rank of input array
size (INT) - Size of the output array. Must be same length as rank of input array
Squared L2 norm: see norm2(String, SDVariable, boolean, int...)
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) -
keepDims - - default = false
dimensions - (Size: AtLeast(min=0))
Remove a single dimension of size 1.
For example, if input has shape [a,b,1,c] then squeeze(input, 2) returns an array of shape [a,b,c]
x (NUMERIC) - Input variable
axis - Size 1 dimension to remove
Stack a set of N INDArray of rank X into one rank X+1 variable.
If inputs have shape [a,b,c] then output has shape:
axis = 0: [N,a,b,c]
axis = 1: [a,N,b,c]
axis = 2: [a,b,N,c]
axis = 3: [a,b,c,N]
see unstack(String[], SDVariable, int, int)
values (NDARRAY) - Input variables to stack. Must have the same shape for all inputs
axis - Axis to stack on
Stardard deviation 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
biasCorrected - If true: divide by (N-1) (i.e., sample stdev). If false: divide by N (population stdev)
keepDims - If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions - default = false
Get a subset of the specified input, by specifying the first element, last element, and the strides.
For example, if input is:
[a, b, c]
[d, e, f]
[g, h, i]
then stridedSlice(input, begin=[0,1], end=[2,2], strides=[2,1], all masks = 0) will return:
[b, c]
[h, i]
in (NUMERIC) - Variable to get subset of
begin - Beginning index (Size: AtLeast(min=1))
end - End index (Size: AtLeast(min=1))
strides
Sum 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 length 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))
Switch operation
Predictate - if false, values are output to left (first) branch/output; if true, to right (second) branch/output
x (NDARRAY) - Input variable
predicate (BOOL) - Predictate - if false, values are output to left (first) branch/output; if true, to right (second) branch/output
//TODO: Ops must be documented.
x (NUMERIC) - Input variable x
y (NUMERIC) - Input variable y
dimensionsX - dimensions for first input array (x) (Size: AtLeast(min=1))
dimensionsY
Repeat (tile) the input tensor the specified number of times.
For example, if input is
[1, 2]
[3, 4]
and repeat is [2, 3]
then output is
[1, 2, 1, 2, 1, 2]
[3, 4, 3, 4, 3, 4]
[1, 2, 1, 2, 1, 2]
[3, 4, 3, 4, 3, 4]
x (NDARRAY) - Input variable
repeat (INT) - Number of times to repeat in each axis. Must have length equal to the rank of the input array
see tile(String, SDVariable, int...)
x (NDARRAY) -
repeat - (Size: AtLeast(min=1))
Matrix transpose operation: If input has shape [a,b] output has shape [b,a]
x (NDARRAY) - Input variable
Unsorted segment max operation. As per segmentMax(String, SDVariable, SDVariable) but without
the requirement for the indices to be sorted.
If data = [1, 3, 2, 6, 4, 9, 8]
segmentIds = [1, 0, 2, 0, 1, 1, 2]
then output = [6, 9, 8] = [max(3,6), max(1,4,9), max(2,8)]
data (NUMERIC) - Data (variable) to perform unsorted segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
numSegments - Number of segments
Unsorted segment mean operation. As per segmentMean(String, SDVariable, SDVariable) but without
the requirement for the indices to be sorted.
If data = [1, 3, 2, 6, 4, 9, 8]
segmentIds = [1, 0, 2, 0, 1, 1, 2]
then output = [4.5, 4.666, 5] = [mean(3,6), mean(1,4,9), mean(2,8)]
data (NUMERIC) - Data (variable) to perform unsorted segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
numSegments - Number of segments
Unsorted segment min operation. As per segmentMin(String, SDVariable, SDVariable) but without
the requirement for the indices to be sorted.
If data = [1, 3, 2, 6, 4, 9, 8]
segmentIds = [1, 0, 2, 0, 1, 1, 2]
then output = [3, 1, 2] = [min(3,6), min(1,4,9), min(2,8)]
data (NUMERIC) - Data (variable) to perform unsorted segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
numSegments - Number of segments
Unsorted segment product operation. As per segmentProd(String, SDVariable, SDVariable) but without
the requirement for the indices to be sorted.
If data = [1, 3, 2, 6, 4, 9, 8]
segmentIds = [1, 0, 2, 0, 1, 1, 2]
then output = [4.5, 4.666, 5] = [mean(3,6), mean(1,4,9), mean(2,8)]
data (NUMERIC) - Data (variable) to perform unsorted segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
numSegments - Number of segments
Unsorted segment sqrtN operation. Simply returns the sqrt of the count of the number of values in each segment
If data = [1, 3, 2, 6, 4, 9, 8]
segmentIds = [1, 0, 2, 0, 1, 1, 2]
then output = [1.414, 1.732, 1.414] = [sqrt(2), sqrtN(3), sqrtN(2)]
data (NUMERIC) - Data (variable) to perform unsorted segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
numSegments - Number of segments
Unsorted segment sum operation. As per segmentSum(String, SDVariable, SDVariable) but without
the requirement for the indices to be sorted.
If data = [1, 3, 2, 6, 4, 9, 8]
segmentIds = [1, 0, 2, 0, 1, 1, 2]
then output = [9, 14, 10] = [sum(3,6), sum(1,4,9), sum(2,8)]
data (NUMERIC) - Data (variable) to perform unsorted segment max on
segmentIds (NUMERIC) - Variable for the segment IDs
numSegments - Number of segments
Unstack a variable of rank X into N rank X-1 variables by taking slices along the specified axis.
If input has shape [a,b,c] then output has shape:
axis = 0: [b,c]
axis = 1: [a,c]
axis = 2: [a,b]
value (NDARRAY) - Input variable to unstack
axis - Axis to unstack on
num - Number of output variables
Variance 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
biasCorrected - If true: divide by (N-1) (i.e., sample variable). If false: divide by N (population variance)
keepDims - If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions - default = false
Return a variable of all 0s, with the same shape as the input variable. Note that this is dynamic:
if the input shape changes in later execution, the returned variable's shape will also be updated
input (NUMERIC) - Input
transposeZ - Transpose result array - default = false
off -
dataType - Output data type - default = DataType.FLOAT
beginMask - Bit mask: If the ith bit is set to 1, then the value in the begin long[] is ignored, and a value of 0 is used instead for the beginning index for that dimension - default = 0
endMask - Bit mask: If the ith bit is set to 1, then the value in the end long[] is ignored, and a value of size(i)-1 is used instead for the end index for that dimension - default = 0
ellipsisMask - Bit mask: only one non-zero value is allowed here. If a non-zero value is set, then other dimensions are inserted as required at the specified position - default = 0
newAxisMask - Bit mask: if the ith bit is set to 1, then the begin/end/stride values are ignored, and a size 1 dimension is inserted at this point - default = 0
shrinkAxisMask - Bit mask: if the ith bit is set to 1, then the begin/end/stride values are ignored, and a size 1 dimension is removed at this point. Note that begin/end/stride values must result in a size 1 output for these dimensions - default = 0
transposeX - Transpose x (first argument) - default = false
transposeY - Transpose y (second argument) - default = false
transposeZ - Transpose result array - default = false
INDArray all(INDArray x, int[] dimensions)
SDVariable all(SDVariable x, int[] dimensions)
SDVariable all(String name, SDVariable x, int[] dimensions)INDArray any(INDArray x, int[] dimensions)
SDVariable any(SDVariable x, int[] dimensions)
SDVariable any(String name, SDVariable x, int[] dimensions)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)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)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)INDArray castTo(INDArray arg, DataType datatype)
SDVariable castTo(SDVariable arg, DataType datatype)
SDVariable castTo(String name, SDVariable arg, DataType datatype)INDArray concat(INDArray inputs, int dimension)
SDVariable concat(SDVariable inputs, int dimension)
SDVariable concat(String name, SDVariable inputs, int dimension)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)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)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)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)input = [1,2,3,4,5]
numPartitions = 2
partitions = [1,0,0,1,0]
out[0] = [2,3,5]
out[1] = [1,4] `INDArray dynamicStitch(INDArray indices, INDArray x)
SDVariable dynamicStitch(SDVariable indices, SDVariable x)
SDVariable dynamicStitch(String name, SDVariable indices, SDVariable x)INDArray eq(INDArray x, double y)
SDVariable eq(SDVariable x, double y)
SDVariable eq(String name, SDVariable x, double y)INDArray eq(INDArray x, INDArray y)
SDVariable eq(SDVariable x, SDVariable y)
SDVariable eq(String name, SDVariable x, SDVariable y)INDArray expandDims(INDArray x, int axis)
SDVariable expandDims(SDVariable x, int axis)
SDVariable expandDims(String name, SDVariable x, int axis)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)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)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)INDArray gatherNd(INDArray df, INDArray indices)
SDVariable gatherNd(SDVariable df, SDVariable indices)
SDVariable gatherNd(String name, SDVariable df, SDVariable indices)INDArray gt(INDArray x, double y)
SDVariable gt(SDVariable x, double y)
SDVariable gt(String name, SDVariable x, double y)INDArray gt(INDArray x, INDArray y)
SDVariable gt(SDVariable x, SDVariable y)
SDVariable gt(String name, SDVariable x, SDVariable y)INDArray gte(INDArray x, double y)
SDVariable gte(SDVariable x, double y)
SDVariable gte(String name, SDVariable x, double y)INDArray gte(INDArray x, INDArray y)
SDVariable gte(SDVariable x, SDVariable y)
SDVariable gte(String name, SDVariable x, SDVariable y)INDArray identity(INDArray input)
SDVariable identity(SDVariable input)
SDVariable identity(String name, SDVariable input)INDArray invertPermutation(INDArray input)
SDVariable invertPermutation(SDVariable input)
SDVariable invertPermutation(String name, SDVariable input)INDArray isNumericTensor(INDArray x)
SDVariable isNumericTensor(SDVariable x)
SDVariable isNumericTensor(String name, SDVariable x)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)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)INDArray lt(INDArray x, double y)
SDVariable lt(SDVariable x, double y)
SDVariable lt(String name, SDVariable x, double y)INDArray lt(INDArray x, INDArray y)
SDVariable lt(SDVariable x, SDVariable y)
SDVariable lt(String name, SDVariable x, SDVariable y)INDArray lte(INDArray x, double y)
SDVariable lte(SDVariable x, double y)
SDVariable lte(String name, SDVariable x, double y)INDArray lte(INDArray x, INDArray y)
SDVariable lte(SDVariable x, SDVariable y)
SDVariable lte(String name, SDVariable x, SDVariable y)INDArray matchCondition(INDArray in, Condition condition)
SDVariable matchCondition(SDVariable in, Condition condition)
SDVariable matchCondition(String name, SDVariable in, Condition condition)INDArray matchConditionCount(INDArray in, Condition condition)
SDVariable matchConditionCount(SDVariable in, Condition condition)
SDVariable matchConditionCount(String name, SDVariable in, Condition condition)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)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)INDArray max(INDArray first, INDArray second)
SDVariable max(SDVariable first, SDVariable second)
SDVariable max(String name, SDVariable first, SDVariable second)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)INDArray merge(INDArray x, INDArray y)
SDVariable merge(SDVariable x, SDVariable y)
SDVariable merge(String name, SDVariable x, SDVariable y)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)INDArray min(INDArray first, INDArray second)
SDVariable min(SDVariable first, SDVariable second)
SDVariable min(String name, SDVariable first, SDVariable second)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)INDArray neq(INDArray x, double y)
SDVariable neq(SDVariable x, double y)
SDVariable neq(String name, SDVariable x, double y)INDArray neq(INDArray x, INDArray y)
SDVariable neq(SDVariable x, SDVariable y)
SDVariable neq(String name, SDVariable x, SDVariable y)INDArray norm1(INDArray x, boolean keepDims, int[] dimensions)
INDArray norm1(INDArray x, int[] dimensions)
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, int[] dimensions)INDArray norm2(INDArray x, boolean keepDims, int[] dimensions)
INDArray norm2(INDArray x, int[] dimensions)
SDVariable norm2(SDVariable x, boolean keepDims, int[] dimensions)
SDVariable norm2(SDVariable x, int[] dimensions)
SDVariable norm2(String name, SDVariable x, boolean keepDims, int[] dimensions)
SDVariable norm2(String name, SDVariable x, int[] dimensions)INDArray normmax(INDArray x, boolean keepDims, int[] dimensions)
INDArray normmax(INDArray x, int[] dimensions)
SDVariable normmax(SDVariable x, boolean keepDims, int[] dimensions)
SDVariable normmax(SDVariable x, int[] dimensions)
SDVariable normmax(String name, SDVariable x, boolean keepDims, int[] dimensions)
SDVariable normmax(String name, SDVariable x, int[] dimensions)INDArray oneHot(INDArray indices, int depth, int axis, double on, double off, DataType dataType)
INDArray oneHot(INDArray indices, int depth, int axis, double on, double off)
SDVariable oneHot(SDVariable indices, int depth, int axis, double on, double off, DataType dataType)
SDVariable oneHot(SDVariable indices, int depth, int axis, double on, double off)
SDVariable oneHot(String name, SDVariable indices, int depth, int axis, double on, double off, DataType dataType)
SDVariable oneHot(String name, SDVariable indices, int depth, int axis, double on, double off)INDArray oneHot(INDArray indices, int depth)
SDVariable oneHot(SDVariable indices, int depth)
SDVariable oneHot(String name, SDVariable indices, int depth)INDArray onesLike(INDArray input)
SDVariable onesLike(SDVariable input)
SDVariable onesLike(String name, SDVariable input)INDArray onesLike(INDArray input, DataType dataType)
SDVariable onesLike(SDVariable input, DataType dataType)
SDVariable onesLike(String name, SDVariable input, DataType dataType)INDArray permute(INDArray x, INDArray dimensions)
SDVariable permute(SDVariable x, SDVariable dimensions)
SDVariable permute(String name, SDVariable x, SDVariable dimensions)INDArray permute(INDArray x, int[] dimensions)
SDVariable permute(SDVariable x, int[] dimensions)
SDVariable permute(String name, SDVariable x, int[] dimensions)INDArray prod(INDArray x, boolean keepDims, int[] dimensions)
INDArray prod(INDArray x, int[] dimensions)
SDVariable prod(SDVariable x, boolean keepDims, int[] dimensions)
SDVariable prod(SDVariable x, int[] dimensions)
SDVariable prod(String name, SDVariable x, boolean keepDims, int[] dimensions)
SDVariable prod(String name, SDVariable x, int[] dimensions)INDArray range(double from, double to, double step, DataType dataType)
SDVariable range(double from, double to, double step, DataType dataType)
SDVariable range(String name, double from, double to, double step, DataType dataType)INDArray range(INDArray from, INDArray to, INDArray step, DataType dataType)
SDVariable range(SDVariable from, SDVariable to, SDVariable step, DataType dataType)
SDVariable range(String name, SDVariable from, SDVariable to, SDVariable step, DataType dataType)INDArray rank(INDArray in)
SDVariable rank(SDVariable in)
SDVariable rank(String name, SDVariable in)INDArray replaceWhere(INDArray update, INDArray from, Condition condition)
SDVariable replaceWhere(SDVariable update, SDVariable from, Condition condition)
SDVariable replaceWhere(String name, SDVariable update, SDVariable from, Condition condition)INDArray replaceWhere(INDArray update, double value, Condition condition)
SDVariable replaceWhere(SDVariable update, double value, Condition condition)
SDVariable replaceWhere(String name, SDVariable update, double value, Condition condition)INDArray reshape(INDArray x, INDArray shape)
SDVariable reshape(SDVariable x, SDVariable shape)
SDVariable reshape(String name, SDVariable x, SDVariable shape)INDArray reshape(INDArray x, long[] shape)
SDVariable reshape(SDVariable x, long[] shape)
SDVariable reshape(String name, SDVariable x, long[] shape)INDArray reverse(INDArray x, int[] dimensions)
SDVariable reverse(SDVariable x, int[] dimensions)
SDVariable reverse(String name, SDVariable x, int[] dimensions)INDArray reverseSequence(INDArray x, INDArray seq_lengths, int seqDim, int batchDim)
INDArray reverseSequence(INDArray x, INDArray seq_lengths)
SDVariable reverseSequence(SDVariable x, SDVariable seq_lengths, int seqDim, int batchDim)
SDVariable reverseSequence(SDVariable x, SDVariable seq_lengths)
SDVariable reverseSequence(String name, SDVariable x, SDVariable seq_lengths, int seqDim, int batchDim)
SDVariable reverseSequence(String name, SDVariable x, SDVariable seq_lengths)INDArray scalarFloorMod(INDArray in, double value)
SDVariable scalarFloorMod(SDVariable in, double value)
SDVariable scalarFloorMod(String name, SDVariable in, double value)INDArray scalarMax(INDArray in, double value)
SDVariable scalarMax(SDVariable in, double value)
SDVariable scalarMax(String name, SDVariable in, double value)INDArray scalarMin(INDArray in, double value)
SDVariable scalarMin(SDVariable in, double value)
SDVariable scalarMin(String name, SDVariable in, double value)INDArray scalarSet(INDArray in, double set)
SDVariable scalarSet(SDVariable in, double set)
SDVariable scalarSet(String name, SDVariable in, double set)INDArray scatterAdd(INDArray ref, INDArray indices, INDArray updates)
SDVariable scatterAdd(SDVariable ref, SDVariable indices, SDVariable updates)
SDVariable scatterAdd(String name, SDVariable ref, SDVariable indices, SDVariable updates)INDArray scatterDiv(INDArray ref, INDArray indices, INDArray updates)
SDVariable scatterDiv(SDVariable ref, SDVariable indices, SDVariable updates)
SDVariable scatterDiv(String name, SDVariable ref, SDVariable indices, SDVariable updates)INDArray scatterMax(INDArray ref, INDArray indices, INDArray updates)
SDVariable scatterMax(SDVariable ref, SDVariable indices, SDVariable updates)
SDVariable scatterMax(String name, SDVariable ref, SDVariable indices, SDVariable updates)INDArray scatterMin(INDArray ref, INDArray indices, INDArray updates)
SDVariable scatterMin(SDVariable ref, SDVariable indices, SDVariable updates)
SDVariable scatterMin(String name, SDVariable ref, SDVariable indices, SDVariable updates)INDArray scatterMul(INDArray ref, INDArray indices, INDArray updates)
SDVariable scatterMul(SDVariable ref, SDVariable indices, SDVariable updates)
SDVariable scatterMul(String name, SDVariable ref, SDVariable indices, SDVariable updates)INDArray scatterSub(INDArray ref, INDArray indices, INDArray updates)
SDVariable scatterSub(SDVariable ref, SDVariable indices, SDVariable updates)
SDVariable scatterSub(String name, SDVariable ref, SDVariable indices, SDVariable updates)INDArray scatterUpdate(INDArray ref, INDArray indices, INDArray updates)
SDVariable scatterUpdate(SDVariable ref, SDVariable indices, SDVariable updates)
SDVariable scatterUpdate(String name, SDVariable ref, SDVariable indices, SDVariable updates)INDArray segmentMax(INDArray data, INDArray segmentIds)
SDVariable segmentMax(SDVariable data, SDVariable segmentIds)
SDVariable segmentMax(String name, SDVariable data, SDVariable segmentIds)INDArray segmentMean(INDArray data, INDArray segmentIds)
SDVariable segmentMean(SDVariable data, SDVariable segmentIds)
SDVariable segmentMean(String name, SDVariable data, SDVariable segmentIds)INDArray segmentMin(INDArray data, INDArray segmentIds)
SDVariable segmentMin(SDVariable data, SDVariable segmentIds)
SDVariable segmentMin(String name, SDVariable data, SDVariable segmentIds)INDArray segmentProd(INDArray data, INDArray segmentIds)
SDVariable segmentProd(SDVariable data, SDVariable segmentIds)
SDVariable segmentProd(String name, SDVariable data, SDVariable segmentIds)INDArray segmentSum(INDArray data, INDArray segmentIds)
SDVariable segmentSum(SDVariable data, SDVariable segmentIds)
SDVariable segmentSum(String name, SDVariable data, SDVariable segmentIds)INDArray sequenceMask(INDArray lengths, int maxLen, DataType dataType)
SDVariable sequenceMask(SDVariable lengths, int maxLen, DataType dataType)
SDVariable sequenceMask(String name, SDVariable lengths, int maxLen, DataType dataType)INDArray sequenceMask(INDArray lengths, INDArray maxLen, DataType dataType)
SDVariable sequenceMask(SDVariable lengths, SDVariable maxLen, DataType dataType)
SDVariable sequenceMask(String name, SDVariable lengths, SDVariable maxLen, DataType dataType)INDArray sequenceMask(INDArray lengths, DataType dataType)
SDVariable sequenceMask(SDVariable lengths, DataType dataType)
SDVariable sequenceMask(String name, SDVariable lengths, DataType dataType)INDArray shape(INDArray input)
SDVariable shape(SDVariable input)
SDVariable shape(String name, SDVariable input)INDArray size(INDArray in)
SDVariable size(SDVariable in)
SDVariable size(String name, SDVariable in)INDArray sizeAt(INDArray in, int dimension)
SDVariable sizeAt(SDVariable in, int dimension)
SDVariable sizeAt(String name, SDVariable in, int dimension)INDArray slice(INDArray input, int[] begin, int[] size)
SDVariable slice(SDVariable input, int[] begin, int[] size)
SDVariable slice(String name, SDVariable input, int[] begin, int[] size)INDArray slice(INDArray input, INDArray begin, INDArray size)
SDVariable slice(SDVariable input, SDVariable begin, SDVariable size)
SDVariable slice(String name, SDVariable input, SDVariable begin, SDVariable size)INDArray squaredNorm(INDArray x, boolean keepDims, int[] dimensions)
INDArray squaredNorm(INDArray x, int[] dimensions)
SDVariable squaredNorm(SDVariable x, boolean keepDims, int[] dimensions)
SDVariable squaredNorm(SDVariable x, int[] dimensions)
SDVariable squaredNorm(String name, SDVariable x, boolean keepDims, int[] dimensions)
SDVariable squaredNorm(String name, SDVariable x, int[] dimensions)INDArray squeeze(INDArray x, int axis)
SDVariable squeeze(SDVariable x, int axis)
SDVariable squeeze(String name, SDVariable x, int axis)INDArray stack(INDArray values, int axis)
SDVariable stack(SDVariable values, int axis)
SDVariable stack(String name, SDVariable values, int axis)INDArray standardDeviation(INDArray x, boolean biasCorrected, boolean keepDims, int[] dimensions)
INDArray standardDeviation(INDArray x, boolean biasCorrected, int[] dimensions)
SDVariable standardDeviation(SDVariable x, boolean biasCorrected, boolean keepDims, int[] dimensions)
SDVariable standardDeviation(SDVariable x, boolean biasCorrected, int[] dimensions)
SDVariable standardDeviation(String name, SDVariable x, boolean biasCorrected, boolean keepDims, int[] dimensions)
SDVariable standardDeviation(String name, SDVariable x, boolean biasCorrected, int[] dimensions)INDArray stridedSlice(INDArray in, long[] begin, long[] end, long[] strides, int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask)
INDArray stridedSlice(INDArray in, long[] begin, long[] end, long[] strides)
SDVariable stridedSlice(SDVariable in, long[] begin, long[] end, long[] strides, int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask)
SDVariable stridedSlice(SDVariable in, long[] begin, long[] end, long[] strides)
SDVariable stridedSlice(String name, SDVariable in, long[] begin, long[] end, long[] strides, int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask)
SDVariable stridedSlice(String name, SDVariable in, long[] begin, long[] end, long[] strides)INDArray sum(INDArray x, boolean keepDims, int[] dimensions)
INDArray sum(INDArray x, int[] dimensions)
SDVariable sum(SDVariable x, boolean keepDims, int[] dimensions)
SDVariable sum(SDVariable x, int[] dimensions)
SDVariable sum(String name, SDVariable x, boolean keepDims, int[] dimensions)
SDVariable sum(String name, SDVariable x, int[] dimensions)INDArray[] switchOp(INDArray x, INDArray predicate)
SDVariable[] switchOp(SDVariable x, SDVariable predicate)
SDVariable[] switchOp(String name, SDVariable x, SDVariable predicate)INDArray tensorMmul(INDArray x, INDArray y, int[] dimensionsX, int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ)
INDArray tensorMmul(INDArray x, INDArray y, int[] dimensionsX, int[] dimensionsY)
SDVariable tensorMmul(SDVariable x, SDVariable y, int[] dimensionsX, int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ)
SDVariable tensorMmul(SDVariable x, SDVariable y, int[] dimensionsX, int[] dimensionsY)
SDVariable tensorMmul(String name, SDVariable x, SDVariable y, int[] dimensionsX, int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ)
SDVariable tensorMmul(String name, SDVariable x, SDVariable y, int[] dimensionsX, int[] dimensionsY)INDArray tile(INDArray x, INDArray repeat)
SDVariable tile(SDVariable x, SDVariable repeat)
SDVariable tile(String name, SDVariable x, SDVariable repeat)INDArray tile(INDArray x, int[] repeat)
SDVariable tile(SDVariable x, int[] repeat)
SDVariable tile(String name, SDVariable x, int[] repeat)INDArray transpose(INDArray x)
SDVariable transpose(SDVariable x)
SDVariable transpose(String name, SDVariable x)INDArray unsortedSegmentMax(INDArray data, INDArray segmentIds, int numSegments)
SDVariable unsortedSegmentMax(SDVariable data, SDVariable segmentIds, int numSegments)
SDVariable unsortedSegmentMax(String name, SDVariable data, SDVariable segmentIds, int numSegments)INDArray unsortedSegmentMean(INDArray data, INDArray segmentIds, int numSegments)
SDVariable unsortedSegmentMean(SDVariable data, SDVariable segmentIds, int numSegments)
SDVariable unsortedSegmentMean(String name, SDVariable data, SDVariable segmentIds, int numSegments)INDArray unsortedSegmentMin(INDArray data, INDArray segmentIds, int numSegments)
SDVariable unsortedSegmentMin(SDVariable data, SDVariable segmentIds, int numSegments)
SDVariable unsortedSegmentMin(String name, SDVariable data, SDVariable segmentIds, int numSegments)INDArray unsortedSegmentProd(INDArray data, INDArray segmentIds, int numSegments)
SDVariable unsortedSegmentProd(SDVariable data, SDVariable segmentIds, int numSegments)
SDVariable unsortedSegmentProd(String name, SDVariable data, SDVariable segmentIds, int numSegments)INDArray unsortedSegmentSqrtN(INDArray data, INDArray segmentIds, int numSegments)
SDVariable unsortedSegmentSqrtN(SDVariable data, SDVariable segmentIds, int numSegments)
SDVariable unsortedSegmentSqrtN(String name, SDVariable data, SDVariable segmentIds, int numSegments)INDArray unsortedSegmentSum(INDArray data, INDArray segmentIds, int numSegments)
SDVariable unsortedSegmentSum(SDVariable data, SDVariable segmentIds, int numSegments)
SDVariable unsortedSegmentSum(String name, SDVariable data, SDVariable segmentIds, int numSegments)void unstack(INDArray value, int axis, int num)
void unstack(SDVariable value, int axis, int num)
void unstack(String name, SDVariable value, int axis, int num)INDArray variance(INDArray x, boolean biasCorrected, boolean keepDims, int[] dimensions)
INDArray variance(INDArray x, boolean biasCorrected, int[] dimensions)
SDVariable variance(SDVariable x, boolean biasCorrected, boolean keepDims, int[] dimensions)
SDVariable variance(SDVariable x, boolean biasCorrected, int[] dimensions)
SDVariable variance(String name, SDVariable x, boolean biasCorrected, boolean keepDims, int[] dimensions)
SDVariable variance(String name, SDVariable x, boolean biasCorrected, int[] dimensions)INDArray zerosLike(INDArray input)
SDVariable zerosLike(SDVariable input)
SDVariable zerosLike(String name, SDVariable input)