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
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
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
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
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
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
Cast the array to a new datatype - for example, Integer -> Float
arg (NDARRAY) - Input variable to cast
datatype - Datatype to cast to
concat
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
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
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
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
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
dynamicStitch
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
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
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
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
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
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
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
Gather slices from df with shape specified by indices.
df (NUMERIC) -
indices (NUMERIC) -
gt
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
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
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
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
Elementwise identity operation: out = x
input (NUMERIC) - Input variable
invertPermutation
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
Is the director a numeric tensor? In the current version of ND4J/SameDiff, this always returns true/1
x (NUMERIC) - Input variable
linspace
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
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
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
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
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
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
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
Returns a count of the number of elements that satisfy the condition
in (NUMERIC) - Input
condition - Condition
matchConditionCount
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
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
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
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
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
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
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
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
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
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
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
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))
normmax
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))
oneHot
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 -
off -
dataType - Output data type - default = DataType.FLOAT
oneHot
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
onesLike
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
onesLike
As per onesLike(String, SDVariable) but the output datatype may be specified
input (NUMERIC) -
dataType -
permute
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
permute
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))
prod
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))
range
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 -
range
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 -
rank
Returns the rank (number of dimensions, i.e., length(shape)) of the specified INDArray as a 0D scalar variable
in (NUMERIC) - Input variable
replaceWhere
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
replaceWhere
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
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
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
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))
reverseSequence
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
scalarFloorMod
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
scalarMax
Element-wise scalar maximum operation: out = max(in, value)
in (NUMERIC) - Input variable
value - Scalar value to compare
scalarMin
Element-wise scalar minimum operation: out = min(in, value)
in (NUMERIC) - Input variable
value - Scalar value to compare
scalarSet
Return a variable with equal shape to the input, but all elements set to value 'set'
in (NUMERIC) - Input variable
set - Value to set
scatterAdd
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
scatterDiv
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
scatterMax
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
scatterMin
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
scatterMul
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
scatterSub
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
scatterUpdate
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
segmentMax
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
segmentMean
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
segmentMin
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
segmentProd
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
segmentSum
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
sequenceMask
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 -
sequenceMask
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 -
sequenceMask
see sequenceMask(String, SDVariable, SDVariable, DataType)
lengths (NUMERIC) -
dataType -
shape
Returns the shape of the specified INDArray as a 1D INDArray
input (NUMERIC) - Input variable
size
Returns the size (number of elements, i.e., prod(shape)) of the specified INDArray as a 0D scalar variable
in (NUMERIC) - Input variable
sizeAt
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
slice
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))
slice
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
squaredNorm
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))
squeeze
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
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
standardDeviation
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
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
stridedSlice
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 - Stride ("step size") for each dimension. For example, stride of 2 means take every second element. (Size: AtLeast(min=1))
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
sum
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))
switchOp
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
tensorMmul
//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 - dimensions for second input array (y) (Size: AtLeast(min=1))
transposeX - Transpose x (first argument) - default = false
transposeY - Transpose y (second argument) - default = false
transposeZ - Transpose result array - default = false
tile
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
tile
see tile(String, SDVariable, int...)
x (NDARRAY) -
repeat - (Size: AtLeast(min=1))
transpose
Matrix transpose operation: If input has shape [a,b] output has shape [b,a]
x (NDARRAY) - Input variable
unsortedSegmentMax
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
unsortedSegmentMean
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
unsortedSegmentMin
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
unsortedSegmentProd
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
unsortedSegmentSqrtN
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
unsortedSegmentSum
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
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
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
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
zerosLike
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
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