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.
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: 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))
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
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 - Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))
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))
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: 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
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: 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
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: 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
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: 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
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: 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
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
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
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: https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
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: https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
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 - Transpose y (second argument) - default = false
transposeZ - Transpose result array - default = false
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: 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 (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 -
off -
dataType - Output data type - default = DataType.FLOAT
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
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
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 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 - 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
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
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
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