Modifier and Type | Method and Description |
---|---|
SDVariable |
all(SDVariable x,
int... dimensions)
Boolean and array reduction operation, optionally along specified dimensions
|
SDVariable |
all(String name,
SDVariable x,
int... dimensions)
Boolean and array reduction operation, optionally along specified dimensions
|
SDVariable |
any(SDVariable x,
int... dimensions)
Boolean or array reduction operation, optionally along specified dimensions
|
SDVariable |
any(String name,
SDVariable x,
int... dimensions)
Boolean or array reduction operation, optionally along specified dimensions
|
SDVariable |
argmax(SDVariable in,
boolean keepDims,
int... dimensions)
Argmax array reduction operation, optionally along specified dimensions.
|
SDVariable |
argmax(SDVariable in,
int... dimensions)
Argmax array reduction operation, optionally along specified dimensions.
|
SDVariable |
argmax(String name,
SDVariable in,
boolean keepDims,
int... dimensions)
Argmax array reduction operation, optionally along specified dimensions.
|
SDVariable |
argmax(String name,
SDVariable in,
int... dimensions)
Argmax array reduction operation, optionally along specified dimensions.
|
SDVariable |
argmin(SDVariable in,
boolean keepDims,
int... dimensions)
Argmin array reduction operation, optionally along specified dimensions.
|
SDVariable |
argmin(SDVariable in,
int... dimensions)
Argmin array reduction operation, optionally along specified dimensions.
|
SDVariable |
argmin(String name,
SDVariable in,
boolean keepDims,
int... dimensions)
Argmin array reduction operation, optionally along specified dimensions.
|
SDVariable |
argmin(String name,
SDVariable in,
int... dimensions)
Argmin array reduction operation, optionally along specified dimensions.
|
SDVariable[] |
batchMmul(SDVariable[] inputsA,
SDVariable... inputsB)
Matrix multiply a batch of matrices. matricesA and matricesB have to be arrays of same
length and each pair taken from these sets has to have dimensions (M, N) and (N, K), respectively. |
SDVariable[] |
batchMmul(SDVariable[] inputsA,
SDVariable[] inputsB,
boolean transposeA,
boolean transposeB)
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. |
SDVariable[] |
batchMmul(String[] names,
SDVariable[] inputsA,
SDVariable... inputsB)
Matrix multiply a batch of matrices. matricesA and matricesB have to be arrays of same
length and each pair taken from these sets has to have dimensions (M, N) and (N, K), respectively. |
SDVariable[] |
batchMmul(String[] names,
SDVariable[] inputsA,
SDVariable[] inputsB,
boolean transposeA,
boolean transposeB)
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. |
SDVariable |
castTo(SDVariable arg,
DataType datatype)
Cast the array to a new datatype - for example, Integer -> Float
|
SDVariable |
castTo(String name,
SDVariable arg,
DataType datatype)
Cast the array to a new datatype - for example, Integer -> Float
|
SDVariable |
concat(int dimension,
SDVariable... inputs)
Concatenate a set of inputs along the specified dimension.
|
SDVariable |
concat(String name,
int dimension,
SDVariable... inputs)
Concatenate a set of inputs along the specified dimension.
|
SDVariable |
cumprod(SDVariable in,
boolean exclusive,
boolean reverse,
int... axis)
Cumulative product operation.
|
SDVariable |
cumprod(SDVariable in,
int... axis)
Cumulative product operation.
|
SDVariable |
cumprod(String name,
SDVariable in,
boolean exclusive,
boolean reverse,
int... axis)
Cumulative product operation.
|
SDVariable |
cumprod(String name,
SDVariable in,
int... axis)
Cumulative product operation.
|
SDVariable |
cumsum(SDVariable in,
boolean exclusive,
boolean reverse,
int... axis)
Cumulative sum operation.
|
SDVariable |
cumsum(SDVariable in,
int... axis)
Cumulative sum operation.
|
SDVariable |
cumsum(String name,
SDVariable in,
boolean exclusive,
boolean reverse,
int... axis)
Cumulative sum operation.
|
SDVariable |
cumsum(String name,
SDVariable in,
int... axis)
Cumulative sum operation.
|
SDVariable |
dot(SDVariable x,
SDVariable y,
int... dimensions)
Pairwise dot product reduction along dimension
output = sum(i=0 ... size(dim)-1) x[i] * y[i] |
SDVariable |
dot(String name,
SDVariable x,
SDVariable y,
int... dimensions)
Pairwise dot product reduction along dimension
output = sum(i=0 ... size(dim)-1) x[i] * y[i] |
SDVariable[] |
dynamicPartition(SDVariable x,
SDVariable partitions,
int numPartitions)
Dynamically partition the input variable values into the specified number of paritions, using the indices.
|
SDVariable[] |
dynamicPartition(String[] names,
SDVariable x,
SDVariable partitions,
int numPartitions)
Dynamically partition the input variable values into the specified number of paritions, using the indices.
|
SDVariable |
dynamicStitch(SDVariable[] indices,
SDVariable... x)
Dynamically merge the specified input arrays into a single array, using the specified indices
|
SDVariable |
dynamicStitch(String name,
SDVariable[] indices,
SDVariable... x)
Dynamically merge the specified input arrays into a single array, using the specified indices
|
SDVariable |
eq(SDVariable x,
double y)
Equals operation: elementwise x == y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
eq(SDVariable x,
SDVariable y)
Equal to operation: elementwise x == y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
eq(String name,
SDVariable x,
double y)
Equals operation: elementwise x == y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
eq(String name,
SDVariable x,
SDVariable y)
Equal to operation: elementwise x == y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
expandDims(SDVariable x,
int axis)
Reshape the input by adding a 1 at the specified location.
|
SDVariable |
expandDims(String name,
SDVariable x,
int axis)
Reshape the input by adding a 1 at the specified location.
|
SDVariable |
fill(SDVariable shape,
DataType dataType,
double value)
Generate an output variable with the specified (dynamic) shape with all elements set to the specified value
|
SDVariable |
fill(String name,
SDVariable shape,
DataType dataType,
double value)
Generate an output variable with the specified (dynamic) shape with all elements set to the specified value
|
SDVariable |
gather(SDVariable df,
int[] indices,
int axis)
Gather slices from the input variable where the indices are specified as fixed int[] values.
|
SDVariable |
gather(SDVariable df,
SDVariable indices,
int axis)
Gather slices from the input variable where the indices are specified as dynamic array values.
|
SDVariable |
gather(String name,
SDVariable df,
int[] indices,
int axis)
Gather slices from the input variable where the indices are specified as fixed int[] values.
|
SDVariable |
gather(String name,
SDVariable df,
SDVariable indices,
int axis)
Gather slices from the input variable where the indices are specified as dynamic array values.
|
SDVariable |
gatherNd(SDVariable df,
SDVariable indices)
Gather slices from df with shape specified by indices.
|
SDVariable |
gatherNd(String name,
SDVariable df,
SDVariable indices)
Gather slices from df with shape specified by indices.
|
SDVariable |
gt(SDVariable x,
double y)
Greater than operation: elementwise x > y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
gt(SDVariable x,
SDVariable y)
Greater than operation: elementwise x > y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
gt(String name,
SDVariable x,
double y)
Greater than operation: elementwise x > y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
gt(String name,
SDVariable x,
SDVariable y)
Greater than operation: elementwise x > y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
gte(SDVariable x,
double y)
Greater than or equals operation: elementwise x >= y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
gte(SDVariable x,
SDVariable y)
Greater than or equal to operation: elementwise x >= y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
gte(String name,
SDVariable x,
double y)
Greater than or equals operation: elementwise x >= y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
gte(String name,
SDVariable x,
SDVariable y)
Greater than or equal to operation: elementwise x >= y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
identity(SDVariable input)
Elementwise identity operation: out = x
|
SDVariable |
identity(String name,
SDVariable input)
Elementwise identity operation: out = x
|
SDVariable |
invertPermutation(SDVariable input)
Compute the inverse permutation indices for a permutation operation
Example: if input is [2, 0, 1] then output is [1, 2, 0] The idea is that x.permute(input).permute(invertPermutation(input)) == x |
SDVariable |
invertPermutation(String name,
SDVariable input)
Compute the inverse permutation indices for a permutation operation
Example: if input is [2, 0, 1] then output is [1, 2, 0] The idea is that x.permute(input).permute(invertPermutation(input)) == x |
SDVariable |
isNumericTensor(SDVariable x)
Is the director a numeric tensor?
|
SDVariable |
isNumericTensor(String name,
SDVariable x)
Is the director a numeric tensor?
|
SDVariable |
linspace(DataType dataType,
double start,
double stop,
long number)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
SDVariable |
linspace(SDVariable start,
SDVariable stop,
SDVariable number,
DataType dataType)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
SDVariable |
linspace(String name,
DataType dataType,
double start,
double stop,
long number)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
SDVariable |
linspace(String name,
SDVariable start,
SDVariable stop,
SDVariable number,
DataType dataType)
Create a new 1d array with values evenly spaced between values 'start' and 'stop'
For example, linspace(start=3.0, stop=4.0, number=3) will generate [3.0, 3.5, 4.0] |
SDVariable |
lt(SDVariable x,
double y)
Less than operation: elementwise x < y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
lt(SDVariable x,
SDVariable y)
Less than operation: elementwise x < y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
lt(String name,
SDVariable x,
double y)
Less than operation: elementwise x < y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
lt(String name,
SDVariable x,
SDVariable y)
Less than operation: elementwise x < y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
lte(SDVariable x,
double y)
Less than or equals operation: elementwise x <= y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
lte(SDVariable x,
SDVariable y)
Less than or equal to operation: elementwise x <= y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
lte(String name,
SDVariable x,
double y)
Less than or equals operation: elementwise x <= y
Return boolean array with values true where satisfied, or false otherwise. |
SDVariable |
lte(String name,
SDVariable x,
SDVariable y)
Less than or equal to operation: elementwise x <= y
If x and y arrays have equal shape, the output shape is the same as these inputs. |
SDVariable |
matchCondition(SDVariable in,
Condition condition)
Returns a boolean mask of equal shape to the input, where the condition is satisfied - value 1 where satisfied, 0 otherwise
|
SDVariable |
matchCondition(String name,
SDVariable in,
Condition condition)
Returns a boolean mask of equal shape to the input, where the condition is satisfied - value 1 where satisfied, 0 otherwise
|
SDVariable |
matchConditionCount(SDVariable in,
Condition condition)
Returns a count of the number of elements that satisfy the condition
|
SDVariable |
matchConditionCount(SDVariable in,
Condition condition,
boolean keepDim,
int... dimensions)
Returns a count of the number of elements that satisfy the condition (for each slice along the specified dimensions)
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
matchConditionCount(SDVariable in,
Condition condition,
int... dimensions)
Returns a count of the number of elements that satisfy the condition (for each slice along the specified dimensions)
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
matchConditionCount(String name,
SDVariable in,
Condition condition)
Returns a count of the number of elements that satisfy the condition
|
SDVariable |
matchConditionCount(String name,
SDVariable in,
Condition condition,
boolean keepDim,
int... dimensions)
Returns a count of the number of elements that satisfy the condition (for each slice along the specified dimensions)
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
matchConditionCount(String name,
SDVariable in,
Condition condition,
int... dimensions)
Returns a count of the number of elements that satisfy the condition (for each slice along the specified dimensions)
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
max(SDVariable x,
boolean keepDims,
int... dimensions)
Max array reduction operation, optionally along specified dimensions
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
max(SDVariable x,
int... dimensions)
Max array reduction operation, optionally along specified dimensions
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
max(SDVariable first,
SDVariable second)
Element-wise maximum operation: out[i] = max(first[i], second[i])
Note: supports broadcasting if x and y have different shapes and are broadcastable. |
SDVariable |
max(String name,
SDVariable x,
boolean keepDims,
int... dimensions)
Max array reduction operation, optionally along specified dimensions
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
max(String name,
SDVariable x,
int... dimensions)
Max array reduction operation, optionally along specified dimensions
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
max(String name,
SDVariable first,
SDVariable second)
Element-wise maximum operation: out[i] = max(first[i], second[i])
Note: supports broadcasting if x and y have different shapes and are broadcastable. |
SDVariable |
mean(SDVariable x,
boolean keepDims,
int... dimensions)
Mean (average) array reduction operation, optionally along specified dimensions
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
mean(SDVariable x,
int... dimensions)
Mean (average) array reduction operation, optionally along specified dimensions
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
mean(String name,
SDVariable x,
boolean keepDims,
int... dimensions)
Mean (average) array reduction operation, optionally along specified dimensions
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
mean(String name,
SDVariable x,
int... dimensions)
Mean (average) array reduction operation, optionally along specified dimensions
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
merge(SDVariable x,
SDVariable y)
The merge operation is a control operation that forwards the either of the inputs to the output, when
the first of them becomes available. |
SDVariable |
merge(String name,
SDVariable x,
SDVariable y)
The merge operation is a control operation that forwards the either of the inputs to the output, when
the first of them becomes available. |
SDVariable |
min(SDVariable x,
boolean keepDims,
int... dimensions)
Minimum array reduction operation, optionally along specified dimensions. out = min(in)
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
min(SDVariable x,
int... dimensions)
Minimum array reduction operation, optionally along specified dimensions. out = min(in)
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
min(SDVariable first,
SDVariable second)
Element-wise minimum operation: out[i] = min(first[i], second[i])
Note: supports broadcasting if x and y have different shapes and are broadcastable. |
SDVariable |
min(String name,
SDVariable x,
boolean keepDims,
int... dimensions)
Minimum array reduction operation, optionally along specified dimensions. out = min(in)
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
min(String name,
SDVariable x,
int... dimensions)
Minimum array reduction operation, optionally along specified dimensions. out = min(in)
Note that if keepDims = true, the output variable has the same rank as the input variable, with the reduced dimensions having size 1. |
SDVariable |
min(String name,
SDVariable first,
SDVariable second)
Element-wise minimum operation: out[i] = min(first[i], second[i])
Note: supports broadcasting if x and y have different shapes and are broadcastable. |
SDVariable |
mmul(SDVariable x,
SDVariable y)
Matrix multiplication: out = mmul(x,y)
Supports specifying transpose argument to perform operation such as mmul(a^T, b), etc. |
SDVariable |
mmul(SDVariable x,
SDVariable y,
boolean transposeX,
boolean transposeY,
boolean transposeZ)
Matrix multiplication: out = mmul(x,y)
Supports specifying transpose argument to perform operation such as mmul(a^T, b), etc. |
SDVariable |
mmul(String name,
SDVariable x,
SDVariable y)
Matrix multiplication: out = mmul(x,y)
Supports specifying transpose argument to perform operation such as mmul(a^T, b), etc. |
SDVariable |
mmul(String name,
SDVariable x,
SDVariable y,
boolean transposeX,
boolean transposeY,
boolean transposeZ)
Matrix multiplication: out = mmul(x,y)
Supports specifying transpose argument to perform operation such as mmul(a^T, b), etc. |
SDVariable |
neq(SDVariable x,
double y)
Not equals operation: elementwise x !
|
SDVariable |
neq(SDVariable x,
SDVariable y)
Not equal to operation: elementwise x !
|
SDVariable |
neq(String name,
SDVariable x,
double y)
Not equals operation: elementwise x !
|
SDVariable |
neq(String name,
SDVariable x,
SDVariable y)
Not equal to operation: elementwise x !
|
SDVariable |
norm1(SDVariable x,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
norm1(SDVariable x,
int... dimensions)
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. |
SDVariable |
norm1(String name,
SDVariable x,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
norm1(String name,
SDVariable x,
int... dimensions)
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. |
SDVariable |
norm2(SDVariable x,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
norm2(SDVariable x,
int... dimensions)
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. |
SDVariable |
norm2(String name,
SDVariable x,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
norm2(String name,
SDVariable x,
int... dimensions)
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. |
SDVariable |
normmax(SDVariable x,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
normmax(SDVariable x,
int... dimensions)
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. |
SDVariable |
normmax(String name,
SDVariable x,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
normmax(String name,
SDVariable x,
int... dimensions)
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. |
SDVariable |
oneHot(SDVariable indices,
int depth)
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,... |
SDVariable |
oneHot(SDVariable indices,
int depth,
int axis,
double on,
double off)
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,... |
SDVariable |
oneHot(SDVariable indices,
int depth,
int axis,
double on,
double off,
DataType dataType)
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,... |
SDVariable |
oneHot(String name,
SDVariable indices,
int depth)
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,... |
SDVariable |
oneHot(String name,
SDVariable indices,
int depth,
int axis,
double on,
double off)
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,... |
SDVariable |
oneHot(String name,
SDVariable indices,
int depth,
int axis,
double on,
double off,
DataType dataType)
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,... |
SDVariable |
onesLike(SDVariable input)
Return a variable of all 1s, with the same shape as the input variable.
|
SDVariable |
onesLike(SDVariable input,
DataType dataType)
As per onesLike(String, SDVariable) but the output datatype may be specified
|
SDVariable |
onesLike(String name,
SDVariable input)
Return a variable of all 1s, with the same shape as the input variable.
|
SDVariable |
onesLike(String name,
SDVariable input,
DataType dataType)
As per onesLike(String, SDVariable) but the output datatype may be specified
|
SDVariable |
permute(SDVariable x,
int... dimensions)
Array permutation operation: permute the dimensions according to the specified permutation indices.
|
SDVariable |
permute(SDVariable x,
SDVariable dimensions)
Array permutation operation: permute the dimensions according to the specified permutation indices.
|
SDVariable |
permute(String name,
SDVariable x,
int... dimensions)
Array permutation operation: permute the dimensions according to the specified permutation indices.
|
SDVariable |
permute(String name,
SDVariable x,
SDVariable dimensions)
Array permutation operation: permute the dimensions according to the specified permutation indices.
|
SDVariable |
prod(SDVariable x,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
prod(SDVariable x,
int... dimensions)
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. |
SDVariable |
prod(String name,
SDVariable x,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
prod(String name,
SDVariable x,
int... dimensions)
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. |
SDVariable |
range(double from,
double to,
double step,
DataType 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. |
SDVariable |
range(SDVariable from,
SDVariable to,
SDVariable step,
DataType 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. |
SDVariable |
range(String name,
double from,
double to,
double step,
DataType 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. |
SDVariable |
range(String name,
SDVariable from,
SDVariable to,
SDVariable step,
DataType 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. |
SDVariable |
rank(SDVariable in)
Returns the rank (number of dimensions, i.e., length(shape)) of the specified INDArray as a 0D scalar variable
|
SDVariable |
rank(String name,
SDVariable in)
Returns the rank (number of dimensions, i.e., length(shape)) of the specified INDArray as a 0D scalar variable
|
SDVariable |
replaceWhere(SDVariable update,
double value,
Condition condition)
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 |
SDVariable |
replaceWhere(SDVariable update,
SDVariable from,
Condition condition)
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 |
SDVariable |
replaceWhere(String name,
SDVariable update,
double value,
Condition condition)
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 |
SDVariable |
replaceWhere(String name,
SDVariable update,
SDVariable from,
Condition condition)
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 |
SDVariable |
reshape(SDVariable x,
long... shape)
Reshape the input variable to the specified (fixed) shape.
|
SDVariable |
reshape(SDVariable x,
SDVariable shape)
Reshape the input variable to the specified (fixed) shape.
|
SDVariable |
reshape(String name,
SDVariable x,
long... shape)
Reshape the input variable to the specified (fixed) shape.
|
SDVariable |
reshape(String name,
SDVariable x,
SDVariable shape)
Reshape the input variable to the specified (fixed) shape.
|
SDVariable |
reverse(SDVariable x,
int... dimensions)
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] |
SDVariable |
reverse(String name,
SDVariable x,
int... dimensions)
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] |
SDVariable |
reverseSequence(SDVariable x,
SDVariable seq_lengths)
Reverse sequence op: for each slice along dimension seqDimension, the first seqLength values are reversed
|
SDVariable |
reverseSequence(SDVariable x,
SDVariable seq_lengths,
int seqDim,
int batchDim)
Reverse sequence op: for each slice along dimension seqDimension, the first seqLength values are reversed
|
SDVariable |
reverseSequence(String name,
SDVariable x,
SDVariable seq_lengths)
Reverse sequence op: for each slice along dimension seqDimension, the first seqLength values are reversed
|
SDVariable |
reverseSequence(String name,
SDVariable x,
SDVariable seq_lengths,
int seqDim,
int batchDim)
Reverse sequence op: for each slice along dimension seqDimension, the first seqLength values are reversed
|
SDVariable |
scalarFloorMod(SDVariable in,
double value)
Element-wise scalar floor modulus operation: out = floorMod(in, value).
|
SDVariable |
scalarFloorMod(String name,
SDVariable in,
double value)
Element-wise scalar floor modulus operation: out = floorMod(in, value).
|
SDVariable |
scalarMax(SDVariable in,
double value)
Element-wise scalar maximum operation: out = max(in, value)
|
SDVariable |
scalarMax(String name,
SDVariable in,
double value)
Element-wise scalar maximum operation: out = max(in, value)
|
SDVariable |
scalarMin(SDVariable in,
double value)
Element-wise scalar minimum operation: out = min(in, value)
|
SDVariable |
scalarMin(String name,
SDVariable in,
double value)
Element-wise scalar minimum operation: out = min(in, value)
|
SDVariable |
scalarSet(SDVariable in,
double set)
Return a variable with equal shape to the input, but all elements set to value 'set'
|
SDVariable |
scalarSet(String name,
SDVariable in,
double set)
Return a variable with equal shape to the input, but all elements set to value 'set'
|
SDVariable |
scatterAdd(SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter addition operation.
|
SDVariable |
scatterAdd(String name,
SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter addition operation.
|
SDVariable |
scatterDiv(SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter division operation.
|
SDVariable |
scatterDiv(String name,
SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter division operation.
|
SDVariable |
scatterMax(SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter max operation.
|
SDVariable |
scatterMax(String name,
SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter max operation.
|
SDVariable |
scatterMin(SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter min operation.
|
SDVariable |
scatterMin(String name,
SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter min operation.
|
SDVariable |
scatterMul(SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter multiplication operation.
|
SDVariable |
scatterMul(String name,
SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter multiplication operation.
|
SDVariable |
scatterSub(SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter subtraction operation.
|
SDVariable |
scatterSub(String name,
SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter subtraction operation.
|
SDVariable |
scatterUpdate(SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter update operation.
|
SDVariable |
scatterUpdate(String name,
SDVariable ref,
SDVariable indices,
SDVariable updates)
Scatter update operation.
|
SDVariable |
segmentMax(SDVariable data,
SDVariable segmentIds)
Segment max operation.
|
SDVariable |
segmentMax(String name,
SDVariable data,
SDVariable segmentIds)
Segment max operation.
|
SDVariable |
segmentMean(SDVariable data,
SDVariable segmentIds)
Segment mean operation.
|
SDVariable |
segmentMean(String name,
SDVariable data,
SDVariable segmentIds)
Segment mean operation.
|
SDVariable |
segmentMin(SDVariable data,
SDVariable segmentIds)
Segment min operation.
|
SDVariable |
segmentMin(String name,
SDVariable data,
SDVariable segmentIds)
Segment min operation.
|
SDVariable |
segmentProd(SDVariable data,
SDVariable segmentIds)
Segment product operation.
|
SDVariable |
segmentProd(String name,
SDVariable data,
SDVariable segmentIds)
Segment product operation.
|
SDVariable |
segmentSum(SDVariable data,
SDVariable segmentIds)
Segment sum operation.
|
SDVariable |
segmentSum(String name,
SDVariable data,
SDVariable segmentIds)
Segment sum operation.
|
SDVariable |
sequenceMask(SDVariable lengths,
DataType dataType)
see sequenceMask(String, SDVariable, SDVariable, DataType)
|
SDVariable |
sequenceMask(SDVariable lengths,
int maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? |
SDVariable |
sequenceMask(SDVariable lengths,
SDVariable maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? |
SDVariable |
sequenceMask(String name,
SDVariable lengths,
DataType dataType)
see sequenceMask(String, SDVariable, SDVariable, DataType)
|
SDVariable |
sequenceMask(String name,
SDVariable lengths,
int maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? |
SDVariable |
sequenceMask(String name,
SDVariable lengths,
SDVariable maxLen,
DataType dataType)
Generate a sequence mask (with values 0 or 1) based on the specified lengths
Specifically, out[i, ..., k, j] = (j < lengths[i, ..., k] ? |
SDVariable |
shape(SDVariable input)
Returns the shape of the specified INDArray as a 1D INDArray
|
SDVariable |
shape(String name,
SDVariable input)
Returns the shape of the specified INDArray as a 1D INDArray
|
SDVariable |
size(SDVariable in)
Returns the size (number of elements, i.e., prod(shape)) of the specified INDArray as a 0D scalar variable
|
SDVariable |
size(String name,
SDVariable in)
Returns the size (number of elements, i.e., prod(shape)) of the specified INDArray as a 0D scalar variable
|
SDVariable |
sizeAt(SDVariable in,
int dimension)
Returns a rank 0 (scalar) variable for the size of the specified dimension.
|
SDVariable |
sizeAt(String name,
SDVariable in,
int dimension)
Returns a rank 0 (scalar) variable for the size of the specified dimension.
|
SDVariable |
slice(SDVariable input,
int[] begin,
int... size)
Get a subset of the specified input, by specifying the first element and the size of the array.
|
SDVariable |
slice(SDVariable input,
SDVariable begin,
SDVariable size)
Get a subset of the specified input, by specifying the first element and the size of the array.
|
SDVariable |
slice(String name,
SDVariable input,
int[] begin,
int... size)
Get a subset of the specified input, by specifying the first element and the size of the array.
|
SDVariable |
slice(String name,
SDVariable input,
SDVariable begin,
SDVariable size)
Get a subset of the specified input, by specifying the first element and the size of the array.
|
SDVariable |
squaredNorm(SDVariable x,
boolean keepDims,
int... dimensions)
Squared L2 norm: see norm2(String, SDVariable, boolean, int...)
|
SDVariable |
squaredNorm(SDVariable x,
int... dimensions)
Squared L2 norm: see norm2(String, SDVariable, boolean, int...)
|
SDVariable |
squaredNorm(String name,
SDVariable x,
boolean keepDims,
int... dimensions)
Squared L2 norm: see norm2(String, SDVariable, boolean, int...)
|
SDVariable |
squaredNorm(String name,
SDVariable x,
int... dimensions)
Squared L2 norm: see norm2(String, SDVariable, boolean, int...)
|
SDVariable |
squeeze(SDVariable x,
int axis)
Remove a single dimension of size 1.
|
SDVariable |
squeeze(String name,
SDVariable x,
int axis)
Remove a single dimension of size 1.
|
SDVariable |
stack(int axis,
SDVariable... values)
Stack a set of N INDArray of rank X into one rank X+1 variable.
|
SDVariable |
stack(String name,
int axis,
SDVariable... values)
Stack a set of N INDArray of rank X into one rank X+1 variable.
|
SDVariable |
standardDeviation(SDVariable x,
boolean biasCorrected,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
standardDeviation(SDVariable x,
boolean biasCorrected,
int... dimensions)
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. |
SDVariable |
standardDeviation(String name,
SDVariable x,
boolean biasCorrected,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
standardDeviation(String name,
SDVariable x,
boolean biasCorrected,
int... dimensions)
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. |
SDVariable |
stridedSlice(SDVariable in,
long[] begin,
long[] end,
long... strides)
Get a subset of the specified input, by specifying the first element, last element, and the strides.
|
SDVariable |
stridedSlice(SDVariable in,
long[] begin,
long[] end,
long[] strides,
int beginMask,
int endMask,
int ellipsisMask,
int newAxisMask,
int shrinkAxisMask)
Get a subset of the specified input, by specifying the first element, last element, and the strides.
|
SDVariable |
stridedSlice(String name,
SDVariable in,
long[] begin,
long[] end,
long... strides)
Get a subset of the specified input, by specifying the first element, last element, and the strides.
|
SDVariable |
stridedSlice(String name,
SDVariable in,
long[] begin,
long[] end,
long[] strides,
int beginMask,
int endMask,
int ellipsisMask,
int newAxisMask,
int shrinkAxisMask)
Get a subset of the specified input, by specifying the first element, last element, and the strides.
|
SDVariable |
sum(SDVariable x,
boolean keepDims,
int... dimensions)
Sum array reduction operation, optionally along specified dimensions.
|
SDVariable |
sum(SDVariable x,
int... dimensions)
Sum array reduction operation, optionally along specified dimensions.
|
SDVariable |
sum(String name,
SDVariable x,
boolean keepDims,
int... dimensions)
Sum array reduction operation, optionally along specified dimensions.
|
SDVariable |
sum(String name,
SDVariable x,
int... dimensions)
Sum array reduction operation, optionally along specified dimensions.
|
SDVariable[] |
switchOp(SDVariable x,
SDVariable predicate)
Switch operation
Predictate - if false, values are output to left (first) branch/output; if true, to right (second) branch/output |
SDVariable[] |
switchOp(String[] names,
SDVariable x,
SDVariable predicate)
Switch operation
Predictate - if false, values are output to left (first) branch/output; if true, to right (second) branch/output |
SDVariable |
tensorMmul(SDVariable x,
SDVariable y,
int[] dimensionsX,
int... dimensionsY)
//TODO: Ops must be documented.
|
SDVariable |
tensorMmul(SDVariable x,
SDVariable y,
int[] dimensionsX,
int[] dimensionsY,
boolean transposeX,
boolean transposeY,
boolean transposeZ)
//TODO: Ops must be documented.
|
SDVariable |
tensorMmul(String name,
SDVariable x,
SDVariable y,
int[] dimensionsX,
int... dimensionsY)
//TODO: Ops must be documented.
|
SDVariable |
tensorMmul(String name,
SDVariable x,
SDVariable y,
int[] dimensionsX,
int[] dimensionsY,
boolean transposeX,
boolean transposeY,
boolean transposeZ)
//TODO: Ops must be documented.
|
SDVariable |
tile(SDVariable x,
int... repeat)
see tile(String, SDVariable, int...)
|
SDVariable |
tile(SDVariable x,
SDVariable repeat)
Repeat (tile) the input tensor the specified number of times.
|
SDVariable |
tile(String name,
SDVariable x,
int... repeat)
see tile(String, SDVariable, int...)
|
SDVariable |
tile(String name,
SDVariable x,
SDVariable repeat)
Repeat (tile) the input tensor the specified number of times.
|
SDVariable |
transpose(SDVariable x)
Matrix transpose operation: If input has shape [a,b] output has shape [b,a]
|
SDVariable |
transpose(String name,
SDVariable x)
Matrix transpose operation: If input has shape [a,b] output has shape [b,a]
|
SDVariable |
unsortedSegmentMax(SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment max operation.
|
SDVariable |
unsortedSegmentMax(String name,
SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment max operation.
|
SDVariable |
unsortedSegmentMean(SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment mean operation.
|
SDVariable |
unsortedSegmentMean(String name,
SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment mean operation.
|
SDVariable |
unsortedSegmentMin(SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment min operation.
|
SDVariable |
unsortedSegmentMin(String name,
SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment min operation.
|
SDVariable |
unsortedSegmentProd(SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment product operation.
|
SDVariable |
unsortedSegmentProd(String name,
SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment product operation.
|
SDVariable |
unsortedSegmentSqrtN(SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment sqrtN operation.
|
SDVariable |
unsortedSegmentSqrtN(String name,
SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment sqrtN operation.
|
SDVariable |
unsortedSegmentSum(SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment sum operation.
|
SDVariable |
unsortedSegmentSum(String name,
SDVariable data,
SDVariable segmentIds,
int numSegments)
Unsorted segment sum operation.
|
SDVariable[] |
unstack(SDVariable value,
int axis,
int num)
Unstack a variable of rank X into N rank X-1 variables by taking slices along the specified axis.
|
SDVariable[] |
unstack(String[] names,
SDVariable value,
int axis,
int num)
Unstack a variable of rank X into N rank X-1 variables by taking slices along the specified axis.
|
SDVariable |
variance(SDVariable x,
boolean biasCorrected,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
variance(SDVariable x,
boolean biasCorrected,
int... dimensions)
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. |
SDVariable |
variance(String name,
SDVariable x,
boolean biasCorrected,
boolean keepDims,
int... dimensions)
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. |
SDVariable |
variance(String name,
SDVariable x,
boolean biasCorrected,
int... dimensions)
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. |
SDVariable |
zerosLike(SDVariable input)
Return a variable of all 0s, with the same shape as the input variable.
|
SDVariable |
zerosLike(String name,
SDVariable input)
Return a variable of all 0s, with the same shape as the input variable.
|
protected SameDiff sd
public SDBaseOps(SameDiff sameDiff)
public SDVariable all(SDVariable x, int... dimensions)
x
- Input variable (NDARRAY type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable all(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NDARRAY type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable any(SDVariable x, int... dimensions)
x
- Input variable (NDARRAY type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable any(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NDARRAY type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable argmax(SDVariable in, boolean keepDims, int... dimensions)
in
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable argmax(String name, SDVariable in, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable argmax(SDVariable in, int... dimensions)
in
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable argmax(String name, SDVariable in, int... dimensions)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable argmin(SDVariable in, boolean keepDims, int... dimensions)
in
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable argmin(String name, SDVariable in, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable argmin(SDVariable in, int... dimensions)
in
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable argmin(String name, SDVariable in, int... dimensions)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable[] batchMmul(SDVariable[] inputsA, SDVariable[] inputsB, boolean transposeA, boolean transposeB)
inputsA
- First array of input matrices, all of shape (M, N) or (N, M) (NUMERIC type)inputsB
- Second array of input matrices, all of shape (N, K) or (K, N) (NUMERIC type)transposeA
- Whether to transpose A arrays or nottransposeB
- Whether to transpose B arrays or notpublic SDVariable[] batchMmul(String[] names, SDVariable[] inputsA, SDVariable[] inputsB, boolean transposeA, boolean transposeB)
names
- names May be null. Arrays of names for the output variables.inputsA
- First array of input matrices, all of shape (M, N) or (N, M) (NUMERIC type)inputsB
- Second array of input matrices, all of shape (N, K) or (K, N) (NUMERIC type)transposeA
- Whether to transpose A arrays or nottransposeB
- Whether to transpose B arrays or notpublic SDVariable[] batchMmul(SDVariable[] inputsA, SDVariable... inputsB)
inputsA
- First array of input matrices, all of shape (M, N) or (N, M) (NUMERIC type)inputsB
- Second array of input matrices, all of shape (N, K) or (K, N) (NUMERIC type)public SDVariable[] batchMmul(String[] names, SDVariable[] inputsA, SDVariable... inputsB)
names
- names May be null. Arrays of names for the output variables.inputsA
- First array of input matrices, all of shape (M, N) or (N, M) (NUMERIC type)inputsB
- Second array of input matrices, all of shape (N, K) or (K, N) (NUMERIC type)public SDVariable castTo(SDVariable arg, DataType datatype)
arg
- Input variable to cast (NDARRAY type)datatype
- Datatype to cast topublic SDVariable castTo(String name, SDVariable arg, DataType datatype)
name
- name May be null. Name for the output variablearg
- Input variable to cast (NDARRAY type)datatype
- Datatype to cast topublic SDVariable concat(int dimension, SDVariable... inputs)
inputs
- Input variables (NUMERIC type)dimension
- Dimension to concatenate onpublic SDVariable concat(String name, int dimension, SDVariable... inputs)
name
- name May be null. Name for the output variabledimension
- Dimension to concatenate oninputs
- Input variables (NUMERIC type)public SDVariable cumprod(SDVariable in, boolean exclusive, boolean reverse, int... axis)
in
- Input variable (NUMERIC type)exclusive
- If true: exclude the first valuereverse
- If true: reverse the direction of the accumulationaxis
- Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))public SDVariable cumprod(String name, SDVariable in, boolean exclusive, boolean reverse, int... axis)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)exclusive
- If true: exclude the first valuereverse
- If true: reverse the direction of the accumulationaxis
- Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))public SDVariable cumprod(SDVariable in, int... axis)
in
- Input variable (NUMERIC type)axis
- Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))public SDVariable cumprod(String name, SDVariable in, int... axis)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)axis
- Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))public SDVariable cumsum(SDVariable in, boolean exclusive, boolean reverse, int... axis)
in
- Input variable (NUMERIC type)exclusive
- If true: exclude the first valuereverse
- If true: reverse the direction of the accumulationaxis
- Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))public SDVariable cumsum(String name, SDVariable in, boolean exclusive, boolean reverse, int... axis)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)exclusive
- If true: exclude the first valuereverse
- If true: reverse the direction of the accumulationaxis
- Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))public SDVariable cumsum(SDVariable in, int... axis)
in
- Input variable (NUMERIC type)axis
- Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))public SDVariable cumsum(String name, SDVariable in, int... axis)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)axis
- Scalar axis argument for dimension to perform cumululative sum operations along (Size: AtLeast(min=1))public SDVariable dot(SDVariable x, SDVariable y, int... dimensions)
x
- first input (NUMERIC type)y
- second input (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable dot(String name, SDVariable x, SDVariable y, int... dimensions)
name
- name May be null. Name for the output variablex
- first input (NUMERIC type)y
- second input (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable[] dynamicPartition(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] }
x
- Input variable (NUMERIC type)partitions
- 1D input with values 0 to numPartitions-1 (INT type)numPartitions
- Number of partitions, >= 1public SDVariable[] dynamicPartition(String[] names, 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] }
names
- names May be null. Arrays of names for the output variables.x
- Input variable (NUMERIC type)partitions
- 1D input with values 0 to numPartitions-1 (INT type)numPartitions
- Number of partitions, >= 1public SDVariable dynamicStitch(SDVariable[] indices, SDVariable... x)
indices
- Indices to use when merging. Must be >= 1, same length as input variables (INT type)x
- Input variables. (NUMERIC type)public SDVariable dynamicStitch(String name, SDVariable[] indices, SDVariable... x)
name
- name May be null. Name for the output variableindices
- Indices to use when merging. Must be >= 1, same length as input variables (INT type)x
- Input variables. (NUMERIC type)public SDVariable eq(SDVariable x, double y)
x
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable eq(String name, SDVariable x, double y)
name
- name May be null. Name for the output variablex
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable eq(SDVariable x, SDVariable y)
x
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable eq(String name, SDVariable x, SDVariable y)
name
- name May be null. Name for the output variablex
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable expandDims(SDVariable x, int axis)
x
- Input variable (NDARRAY type)axis
- Axis to expandpublic SDVariable expandDims(String name, SDVariable x, int axis)
name
- name May be null. Name for the output variablex
- Input variable (NDARRAY type)axis
- Axis to expandpublic SDVariable fill(SDVariable shape, DataType dataType, double value)
shape
- Shape: must be a 1D array/variable (INT type)dataType
- Datatype of the output arrayvalue
- Value to set all elements topublic SDVariable fill(String name, SDVariable shape, DataType dataType, double value)
name
- name May be null. Name for the output variableshape
- Shape: must be a 1D array/variable (INT type)dataType
- Datatype of the output arrayvalue
- Value to set all elements topublic SDVariable gather(SDVariable df, int[] indices, int axis)
df
- Input variable (NUMERIC type)indices
- Indices to get (Size: AtLeast(min=1))axis
- Axis that the indices refer topublic SDVariable gather(String name, SDVariable df, int[] indices, int axis)
name
- name May be null. Name for the output variabledf
- Input variable (NUMERIC type)indices
- Indices to get (Size: AtLeast(min=1))axis
- Axis that the indices refer topublic SDVariable gather(SDVariable df, SDVariable indices, int axis)
df
- Input variable (NUMERIC type)indices
- Indices to get slices for. Rank 0 or 1 input (INT type)axis
- Axis that the indices refer topublic SDVariable gather(String name, SDVariable df, SDVariable indices, int axis)
name
- name May be null. Name for the output variabledf
- Input variable (NUMERIC type)indices
- Indices to get slices for. Rank 0 or 1 input (INT type)axis
- Axis that the indices refer topublic SDVariable gatherNd(SDVariable df, SDVariable indices)
df
- (NUMERIC type)indices
- (NUMERIC type)public SDVariable gatherNd(String name, SDVariable df, SDVariable indices)
name
- name May be null. Name for the output variabledf
- (NUMERIC type)indices
- (NUMERIC type)public SDVariable gt(SDVariable x, double y)
x
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable gt(String name, SDVariable x, double y)
name
- name May be null. Name for the output variablex
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable gt(SDVariable x, SDVariable y)
x
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable gt(String name, SDVariable x, SDVariable y)
name
- name May be null. Name for the output variablex
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable gte(SDVariable x, double y)
x
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable gte(String name, SDVariable x, double y)
name
- name May be null. Name for the output variablex
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable gte(SDVariable x, SDVariable y)
x
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable gte(String name, SDVariable x, SDVariable y)
name
- name May be null. Name for the output variablex
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable identity(SDVariable input)
input
- Input variable (NUMERIC type)public SDVariable identity(String name, SDVariable input)
name
- name May be null. Name for the output variableinput
- Input variable (NUMERIC type)public SDVariable invertPermutation(SDVariable input)
input
- 1D indices for permutation (INT type)public SDVariable invertPermutation(String name, SDVariable input)
name
- name May be null. Name for the output variableinput
- 1D indices for permutation (INT type)public SDVariable isNumericTensor(SDVariable x)
x
- Input variable (NUMERIC type)public SDVariable isNumericTensor(String name, SDVariable x)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)public SDVariable linspace(DataType dataType, double start, double stop, long number)
dataType
- Data type of the output arraystart
- Start valuestop
- Stop valuenumber
- Number of values to generatepublic SDVariable linspace(String name, DataType dataType, double start, double stop, long number)
name
- name May be null. Name for the output variabledataType
- Data type of the output arraystart
- Start valuestop
- Stop valuenumber
- Number of values to generatepublic SDVariable linspace(SDVariable start, SDVariable stop, SDVariable number, DataType dataType)
start
- Start value (NUMERIC type)stop
- Stop value (NUMERIC type)number
- Number of values to generate (LONG type)dataType
- Data type of the output arraypublic SDVariable linspace(String name, SDVariable start, SDVariable stop, SDVariable number, DataType dataType)
name
- name May be null. Name for the output variablestart
- Start value (NUMERIC type)stop
- Stop value (NUMERIC type)number
- Number of values to generate (LONG type)dataType
- Data type of the output arraypublic SDVariable lt(SDVariable x, double y)
x
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable lt(String name, SDVariable x, double y)
name
- name May be null. Name for the output variablex
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable lt(SDVariable x, SDVariable y)
x
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable lt(String name, SDVariable x, SDVariable y)
name
- name May be null. Name for the output variablex
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable lte(SDVariable x, double y)
x
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable lte(String name, SDVariable x, double y)
name
- name May be null. Name for the output variablex
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable lte(SDVariable x, SDVariable y)
x
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable lte(String name, SDVariable x, SDVariable y)
name
- name May be null. Name for the output variablex
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable matchCondition(SDVariable in, Condition condition)
in
- Input (NUMERIC type)condition
- Conditionpublic SDVariable matchCondition(String name, SDVariable in, Condition condition)
name
- name May be null. Name for the output variablein
- Input (NUMERIC type)condition
- Conditionpublic SDVariable matchConditionCount(SDVariable in, Condition condition)
in
- Input (NUMERIC type)condition
- Conditionpublic SDVariable matchConditionCount(String name, SDVariable in, Condition condition)
name
- name May be null. Name for the output variablein
- Input (NUMERIC type)condition
- Conditionpublic SDVariable matchConditionCount(SDVariable in, Condition condition, boolean keepDim, int... dimensions)
in
- Input variable (NUMERIC type)condition
- ConditionkeepDim
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable matchConditionCount(String name, SDVariable in, Condition condition, boolean keepDim, int... dimensions)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)condition
- ConditionkeepDim
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable matchConditionCount(SDVariable in, Condition condition, int... dimensions)
in
- Input variable (NUMERIC type)condition
- Conditiondimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable matchConditionCount(String name, SDVariable in, Condition condition, int... dimensions)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)condition
- Conditiondimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable max(SDVariable x, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable max(String name, SDVariable x, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable max(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable max(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable max(SDVariable first, SDVariable second)
first
- First input array (NUMERIC type)second
- Second input array (NUMERIC type)public SDVariable max(String name, SDVariable first, SDVariable second)
name
- name May be null. Name for the output variablefirst
- First input array (NUMERIC type)second
- Second input array (NUMERIC type)public SDVariable mean(SDVariable x, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable mean(String name, SDVariable x, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable mean(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable mean(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable merge(SDVariable x, SDVariable y)
x
- Input variable (NUMERIC type)y
- Input variable (NUMERIC type)public SDVariable merge(String name, SDVariable x, SDVariable y)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)y
- Input variable (NUMERIC type)public SDVariable min(SDVariable x, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable min(String name, SDVariable x, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable min(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable min(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable min(SDVariable first, SDVariable second)
first
- First input array (NUMERIC type)second
- Second input array (NUMERIC type)public SDVariable min(String name, SDVariable first, SDVariable second)
name
- name May be null. Name for the output variablefirst
- First input array (NUMERIC type)second
- Second input array (NUMERIC type)public SDVariable mmul(SDVariable x, SDVariable y, boolean transposeX, boolean transposeY, boolean transposeZ)
x
- First input variable (NUMERIC type)y
- Second input variable (NUMERIC type)transposeX
- Transpose x (first argument)transposeY
- Transpose y (second argument)transposeZ
- Transpose result arraypublic SDVariable mmul(String name, SDVariable x, SDVariable y, boolean transposeX, boolean transposeY, boolean transposeZ)
name
- name May be null. Name for the output variablex
- First input variable (NUMERIC type)y
- Second input variable (NUMERIC type)transposeX
- Transpose x (first argument)transposeY
- Transpose y (second argument)transposeZ
- Transpose result arraypublic SDVariable mmul(SDVariable x, SDVariable y)
x
- First input variable (NUMERIC type)y
- Second input variable (NUMERIC type)public SDVariable mmul(String name, SDVariable x, SDVariable y)
name
- name May be null. Name for the output variablex
- First input variable (NUMERIC type)y
- Second input variable (NUMERIC type)public SDVariable neq(SDVariable x, double y)
x
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable neq(String name, SDVariable x, double y)
name
- name May be null. Name for the output variablex
- Input array (NUMERIC type)y
- Double value argument to use in operationpublic SDVariable neq(SDVariable x, SDVariable y)
x
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable neq(String name, SDVariable x, SDVariable y)
name
- name May be null. Name for the output variablex
- Input 1 (NUMERIC type)y
- Input 2 (NUMERIC type)public SDVariable norm1(SDVariable x, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- dimensions to reduce over (Size: AtLeast(min=0))public SDVariable norm1(String name, SDVariable x, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- dimensions to reduce over (Size: AtLeast(min=0))public SDVariable norm1(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- dimensions to reduce over (Size: AtLeast(min=0))public SDVariable norm1(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- dimensions to reduce over (Size: AtLeast(min=0))public SDVariable norm2(SDVariable x, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- dimensions dimensions to reduce over (Size: AtLeast(min=0))public SDVariable norm2(String name, SDVariable x, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- dimensions dimensions to reduce over (Size: AtLeast(min=0))public SDVariable norm2(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- dimensions dimensions to reduce over (Size: AtLeast(min=0))public SDVariable norm2(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- dimensions dimensions to reduce over (Size: AtLeast(min=0))public SDVariable normmax(SDVariable x, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- dimensions to reduce over (Size: AtLeast(min=0))public SDVariable normmax(String name, SDVariable x, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- dimensions to reduce over (Size: AtLeast(min=0))public SDVariable normmax(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- dimensions to reduce over (Size: AtLeast(min=0))public SDVariable normmax(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- dimensions to reduce over (Size: AtLeast(min=0))public SDVariable oneHot(SDVariable indices, int depth, int axis, double on, double off, DataType dataType)
indices
- Indices - value 0 to depth-1 (NUMERIC type)depth
- Number of classesaxis
- on
- off
- dataType
- Output data typepublic SDVariable oneHot(String name, SDVariable indices, int depth, int axis, double on, double off, DataType dataType)
name
- name May be null. Name for the output variableindices
- Indices - value 0 to depth-1 (NUMERIC type)depth
- Number of classesaxis
- on
- off
- dataType
- Output data typepublic SDVariable oneHot(SDVariable indices, int depth, int axis, double on, double off)
indices
- Indices - value 0 to depth-1 (NUMERIC type)depth
- Number of classesaxis
- on
- off
- public SDVariable oneHot(String name, SDVariable indices, int depth, int axis, double on, double off)
name
- name May be null. Name for the output variableindices
- Indices - value 0 to depth-1 (NUMERIC type)depth
- Number of classesaxis
- on
- off
- public SDVariable oneHot(SDVariable indices, int depth)
indices
- Indices - value 0 to depth-1 (NUMERIC type)depth
- Number of classespublic SDVariable oneHot(String name, SDVariable indices, int depth)
name
- name May be null. Name for the output variableindices
- Indices - value 0 to depth-1 (NUMERIC type)depth
- Number of classespublic SDVariable onesLike(SDVariable input)
input
- Input INDArray (NUMERIC type)public SDVariable onesLike(String name, SDVariable input)
name
- name May be null. Name for the output variableinput
- Input INDArray (NUMERIC type)public SDVariable onesLike(SDVariable input, DataType dataType)
input
- (NUMERIC type)dataType
- public SDVariable onesLike(String name, SDVariable input, DataType dataType)
name
- name May be null. Name for the output variableinput
- (NUMERIC type)dataType
- public SDVariable permute(SDVariable x, SDVariable dimensions)
x
- Input variable (NUMERIC type)dimensions
- Permute dimensions (INT type)public SDVariable permute(String name, SDVariable x, SDVariable dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- Permute dimensions (INT type)public SDVariable permute(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- (Size: AtLeast(min=0))public SDVariable permute(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- (Size: AtLeast(min=0))public SDVariable prod(SDVariable x, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable prod(String name, SDVariable x, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable prod(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable prod(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable range(double from, double to, double step, DataType dataType)
from
- Initial/smallest valueto
- Largest value (exclusive)step
- Step sizedataType
- public SDVariable range(String name, double from, double to, double step, DataType dataType)
name
- name May be null. Name for the output variablefrom
- Initial/smallest valueto
- Largest value (exclusive)step
- Step sizedataType
- public SDVariable range(SDVariable from, SDVariable to, SDVariable step, DataType dataType)
from
- Initial/smallest value (NUMERIC type)to
- Largest value (exclusive) (NUMERIC type)step
- Step size (NUMERIC type)dataType
- public SDVariable range(String name, SDVariable from, SDVariable to, SDVariable step, DataType dataType)
name
- name May be null. Name for the output variablefrom
- Initial/smallest value (NUMERIC type)to
- Largest value (exclusive) (NUMERIC type)step
- Step size (NUMERIC type)dataType
- public SDVariable rank(SDVariable in)
in
- Input variable (NUMERIC type)public SDVariable rank(String name, SDVariable in)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)public SDVariable replaceWhere(SDVariable update, SDVariable from, Condition condition)
update
- Source array (NUMERIC type)from
- Replacement values array (used conditionally). Must be same shape as 'update' array (NUMERIC type)condition
- Condition to check on update array elementspublic SDVariable replaceWhere(String name, SDVariable update, SDVariable from, Condition condition)
name
- name May be null. Name for the output variableupdate
- Source array (NUMERIC type)from
- Replacement values array (used conditionally). Must be same shape as 'update' array (NUMERIC type)condition
- Condition to check on update array elementspublic SDVariable replaceWhere(SDVariable update, double value, Condition condition)
update
- Source array (NUMERIC type)value
- Value to set at the output, if the condition is satisfiedcondition
- Condition to check on update array elementspublic SDVariable replaceWhere(String name, SDVariable update, double value, Condition condition)
name
- name May be null. Name for the output variableupdate
- Source array (NUMERIC type)value
- Value to set at the output, if the condition is satisfiedcondition
- Condition to check on update array elementspublic SDVariable reshape(SDVariable x, SDVariable shape)
x
- Input variable (NUMERIC type)shape
- New shape for variable (NUMERIC type)public SDVariable reshape(String name, SDVariable x, SDVariable shape)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)shape
- New shape for variable (NUMERIC type)public SDVariable reshape(SDVariable x, long... shape)
x
- Input variable (NUMERIC type)shape
- New shape for variable (Size: AtLeast(min=0))public SDVariable reshape(String name, SDVariable x, long... shape)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)shape
- New shape for variable (Size: AtLeast(min=0))public SDVariable reverse(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- Input variable (Size: AtLeast(min=0))public SDVariable reverse(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- Input variable (Size: AtLeast(min=0))public SDVariable reverseSequence(SDVariable x, SDVariable seq_lengths, int seqDim, int batchDim)
x
- Input variable (NUMERIC type)seq_lengths
- Length of the sequences (INT type)seqDim
- Sequence dimensionbatchDim
- Batch dimensionpublic SDVariable reverseSequence(String name, SDVariable x, SDVariable seq_lengths, int seqDim, int batchDim)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)seq_lengths
- Length of the sequences (INT type)seqDim
- Sequence dimensionbatchDim
- Batch dimensionpublic SDVariable reverseSequence(SDVariable x, SDVariable seq_lengths)
x
- Input variable (NUMERIC type)seq_lengths
- Length of the sequences (INT type)public SDVariable reverseSequence(String name, SDVariable x, SDVariable seq_lengths)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)seq_lengths
- Length of the sequences (INT type)public SDVariable scalarFloorMod(SDVariable in, double value)
in
- Input variable (NUMERIC type)value
- Scalar value to comparepublic SDVariable scalarFloorMod(String name, SDVariable in, double value)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)value
- Scalar value to comparepublic SDVariable scalarMax(SDVariable in, double value)
in
- Input variable (NUMERIC type)value
- Scalar value to comparepublic SDVariable scalarMax(String name, SDVariable in, double value)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)value
- Scalar value to comparepublic SDVariable scalarMin(SDVariable in, double value)
in
- Input variable (NUMERIC type)value
- Scalar value to comparepublic SDVariable scalarMin(String name, SDVariable in, double value)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)value
- Scalar value to comparepublic SDVariable scalarSet(SDVariable in, double set)
in
- Input variable (NUMERIC type)set
- Value to setpublic SDVariable scalarSet(String name, SDVariable in, double set)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)set
- Value to setpublic SDVariable scatterAdd(SDVariable ref, SDVariable indices, SDVariable updates)
ref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterAdd(String name, SDVariable ref, SDVariable indices, SDVariable updates)
name
- name May be null. Name for the output variableref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterDiv(SDVariable ref, SDVariable indices, SDVariable updates)
ref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterDiv(String name, SDVariable ref, SDVariable indices, SDVariable updates)
name
- name May be null. Name for the output variableref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterMax(SDVariable ref, SDVariable indices, SDVariable updates)
ref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterMax(String name, SDVariable ref, SDVariable indices, SDVariable updates)
name
- name May be null. Name for the output variableref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterMin(SDVariable ref, SDVariable indices, SDVariable updates)
ref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterMin(String name, SDVariable ref, SDVariable indices, SDVariable updates)
name
- name May be null. Name for the output variableref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterMul(SDVariable ref, SDVariable indices, SDVariable updates)
ref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterMul(String name, SDVariable ref, SDVariable indices, SDVariable updates)
name
- name May be null. Name for the output variableref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterSub(SDVariable ref, SDVariable indices, SDVariable updates)
ref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterSub(String name, SDVariable ref, SDVariable indices, SDVariable updates)
name
- name May be null. Name for the output variableref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterUpdate(SDVariable ref, SDVariable indices, SDVariable updates)
ref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable scatterUpdate(String name, SDVariable ref, SDVariable indices, SDVariable updates)
name
- name May be null. Name for the output variableref
- Initial/source variable (NUMERIC type)indices
- Indices array (NUMERIC type)updates
- Updates to add to the initial/source array (NUMERIC type)public SDVariable segmentMax(SDVariable data, SDVariable segmentIds)
data
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable segmentMax(String name, SDVariable data, SDVariable segmentIds)
name
- name May be null. Name for the output variabledata
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable segmentMean(SDVariable data, SDVariable segmentIds)
data
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable segmentMean(String name, SDVariable data, SDVariable segmentIds)
name
- name May be null. Name for the output variabledata
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable segmentMin(SDVariable data, SDVariable segmentIds)
data
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable segmentMin(String name, SDVariable data, SDVariable segmentIds)
name
- name May be null. Name for the output variabledata
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable segmentProd(SDVariable data, SDVariable segmentIds)
data
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable segmentProd(String name, SDVariable data, SDVariable segmentIds)
name
- name May be null. Name for the output variabledata
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable segmentSum(SDVariable data, SDVariable segmentIds)
data
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable segmentSum(String name, SDVariable data, SDVariable segmentIds)
name
- name May be null. Name for the output variabledata
- Data to perform segment max on (NDARRAY type)segmentIds
- Variable for the segment IDs (NUMERIC type)public SDVariable sequenceMask(SDVariable lengths, int maxLen, DataType dataType)
lengths
- Lengths of the sequences (NUMERIC type)maxLen
- Maximum sequence lengthdataType
- public SDVariable sequenceMask(String name, SDVariable lengths, int maxLen, DataType dataType)
name
- name May be null. Name for the output variablelengths
- Lengths of the sequences (NUMERIC type)maxLen
- Maximum sequence lengthdataType
- public SDVariable sequenceMask(SDVariable lengths, SDVariable maxLen, DataType dataType)
lengths
- Lengths of the sequences (NUMERIC type)maxLen
- Maximum sequence length (INT type)dataType
- public SDVariable sequenceMask(String name, SDVariable lengths, SDVariable maxLen, DataType dataType)
name
- name May be null. Name for the output variablelengths
- Lengths of the sequences (NUMERIC type)maxLen
- Maximum sequence length (INT type)dataType
- public SDVariable sequenceMask(SDVariable lengths, DataType dataType)
lengths
- (NUMERIC type)dataType
- public SDVariable sequenceMask(String name, SDVariable lengths, DataType dataType)
name
- name May be null. Name for the output variablelengths
- (NUMERIC type)dataType
- public SDVariable shape(SDVariable input)
input
- Input variable (NUMERIC type)public SDVariable shape(String name, SDVariable input)
name
- name May be null. Name for the output variableinput
- Input variable (NUMERIC type)public SDVariable size(SDVariable in)
in
- Input variable (NUMERIC type)public SDVariable size(String name, SDVariable in)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)public SDVariable sizeAt(SDVariable in, int dimension)
in
- Input variable (NUMERIC type)dimension
- Dimension to get size ofpublic SDVariable sizeAt(String name, SDVariable in, int dimension)
name
- name May be null. Name for the output variablein
- Input variable (NUMERIC type)dimension
- Dimension to get size ofpublic SDVariable slice(SDVariable input, int[] begin, int... size)
input
- input Variable to get subset of (NUMERIC type)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))public SDVariable slice(String name, SDVariable input, int[] begin, int... size)
name
- name May be null. Name for the output variableinput
- input Variable to get subset of (NUMERIC type)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))public SDVariable slice(SDVariable input, SDVariable begin, SDVariable size)
input
- input Variable to get subset of (NUMERIC type)begin
- Beginning index. Must be same length as rank of input array (INT type)size
- Size of the output array. Must be same length as rank of input array (INT type)public SDVariable slice(String name, SDVariable input, SDVariable begin, SDVariable size)
name
- name May be null. Name for the output variableinput
- input Variable to get subset of (NUMERIC type)begin
- Beginning index. Must be same length as rank of input array (INT type)size
- Size of the output array. Must be same length as rank of input array (INT type)public SDVariable squaredNorm(SDVariable x, boolean keepDims, int... dimensions)
x
- (NUMERIC type)keepDims
- dimensions
- (Size: AtLeast(min=0))public SDVariable squaredNorm(String name, SDVariable x, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- (NUMERIC type)keepDims
- dimensions
- (Size: AtLeast(min=0))public SDVariable squaredNorm(SDVariable x, int... dimensions)
x
- (NUMERIC type)dimensions
- (Size: AtLeast(min=0))public SDVariable squaredNorm(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- (NUMERIC type)dimensions
- (Size: AtLeast(min=0))public SDVariable squeeze(SDVariable x, int axis)
x
- Input variable (NUMERIC type)axis
- Size 1 dimension to removepublic SDVariable squeeze(String name, SDVariable x, int axis)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)axis
- Size 1 dimension to removepublic SDVariable stack(int axis, SDVariable... values)
values
- Input variables to stack. Must have the same shape for all inputs (NDARRAY type)axis
- Axis to stack onpublic SDVariable stack(String name, int axis, SDVariable... values)
name
- name May be null. Name for the output variableaxis
- Axis to stack onvalues
- Input variables to stack. Must have the same shape for all inputs (NDARRAY type)public SDVariable standardDeviation(SDVariable x, boolean biasCorrected, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)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 dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable standardDeviation(String name, SDVariable x, boolean biasCorrected, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)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 dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable standardDeviation(SDVariable x, boolean biasCorrected, int... dimensions)
x
- Input variable (NUMERIC type)biasCorrected
- If true: divide by (N-1) (i.e., sample stdev). If false: divide by N (population stdev)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable standardDeviation(String name, SDVariable x, boolean biasCorrected, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)biasCorrected
- If true: divide by (N-1) (i.e., sample stdev). If false: divide by N (population stdev)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable stridedSlice(SDVariable in, long[] begin, long[] end, long[] strides, int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask)
in
- Variable to get subset of (NUMERIC type)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 dimensionendMask
- 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 dimensionellipsisMask
- 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 positionnewAxisMask
- 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 pointshrinkAxisMask
- 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 dimensionspublic SDVariable stridedSlice(String name, SDVariable in, long[] begin, long[] end, long[] strides, int beginMask, int endMask, int ellipsisMask, int newAxisMask, int shrinkAxisMask)
name
- name May be null. Name for the output variablein
- Variable to get subset of (NUMERIC type)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 dimensionendMask
- 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 dimensionellipsisMask
- 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 positionnewAxisMask
- 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 pointshrinkAxisMask
- 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 dimensionspublic SDVariable stridedSlice(SDVariable in, long[] begin, long[] end, long... strides)
in
- Variable to get subset of (NUMERIC type)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))public SDVariable stridedSlice(String name, SDVariable in, long[] begin, long[] end, long... strides)
name
- name May be null. Name for the output variablein
- Variable to get subset of (NUMERIC type)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))public SDVariable sum(SDVariable x, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable sum(String name, SDVariable x, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)keepDims
- If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable sum(SDVariable x, int... dimensions)
x
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable sum(String name, SDVariable x, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable[] switchOp(SDVariable x, SDVariable predicate)
x
- Input variable (NDARRAY type)predicate
- Predictate - if false, values are output to left (first) branch/output; if true, to right (second) branch/output (BOOL type)public SDVariable[] switchOp(String[] names, SDVariable x, SDVariable predicate)
names
- names May be null. Arrays of names for the output variables.x
- Input variable (NDARRAY type)predicate
- Predictate - if false, values are output to left (first) branch/output; if true, to right (second) branch/output (BOOL type)public SDVariable tensorMmul(SDVariable x, SDVariable y, int[] dimensionsX, int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ)
x
- Input variable x (NUMERIC type)y
- Input variable y (NUMERIC type)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)transposeY
- Transpose y (second argument)transposeZ
- Transpose result arraypublic SDVariable tensorMmul(String name, SDVariable x, SDVariable y, int[] dimensionsX, int[] dimensionsY, boolean transposeX, boolean transposeY, boolean transposeZ)
name
- name May be null. Name for the output variablex
- Input variable x (NUMERIC type)y
- Input variable y (NUMERIC type)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)transposeY
- Transpose y (second argument)transposeZ
- Transpose result arraypublic SDVariable tensorMmul(SDVariable x, SDVariable y, int[] dimensionsX, int... dimensionsY)
x
- Input variable x (NUMERIC type)y
- Input variable y (NUMERIC type)dimensionsX
- dimensions for first input array (x) (Size: AtLeast(min=1))dimensionsY
- dimensions for second input array (y) (Size: AtLeast(min=1))public SDVariable tensorMmul(String name, SDVariable x, SDVariable y, int[] dimensionsX, int... dimensionsY)
name
- name May be null. Name for the output variablex
- Input variable x (NUMERIC type)y
- Input variable y (NUMERIC type)dimensionsX
- dimensions for first input array (x) (Size: AtLeast(min=1))dimensionsY
- dimensions for second input array (y) (Size: AtLeast(min=1))public SDVariable tile(SDVariable x, SDVariable repeat)
x
- Input variable (NDARRAY type)repeat
- Number of times to repeat in each axis. Must have length equal to the rank of the input array (INT type)public SDVariable tile(String name, SDVariable x, SDVariable repeat)
name
- name May be null. Name for the output variablex
- Input variable (NDARRAY type)repeat
- Number of times to repeat in each axis. Must have length equal to the rank of the input array (INT type)public SDVariable tile(SDVariable x, int... repeat)
x
- (NDARRAY type)repeat
- (Size: AtLeast(min=1))public SDVariable tile(String name, SDVariable x, int... repeat)
name
- name May be null. Name for the output variablex
- (NDARRAY type)repeat
- (Size: AtLeast(min=1))public SDVariable transpose(SDVariable x)
x
- Input variable (NDARRAY type)public SDVariable transpose(String name, SDVariable x)
name
- name May be null. Name for the output variablex
- Input variable (NDARRAY type)public SDVariable unsortedSegmentMax(SDVariable data, SDVariable segmentIds, int numSegments)
data
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentMax(String name, SDVariable data, SDVariable segmentIds, int numSegments)
name
- name May be null. Name for the output variabledata
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentMean(SDVariable data, SDVariable segmentIds, int numSegments)
data
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentMean(String name, SDVariable data, SDVariable segmentIds, int numSegments)
name
- name May be null. Name for the output variabledata
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentMin(SDVariable data, SDVariable segmentIds, int numSegments)
data
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentMin(String name, SDVariable data, SDVariable segmentIds, int numSegments)
name
- name May be null. Name for the output variabledata
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentProd(SDVariable data, SDVariable segmentIds, int numSegments)
data
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentProd(String name, SDVariable data, SDVariable segmentIds, int numSegments)
name
- name May be null. Name for the output variabledata
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentSqrtN(SDVariable data, SDVariable segmentIds, int numSegments)
data
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentSqrtN(String name, SDVariable data, SDVariable segmentIds, int numSegments)
name
- name May be null. Name for the output variabledata
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentSum(SDVariable data, SDVariable segmentIds, int numSegments)
data
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable unsortedSegmentSum(String name, SDVariable data, SDVariable segmentIds, int numSegments)
name
- name May be null. Name for the output variabledata
- Data (variable) to perform unsorted segment max on (NUMERIC type)segmentIds
- Variable for the segment IDs (NUMERIC type)numSegments
- Number of segmentspublic SDVariable[] unstack(SDVariable value, int axis, int num)
value
- Input variable to unstack (NDARRAY type)axis
- Axis to unstack onnum
- Number of output variablespublic SDVariable[] unstack(String[] names, SDVariable value, int axis, int num)
names
- names May be null. Arrays of names for the output variables.value
- Input variable to unstack (NDARRAY type)axis
- Axis to unstack onnum
- Number of output variablespublic SDVariable variance(SDVariable x, boolean biasCorrected, boolean keepDims, int... dimensions)
x
- Input variable (NUMERIC type)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 dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable variance(String name, SDVariable x, boolean biasCorrected, boolean keepDims, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)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 dimensionsdimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable variance(SDVariable x, boolean biasCorrected, int... dimensions)
x
- Input variable (NUMERIC type)biasCorrected
- If true: divide by (N-1) (i.e., sample variable). If false: divide by N (population variance)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable variance(String name, SDVariable x, boolean biasCorrected, int... dimensions)
name
- name May be null. Name for the output variablex
- Input variable (NUMERIC type)biasCorrected
- If true: divide by (N-1) (i.e., sample variable). If false: divide by N (population variance)dimensions
- Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))public SDVariable zerosLike(SDVariable input)
input
- Input (NUMERIC type)public SDVariable zerosLike(String name, SDVariable input)
name
- name May be null. Name for the output variableinput
- Input (NUMERIC type)Copyright © 2020. All rights reserved.