Clips tensor values to a maximum average L2-norm.
x (NUMERIC) - Input variable
clipValue - Value for clipping
dimensions - Dimensions to reduce over (Size: AtLeast(min=0))
Looks up ids in a list of embedding tensors.
x (NUMERIC) - Input tensor
indices (INT) - A Tensor containing the ids to be looked up.
PartitionMode - partition_mode == 0 - i.e. 'mod' , 1 - 'div'
Return array of max elements indices with along tensor dimensions
x (NUMERIC) - Input tensor
dataType - Data type - default = DataType.INT
Elementwise absolute value operation: out = abs(x)
x (NUMERIC) - Input variable
Elementwise acos (arccosine, inverse cosine) operation: out = arccos(x)
x (NUMERIC) - Input variable
Elementwise acosh (inverse hyperbolic cosine) function: out = acosh(x)
x (NUMERIC) - Input variable
Pairwise addition operation, out = x + y
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Scalar add operation, out = in + scalar
x (NUMERIC) - Input variable
value - Scalar value for op
Absolute max array reduction operation, optionally along specified dimensions: out = max(abs(x))
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Absolute mean array reduction operation, optionally along specified dimensions: out = mean(abs(x))
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Absolute min array reduction operation, optionally along specified dimensions: out = min(abs(x))
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Boolean AND operation: elementwise (x != 0) && (y != 0)
If x and y arrays have equal shape, the output shape is the same as these inputs.
Note: supports broadcasting if x and y have different shapes and are broadcastable.
Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
x (BOOL) - Input 1
y (BOOL) - Input 2
Elementwise asin (arcsin, inverse sine) operation: out = arcsin(x)
x (NUMERIC) - Input variable
Elementwise asinh (inverse hyperbolic sine) function: out = asinh(x)
x (NUMERIC) - Input variable
Absolute sum array reduction operation, optionally along specified dimensions: out = sum(abs(x))
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Elementwise atan (arctangent, inverse tangent) operation: out = arctangent(x)
x (NUMERIC) - Input variable
Elementwise atan (arctangent, inverse tangent) operation: out = atan2(x,y).
Similar to atan(y/x) but sigts of x and y are used to determine the location of the result
y (NUMERIC) - Input Y variable
x (NUMERIC) - Input X variable
Elementwise atanh (inverse hyperbolic tangent) function: out = atanh(x)
x (NUMERIC) - Input variable
Bit shift operation
x (NUMERIC) - input
shift (NUMERIC) - shift value
Right bit shift operation
x (NUMERIC) - Input tensor
shift (NUMERIC) - shift argument
Cyclic bit shift operation
x (NUMERIC) - Input tensor
shift (NUMERIC) - shift argy=ument
Cyclic right shift operation
x (NUMERIC) - Input tensor
shift (NUMERIC) - Shift argument
Element-wise ceiling function: out = ceil(x).
Rounds each value up to the nearest integer value (if not already an integer)
x (NUMERIC) - Input variable
Clipping by L2 norm, optionally along dimension(s)
if l2Norm(x,dimension) < clipValue, then input is returned unmodifed
Otherwise, out[i] = in[i] * clipValue / l2Norm(in, dimensions) where each value is clipped according
to the corresponding l2Norm along the specified dimensions
x (NUMERIC) - Input variable
clipValue - Clipping value (maximum l2 norm)
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Element-wise clipping function:
out[i] = in[i] if in[i] >= clipValueMin and in[i] <= clipValueMax
out[i] = clipValueMin if in[i] < clipValueMin
out[i] = clipValueMax if in[i] > clipValueMax
x (NUMERIC) - Input variable
clipValueMin - Minimum value for clipping
clipValueMax - Maximum value for clipping
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values. This version assumes the number of classes is 1 + max(max(labels), max(pred))
For example, if labels = [0, 1, 1] and predicted = [0, 2, 1] then output is:
[1, 0, 0]
[0, 1, 1]
[0, 0, 0]
labels (NUMERIC) - Labels - 1D array of integer values representing label values
pred (NUMERIC) - Predictions - 1D array of integer values representing predictions. Same length as labels
dataType - Data type
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values.
For example, if labels = [0, 1, 1], predicted = [0, 2, 1], and numClasses=4 then output is:
[1, 0, 0, 0]
[0, 1, 1, 0]
[0, 0, 0, 0]
[0, 0, 0, 0]
labels (NUMERIC) - Labels - 1D array of integer values representing label values
pred (NUMERIC) - Predictions - 1D array of integer values representing predictions. Same length as labels
numClasses - Number of classes
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values. This version assumes the number of classes is 1 + max(max(labels), max(pred))
For example, if labels = [0, 1, 1], predicted = [0, 2, 1] and weights = [1, 2, 3]
[1, 0, 0]
[0, 3, 2]
[0, 0, 0]
labels (NUMERIC) - Labels - 1D array of integer values representing label values
pred (NUMERIC) - Predictions - 1D array of integer values representing predictions. Same length as labels
weights (NUMERIC) - Weights - 1D array of values (may be real/decimal) representing the weight/contribution of each prediction. Must be same length as both labels and predictions arrays
Compute the 2d confusion matrix of size [numClasses, numClasses] from a pair of labels and predictions, both of
which are represented as integer values.
For example, if labels = [0, 1, 1], predicted = [0, 2, 1], numClasses = 4, and weights = [1, 2, 3]
[1, 0, 0, 0]
[0, 3, 2, 0]
[0, 0, 0, 0]
[0, 0, 0, 0]
labels (NUMERIC) - Labels - 1D array of integer values representing label values
pred (NUMERIC) - Predictions - 1D array of integer values representing predictions. Same length as labels
weights (NUMERIC) - Weights - 1D array of values (may be real/decimal) representing the weight/contribution of each prediction. Must be same length as both labels and predictions arrays
Elementwise cosine operation: out = cos(x)
x (NUMERIC) - Input variable
Elementwise cosh (hyperbolic cosine) operation: out = cosh(x)
x (NUMERIC) - Input variable
Cosine distance reduction operation. The output contains the cosine distance for each
tensor/subset along the specified dimensions:
out = 1.0 - cosineSimilarity(x,y)
x (NUMERIC) - Input variable x
y (NUMERIC) - Input variable y
dimensions - Dimensions to calculate cosineDistance over (Size: AtLeast(min=0))
Cosine similarity pairwise reduction operation. The output contains the cosine similarity for each tensor/subset
along the specified dimensions:
out = (sum_i x[i] y[i]) / ( sqrt(sum_i x[i]^2) sqrt(sum_i y[i]^2)
x (NUMERIC) - Input variable x
y (NUMERIC) - Input variable y
dimensions - Dimensions to calculate cosineSimilarity over (Size: AtLeast(min=0))
Count non zero array reduction operation, optionally along specified dimensions: out = count(x != 0)
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Count zero array reduction operation, optionally along specified dimensions: out = count(x == 0)
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Returns the pair-wise cross product of equal size arrays a and b: a x b = ||a||x||b|| sin(theta).
Can take rank 1 or above inputs (of equal shapes), but note that the last dimension must have dimension 3
a (NUMERIC) - First input
b (NUMERIC) - Second input
Element-wise cube function: out = x^3
x (NUMERIC) - Input variable
Returns an output variable with diagonal values equal to the specified values; off-diagonal values will be set to 0
For example, if input = [1,2,3], then output is given by:
[ 1, 0, 0]
[ 0, 2, 0]
[ 0, 0, 3]
Higher input ranks are also supported: if input has shape [a,...,R-1] then output[i,...,k,i,...,k] = input[i,...,k].
i.e., for input rank R, output has rank 2R
x (NUMERIC) - Input variable
Extract the diagonal part from the input array.
If input is
[ 1, 0, 0]
[ 0, 2, 0]
[ 0, 0, 3]
then output is [1, 2, 3].
Supports higher dimensions: in general, out[i,...,k] = in[i,...,k,i,...,k]
x (NUMERIC) - Input variable
Pairwise division operation, out = x / y
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Scalar division operation, out = in / scalar
x (NUMERIC) - Input variable
value - Scalar value for op
Entropy reduction: -sum(x * log(x))
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Element-wise Gaussian error function - out = erf(in)
x (NUMERIC) - Input variable
Element-wise complementary Gaussian error function - out = erfc(in) = 1 - erf(in)
x (NUMERIC) - Input variable
Euclidean distance (l2 norm, l2 distance) reduction operation. The output contains the Euclidean distance for each
tensor/subset along the specified dimensions:
out = sqrt( sum_i (x[i] - y[i])^2 )
x (NUMERIC) - Input variable x
y (NUMERIC) - Input variable y
dimensions - Dimensions to calculate euclideanDistance over (Size: AtLeast(min=0))
Elementwise exponent function: out = exp(x) = 2.71828...^x
x (NUMERIC) - Input variable
Elementwise 1.0 - exponent function: out = 1.0 - exp(x) = 1.0 - 2.71828...^x
x (NUMERIC) - Input variable
Generate an identity matrix with the specified number of rows and columns.
rows - Number of rows
As per eye(String, int, int, DataType) but with the default datatype, Eye.DEFAULT_DTYPE
rows - Number of rows
cols - Number of columns
Generate an identity matrix with the specified number of rows and columns
Example:
rows - Number of rows
cols - Number of columns
dataType - Data type
dimensions - (Size: AtLeast(min=0))
As per eye(int, int) bit with the number of rows/columns specified as scalar INDArrays
rows (INT) - Number of rows
cols (INT) - Number of columns
As per eye(String, int) but with the number of rows specified as a scalar INDArray
rows (INT) - Number of rows
First index reduction operation.
Returns a variable that contains the index of the first element that matches the specified condition (for each
slice along the specified dimensions)
Note that if keepDims = true, the output variable has the same rank as the input variable,
with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
the mean along a dimension).
Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
keepDims = true: [a,1,c]
keepDims = false: [a,c]
in (NUMERIC) - Input variable
condition - Condition to check on input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=1))
Element-wise floor function: out = floor(x).
Rounds each value down to the nearest integer value (if not already an integer)
x (NUMERIC) - Input variable
Pairwise floor division operation, out = floor(x / y)
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Pairwise Modulus division operation
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Scalar floor modulus operation
x (NUMERIC) - Input variable
value - Scalar value for op
Hamming distance reduction operation. The output contains the cosine distance for each
tensor/subset along the specified dimensions:
out = count( x[i] != y[i] )
x (NUMERIC) - Input variable x
y (NUMERIC) - Input variable y
dimensions - Dimensions to calculate hammingDistance over (Size: AtLeast(min=0))
Index of the max absolute value: argmax(abs(in))
see argmax(String, INDArray, boolean, int...)
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=1))
keepDims - If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensions - default = false
Index of the min absolute value: argmin(abs(in))
see argmin(String, INDArray, boolean, int...)
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=1))
keepDims - If true: keep the dimensions that are reduced on (as length 1). False: remove the reduction dimensions - default = false
Is finite operation: elementwise isFinite(x)
Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
value 0 otherwise
x (NUMERIC) - Input variable
Is infinite operation: elementwise isInfinite(x)
Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
value 0 otherwise
x (NUMERIC) - Input variable
Is maximum operation: elementwise x == max(x)
Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
value 0 otherwise
x (NUMERIC) - Input variable
Is Not a Number operation: elementwise isNaN(x)
Returns an array with the same shape/size as the input, with values 1 where condition is satisfied, or
value 0 otherwise
x (NUMERIC) - Input variable
Is the array non decreasing?
An array is non-decreasing if for every valid i, x[i] <= x[i+1]. For Rank 2+ arrays, values are compared
in 'c' (row major) order
x (NUMERIC) - Input variable
Is the array strictly increasing?
An array is strictly increasing if for every valid i, x[i] < x[i+1]. For Rank 2+ arrays, values are compared
in 'c' (row major) order
x (NUMERIC) - Input variable
Jaccard similarity reduction operation. The output contains the Jaccard distance for each
x (NUMERIC) - Input variable x
y (NUMERIC) - Input variable y
dimensions - Dimensions to calculate jaccardDistance over (Size: AtLeast(min=0))
Last index reduction operation.
Returns a variable that contains the index of the last element that matches the specified condition (for each
slice along the specified dimensions)
Note that if keepDims = true, the output variable has the same rank as the input variable,
with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
the mean along a dimension).
Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
keepDims = true: [a,1,c]
keepDims = false: [a,c]
in (NUMERIC) - Input variable
condition - Condition to check on input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=1))
Calculates difference between inputs X and Y.
x (NUMERIC) - Input variable X
y (NUMERIC) - Input variable Y
Element-wise logarithm function (base e - natural logarithm): out = log(x)
x (NUMERIC) - Input variable
Element-wise logarithm function (with specified base): out = log_{base`(x)
x (NUMERIC) - Input variable
base - Logarithm base
Elementwise natural logarithm function: out = log_e (1 + x)
x (NUMERIC) - Input variable
Log entropy reduction: log(-sum(x * log(x)))
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Log-sum-exp reduction (optionally along dimension).
Computes log(sum(exp(x))
input (NUMERIC) - Input variable
dimensions - Optional dimensions to reduce along (Size: AtLeast(min=0))
Manhattan distance (l1 norm, l1 distance) reduction operation. The output contains the Manhattan distance for each
tensor/subset along the specified dimensions:
out = sum_i abs(x[i]-y[i])
x (NUMERIC) - Input variable x
y (NUMERIC) - Input variable y
dimensions - Dimensions to calculate manhattanDistance over (Size: AtLeast(min=0))
Matrix determinant op. For 2D input, this returns the standard matrix determinant.
For higher dimensional input with shape [..., m, m] the matrix determinant is returned for each
shape [m,m] sub-matrix.
in (NUMERIC) - Input
Matrix inverse op. For 2D input, this returns the standard matrix inverse.
For higher dimensional input with shape [..., m, m] the matrix inverse is returned for each
shape [m,m] sub-matrix.
in (NUMERIC) - Input
Pairwise max operation, out = max(x, y)
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - First input variable, x
y (NUMERIC) - Second input variable, y
Merge add function: merges an arbitrary number of equal shaped arrays using element-wise addition:
out = sum_i in[i]
inputs (NUMERIC) - Input variables
Merge average function: merges an arbitrary number of equal shaped arrays using element-wise mean operation:
out = mean_i in[i]
inputs (NUMERIC) - Input variables
Merge max function: merges an arbitrary number of equal shaped arrays using element-wise maximum operation:
out = max_i in[i]
inputs (NUMERIC) - Input variables
Broadcasts parameters for evaluation on an N-D grid.
inputs (NUMERIC) -
cartesian -
Pairwise max operation, out = min(x, y)
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - First input variable, x
y (NUMERIC) - Second input variable, y
Pairwise modulus (remainder) operation, out = x % y
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Calculate the mean and (population) variance for the input variable, for the specified axis
input (NUMERIC) - Input to calculate moments for
axes - Dimensions to perform calculation over (Size: AtLeast(min=0))
Pairwise multiplication operation, out = x * y
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Scalar multiplication operation, out = in * scalar
x (NUMERIC) - Input variable
value - Scalar value for op
Elementwise negative operation: out = -x
x (NUMERIC) - Input variable
Calculate the mean and variance from the sufficient statistics
counts (NUMERIC) - Rank 0 (scalar) value with the total number of values used to calculate the sufficient statistics
means (NUMERIC) - Mean-value sufficient statistics: this is the SUM of all data values
variances (NUMERIC) - Variaance sufficient statistics: this is the squared sum of all data values
Boolean OR operation: elementwise (x != 0) || (y != 0)
If x and y arrays have equal shape, the output shape is the same as these inputs.
Note: supports broadcasting if x and y have different shapes and are broadcastable.
Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
x (BOOL) - Input 1
y (BOOL) - Input 2
Element-wise power function: out = x^value
x (NUMERIC) - Input variable
value - Scalar value for op
Element-wise (broadcastable) power function: out = x[i]^y[i]
x (NUMERIC) - Input variable
y (NUMERIC) - Power
Rational Tanh Approximation elementwise function, as described in the paper:
Compact Convolutional Neural Network Cascade for Face Detection
This is a faster Tanh approximation
x (NUMERIC) - Input variable
Pairwise reverse division operation, out = y / x
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Scalar reverse division operation, out = scalar / in
x (NUMERIC) - Input variable
value - Scalar value for op
Element-wise reciprocal (inverse) function: out[i] = 1 / in[i]
x (NUMERIC) - Input variable
Rectified tanh operation: max(0, tanh(in))
x (NUMERIC) - Input variable
Element-wise round function: out = round(x).
Rounds (up or down depending on value) to the nearest integer value.
x (NUMERIC) - Input variable
Element-wise reciprocal (inverse) of square root: out = 1.0 / sqrt(x)
x (NUMERIC) - Input variable
Pairwise reverse subtraction operation, out = y - x
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Scalar reverse subtraction operation, out = scalar - in
x (NUMERIC) - Input variable
value - Scalar value for op
Set the diagonal value to the specified values
If input is
[ a, b, c]
[ d, e, f]
[ g, h, i]
and diag = [ 1, 2, 3] then output is
[ 1, b, c]
[ d, 2, f]
[ g, h, 3]
in (NUMERIC) - Input variable
diag (NUMERIC) - Diagonal
Shannon Entropy reduction: -sum(x * log2(x))
in (NUMERIC) - Input variable
dimensions - Dimensions to reduce over. If dimensions are not specified, full array reduction is performed (Size: AtLeast(min=0))
Element-wise sign (signum) function:
out = -1 if in < 0
out = 0 if in = 0
out = 1 if in > 0
x (NUMERIC) - Input variable
Elementwise sine operation: out = sin(x)
x (NUMERIC) - Input variable
Elementwise sinh (hyperbolic sine) operation: out = sinh(x)
x (NUMERIC) - Input variable
Element-wise square root function: out = sqrt(x)
x (NUMERIC) - Input variable
Element-wise square function: out = x^2
x (NUMERIC) - Input variable
Pairwise squared difference operation.
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Standardize input variable along given axis
out = (x - mean) / stdev
with mean and stdev being calculated along the given dimension.
For example: given x as a mini batch of the shape [numExamples, exampleLength]:
use dimension 1 too use the statistics (mean, stdev) for each example
use dimension 0 if you want to use the statistics for each column across all examples
use dimensions 0,1 if you want to use the statistics across all columns and examples
x (NUMERIC) - Input variable
Elementwise step function:
out(x) = 1 if x >= cutoff
out(x) = 0 otherwise
x (NUMERIC) - Input variable
value - Scalar value for op
Pairwise subtraction operation, out = x - y
Note: supports broadcasting if x and y have different shapes and are broadcastable.
For example, if X has shape [1,10] and Y has shape [5,10] then op(X,Y) has output shape [5,10]
Broadcast rules are the same as NumPy:
x (NUMERIC) - Input variable
y (NUMERIC) - Input variable
Scalar subtraction operation, out = in - scalar
x (NUMERIC) - Input variable
value - Scalar value for op
Elementwise tangent operation: out = tan(x)
x (NUMERIC) - Input variable
Elementwise tanh (hyperbolic tangent) operation: out = tanh(x)
x (NUMERIC) - Input variable
Matrix trace operation
For rank 2 matrices, the output is a scalar vith the trace - i.e., sum of the main diagonal.
For higher rank inputs, output[a,b,c] = trace(in[a,b,c,:,:])
in (NUMERIC) - Input variable
Boolean XOR (exclusive OR) operation: elementwise (x != 0) XOR (y != 0)
If x and y arrays have equal shape, the output shape is the same as these inputs.
Note: supports broadcasting if x and y have different shapes and are broadcastable.
Returns an array with values 1 where condition is satisfied, or value 0 otherwise.
x (BOOL) - Input 1
y (BOOL) - Input 2
Full array zero fraction array reduction operation, optionally along specified dimensions: out = (count(x == 0) / length(x))
input (NUMERIC) - Input variable
INDArray ClipByAvgNorm(INDArray x, double clipValue, int[] dimensions)
SDVariable ClipByAvgNorm(SDVariable x, double clipValue, int[] dimensions)
SDVariable ClipByAvgNorm(String name, SDVariable x, double clipValue, int[] dimensions)dimensions - (Size: AtLeast(min=1))
INDArray EmbeddingLookup(INDArray x, INDArray indices, PartitionMode PartitionMode)
SDVariable EmbeddingLookup(SDVariable x, SDVariable indices, PartitionMode PartitionMode)
SDVariable EmbeddingLookup(String name, SDVariable x, SDVariable indices, PartitionMode PartitionMode)INDArray MergeMaxIndex(INDArray x, DataType dataType)
INDArray MergeMaxIndex(INDArray x)
SDVariable MergeMaxIndex(SDVariable x, DataType dataType)
SDVariable MergeMaxIndex(SDVariable x)
SDVariable MergeMaxIndex(String name, SDVariable x, DataType dataType)
SDVariable MergeMaxIndex(String name, SDVariable x)INDArray abs(INDArray x)
SDVariable abs(SDVariable x)
SDVariable abs(String name, SDVariable x)INDArray acos(INDArray x)
SDVariable acos(SDVariable x)
SDVariable acos(String name, SDVariable x)INDArray acosh(INDArray x)
SDVariable acosh(SDVariable x)
SDVariable acosh(String name, SDVariable x)INDArray add(INDArray x, INDArray y)
SDVariable add(SDVariable x, SDVariable y)
SDVariable add(String name, SDVariable x, SDVariable y)INDArray add(INDArray x, double value)
SDVariable add(SDVariable x, double value)
SDVariable add(String name, SDVariable x, double value)INDArray amax(INDArray in, int[] dimensions)
SDVariable amax(SDVariable in, int[] dimensions)
SDVariable amax(String name, SDVariable in, int[] dimensions)INDArray amean(INDArray in, int[] dimensions)
SDVariable amean(SDVariable in, int[] dimensions)
SDVariable amean(String name, SDVariable in, int[] dimensions)INDArray amin(INDArray in, int[] dimensions)
SDVariable amin(SDVariable in, int[] dimensions)
SDVariable amin(String name, SDVariable in, int[] dimensions)INDArray and(INDArray x, INDArray y)
SDVariable and(SDVariable x, SDVariable y)
SDVariable and(String name, SDVariable x, SDVariable y)INDArray asin(INDArray x)
SDVariable asin(SDVariable x)
SDVariable asin(String name, SDVariable x)INDArray asinh(INDArray x)
SDVariable asinh(SDVariable x)
SDVariable asinh(String name, SDVariable x)INDArray asum(INDArray in, int[] dimensions)
SDVariable asum(SDVariable in, int[] dimensions)
SDVariable asum(String name, SDVariable in, int[] dimensions)INDArray atan(INDArray x)
SDVariable atan(SDVariable x)
SDVariable atan(String name, SDVariable x)INDArray atan2(INDArray y, INDArray x)
SDVariable atan2(SDVariable y, SDVariable x)
SDVariable atan2(String name, SDVariable y, SDVariable x)INDArray atanh(INDArray x)
SDVariable atanh(SDVariable x)
SDVariable atanh(String name, SDVariable x)INDArray bitShift(INDArray x, INDArray shift)
SDVariable bitShift(SDVariable x, SDVariable shift)
SDVariable bitShift(String name, SDVariable x, SDVariable shift)INDArray bitShiftRight(INDArray x, INDArray shift)
SDVariable bitShiftRight(SDVariable x, SDVariable shift)
SDVariable bitShiftRight(String name, SDVariable x, SDVariable shift)INDArray bitShiftRotl(INDArray x, INDArray shift)
SDVariable bitShiftRotl(SDVariable x, SDVariable shift)
SDVariable bitShiftRotl(String name, SDVariable x, SDVariable shift)INDArray bitShiftRotr(INDArray x, INDArray shift)
SDVariable bitShiftRotr(SDVariable x, SDVariable shift)
SDVariable bitShiftRotr(String name, SDVariable x, SDVariable shift)INDArray ceil(INDArray x)
SDVariable ceil(SDVariable x)
SDVariable ceil(String name, SDVariable x)INDArray clipByNorm(INDArray x, double clipValue, int[] dimensions)
SDVariable clipByNorm(SDVariable x, double clipValue, int[] dimensions)
SDVariable clipByNorm(String name, SDVariable x, double clipValue, int[] dimensions)INDArray clipByValue(INDArray x, double clipValueMin, double clipValueMax)
SDVariable clipByValue(SDVariable x, double clipValueMin, double clipValueMax)
SDVariable clipByValue(String name, SDVariable x, double clipValueMin, double clipValueMax)INDArray confusionMatrix(INDArray labels, INDArray pred, DataType dataType)
SDVariable confusionMatrix(SDVariable labels, SDVariable pred, DataType dataType)
SDVariable confusionMatrix(String name, SDVariable labels, SDVariable pred, DataType dataType)INDArray confusionMatrix(INDArray labels, INDArray pred, int numClasses)
SDVariable confusionMatrix(SDVariable labels, SDVariable pred, int numClasses)
SDVariable confusionMatrix(String name, SDVariable labels, SDVariable pred, int numClasses)INDArray confusionMatrix(INDArray labels, INDArray pred, INDArray weights)
SDVariable confusionMatrix(SDVariable labels, SDVariable pred, SDVariable weights)
SDVariable confusionMatrix(String name, SDVariable labels, SDVariable pred, SDVariable weights)INDArray confusionMatrix(INDArray labels, INDArray pred, INDArray weights, int numClasses)
SDVariable confusionMatrix(SDVariable labels, SDVariable pred, SDVariable weights, int numClasses)
SDVariable confusionMatrix(String name, SDVariable labels, SDVariable pred, SDVariable weights, int numClasses)INDArray cos(INDArray x)
SDVariable cos(SDVariable x)
SDVariable cos(String name, SDVariable x)INDArray cosh(INDArray x)
SDVariable cosh(SDVariable x)
SDVariable cosh(String name, SDVariable x)INDArray cosineDistance(INDArray x, INDArray y, int[] dimensions)
SDVariable cosineDistance(SDVariable x, SDVariable y, int[] dimensions)
SDVariable cosineDistance(String name, SDVariable x, SDVariable y, int[] dimensions)INDArray cosineSimilarity(INDArray x, INDArray y, int[] dimensions)
SDVariable cosineSimilarity(SDVariable x, SDVariable y, int[] dimensions)
SDVariable cosineSimilarity(String name, SDVariable x, SDVariable y, int[] dimensions)INDArray countNonZero(INDArray in, int[] dimensions)
SDVariable countNonZero(SDVariable in, int[] dimensions)
SDVariable countNonZero(String name, SDVariable in, int[] dimensions)INDArray countZero(INDArray in, int[] dimensions)
SDVariable countZero(SDVariable in, int[] dimensions)
SDVariable countZero(String name, SDVariable in, int[] dimensions)INDArray cross(INDArray a, INDArray b)
SDVariable cross(SDVariable a, SDVariable b)
SDVariable cross(String name, SDVariable a, SDVariable b)INDArray cube(INDArray x)
SDVariable cube(SDVariable x)
SDVariable cube(String name, SDVariable x)INDArray diag(INDArray x)
SDVariable diag(SDVariable x)
SDVariable diag(String name, SDVariable x)INDArray diagPart(INDArray x)
SDVariable diagPart(SDVariable x)
SDVariable diagPart(String name, SDVariable x)INDArray div(INDArray x, INDArray y)
SDVariable div(SDVariable x, SDVariable y)
SDVariable div(String name, SDVariable x, SDVariable y)INDArray div(INDArray x, double value)
SDVariable div(SDVariable x, double value)
SDVariable div(String name, SDVariable x, double value)INDArray entropy(INDArray in, int[] dimensions)
SDVariable entropy(SDVariable in, int[] dimensions)
SDVariable entropy(String name, SDVariable in, int[] dimensions)INDArray erf(INDArray x)
SDVariable erf(SDVariable x)
SDVariable erf(String name, SDVariable x)INDArray erfc(INDArray x)
SDVariable erfc(SDVariable x)
SDVariable erfc(String name, SDVariable x)INDArray euclideanDistance(INDArray x, INDArray y, int[] dimensions)
SDVariable euclideanDistance(SDVariable x, SDVariable y, int[] dimensions)
SDVariable euclideanDistance(String name, SDVariable x, SDVariable y, int[] dimensions)INDArray exp(INDArray x)
SDVariable exp(SDVariable x)
SDVariable exp(String name, SDVariable x)INDArray expm1(INDArray x)
SDVariable expm1(SDVariable x)
SDVariable expm1(String name, SDVariable x)INDArray eye(int rows)
SDVariable eye(int rows)
SDVariable eye(String name, int rows)INDArray eye(int rows, int cols)
SDVariable eye(int rows, int cols)
SDVariable eye(String name, int rows, int cols)INDArray eye(int rows, int cols, DataType dataType, int[] dimensions)
SDVariable eye(int rows, int cols, DataType dataType, int[] dimensions)
SDVariable eye(String name, int rows, int cols, DataType dataType, int[] dimensions)`INDArray eye = eye(3,2)
eye:
[ 1, 0]
[ 0, 1]
[ 0, 0]`INDArray eye(INDArray rows, INDArray cols)
SDVariable eye(SDVariable rows, SDVariable cols)
SDVariable eye(String name, SDVariable rows, SDVariable cols)INDArray eye(INDArray rows)
SDVariable eye(SDVariable rows)
SDVariable eye(String name, SDVariable rows)INDArray firstIndex(INDArray in, Condition condition, int[] dimensions)
INDArray firstIndex(INDArray in, Condition condition, boolean keepDims, int[] dimensions)
SDVariable firstIndex(SDVariable in, Condition condition, int[] dimensions)
SDVariable firstIndex(SDVariable in, Condition condition, boolean keepDims, int[] dimensions)
SDVariable firstIndex(String name, SDVariable in, Condition condition, int[] dimensions)
SDVariable firstIndex(String name, SDVariable in, Condition condition, boolean keepDims, int[] dimensions)INDArray floor(INDArray x)
SDVariable floor(SDVariable x)
SDVariable floor(String name, SDVariable x)INDArray floorDiv(INDArray x, INDArray y)
SDVariable floorDiv(SDVariable x, SDVariable y)
SDVariable floorDiv(String name, SDVariable x, SDVariable y)INDArray floorMod(INDArray x, INDArray y)
SDVariable floorMod(SDVariable x, SDVariable y)
SDVariable floorMod(String name, SDVariable x, SDVariable y)INDArray floorMod(INDArray x, double value)
SDVariable floorMod(SDVariable x, double value)
SDVariable floorMod(String name, SDVariable x, double value)INDArray hammingDistance(INDArray x, INDArray y, int[] dimensions)
SDVariable hammingDistance(SDVariable x, SDVariable y, int[] dimensions)
SDVariable hammingDistance(String name, SDVariable x, SDVariable y, int[] dimensions)INDArray iamax(INDArray in, int[] dimensions)
INDArray iamax(INDArray in, boolean keepDims, int[] dimensions)
SDVariable iamax(SDVariable in, int[] dimensions)
SDVariable iamax(SDVariable in, boolean keepDims, int[] dimensions)
SDVariable iamax(String name, SDVariable in, int[] dimensions)
SDVariable iamax(String name, SDVariable in, boolean keepDims, int[] dimensions)INDArray iamin(INDArray in, int[] dimensions)
INDArray iamin(INDArray in, boolean keepDims, int[] dimensions)
SDVariable iamin(SDVariable in, int[] dimensions)
SDVariable iamin(SDVariable in, boolean keepDims, int[] dimensions)
SDVariable iamin(String name, SDVariable in, int[] dimensions)
SDVariable iamin(String name, SDVariable in, boolean keepDims, int[] dimensions)INDArray isFinite(INDArray x)
SDVariable isFinite(SDVariable x)
SDVariable isFinite(String name, SDVariable x)INDArray isInfinite(INDArray x)
SDVariable isInfinite(SDVariable x)
SDVariable isInfinite(String name, SDVariable x)INDArray isMax(INDArray x)
SDVariable isMax(SDVariable x)
SDVariable isMax(String name, SDVariable x)INDArray isNaN(INDArray x)
SDVariable isNaN(SDVariable x)
SDVariable isNaN(String name, SDVariable x)INDArray isNonDecreasing(INDArray x)
SDVariable isNonDecreasing(SDVariable x)
SDVariable isNonDecreasing(String name, SDVariable x)INDArray isStrictlyIncreasing(INDArray x)
SDVariable isStrictlyIncreasing(SDVariable x)
SDVariable isStrictlyIncreasing(String name, SDVariable x)INDArray jaccardDistance(INDArray x, INDArray y, int[] dimensions)
SDVariable jaccardDistance(SDVariable x, SDVariable y, int[] dimensions)
SDVariable jaccardDistance(String name, SDVariable x, SDVariable y, int[] dimensions) tensor along the specified dimensions.INDArray lastIndex(INDArray in, Condition condition, int[] dimensions)
INDArray lastIndex(INDArray in, Condition condition, boolean keepDims, int[] dimensions)
SDVariable lastIndex(SDVariable in, Condition condition, int[] dimensions)
SDVariable lastIndex(SDVariable in, Condition condition, boolean keepDims, int[] dimensions)
SDVariable lastIndex(String name, SDVariable in, Condition condition, int[] dimensions)
SDVariable lastIndex(String name, SDVariable in, Condition condition, boolean keepDims, int[] dimensions)INDArray[] listDiff(INDArray x, INDArray y)
SDVariable[] listDiff(SDVariable x, SDVariable y)
SDVariable[] listDiff(String name, SDVariable x, SDVariable y)INDArray log(INDArray x)
SDVariable log(SDVariable x)
SDVariable log(String name, SDVariable x)INDArray log(INDArray x, double base)
SDVariable log(SDVariable x, double base)
SDVariable log(String name, SDVariable x, double base)INDArray log1p(INDArray x)
SDVariable log1p(SDVariable x)
SDVariable log1p(String name, SDVariable x)INDArray logEntropy(INDArray in, int[] dimensions)
SDVariable logEntropy(SDVariable in, int[] dimensions)
SDVariable logEntropy(String name, SDVariable in, int[] dimensions)INDArray logSumExp(INDArray input, int[] dimensions)
SDVariable logSumExp(SDVariable input, int[] dimensions)
SDVariable logSumExp(String name, SDVariable input, int[] dimensions)INDArray manhattanDistance(INDArray x, INDArray y, int[] dimensions)
SDVariable manhattanDistance(SDVariable x, SDVariable y, int[] dimensions)
SDVariable manhattanDistance(String name, SDVariable x, SDVariable y, int[] dimensions)INDArray matrixDeterminant(INDArray in)
SDVariable matrixDeterminant(SDVariable in)
SDVariable matrixDeterminant(String name, SDVariable in)INDArray matrixInverse(INDArray in)
SDVariable matrixInverse(SDVariable in)
SDVariable matrixInverse(String name, SDVariable in)INDArray max(INDArray x, INDArray y)
SDVariable max(SDVariable x, SDVariable y)
SDVariable max(String name, SDVariable x, SDVariable y)INDArray mergeAdd(INDArray inputs)
SDVariable mergeAdd(SDVariable inputs)
SDVariable mergeAdd(String name, SDVariable inputs)INDArray mergeAvg(INDArray inputs)
SDVariable mergeAvg(SDVariable inputs)
SDVariable mergeAvg(String name, SDVariable inputs)INDArray mergeMax(INDArray inputs)
SDVariable mergeMax(SDVariable inputs)
SDVariable mergeMax(String name, SDVariable inputs)INDArray[] meshgrid(INDArray inputs, boolean cartesian)
SDVariable[] meshgrid(SDVariable inputs, boolean cartesian)
SDVariable[] meshgrid(String name, SDVariable inputs, boolean cartesian)INDArray min(INDArray x, INDArray y)
SDVariable min(SDVariable x, SDVariable y)
SDVariable min(String name, SDVariable x, SDVariable y)INDArray mod(INDArray x, INDArray y)
SDVariable mod(SDVariable x, SDVariable y)
SDVariable mod(String name, SDVariable x, SDVariable y)INDArray[] moments(INDArray input, int[] axes)
SDVariable[] moments(SDVariable input, int[] axes)
SDVariable[] moments(String name, SDVariable input, int[] axes)INDArray mul(INDArray x, INDArray y)
SDVariable mul(SDVariable x, SDVariable y)
SDVariable mul(String name, SDVariable x, SDVariable y)INDArray mul(INDArray x, double value)
SDVariable mul(SDVariable x, double value)
SDVariable mul(String name, SDVariable x, double value)INDArray neg(INDArray x)
SDVariable neg(SDVariable x)
SDVariable neg(String name, SDVariable x)INDArray[] normalizeMoments(INDArray counts, INDArray means, INDArray variances, double shift)
SDVariable[] normalizeMoments(SDVariable counts, SDVariable means, SDVariable variances, double shift)
SDVariable[] normalizeMoments(String name, SDVariable counts, SDVariable means, SDVariable variances, double shift)INDArray or(INDArray x, INDArray y)
SDVariable or(SDVariable x, SDVariable y)
SDVariable or(String name, SDVariable x, SDVariable y)INDArray pow(INDArray x, double value)
SDVariable pow(SDVariable x, double value)
SDVariable pow(String name, SDVariable x, double value)INDArray pow(INDArray x, INDArray y)
SDVariable pow(SDVariable x, SDVariable y)
SDVariable pow(String name, SDVariable x, SDVariable y)INDArray rationalTanh(INDArray x)
SDVariable rationalTanh(SDVariable x)
SDVariable rationalTanh(String name, SDVariable x)INDArray rdiv(INDArray x, INDArray y)
SDVariable rdiv(SDVariable x, SDVariable y)
SDVariable rdiv(String name, SDVariable x, SDVariable y)INDArray rdiv(INDArray x, double value)
SDVariable rdiv(SDVariable x, double value)
SDVariable rdiv(String name, SDVariable x, double value)INDArray reciprocal(INDArray x)
SDVariable reciprocal(SDVariable x)
SDVariable reciprocal(String name, SDVariable x)INDArray rectifiedTanh(INDArray x)
SDVariable rectifiedTanh(SDVariable x)
SDVariable rectifiedTanh(String name, SDVariable x)INDArray round(INDArray x)
SDVariable round(SDVariable x)
SDVariable round(String name, SDVariable x)INDArray rsqrt(INDArray x)
SDVariable rsqrt(SDVariable x)
SDVariable rsqrt(String name, SDVariable x)INDArray rsub(INDArray x, INDArray y)
SDVariable rsub(SDVariable x, SDVariable y)
SDVariable rsub(String name, SDVariable x, SDVariable y)INDArray rsub(INDArray x, double value)
SDVariable rsub(SDVariable x, double value)
SDVariable rsub(String name, SDVariable x, double value)INDArray setDiag(INDArray in, INDArray diag)
SDVariable setDiag(SDVariable in, SDVariable diag)
SDVariable setDiag(String name, SDVariable in, SDVariable diag)INDArray shannonEntropy(INDArray in, int[] dimensions)
SDVariable shannonEntropy(SDVariable in, int[] dimensions)
SDVariable shannonEntropy(String name, SDVariable in, int[] dimensions)INDArray sign(INDArray x)
SDVariable sign(SDVariable x)
SDVariable sign(String name, SDVariable x)INDArray sin(INDArray x)
SDVariable sin(SDVariable x)
SDVariable sin(String name, SDVariable x)INDArray sinh(INDArray x)
SDVariable sinh(SDVariable x)
SDVariable sinh(String name, SDVariable x)INDArray sqrt(INDArray x)
SDVariable sqrt(SDVariable x)
SDVariable sqrt(String name, SDVariable x)INDArray square(INDArray x)
SDVariable square(SDVariable x)
SDVariable square(String name, SDVariable x)INDArray squaredDifference(INDArray x, INDArray y)
SDVariable squaredDifference(SDVariable x, SDVariable y)
SDVariable squaredDifference(String name, SDVariable x, SDVariable y)INDArray standardize(INDArray x, int[] dimensions)
SDVariable standardize(SDVariable x, int[] dimensions)
SDVariable standardize(String name, SDVariable x, int[] dimensions)INDArray step(INDArray x, double value)
SDVariable step(SDVariable x, double value)
SDVariable step(String name, SDVariable x, double value)INDArray sub(INDArray x, INDArray y)
SDVariable sub(SDVariable x, SDVariable y)
SDVariable sub(String name, SDVariable x, SDVariable y)INDArray sub(INDArray x, double value)
SDVariable sub(SDVariable x, double value)
SDVariable sub(String name, SDVariable x, double value)INDArray tan(INDArray x)
SDVariable tan(SDVariable x)
SDVariable tan(String name, SDVariable x)INDArray tanh(INDArray x)
SDVariable tanh(SDVariable x)
SDVariable tanh(String name, SDVariable x)INDArray trace(INDArray in)
SDVariable trace(SDVariable in)
SDVariable trace(String name, SDVariable in)INDArray xor(INDArray x, INDArray y)
SDVariable xor(SDVariable x, SDVariable y)
SDVariable xor(String name, SDVariable x, SDVariable y)INDArray zeroFraction(INDArray input)
SDVariable zeroFraction(SDVariable input)
SDVariable zeroFraction(String name, SDVariable input)