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This file was autogenerated. Do not edit it by hand, since your modifications would be overwritten.
Modules
image module: DO NOT EDIT.
linalg module: DO NOT EDIT.
nn module: DO NOT EDIT.
numpy module: DO NOT EDIT.
Functions
abs(...): Shorthand for keras.ops.absolute.
absolute(...): Compute the absolute value element-wise.
adaptive_average_pool(...): Adaptive average pooling operation.
adaptive_max_pool(...): Adaptive max pooling operation.
add(...): Add arguments element-wise.
all(...): Test whether all array elements along a given axis evaluate to True.
allclose(...): Returns True if two arrays are element-wise equal within a tolerance.
amax(...): Returns the maximum of an array or maximum value along an axis.
amin(...): Returns the minimum of an array or minimum value along an axis.
angle(...): Element-wise angle of a complex tensor.
any(...): Test whether any array element along a given axis evaluates to True.
append(...): Append tensor x2 to the end of tensor x1.
arange(...): Return evenly spaced values within a given interval.
arccos(...): Trigonometric inverse cosine, element-wise.
arccosh(...): Inverse hyperbolic cosine, element-wise.
arcsin(...): Inverse sine, element-wise.
arcsinh(...): Inverse hyperbolic sine, element-wise.
arctan(...): Trigonometric inverse tangent, element-wise.
arctan2(...): Element-wise arc tangent of x1/x2 choosing the quadrant correctly.
arctanh(...): Inverse hyperbolic tangent, element-wise.
argmax(...): Returns the indices of the maximum values along an axis.
argmin(...): Returns the indices of the minimum values along an axis.
argpartition(...): Performs an indirect partition along the given axis.
argsort(...): Returns the indices that would sort a tensor.
array(...): Create a tensor.
array_split(...): Splits an array into multiple sub-arrays (unevenly).
associative_scan(...): Performs a scan with an associative binary operation, in parallel.
average(...): Compute the weighted average along the specified axis.
average_pool(...): Average pooling operation.
bartlett(...): Bartlett window function.
batch_normalization(...): Normalizes x by mean and variance.
binary_crossentropy(...): Computes binary cross-entropy loss between target and output tensor.
bincount(...): Count the number of occurrences of each value in a tensor of integers.
bitwise_and(...): Compute the bit-wise AND of two arrays element-wise.
bitwise_invert(...): Compute bit-wise inversion, or bit-wise NOT, element-wise.
bitwise_left_shift(...): Shift the bits of an integer to the left.
bitwise_not(...): Compute bit-wise inversion, or bit-wise NOT, element-wise.
bitwise_or(...): Compute the bit-wise OR of two arrays element-wise.
bitwise_right_shift(...): Shift the bits of an integer to the right.
bitwise_xor(...): Compute the bit-wise XOR of two arrays element-wise.
blackman(...): Blackman window function.
broadcast_to(...): Broadcast a tensor to a new shape.
cast(...): Cast a tensor to the desired dtype.
categorical_crossentropy(...): Computes categorical cross-entropy loss between target and output tensor.
cbrt(...): Computes the cube root of the input tensor, element-wise.
cdist(...): Computes pairwise distances between two collections of vectors.
ceil(...): Return the ceiling of the input, element-wise.
celu(...): Continuously-differentiable exponential linear unit.
cholesky(...): Computes the Cholesky decomposition of a positive semi-definite matrix.
cholesky_inverse(...): Computes the inverse of a symmetric positive-definite matrix.
clip(...): Clip (limit) the values in a tensor.
concatenate(...): Join a sequence of tensors along an existing axis.
cond(...): Conditionally applies true_fn or false_fn.
conj(...): Shorthand for keras.ops.conjugate.
conjugate(...): Returns the complex conjugate, element-wise.
conv(...): General N-D convolution.
conv_transpose(...): General N-D convolution transpose.
convert_to_numpy(...): Convert a tensor to a NumPy array.
convert_to_tensor(...): Convert a NumPy array or Python array to a tensor.
copy(...): Returns a copy of x.
corrcoef(...): Compute the Pearson correlation coefficient matrix.
correlate(...): Compute the cross-correlation of two 1-dimensional tensors.
cos(...): Cosine, element-wise.
cosh(...): Hyperbolic cosine, element-wise.
count_nonzero(...): Counts the number of non-zero values in x along the given axis.
cross(...): Returns the cross product of two (arrays of) vectors.
ctc_decode(...): Decodes the output of a CTC model.
ctc_loss(...): CTC (Connectionist Temporal Classification) loss.
cumprod(...): Return the cumulative product of elements along a given axis.
cumsum(...): Returns the cumulative sum of elements along a given axis.
custom_gradient(...): Decorator to define a function with a custom gradient.
deg2rad(...): Convert angles from degrees to radians.
depth_to_space(...): Rearranges data from depth into blocks of spatial data.
depthwise_conv(...): General N-D depthwise convolution.
det(...): Computes the determinant of a square tensor.
diag(...): Extract a diagonal or construct a diagonal array.
diagflat(...): Create a two-dimensional array with the flattened input on the k-th diagonal.
diagonal(...): Return specified diagonals.
diff(...): Calculate the n-th discrete difference along the given axis.
digitize(...): Returns the indices of the bins to which each value in x belongs.
divide(...): Divide arguments element-wise.
divide_no_nan(...): Safe element-wise division which returns 0 where the denominator is 0.
dot(...): Dot product of two tensors.
dot_product_attention(...): Scaled dot product attention function.
dsplit(...): Split an array into multiple sub-arrays depth-wise.
dstack(...): Stack tensors in sequence depth wise (along third axis).
dtype(...): Return the dtype of the tensor input as a standardized string.
eig(...): Computes the eigenvalues and eigenvectors of a square matrix.
eigh(...): Computes the eigenvalues and eigenvectors of a complex Hermitian.
einsum(...): Evaluates the Einstein summation convention on the operands.
elu(...): Exponential Linear Unit activation function.
empty(...): Return a tensor of given shape and type filled with uninitialized data.
empty_like(...): Return a new uninitialized tensor with the same shape and dtype as x.
equal(...): Returns (x1 == x2) element-wise.
erf(...): Computes the error function of x, element-wise.
erfc(...): Computes the complementary error function of x, element-wise.
erfinv(...): Computes the inverse error function of x, element-wise.
exp(...): Calculate the exponential of all elements in the input tensor.
exp2(...): Calculate the base-2 exponential of all elements in the input tensor.
expand_dims(...): Expand the shape of a tensor.
expm1(...): Calculate exp(x) - 1 for all elements in the tensor.
extract_sequences(...): Expands the dimension of last axis into sequences of sequence_length.
eye(...): Return a 2-D tensor with ones on the diagonal and zeros elsewhere.
fabs(...): Compute the absolute values element-wise.
fft(...): Computes the Fast Fourier Transform along last axis of input.
fft2(...): Computes the 2D Fast Fourier Transform along the last two axes of input.
flip(...): Reverse the order of elements in the tensor along the given axis.
fliplr(...): Reverse the order of elements along axis 1 (left/right).
flipud(...): Reverse the order of elements along axis 0 (up/down).
floor(...): Return the floor of the input, element-wise.
floor_divide(...): Returns the largest integer smaller or equal to the division of inputs.
fmax(...): Element-wise maximum of tensor elements, ignoring NaNs.
fmin(...): Element-wise minimum of tensor elements, ignoring NaNs.
fmod(...): Returns the element-wise remainder of division with truncation.
fold(...): Combines an array of sliding local blocks into a large containing tensor (reverses unfold).
fori_loop(...): For loop implementation.
full(...): Return a new tensor of given shape and type, filled with fill_value.
full_like(...): Return a full tensor with the same shape and type as the given tensor.
gcd(...): Greatest common divisor of x1 and x2, element-wise.
gelu(...): Gaussian Error Linear Unit (GELU) activation function.
geomspace(...): Returns numbers spaced evenly on a log scale (a geometric progression).
get_item(...): Return x[key].
glu(...): Gated Linear Unit (GLU) activation function.
greater(...): Return the truth value of x1 > x2 element-wise.
greater_equal(...): Return the truth value of x1 >= x2 element-wise.
hamming(...): Hamming window function.
hanning(...): Hanning window function.
hard_shrink(...): Hard Shrink activation function.
hard_sigmoid(...): Hard sigmoid activation function.
hard_silu(...): Hard SiLU activation function, also known as Hard Swish.
hard_swish(...): Hard SiLU activation function, also known as Hard Swish.
hard_tanh(...): Applies the HardTanh function element-wise.
heaviside(...): Heaviside step function.
histogram(...): Computes a histogram of the data tensor x.
hsplit(...): Split an array into multiple sub-arrays horizontally (column-wise).
hstack(...): Stack tensors in sequence horizontally (column wise).
hypot(...): Element-wise hypotenuse of right triangles with legs x1 and x2.
i0(...): Modified Bessel function of the first kind, order 0.
identity(...): Return the identity tensor.
ifft2(...): Computes the 2D Inverse Fast Fourier Transform along the last two axes of input.
imag(...): Return the imaginary part of the complex argument.
in_top_k(...): Checks if the targets are in the top-k predictions.
inner(...): Return the inner product of two tensors.
inv(...): Computes the inverse of a square tensor.
irfft(...): Inverse real-valued Fast Fourier transform along the last axis.
is_tensor(...): Check whether the given object is a tensor.
isclose(...): Return whether two tensors are element-wise almost equal.
isfinite(...): Return whether a tensor is finite, element-wise.
isin(...): Test whether each element of x1 is present in x2.
isinf(...): Test element-wise for positive or negative infinity.
isnan(...): Test element-wise for NaN and return result as a boolean tensor.
isneginf(...): Test element-wise for negative infinity.
isposinf(...): Test element-wise for positive infinity.
isreal(...): Test element-wise for real numbers.
istft(...): Inverse Short-Time Fourier Transform along the last axis of the input.
jvp(...): Computes a (forward-mode) Jacobian-vector product of fun.
kaiser(...): Kaiser window function.
kron(...): Kronecker product of x1 and x2.
layer_normalization(...): Layer normalization layer (Ba et al., 2016).
lcm(...): Least common multiple of x1 and x2, element-wise.
ldexp(...): Multiply x1 by 2 raised to the power of x2, element-wise.
leaky_relu(...): Leaky version of a Rectified Linear Unit activation function.
left_shift(...): Shift the bits of an integer to the left.
less(...): Return the truth value of x1 < x2 element-wise.
less_equal(...): Return the truth value of x1 <= x2 element-wise.
linspace(...): Return evenly spaced numbers over a specified interval.
log(...): Natural logarithm, element-wise.
log10(...): Return the base 10 logarithm of the input tensor, element-wise.
log1p(...): Returns the natural logarithm of one plus the x, element-wise.
log2(...): Base-2 logarithm of x, element-wise.
log_sigmoid(...): Logarithm of the sigmoid activation function.
log_softmax(...): Log-softmax activation function.
logaddexp(...): Logarithm of the sum of exponentiations of the inputs.
logaddexp2(...): Base-2 logarithm of the sum of exponentiations of the inputs.
logdet(...): Computes log of the determinant of a hermitian positive definite matrix.
logical_and(...): Computes the element-wise logical AND of the given input tensors.
logical_not(...): Computes the element-wise NOT of the given input tensor.
logical_or(...): Computes the element-wise logical OR of the given input tensors.
logical_xor(...): Compute the truth value of x1 XOR x2, element-wise.
logspace(...): Returns numbers spaced evenly on a log scale.
logsumexp(...): Computes the logarithm of sum of exponentials of elements in a tensor.
lstsq(...): Return the least-squares solution to a linear matrix equation.
lu_factor(...): Computes the lower-upper decomposition of a square matrix.
map(...): Map a function over leading array axes.
matmul(...): Matrix product of two tensors.
matrix_rank(...): Return the matrix rank of one or more matrices using SVD.
max(...): Return the maximum of a tensor or maximum along an axis.
max_pool(...): Max pooling operation.
maximum(...): Element-wise maximum of x1 and x2.
mean(...): Compute the arithmetic mean along the specified axes.
median(...): Compute the median along the specified axis.
meshgrid(...): Creates grids of coordinates from coordinate vectors.
min(...): Return the minimum of a tensor or minimum along an axis.
minimum(...): Element-wise minimum of x1 and x2.
mod(...): Returns the element-wise remainder of division.
moments(...): Calculates the mean and variance of x.
moveaxis(...): Move axes of a tensor to new positions.
multi_hot(...): Encodes integer labels as multi-hot vectors.
multiply(...): Multiply arguments element-wise.
nan_to_num(...): Replace NaN with zero and infinity with large finite numbers.
nanargmax(...): Returns the indices of the maximum values along an axis, ignoring NaNs.
nanargmin(...): Returns the indices of the minimum values along an axis, ignoring NaNs.
nancumprod(...): Returns the cumulative product of elements along a given axis, treating NaNs as one.
nancumsum(...): Returns the cumulative sum of elements along a given axis, treating NaNs as zero.
nanmax(...): Maximum of a tensor over the given axes, ignoring NaNs.
nanmean(...): Mean of a tensor over the given axes, ignoring NaNs.
nanmedian(...): Median of a tensor over the given axes, ignoring NaNs.
nanmin(...): Minimum of a tensor over the given axes, ignoring NaNs.
nanpercentile(...): Compute the q-th percentile(s) of the data along the specified axis, while ignoring NaNs.
nanprod(...): Product of a tensor over the given axes, ignoring NaNs.
nanquantile(...): Compute the q-th quantile(s) of the data along the specified axis, while ignoring NaNs.
nanstd(...): Compute the standard deviation along the specified axes, ignoring NaNs.
nansum(...): Sum of a tensor over the given axes, ignoring NaNs.
nanvar(...): Compute the variance along the specified axes, ignoring NaNs.
ndim(...): Return the number of dimensions of a tensor.
negative(...): Numerical negative, element-wise.
nextafter(...): Return the next representable floating-point value after x1 towards x2.
nonzero(...): Return the indices of the elements that are non-zero.
norm(...): Matrix or vector norm.
normalize(...): Normalizes x over the specified axis.
not_equal(...): Return (x1 != x2) element-wise.
one_hot(...): Converts integer tensor x into a one-hot tensor.
ones(...): Return a new tensor of given shape and type, filled with ones.
ones_like(...): Return a tensor of ones with the same shape and type of x.
outer(...): Compute the outer product of two vectors.
pad(...): Pad a tensor.
percentile(...): Compute the q-th percentile(s) of the data along the specified axis.
pinv(...): Compute the (Moore-Penrose) pseudoinverse of a matrix.
polar(...): Constructs a complex tensor whose elements are Cartesian coordinates corresponding to the polar coordinates with absolute value abs and angle angle.
power(...): First tensor elements raised to powers from second tensor, element-wise.
prod(...): Return the product of tensor elements over a given axis.
psnr(...): Peak Signal-to-Noise Ratio (PSNR) function.
ptp(...): Return the peak-to-peak (max - min) value of tensor elements over a given axis.
qr(...): Computes the QR decomposition of a tensor.
quantile(...): Compute the q-th quantile(s) of the data along the specified axis.
rad2deg(...): Convert angles from radians to degrees.
ravel(...): Return a contiguous flattened tensor.
real(...): Return the real part of the complex argument.
rearrange(...): Rearranges the axes of a Keras tensor according to a specified pattern, einops-style.
reciprocal(...): Return the reciprocal of the argument, element-wise.
relu(...): Rectified linear unit activation function.
relu6(...): Rectified linear unit activation function with upper bound of 6.
repeat(...): Repeat each element of a tensor after themselves.
reshape(...): Gives a new shape to a tensor without changing its data.
rfft(...): Real-valued Fast Fourier Transform along the last axis of the input.
right_shift(...): Shift the bits of an integer to the right.
rms_normalization(...): Performs Root Mean Square (RMS) normalization on x.
roll(...): Roll tensor elements along a given axis.
rot90(...): Rotate an array by 90 degrees in the plane specified by axes.
round(...): Evenly round to the given number of decimals.
rsqrt(...): Computes reciprocal of square root of x element-wise.
saturate_cast(...): Performs a safe saturating cast to the desired dtype.
scan(...): Scan a function over leading array axes while carrying along state.
scatter(...): Returns a tensor of shape shape where indices are set to values.
scatter_update(...): Update inputs via updates at scattered (sparse) indices.
searchsorted(...): Perform a binary search, returning indices for insertion of values into sorted_sequence that maintain the sorting order.
segment_max(...): Computes the max of segments in a tensor.
segment_min(...): Computes the min of segments in a tensor.
segment_prod(...): Computes the product of segments in a tensor.
segment_sum(...): Computes the sum of segments in a tensor.
select(...): Return elements from choicelist, based on conditions in condlist.
selu(...): Scaled Exponential Linear Unit (SELU) activation function.
separable_conv(...): General N-D separable convolution.
shape(...): Gets the shape of the tensor input.
sigmoid(...): Sigmoid activation function.
sign(...): Returns a tensor with the signs of the elements of x.
signbit(...): Return the sign bit of the elements of x.
silu(...): Sigmoid Linear Unit (SiLU) activation function, also known as Swish.
sin(...): Trigonometric sine, element-wise.
sinc(...): Return the normalized sinc function.
sinh(...): Hyperbolic sine, element-wise.
size(...): Return the number of elements in a tensor.
slice(...): Return a slice of an input tensor.
slice_update(...): Update an input by slicing in a tensor of updated values.
slogdet(...): Compute the sign and natural logarithm of the determinant of a matrix.
soft_shrink(...): Soft Shrink activation function.
softmax(...): Softmax activation function.
softplus(...): Softplus activation function.
softsign(...): Softsign activation function.
solve(...): Solves a linear system of equations given by a x = b.
solve_triangular(...): Solves a linear system of equations given by a x = b.
sort(...): Sorts the elements of x along a given axis in ascending order.
space_to_depth(...): Rearranges blocks of spatial data into depth.
sparse_categorical_crossentropy(...): Computes sparse categorical cross-entropy loss.
sparse_plus(...): SparsePlus activation function.
sparse_sigmoid(...): Sparse sigmoid activation function.
sparsemax(...): Sparsemax activation function.
split(...): Split a tensor into chunks.
sqrt(...): Return the non-negative square root of a tensor, element-wise.
square(...): Return the element-wise square of the input.
squareplus(...): Squareplus activation function.
squeeze(...): Remove axes of length one from x.
stack(...): Join a sequence of tensors along a new axis.
std(...): Compute the standard deviation along the specified axis.
stft(...): Short-Time Fourier Transform along the last axis of the input.
stop_gradient(...): Stops gradient computation.
subtract(...): Subtract arguments element-wise.
sum(...): Sum of a tensor over the given axes.
svd(...): Computes the singular value decomposition of a matrix.
swapaxes(...): Interchange two axes of a tensor.
swish(...): Sigmoid Linear Unit (SiLU) activation function, also known as Swish.
switch(...): Apply exactly one of the branches given by index.
take(...): Take elements from a tensor along an axis.
take_along_axis(...): Select values from x at the 1-D indices along the given axis.
tan(...): Compute tangent, element-wise.
tanh(...): Hyperbolic tangent, element-wise.
tanh_shrink(...): Applies the tanh shrink function element-wise.
tensordot(...): Compute the tensor dot product along specified axes.
threshold(...): Threshold activation function.
tile(...): Repeat x the number of times given by repeats.
top_k(...): Finds the top-k values and their indices in a tensor.
trace(...): Return the sum along diagonals of the tensor.
transpose(...): Returns a tensor with axes transposed.
trapezoid(...): Integrate along the given axis using the composite trapezoidal rule.
tri(...): Return a tensor with ones at and below a diagonal and zeros elsewhere.
tril(...): Return lower triangle of a tensor.
triu(...): Return upper triangle of a tensor.
true_divide(...): Alias for keras.ops.divide.
trunc(...): Return the truncated value of the input, element-wise.
unfold(...): Extract sliding local blocks from a 4-D input (batched image).
unique(...): Finds the unique elements of a tensor.
unravel_index(...): Convert flat indices to coordinate arrays in a given array shape.
unstack(...): Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.
vander(...): Generate a Vandermonde matrix.
var(...): Compute the variance along the specified axes.
vdot(...): Return the dot product of two vectors.
vectorize(...): Turn a function into a vectorized function.
vectorized_map(...): Parallel map of function on axis 0 of tensor(s) elements.
view(...): Create a new bitwise view of the same data with the specified dtype.
view_as_complex(...): Converts a real tensor with shape (..., 2) to a complex tensor, where the last dimension represents the real and imaginary components of a complex tensor.
view_as_real(...): Converts a complex tensor to a real tensor with shape (..., 2), where the last dimension represents the real and imaginary components.
vsplit(...): Split an array into multiple sub-arrays vertically (row-wise).
vstack(...): Stack tensors in sequence vertically (row wise).
where(...): Return elements chosen from x1 or x2 depending on condition.
while_loop(...): While loop implementation.
zeros(...): Return a new tensor of given shape and type, filled with zeros.
zeros_like(...): Return a tensor of zeros with the same shape and type as x.