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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.
add(...): Add arguments element-wise.
all(...): Test whether all array elements along a given axis evaluate to True.
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.
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 minium values along an axis.
argsort(...): Returns the indices that would sort a tensor.
array(...): Create a tensor.
average(...): Compute the weighted average along the specified axis.
average_pool(...): Average pooling operation.
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.
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.
ceil(...): Return the ceiling of the input, element-wise.
cholesky(...): Computes the Cholesky decomposition of a positive semi-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 to a tensor.
copy(...): Returns a copy of x.
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.
depthwise_conv(...): General N-D depthwise convolution.
det(...): Computes the determinant of a square tensor.
diag(...): Extract a diagonal or construct a diagonal array.
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.
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.
equal(...): Returns (x1 == x2) element-wise.
erf(...): Computes the 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.
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.
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.
floor(...): Return the floor of the input, element-wise.
floor_divide(...): Returns the largest integer smaller or equal to the division of inputs.
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.
gelu(...): Gaussian Error Linear Unit (GELU) activation function.
get_item(...): Return x[key].
greater(...): Return the truth value of x1 > x2 element-wise.
greater_equal(...): Return the truth value of x1 >= x2 element-wise.
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.
hstack(...): Stack tensors in sequence horizontally (column wise).
identity(...): Return the identity tensor.
imag(...): Return the imaginary part of the complex argument.
in_top_k(...): Checks if the targets are in the top-k predictions.
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.
isinf(...): Test element-wise for positive or negative infinity.
isnan(...): Test element-wise for NaN and return result as a boolean tensor.
istft(...): Inverse Short-Time Fourier Transform along the last axis of the input.
leaky_relu(...): Leaky version of a Rectified Linear Unit activation function.
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.
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.
lu_factor(...): Computes the lower-upper decomposition of a square matrix.
matmul(...): Matrix product of two tensors.
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.
ndim(...): Return the number of dimensions of a tensor.
negative(...): Numerical negative, element-wise.
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.
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.
qr(...): Computes the QR decomposition of a tensor.
quantile(...): Compute the q-th quantile(s) of the data along the specified axis.
ravel(...): Return a contiguous flattened tensor.
real(...): Return the real part of the complex argument.
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.
roll(...): Roll tensor elements along a given axis.
round(...): Evenly round to the given number of decimals.
rsqrt(...): Computes reciprocal of square root of x element-wise.
scatter(...): Returns a tensor of shape shape where indices are set to values.
scatter_update(...): Update inputs via updates at scattered (sparse) indices.
segment_max(...): Computes the max 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.
silu(...): Sigmoid Linear Unit (SiLU) activation function, also known as Swish.
sin(...): Trigonometric sine, element-wise.
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.
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.
sparse_categorical_crossentropy(...): Computes sparse categorical cross-entropy loss.
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.
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.
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.
tensordot(...): Compute the tensor dot product along specified axes.
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.
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.
unstack(...): Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.
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.
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.