symjax.nn.layers.RandomFlip

class symjax.nn.layers.RandomFlip(input, p, axis, deterministic, seed=None)

random axis flip on the input

Random layer that will randomly flip the axis of the input. Note that all the involved operations are GPU compatible and allow for backpropagation

Parameters:

• input_or_shape (shape or Tensor) – the input of the layer or the shape of the layer input
• crop_shape (shape) – the shape of the cropped part of the input. It must have the same length as the input shape minus one for the first dimension
• deterministic (bool or Tensor) – if the layer is in deterministic mode or not
• padding (shape) – the amount of padding to apply on each dimension (except the first one) each dimension should have a couple for the before and after padding parts
• seed (seed (optional)) – to control reproducibility

Returns:

output

Return type:

the output tensor which containts the internal variables

__init__(input, p, axis, deterministic, seed=None)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(input, p, axis, deterministic[, seed])	Initialize self.
RandomFlip.add_update
RandomFlip.add_variable
argmax([axis, out])	Returns the indices of the maximum values along an axis.
argmin([axis, out])	Returns the indices of the minimum values along an axis.
astype(new_dtype)	Elementwise cast.
cast(new_dtype)	Elementwise cast.
clone(givens)
conj()	Return the complex conjugate, element-wise.
conjugate()	Return the complex conjugate, element-wise.
RandomFlip.create_tensor
RandomFlip.create_variable
dot(b, *[, precision])	Dot product of two arrays.
expand_dims(axis, Tuple[int, …]])	Expand the shape of an array.
flatten()	reshape the input into a vector
forward()
imag()	Return the imaginary part of the complex argument.
RandomFlip.init_input
matmul(b, *[, precision])	Matrix product of two arrays.
max([axis, out, keepdims, initial, where])	Return the maximum of an array or maximum along an axis.
mean([axis, dtype, out, keepdims])	Compute the arithmetic mean along the specified axis.
min([axis, out, keepdims, initial, where])	Return the minimum of an array or minimum along an axis.
prod([axis, dtype, out, keepdims, initial, …])	Return the product of array elements over a given axis.
real()	Return the real part of the complex argument.
repeat(repeats[, axis, total_repeat_length])	Repeat elements of an array.
reshape(newshape[, order])	Gives a new shape to an array without changing its data.
round([decimals, out])	Round an array to the given number of decimals.
squeeze(axis, Tuple[int, …]] = None)	Remove single-dimensional entries from the shape of an array.
std([axis, dtype, out, ddof, keepdims])	Compute the standard deviation along the specified axis.
sum([axis, dtype, out, keepdims, initial, where])	Sum of array elements over a given axis.
transpose([axes])	Reverse or permute the axes of an array; returns the modified array.
var([axis, dtype, out, ddof, keepdims])	Compute the variance along the specified axis.
variables([trainable])

Attributes

dtype
RandomFlip.fn_name
name
ndim
scope
shape
RandomFlip.updates