symjax.nn.layers.RandomCrop

class symjax.nn.layers.RandomCrop(input, crop_shape, deterministic, padding=0, seed=None)

random crop selection form the input

Random layer that will select a window of the input based on the given parameters, with the possibility to first apply a padding. This layer is commonly used as a data augmentation technique and positioned at the beginning of the deep network topology. 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, crop_shape, deterministic, padding=0, seed=None)

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

Methods

__init__(input, crop_shape, deterministic[, …])	Initialize self.
RandomCrop.add_update
RandomCrop.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.
RandomCrop.create_tensor
RandomCrop.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.
RandomCrop.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
RandomCrop.fn_name
name
ndim
scope
shape
RandomCrop.updates