symjax.nn.layers.RandomCrop¶
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class
symjax.nn.layers.
RandomCrop
(input, crop_shape, deterministic, padding=0, seed=None)[source]¶ 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
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__init__
(input, crop_shape, deterministic, padding=0, seed=None)[source]¶ 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