symjax.nn.layers.BatchNormalization

class symjax.nn.layers.BatchNormalization(input, axis, deterministic, const=0.001, beta_1=0.99, beta_2=0.99, W=<function ones>, b=<function zeros>, trainable_W=True, trainable_b=True)

batch-normalization layer

input_or_shape: shape or Tensor

the layer input tensor or shape

axis: list or tuple of ints

the axis to normalize on. If using BN on a dense layer then axis should be [0] to normalize over the samples. If the layer if a convolutional layer with data format NCHW then axis should be [0, 2, 3] to normalize over the samples and spatial dimensions (commonly done)

deterministic: bool or Tensor

controlling the state of the layer

const: float32 (optional)

the constant used in the standard deviation renormalization

beta1: flaot32 (optional)

the parameter for the exponential moving average of the mean

beta2: float32 (optional)

the parameters for the exponential moving average of the std

Returns:output Return type:the layer output with attributes given by the layer options

__init__(input, axis, deterministic, const=0.001, beta_1=0.99, beta_2=0.99, W=<function ones>, b=<function zeros>, trainable_W=True, trainable_b=True)

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

Methods

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