alibi_detect.models.tensorflow.resnet

Constants

BATCH_NORM_DECAY

BATCH_NORM_DECAY: float = 0.997

Convert a string or number to a floating point number, if possible.

BATCH_NORM_EPSILON

BATCH_NORM_EPSILON: float = 1e-05

Convert a string or number to a floating point number, if possible.

L2_WEIGHT_DECAY

L2_WEIGHT_DECAY: float = 0.0002

Convert a string or number to a floating point number, if possible.

LR_SCHEDULE

LR_SCHEDULE: list = [(0.1, 91), (0.01, 136), (0.001, 182)]

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

BASE_LEARNING_RATE

BASE_LEARNING_RATE: float = 0.1

Convert a string or number to a floating point number, if possible.

HEIGHT

HEIGHT: int = 32

int([x]) -> integer int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating point numbers, this truncates towards zero.

If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.

int('0b100', base=0) 4

WIDTH

WIDTH: int = 32

int([x]) -> integer int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating point numbers, this truncates towards zero.

If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.

int('0b100', base=0) 4

NUM_CHANNELS

NUM_CHANNELS: int = 3

int([x]) -> integer int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.int(). For floating point numbers, this truncates towards zero.

If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by '+' or '-' and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.

int('0b100', base=0) 4

LearningRateBatchScheduler

Inherits from: Callback

Constructor

LearningRateBatchScheduler(self, schedule: Callable, batch_size: int, steps_per_epoch: int)

Name	Type	Default	Description
schedule	Callable		Function taking the epoch and batch index as input which returns the new learning rate as output.
batch_size	int		Batch size.
steps_per_epoch	int		Number of batches or steps per epoch.

Methods

on_batch_begin

on_batch_begin(batch, logs = None)

Executes before step begins.

Name	Type	Default	Description
batch
logs		None

on_epoch_begin

on_epoch_begin(epoch, logs = None)

Name	Type	Default	Description
epoch
logs		None

Functions

conv_block

conv_block(x_in: tensorflow.python.framework.tensor.Tensor, filters: Tuple[int, int], kernel_size: Union[int, list, Tuple[int]], stage: int, block: str, strides: Tuple[int, int] = (2, 2), l2_regularisation: bool = True) -> tensorflow.python.framework.tensor.Tensor

Conv block in ResNet with a parameterised skip connection to reduce the width and height

controlled by the strides.

Name	Type	Default	Description
x_in	tensorflow.python.framework.tensor.Tensor		Input Tensor.
filters	Tuple[int, int]		Number of filters for each of the 2 conv layers.
kernel_size	Union[int, list, Tuple[int]]		Kernel size for the conv layers.
stage	int		Stage of the block in the ResNet.
block	str		Block within a stage in the ResNet.
strides	Tuple[int, int]	(2, 2)	Stride size applied to reduce the image size.
l2_regularisation	bool	True	Whether to apply L2 regularisation.

Returns

• Type: tensorflow.python.framework.tensor.Tensor

identity_block

identity_block(x_in: tensorflow.python.framework.tensor.Tensor, filters: Tuple[int, int], kernel_size: Union[int, list, Tuple[int]], stage: int, block: str, l2_regularisation: bool = True) -> tensorflow.python.framework.tensor.Tensor

Identity block in ResNet.

Name	Type	Default	Description
x_in	tensorflow.python.framework.tensor.Tensor		Input Tensor.
filters	Tuple[int, int]		Number of filters for each of the 2 conv layers.
kernel_size	Union[int, list, Tuple[int]]		Kernel size for the conv layers.
stage	int		Stage of the block in the ResNet.
block	str		Block within a stage in the ResNet.
l2_regularisation	bool	True	Whether to apply L2 regularisation.

Returns

• Type: tensorflow.python.framework.tensor.Tensor

l2_regulariser

l2_regulariser(l2_regularisation: bool = True)

Apply L2 regularisation to kernel.

Name	Type	Default	Description
l2_regularisation	bool	True	Whether to apply L2 regularisation.

learning_rate_schedule

learning_rate_schedule(current_epoch: int, current_batch: int, batches_per_epoch: int, batch_size: int) -> float

Linear learning rate scaling and learning rate decay at specified epochs.

Name	Type	Default	Description
current_epoch	int		Current training epoch.
current_batch	int		Current batch with current epoch, not used.
batches_per_epoch	int		Number of batches or steps in an epoch, not used.
batch_size	int		Batch size.

Returns

• Type: float

preprocess_image

preprocess_image(x: numpy.ndarray, is_training: bool = True) -> numpy.ndarray

Name	Type	Default	Description
x	numpy.ndarray
is_training	bool	True

Returns

• Type: numpy.ndarray

resnet

resnet(num_blocks: int, classes: int = 10, input_shape: Tuple[int, int, int] = (32, 32, 3)) -> keras.src.models.model.Model

Define ResNet.

Name	Type	Default	Description
num_blocks	int		Number of ResNet blocks.
classes	int	10	Number of classification classes.
input_shape	Tuple[int, int, int]	(32, 32, 3)	Input shape of an image.

Returns

• Type: keras.src.models.model.Model

resnet_block

resnet_block(x_in: tensorflow.python.framework.tensor.Tensor, size: int, filters: Tuple[int, int], kernel_size: Union[int, list, Tuple[int]], stage: int, strides: Tuple[int, int] = (2, 2), l2_regularisation: bool = True) -> tensorflow.python.framework.tensor.Tensor

Block in ResNet combining a conv block with identity blocks.

Name	Type	Default	Description
x_in	tensorflow.python.framework.tensor.Tensor		Input Tensor.
size	int		The ResNet block consists of 1 conv block and size-1 identity blocks.
filters	Tuple[int, int]		Number of filters for each of the conv layers.
kernel_size	Union[int, list, Tuple[int]]		Kernel size for the conv layers.
stage	int		Stage of the block in the ResNet.
strides	Tuple[int, int]	(2, 2)	Stride size applied to reduce the image size.
l2_regularisation	bool	True	Whether to apply L2 regularisation.

Returns

• Type: tensorflow.python.framework.tensor.Tensor

run

run(num_blocks: int, epochs: int, batch_size: int, model_dir: Union[str, os.PathLike], num_classes: int = 10, input_shape: Tuple[int, int, int] = (32, 32, 3), validation_freq: int = 10, verbose: int = 2, seed: int = 1, serving: bool = False) -> None

Name	Type	Default	Description
num_blocks	int
epochs	int
batch_size	int
model_dir	Union[str, os.PathLike]
num_classes	int	10
input_shape	Tuple[int, int, int]	(32, 32, 3)
validation_freq	int	10
verbose	int	2
seed	int	1
serving	bool	False

Returns

• Type: None

scale_by_instance

scale_by_instance(x: numpy.ndarray, eps: float = 1e-12) -> numpy.ndarray

Name	Type	Default	Description
x	numpy.ndarray
eps	float	1e-12

Returns

• Type: numpy.ndarray