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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

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