Module brevettiai.model.factory
Factory for configuration of implemented tensorflow models
Expand source code
"""
Factory for configuration of implemented tensorflow models
"""
from tensorflow.python.keras.engine.functional import Functional
from abc import ABC, abstractmethod
from pydantic import BaseModel
class ModelFactory(ABC, BaseModel):
    """Abstract model factory class"""
    @staticmethod
    def custom_objects():
        """Custom objects used by the model"""
        return {}
    @abstractmethod
    def build(self, input_shape, output_shape, **kwargs) -> Functional:
        """Function to build segmentation backbone"""
Sub-modules
brevettiai.model.factory.lenet_backbonebrevettiai.model.factory.lrasppbrevettiai.model.factory.mobilenetv2_backbonebrevettiai.model.factory.segmentation
Classes
class ModelFactory (**data: Any)- 
Abstract model factory class
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
Expand source code
class ModelFactory(ABC, BaseModel): """Abstract model factory class""" @staticmethod def custom_objects(): """Custom objects used by the model""" return {} @abstractmethod def build(self, input_shape, output_shape, **kwargs) -> Functional: """Function to build segmentation backbone"""Ancestors
- abc.ABC
 - pydantic.main.BaseModel
 - pydantic.utils.Representation
 
Subclasses
Static methods
def custom_objects()- 
Custom objects used by the model
Expand source code
@staticmethod def custom_objects(): """Custom objects used by the model""" return {} 
Methods
def build(self, input_shape, output_shape, **kwargs) ‑> tensorflow.python.keras.engine.functional.Functional- 
Function to build segmentation backbone
Expand source code
@abstractmethod def build(self, input_shape, output_shape, **kwargs) -> Functional: """Function to build segmentation backbone"""