gianlp.models.base_model.BaseModel

class gianlp.models.base_model.BaseModel

Bases: ABC

Serializable keras wrapper model class.

Guarantees serializing and deserializing from and to objects with the same behaviour. All base models contains at least one model from Keras.

Methods

`build`	Builds the whole chain of models in a recursive manner using the functional API.
`deserialize`	Deserializes a model
`preprocess_texts`	Given texts returns the array representation needed for forwarding the keras model
`serialize`	Serializes the model to be deserialized with the deserialize method

Attributes

`inputs`	Method for getting all models that serve as input
`inputs_shape`	Returns the shapes of the inputs of the model
`outputs_shape`	Returns the output shape of the model
`trainable_weights_amount`	Computes the total amount of trainable weights
`weights_amount`	Computes the total amount of weights

abstract property inputs: ModelInputsWrapper

Method for getting all models that serve as input

abstract property outputs_shape: Union[List[ModelIOShape], ModelIOShape]

Returns the output shape of the model

abstract property inputs_shape: Union[List[ModelIOShape], ModelIOShape]

Returns the shapes of the inputs of the model

property weights_amount: Optional[int]

Computes the total amount of weights

property trainable_weights_amount: Optional[int]

Computes the total amount of trainable weights

build(texts: Union[List[str], Series, Dict[str, List[str]], DataFrame]) → None

Builds the whole chain of models in a recursive manner using the functional API. Some operations may need the model to be built.

Parameters: texts – the texts for building if needed, some models have to learn from a sample corpus before working
Raises: ValueError – If the multi-text input keys do not match with the ones in a multi-text model

serialize() → bytes

Serializes the model to be deserialized with the deserialize method

classmethod deserialize(data: bytes) → BaseModel

Deserializes a model

static get_bytes_from_model(model: Model, copy: bool = False) → bytes

Transforms a keras model into bytes

Parameters

model – the keras model
copy – whether to copy the model before saving. copying the model is needed for complex nested models because the keras save/load can fail

Returns

a byte array

static get_model_from_bytes(data: bytes) → Model

Given bytes from keras model serialized with get_bytes_from_model method returns the model

abstract preprocess_texts(texts: Union[List[str], Series, Dict[str, List[str]], DataFrame]) → Union[List[ndarray], ndarray]

Given texts returns the array representation needed for forwarding the keras model