Adicionando recursos no código

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Construímos uma lib para treinar um modelo
A lib pode ser usada em notebooks e aplicações independentes
Métricas são exportadas para o standard output

Dependencia na interface

def train_model(model: ImageClassifier, learning_rate: float, train_dataset: tf.data.Dataset,
                evaluation_dataset: tf.data.Dataset, train_epochs: int,
                report_step: int) -> ImageClassifier

def train_model(model: ImageClassifier, learning_rate: float, train_dataset: tf.data.Dataset,
                evaluation_dataset: tf.data.Dataset, train_epochs: int, report_step: int,
                metric_exporter: Optional[BaseMetricExporter] = None) -> ImageClassifier:

Nova assinatura

def train_model(model: ImageClassifier, learning_rate: float, train_dataset: tf.data.Dataset,
                evaluation_dataset: tf.data.Dataset, train_epochs: int, report_step: int,
                metric_exporter: Optional[BaseMetricExporter] = None) -> ImageClassifier:

Dependência na interface BaseMetricExporter
Mantém retrocompabilidade (parametro opcional)

def train_model(model: ImageClassifier, learning_rate: float, train_dataset: tf.data.Dataset,
                evaluation_dataset: tf.data.Dataset, train_epochs: int,
                report_step: int) -> ImageClassifier:

    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

    for epoch in range(train_epochs):
        logging.info(f"Starging epoch {epoch}")

        for step, (batch_inputs, batch_labels) in enumerate(train_dataset):
            with tf.GradientTape() as tape:
                logits = model(batch_inputs, training=True)
                loss_value = tf.keras.losses.categorical_crossentropy(batch_labels, logits)
            gradients = tape.gradient(loss_value, model.trainable_weights)
            optimizer.apply_gradients(zip(gradients, model.trainable_weights))

            if step % report_step == 0:
                mean_loss = tf.reduce_mean(loss_value)
                logging.info(f"epoch = {epoch:4d} step = {step:5d} "
                             f"categorical cross entroypy = {mean_loss:8.8f}")

        evaluation_loss = tf.keras.metrics.CategoricalCrossentropy()
        for step, (batch_inputs, batch_labels) in enumerate(evaluation_dataset):
            logits = model(batch_inputs)
            evaluation_loss.update_state(batch_labels, logits)

        evaluation_loss_value = evaluation_loss.result()
        mean_evaluation_loss_value = tf.reduce_mean(evaluation_loss_value)
        logging.info(f"test epoch = {epoch:4d} step = {step:5d} "
                     f"categorical cross entroypy = {mean_evaluation_loss_value:8.8f}")
        evaluation_loss.reset_state()

    return model

def train_model(model: ImageClassifier, learning_rate: float, train_dataset: tf.data.Dataset,
                evaluation_dataset: tf.data.Dataset, train_epochs: int,
                report_step: int) -> ImageClassifier:

    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

    for epoch in range(train_epochs):
        logging.info(f"Starging epoch {epoch}")

        for step, (batch_inputs, batch_labels) in enumerate(train_dataset):
            with tf.GradientTape() as tape:
                logits = model(batch_inputs, training=True)
                loss_value = tf.keras.losses.categorical_crossentropy(batch_labels, logits)
            gradients = tape.gradient(loss_value, model.trainable_weights)
            optimizer.apply_gradients(zip(gradients, model.trainable_weights))

            if step % report_step == 0:
                mean_loss = tf.reduce_mean(loss_value)
                logging.info(f"epoch = {epoch:4d} step = {step:5d} "
                             f"categorical cross entroypy = {mean_loss:8.8f}")

        evaluation_loss = tf.keras.metrics.CategoricalCrossentropy()
        for step, (batch_inputs, batch_labels) in enumerate(evaluation_dataset):
            logits = model(batch_inputs)
            evaluation_loss.update_state(batch_labels, logits)

        evaluation_loss_value = evaluation_loss.result()
        mean_evaluation_loss_value = tf.reduce_mean(evaluation_loss_value)
        logging.info(f"test epoch = {epoch:4d} step = {step:5d} "
                     f"categorical cross entroypy = {mean_evaluation_loss_value:8.8f}")
        evaluation_loss.reset_state()

    return model

def train_model(model: ImageClassifier, learning_rate: float, train_dataset: tf.data.Dataset,
                evaluation_dataset: tf.data.Dataset, train_epochs: int, report_step: int,
                metric_exporter: Optional[BaseMetricExporter] = None) -> ImageClassifier:

    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

    for epoch in range(train_epochs):
        logging.info(f"Starging epoch {epoch}")

        for step, (batch_inputs, batch_labels) in enumerate(train_dataset):
            with tf.GradientTape() as tape:
                logits = model(batch_inputs, training=True)
                loss_value = tf.keras.losses.categorical_crossentropy(batch_labels, logits)
            gradients = tape.gradient(loss_value, model.trainable_weights)
            optimizer.apply_gradients(zip(gradients, model.trainable_weights))

            if step % report_step == 0:
                mean_loss = tf.reduce_mean(loss_value)
                logging.info(f"epoch = {epoch:4d} step = {step:5d} "
                             f"categorical cross entroypy = {mean_loss:8.8f}")
                if metric_exporter:
                    metric_exporter.write_scalar("training_loss", mean_loss, step, epoch)

        evaluation_loss = tf.keras.metrics.CategoricalCrossentropy()
        for step, (batch_inputs, batch_labels) in enumerate(evaluation_dataset):
            logits = model(batch_inputs)
            evaluation_loss.update_state(batch_labels, logits)

        evaluation_loss_value = evaluation_loss.result()
        mean_evaluation_loss_value = tf.reduce_mean(evaluation_loss_value)
        logging.info(f"test epoch = {epoch:4d} step = {step:5d} "
                     f"categorical cross entroypy = {mean_evaluation_loss_value:8.8f}")
        if metric_exporter:
            metric_exporter.write_scalar("evaluation_loss",
            	mean_evaluation_loss_value, 0, epoch)

        evaluation_loss.reset_state()

    return model

Depência de interface

train_model depende da classe abstrata, não da implementação
Podemos ter várias iimplementações sem afetar train_model
Responsabilidade segregada

MetricValue = Union[tf.Tensor, float, int]

class BaseMetricExporter(abc.ABC):

    @abc.abstractmethod
    def write_scalar(
    	self, metric_name: str,
        metric_value: MetricValue,
        step: int, epoch: int) -> None:
        	pass

Implementação

import os
import json
from datetime import datetime
from typing import Any, Union, Dict
from uuid import uuid4

from fine_tunner.persistence.metadata import MetricValue, BaseMetricExporter


class FileExporter(BaseMetricExporter):

    def __init__(self, ouput_folder: str):
        os.makedirs(ouput_folder, exist_ok=True)
        self._ouput_folder = ouput_folder
        

    def write_scalar(self, metric_name: str, metric_value: MetricValue, step: int,
    	epoch: int) -> None:
          filename = f"{uuid4()}.json"
          now = datetime.now().timestamp()
          output_path = os.path.join(self._ouput_folder, metric_name, filename)
          data = dict(value=metric_value, step=step, epoch=epoch, timestamp=now)

          with open(output_path, "w") as output_file:
              json.dump(data, output_file)

Implementação

import os
import requests
import json
from datetime import datetime
from typing import Dict, Optional
from uuid import uuid4

from fine_tunner.persistence.metadata import MetricValue, BaseMetricExporter


class ApiExporter(BaseMetricExporter):

    def __init__(self, endpoint: str, prefix: str,
    	headers: Optional[Dict[str, str]] = None):
          self._endpoint = endpoint
          self._headers = headers
          self._prefix = prefix


    def write_scalar(self, metric_name: str, metric_value: MetricValue,
    	step: int, epoch: int) -> None:
          now = datetime.now().timestamp()        
          data = dict(value=metric_value, step=step, epoch=epoch, timestamp=now)
			
          endpoint = f"{self._endpoint}/{self._prefix}"
          requests.post(endpoint, json=data, headers=self._headers)

Implementação

endpoint = "http://metric"
metric_exporter = ApiExporter(endpoint, "treinamento 001")

trained_model = train_model(model, learning_rate, training_dataset,
                            evaluation_dataset, train_epochs, REPORT_STEP, metric_exporter)
                            
                            
#################

output_folder = "~/treinamento-001/metrics"
metric_exporter = FileExporter(output_folder, "treinamento 001")

trained_model = train_model(model, learning_rate, training_dataset,
                            evaluation_dataset, train_epochs, REPORT_STEP, metric_exporter)
                            
################


trained_model = train_model(model, learning_rate, training_dataset,
                            evaluation_dataset, train_epochs, REPORT_STEP)

Adicionando recursos no código

Lembrando...

Dependencia na interface

Nova assinatura

Depência de interface

Implementação

Implementação

Implementação

Adicionando recursos

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