endpoint_layer_pattern.ipynb

Kernel: Python 3

Endpoint layer pattern

Author: fchollet
Date created: 2019/05/10
Last modified: 2023/11/22
Description: Demonstration of the "endpoint layer" pattern (layer that handles loss management).

Setup

import os

os.environ["KERAS_BACKEND"] = "tensorflow"

import tensorflow as tf
import keras
import numpy as np

Usage of endpoint layers in the Functional API

An "endpoint layer" has access to the model's targets, and creates arbitrary losses in call() using self.add_loss() and Metric.update_state(). This enables you to define losses and metrics that don't match the usual signature fn(y_true, y_pred, sample_weight=None).

Note that you could have separate metrics for training and eval with this pattern.

class LogisticEndpoint(keras.layers.Layer):
    def __init__(self, name=None):
        super().__init__(name=name)
        self.loss_fn = keras.losses.BinaryCrossentropy(from_logits=True)
        self.accuracy_metric = keras.metrics.BinaryAccuracy(name="accuracy")

    def call(self, logits, targets=None, sample_weight=None):
        if targets is not None:
            # Compute the training-time loss value and add it
            # to the layer using `self.add_loss()`.
            loss = self.loss_fn(targets, logits, sample_weight)
            self.add_loss(loss)

            # Log the accuracy as a metric (we could log arbitrary metrics,
            # including different metrics for training and inference.)
            self.accuracy_metric.update_state(targets, logits, sample_weight)

        # Return the inference-time prediction tensor (for `.predict()`).
        return tf.nn.softmax(logits)


inputs = keras.Input((764,), name="inputs")
logits = keras.layers.Dense(1)(inputs)
targets = keras.Input((1,), name="targets")
sample_weight = keras.Input((1,), name="sample_weight")
preds = LogisticEndpoint()(logits, targets, sample_weight)
model = keras.Model([inputs, targets, sample_weight], preds)

data = {
    "inputs": np.random.random((1000, 764)),
    "targets": np.random.random((1000, 1)),
    "sample_weight": np.random.random((1000, 1)),
}

model.compile(keras.optimizers.Adam(1e-3))
model.fit(data, epochs=2)

Exporting an inference-only model

Simply don't include targets in the model. The weights stay the same.

inputs = keras.Input((764,), name="inputs")
logits = keras.layers.Dense(1)(inputs)
preds = LogisticEndpoint()(logits, targets=None, sample_weight=None)
inference_model = keras.Model(inputs, preds)

inference_model.set_weights(model.get_weights())

preds = inference_model.predict(np.random.random((1000, 764)))

Usage of loss endpoint layers in subclassed models

class LogReg(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense = keras.layers.Dense(1)
        self.logistic_endpoint = LogisticEndpoint()

    def call(self, inputs):
        # Note that all inputs should be in the first argument
        # since we want to be able to call `model.fit(inputs)`.
        logits = self.dense(inputs["inputs"])
        preds = self.logistic_endpoint(
            logits=logits,
            targets=inputs["targets"],
            sample_weight=inputs["sample_weight"],
        )
        return preds


model = LogReg()
data = {
    "inputs": np.random.random((1000, 764)),
    "targets": np.random.random((1000, 1)),
    "sample_weight": np.random.random((1000, 1)),
}

model.compile(keras.optimizers.Adam(1e-3))
model.fit(data, epochs=2)