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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)
Epoch 1/2
27/32 ━━━━━━━━━━━━━━━━[37m━━━━ 0s 2ms/step - loss: 0.3664
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1700705222.380735 3351467 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
32/32 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step - loss: 0.3663
Epoch 2/2
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3627
<keras.src.callbacks.history.History at 0x7f13401b1e10>
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)))
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step
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)
Epoch 1/2
32/32 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 0.3529
Epoch 2/2
32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.3509
<keras.src.callbacks.history.History at 0x7f132c1d1450>