代码示例 / Keras 快速指南 / 端点层模式

端点层模式

作者: fchollet
创建时间 2019/05/10
上次修改时间 2023/11/22
描述:演示“端点层”模式(处理损失管理的层)。

ⓘ 此示例使用 Keras 3

在 Colab 中查看 GitHub 源代码


设置

import os

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

import tensorflow as tf
import keras
import numpy as np

在函数式 API 中使用端点层

“端点层”可以访问模型的目标,并在 call() 中使用 self.add_loss()Metric.update_state() 创建任意损失。这使您能够定义与通常签名 fn(y_true, y_pred, sample_weight=None) 不匹配的损失和指标。

请注意,使用此模式,您可以为训练和评估设置单独的指标。

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 ━━━━━━━━━━━━━━━━━━━━  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>

导出仅推理模型

只需在模型中不包含 targets 即可。权重保持不变。

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

在子类化模型中使用损失端点层

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>