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tensorflow2 自定義損失函數(shù)使用的隱藏坑

更新時(shí)間：2021年07月27日 15:22:43 作者：geeks_reign

本文主要介紹了tensorflow2 自定義損失函數(shù)使用的隱藏坑，文中通過示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧

Keras的核心原則是逐步揭示復(fù)雜性，可以在保持相應(yīng)的高級(jí)便利性的同時(shí)，對(duì)操作細(xì)節(jié)進(jìn)行更多控制。當(dāng)我們要自定義fit中的訓(xùn)練算法時(shí)，可以重寫模型中的train_step方法，然后調(diào)用fit來訓(xùn)練模型。

這里以tensorflow2官網(wǎng)中的例子來說明：

import numpy as np
import tensorflow as tf
from tensorflow import keras
x = np.random.random((1000, 32))
y = np.random.random((1000, 1))
class CustomModel(keras.Model):
    tf.random.set_seed(100)
    def train_step(self, data):
        # Unpack the data. Its structure depends on your model and
        # on what you pass to `fit()`.
        x, y = data

        with tf.GradientTape() as tape:
            y_pred = self(x, training=True)  # Forward pass
            # Compute the loss value
            # (the loss function is configured in `compile()`)
            loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses)

        # Compute gradients
        trainable_vars = self.trainable_variables
        gradients = tape.gradient(loss, trainable_vars)
        # Update weights
        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
        # Update metrics (includes the metric that tracks the loss)
        self.compiled_metrics.update_state(y, y_pred)
        # Return a dict mapping metric names to current value
        return {m.name: m.result() for m in self.metrics}
    


# Construct and compile an instance of CustomModel
inputs = keras.Input(shape=(32,))
outputs = keras.layers.Dense(1)(inputs)
model = CustomModel(inputs, outputs)
model.compile(optimizer="adam", loss=tf.losses.MSE, metrics=["mae"])

# Just use `fit` as usual

model.fit(x, y, epochs=1, shuffle=False)
32/32 [==============================] - 0s 1ms/step - loss: 0.2783 - mae: 0.4257

 
<tensorflow.python.keras.callbacks.History at 0x7ff7edf6dfd0>

這里的loss是tensorflow庫中實(shí)現(xiàn)了的損失函數(shù)，如果想自定義損失函數(shù)，然后將損失函數(shù)傳入model.compile中，能正常按我們預(yù)想的work嗎？

答案竟然是否定的，而且沒有錯(cuò)誤提示，只是loss計(jì)算不會(huì)符合我們的預(yù)期。

def custom_mse(y_true, y_pred):
    return tf.reduce_mean((y_true - y_pred)**2, axis=-1)
a_true = tf.constant([1., 1.5, 1.2])
a_pred = tf.constant([1., 2, 1.5])
custom_mse(a_true, a_pred)
<tf.Tensor: shape=(), dtype=float32, numpy=0.11333332>
tf.losses.MSE(a_true, a_pred)
<tf.Tensor: shape=(), dtype=float32, numpy=0.11333332>

以上結(jié)果證實(shí)了我們自定義loss的正確性，下面我們直接將自定義的loss置入compile中的loss參數(shù)中，看看會(huì)發(fā)生什么。

my_model = CustomModel(inputs, outputs)
my_model.compile(optimizer="adam", loss=custom_mse, metrics=["mae"])
my_model.fit(x, y, epochs=1, shuffle=False)
32/32 [==============================] - 0s 820us/step - loss: 0.1628 - mae: 0.3257

<tensorflow.python.keras.callbacks.History at 0x7ff7edeb7810>

我們看到，這里的loss與我們與標(biāo)準(zhǔn)的tf.losses.MSE明顯不同。這說明我們自定義的loss以這種方式直接傳遞進(jìn)model.compile中，是完全錯(cuò)誤的操作。

正確運(yùn)用自定義loss的姿勢(shì)是什么呢？下面揭曉。

loss_tracker = keras.metrics.Mean(name="loss")
mae_metric = keras.metrics.MeanAbsoluteError(name="mae")

class MyCustomModel(keras.Model):
    tf.random.set_seed(100)
    def train_step(self, data):
        # Unpack the data. Its structure depends on your model and
        # on what you pass to `fit()`.
        x, y = data

        with tf.GradientTape() as tape:
            y_pred = self(x, training=True)  # Forward pass
            # Compute the loss value
            # (the loss function is configured in `compile()`)
            loss = custom_mse(y, y_pred)
            # loss += self.losses

        # Compute gradients
        trainable_vars = self.trainable_variables
        gradients = tape.gradient(loss, trainable_vars)
        # Update weights
        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
        
        # Compute our own metrics
        loss_tracker.update_state(loss)
        mae_metric.update_state(y, y_pred)
        return {"loss": loss_tracker.result(), "mae": mae_metric.result()}
    
    @property
    def metrics(self):
        # We list our `Metric` objects here so that `reset_states()` can be
        # called automatically at the start of each epoch
        # or at the start of `evaluate()`.
        # If you don't implement this property, you have to call
        # `reset_states()` yourself at the time of your choosing.
        return [loss_tracker, mae_metric]
    
# Construct and compile an instance of CustomModel
inputs = keras.Input(shape=(32,))
outputs = keras.layers.Dense(1)(inputs)
my_model_beta = MyCustomModel(inputs, outputs)
my_model_beta.compile(optimizer="adam")

# Just use `fit` as usual

my_model_beta.fit(x, y, epochs=1, shuffle=False)
32/32 [==============================] - 0s 960us/step - loss: 0.2783 - mae: 0.4257

<tensorflow.python.keras.callbacks.History at 0x7ff7eda3d810>

終于，通過跳過在 compile() 中傳遞損失函數(shù)，而在 train_step 中手動(dòng)完成所有計(jì)算內(nèi)容，我們獲得了與之前默認(rèn)tf.losses.MSE完全一致的輸出，這才是我們想要的結(jié)果。

總結(jié)一下，當(dāng)我們?cè)谀Ｐ椭邢胗米远x的損失函數(shù)，不能直接傳入fit函數(shù)，而是需要在train_step中手動(dòng)傳入，完成計(jì)算過程。

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