Keras的核心原則是逐步揭示復雜性，可以在保持相應的高級便利性的同時，對操作細節進行更多控制。當我們要自定義fit中的訓練算法時，可以重寫模型中的train_step方法，然后調用fit來訓練模型。
這里以tensorflow2官網中的例子來說明：
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庫中實現了的損失函數，如果想自定義損失函數，然后將損失函數傳入model.compile中，能正常按我們預想的work嗎？
答案竟然是否定的，而且沒有錯誤提示，只是loss計算不會符合我們的預期。
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>
以上結果證實了我們自定義loss的正確性，下面我們直接將自定義的loss置入compile中的loss參數中，看看會發生什么。
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與我們與標準的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model.compile中，是完全錯誤的操作。
正確運用自定義loss的姿勢是什么呢？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，而在 train_step 中手動完成所有計算內容，我們獲得了與之前默認tf.losses.MSE完全一致的輸出，這才是我們想要的結果。
總結一下，當我們在模型中想用自定義的損失函數，不能直接傳入fit函數，而是需要在train_step中手動傳入，完成計算過程。
到此這篇關于tensorflow2 自定義損失函數使用的隱藏坑的文章就介紹到這了,更多相關tensorflow2 自定義損失函數內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
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