Keras的核心原则是逐步揭示复杂性，可以在保持相应的高级便利性的同时，对操作细节进行更多控制。当我们要自定义fit中的训练算法时，可以重写模型中的train_step方法，然后调用fit来训练模型。
这里以tensorflow2官网中的例子来说明：
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import numpy as npimport tensorflow as tffrom tensorflow import kerasx = 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 CustomModelinputs = 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 usualmodel.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> |
这里的loss是tensorflow库中实现了的损失函数，如果想自定义损失函数，然后将损失函数传入model.compile中，能正常按我们预想的work吗？
答案竟然是否定的，而且没有错误提示，只是loss计算不会符合我们的预期。
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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> |
以上结果证实了我们自定义loss的正确性，下面我们直接将自定义的loss置入compile中的loss参数中，看看会发生什么。
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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> |
我们看到，这里的loss与我们与标准的tf.losses.MSE明显不同。这说明我们自定义的loss以这种方式直接传递进model.compile中，是完全错误的操作。
正确运用自定义loss的姿势是什么呢？下面揭晓。
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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 CustomModelinputs = 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 usualmy_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> |
终于，通过跳过在 compile() 中传递损失函数，而在 train_step 中手动完成所有计算内容，我们获得了与之前默认tf.losses.MSE完全一致的输出，这才是我们想要的结果。
总结一下，当我们在模型中想用自定义的损失函数，不能直接传入fit函数，而是需要在train_step中手动传入，完成计算过程。
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原文链接：https://www.cnblogs.com/geeks-reign/p/15060924.html
