Keras的核心原则是渐渐揭示复杂性,可以在保持相应的高级便利性的同时,对操纵细节举行更多控制。当我们要自界说fit中的训练算法时,可以重写模子中的train_step方法,然后调用fit来训练模子。
这里以tensorflow2官网中的例子来分析:
- 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>
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这里的loss是tensorflow库中实现了的丧失函数,如果想自界说丧失函数,然后将丧失函数传入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盘算不会符合我们的预期。- 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>
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以上效果证实了我们自界说loss的正确性,下面我们直接将自界说的loss置入compile中的loss参数中,看看会发生什么。- 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>
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我们看到,这里的loss与我们与尺度的tf.losses.MSE明显不同。这分析我们自界说的loss以这种方式直接通报进model.compile中,是完全错误的操纵。
正确运用自界说loss的姿势是什么呢?下面发表。- 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>
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终于,通过跳过在 compile() 中通报丧失函数,而在 train_step 中手动完成所有盘算内容,我们得到了与之前默认tf.losses.MSE完全同等的输出,这才是我们想要的效果。
总结一下,当我们在模子中想用自界说的丧失函数,不能直接传入fit函数,而是需要在train_step中手动传入,完成盘算过程。
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