experiments/STT-tensorflow
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aweers 2019-04-26 19:26:02 +02:00 committed by GitHub
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tensorflow/python/keras/callbacks.py
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@ -1172,15 +1172,15 @@ class LearningRateScheduler(Callback):
```python ```python
# Firstly, let's create a function which # Firstly, let's create a function which
# maps a given epoch to a learning rate. # maps a given epoch to a learning rate.
# This function would keep the learning rate # This function keeps the learning rate
# constant at 0.001 for the first ten epochs and # at 0.001 for the first ten epochs and
# let it decrease exponentially after that. # decreases it exponentially after that.
def scheduler(epoch): def scheduler(epoch):
if epoch < 10: if epoch < 10:
return 0.001 return 0.001
else: else:
return 0.001 * tf.math.exp(0.1 * (10 - epoch)) return 0.001 * tf.math.exp(0.1 * (10 - epoch))
# Next, we need to set up the callback and train the model.
callback = tf.keras.callbacks.LearningRateScheduler(scheduler) callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
model.fit(data, labels, epochs=100, callbacks=[callback], model.fit(data, labels, epochs=100, callbacks=[callback],
validation_data=(val_data, val_labels)) validation_data=(val_data, val_labels))