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learningrate scheduler tensorflow

Shanti answered on January 19, 2021 Popularity 8/10 Helpfulness 5/10

answer learningrate scheduler tensorflow

learningrate scheduler tensorflow

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tf.keras.callbacks.LearningRateScheduler(
    schedule, verbose=0)

# This function keeps the initial learning rate for the first ten epochs
# and decreases it exponentially after that.

def scheduler(epoch, lr):
  if epoch < 10:
    return lr
  else:
    return lr * tf.math.exp(-0.1)

model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
model.compile(tf.keras.optimizers.SGD(), loss='mse')

callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
                    epochs=15, callbacks=[callback], verbose=0)

Popularity 8/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: learning-rate python scheduler tensorflow

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Contributed on Jan 19 2021

Shanti

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