How to set adaptive learning rate for GradientDescentOptimizer?

Question

I am using TensorFlow to train a neural network. This is how I am initializing the GradientDescentOptimizer:

init = tf.initialize_all_variables() sess = tf.Session() sess.run(init)  mse        = tf.reduce_mean(tf.square(out - out_)) train_step = tf.train.GradientDescentOptimizer(0.3).minimize(mse) 

The thing here is that I don't know how to set an update rule for the learning rate or a decay value for that.

How can I use an adaptive learning rate here?

Answer 1

First of all, tf.train.GradientDescentOptimizer is designed to use a constant learning rate for all variables in all steps. TensorFlow also provides out-of-the-box adaptive optimizers including the tf.train.AdagradOptimizer and the tf.train.AdamOptimizer, and these can be used as drop-in replacements.

However, if you want to control the learning rate with otherwise-vanilla gradient descent, you can take advantage of the fact that the learning_rate argument to the tf.train.GradientDescentOptimizer constructor can be a Tensor object. This allows you to compute a different value for the learning rate in each step, for example:

learning_rate = tf.placeholder(tf.float32, shape=[]) # ... train_step = tf.train.GradientDescentOptimizer(     learning_rate=learning_rate).minimize(mse)  sess = tf.Session()  # Feed different values for learning rate to each training step. sess.run(train_step, feed_dict={learning_rate: 0.1}) sess.run(train_step, feed_dict={learning_rate: 0.1}) sess.run(train_step, feed_dict={learning_rate: 0.01}) sess.run(train_step, feed_dict={learning_rate: 0.01}) 

Alternatively, you could create a scalar tf.Variable that holds the learning rate, and assign it each time you want to change the learning rate.