How to set adaptive learning rate for GradientDescentOptimizer?

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()

    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?

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(

    sess = tf.Session()

    # Feed different values for learning rate to each training step., feed_dict={learning_rate: 0.1}), feed_dict={learning_rate: 0.1}), feed_dict={learning_rate: 0.01}), 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.