Contents
What does TF train AdamOptimizer do?
The AdamOptimizer class creates additional variables, called “slots”, to hold values for the “m” and “v” accumulators. See the source here if you’re curious, it’s actually quite readable: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/training/adam.py#L39 .
What is AdamOptimizer?
Adam optimizer involves a combination of two gradient descent methodologies: Momentum: This algorithm is used to accelerate the gradient descent algorithm by taking into consideration the ‘exponentially weighted average’ of the gradients. Using averages makes the algorithm converge towards the minima in a faster pace.
What are the different optimizers in Tensorflow?
class Adagrad : Optimizer that implements the Adagrad algorithm. class Adam : Optimizer that implements the Adam algorithm. class Adamax : Optimizer that implements the Adamax algorithm. class Ftrl : Optimizer that implements the FTRL algorithm.
What’s the difference between Adam and gradientdescentoptimizer?
Foremost is that it uses moving averages of the parameters (momentum); Bengio discusses the reasons for why this is beneficial in Section 3.1.1 of this paper. Simply put, this enables Adam to use a larger effective step size, and the algorithm will converge to this step size without fine tuning.
What’s the difference between tf.train and adamoptimizer?
The tf.train.AdamOptimizer uses Kingma and Ba’s Adam algorithm to control the learning rate. Adam offers several advantages over the simple tf.train.GradientDescentOptimizer. Foremost is that it uses moving averages of the parameters (momentum); Bengio discusses the reasons for why this is beneficial in Section 3.1.1 of this paper.
Which is an advantage of gradient descent in TensorFlow?
Another advantage is that it basically eliminates the need to tune the learning rate. Each parameter has its own learning rate and due to the peculiarities of the algorithm the learning rate is monotonically decreasing.
Which is better tf.train or gradientdescentoptimizer?
A simple tf.train.GradientDescentOptimizer could equally be used in your MLP, but would require more hyperparameter tuning before it would converge as quickly. Thanks for contributing an answer to Cross Validated!