tf.train.AdamOptimizer

tf.train.AdamOptimizer is a popular optimization algorithm used for training deep neural networks. It is an adaptive learning rate optimization algorithm that is particularly well suited for training large scale deep neural networks. The algorithm works by maintaining a set of exponentially decaying average of past gradients and squared gradients, which are used to update the parameters of the network in a way that adapts the learning rate to the gradient magnitude. This helps to avoid issues such as vanishing or exploding gradients, and can lead to faster and more stable convergence during training. The Adam optimizer is widely used in the deep learning community due to its effectiveness and ease of use.