Adagrad optimizer as described in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
optimizer_adagrad( lr = 0.01, epsilon = NULL, decay = 0, clipnorm = NULL, clipvalue = NULL )
float >= 0. Learning rate.
float >= 0. Fuzz factor. If
float >= 0. Learning rate decay over each update.
Gradients will be clipped when their L2 norm exceeds this value.
Gradients will be clipped when their absolute value exceeds this value.
It is recommended to leave the parameters of this optimizer at their default values.