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 NULL, defaults to k_epsilon().
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.
Other optimizers: optimizer_adadelta,
optimizer_adamax,
optimizer_adam,
optimizer_nadam,
optimizer_rmsprop,
optimizer_sgd