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()