def __init__(<EOL>self,<EOL>learning_rate=<NUM_LIT>,<EOL>momentum=<NUM_LIT>,<EOL>clip_kl=<NUM_LIT>,<EOL>kfac_update=<NUM_LIT:2>,<EOL>stats_accum_iter=<NUM_LIT>,<EOL>full_stats_init=False,<EOL>cold_iter=<NUM_LIT:100>,<EOL>cold_lr=None,<EOL>async_=False,<EOL>async_stats=False,<EOL>epsilon=<NUM_LIT>,<EOL>stats_decay=<NUM_LIT>,<EOL>blockdiag_bias=False,<EOL>channel_fac=False,<EOL>factored_damping=False,<EOL>approxT2=False,<EOL>use_float64=False,<EOL>weight_decay_dict={},<EOL>max_grad_norm=<NUM_LIT:0.5>,<EOL>scope='<STR_LIT>',<EOL>summary_labels=()<EOL>):
self.max_grad_norm = max_grad_norm<EOL>self._lr = learning_rate<EOL>self._momentum = momentum<EOL>self._clip_kl = clip_kl<EOL>self._channel_fac = channel_fac<EOL>self._kfac_update = kfac_update<EOL>self._async = async_<EOL>self._async_stats = async_stats<EOL>self._epsilon = epsilon<EOL>self._stats_decay = stats_decay<EOL>self._blockdiag_bias = blockdiag_bias<EOL>self._approxT2 = approxT2<EOL>self._use_float64 = use_float64<EOL>self._factored_damping = factored_damping<EOL>self._cold_iter = cold_iter<EOL>if cold_lr == None:<EOL><INDENT>self._cold_lr = self._lr<EOL><DEDENT>else:<EOL><INDENT>self._cold_lr = cold_lr<EOL><DEDENT>self._stats_accum_iter = stats_accum_iter<EOL>self._weight_decay_dict = weight_decay_dict<EOL>self._diag_init_coeff = <NUM_LIT:0.><EOL>self._full_stats_init = full_stats_init<EOL>if not self._full_stats_init:<EOL><INDENT>self._stats_accum_iter = self._cold_iter<EOL><DEDENT>self.sgd_step = tf.Variable(<NUM_LIT:0>, name='<STR_LIT>', trainable=False)<EOL>self.global_step = tf.Variable(<EOL><NUM_LIT:0>, name='<STR_LIT>', trainable=False)<EOL>self.cold_step = tf.Variable(<NUM_LIT:0>, name='<STR_LIT>', trainable=False)<EOL>self.factor_step = tf.Variable(<EOL><NUM_LIT:0>, name='<STR_LIT>', trainable=False)<EOL>self.stats_step = tf.Variable(<EOL><NUM_LIT:0>, name='<STR_LIT>', trainable=False)<EOL>self.vFv = tf.Variable(<NUM_LIT:0.>, name='<STR_LIT>', trainable=False)<EOL>self.factors = {}<EOL>self.param_vars = []<EOL>self.stats = {}<EOL>self.stats_eigen = {}<EOL>super(KFAC, self).__init__(scope=scope, summary_labels=summary_labels)<EOL>
Initializes a KFAC optimizer.

For more information on arguments, see the Kfac Optimization paper https://arxiv.org/pdf/1503.05671.pdf