/*------------------------------------------------------------------*/
void AzTrTreeFeat::printHelp(AzHelp &h) const
{
h.begin(Azforest_config, "AzTrTreeFeat");
h.item_experimental(kw_doCountRules, help_doCountRules);
h.item_experimental(kw_doCheckConsistency, help_doCheckConsistency);
h.end();
}
virtual void printHelp(AzHelp &h) const {
h.item(kw_pl_type, "Pooling type. \"Max\" | \"Avg\" | \"L2\" | \"None\"", "None");
h.item(kw_pl_sz, "Pooling region size.");
h.item(kw_pl_step, "Pooling region stride.");
h.item(kw_pl_num, "Number of pooling units. Specify either pooling_size or num_pooling. Not both.");
/* kw_do_pl_simple_grid */
}
/*--------------------------------------------------------*/
void AzOptOnTree::printHelp(AzHelp &h) const
{
h.begin(Azopt_config, "AzOptOnTree");
h.item_required_lvl(kw_lambda, help_lambda, 1);
h.item_experimental(kw_sigma, help_sigma, sigma_dflt);
h.item(kw_doUseAvg, help_doUseAvg);
AzBytArr s_dflt;
s_dflt.cn(max_ite_num_dflt_oth); s_dflt.c(help_oth_loss); s_dflt.c("; ");
s_dflt.cn(max_ite_num_dflt_expo); s_dflt.c(help_expo_loss);
h.item(kw_max_ite_num, help_max_ite_num, s_dflt.c_str());
h.item_experimental(kw_doIntercept, help_doIntercept);
h.item(kw_eta, help_eta, eta_dflt);
h.item_experimental(kw_exit_delta, help_exit_delta, exit_delta_dflt);
h.end();
}
/*--------------------------------------------------------*/
void AzRgfTree::printHelp(AzHelp &h) const
{
h.begin(Aztree_config, "AzRgfTree", "Tree-wise control");
h.item(kw_min_size, help_min_size, min_size_dflt);
h.item_experimental(kw_max_depth, help_max_depth, "-1: Don't care");
h.item_experimental(kw_max_leaf_num, help_max_leaf_num, "-1: Don't care");
h.item_experimental(kw_doUseInternalNodes, help_doUseInternalNodes);
h.item_experimental(kw_tree_beVerbose, help_tree_beVerbose);
h.end();
}
virtual void printHelp(AzHelp &h) const {
h.item(kw_activ_typ, "Non-linear activation type. \"None\" | \"Log\" (sigmoid) | \"Rect\" (rectifier) | \"Softplus\" | \"Tanh\"");
/* kw_do_stat kw_trunc */
}
/*------------------------------------------------*/
void AzRgforest::printHelp(AzHelp &h) const
{
fs->printHelp(h);
h.begin(Azforest_config, "AzRgforest", "Forest-wide control");
h.item(kw_loss, help_loss, AzLoss::lossName(loss_type_dflt));
AzDataPool<AzBytArr> pool_desc;
AzLoss::help_lines(h.getLevel(), &pool_desc);
int ix;
for (ix = 0; ix < pool_desc.size(); ++ix) {
h.writeln_desc(pool_desc.point(ix)->c_str());
}
h.item(kw_max_lnum, help_max_lnum, max_lnum_dflt);
h.item_experimental(kw_max_tree_num, help_max_tree_num, "Don't care");
h.item(kw_lnum_inc_opt, help_lnum_inc_opt, lnum_inc_opt_dflt);
h.item(kw_lnum_inc_test, help_lnum_inc_test, lnum_inc_test_dflt);
h.item(kw_s_tree_num, help_s_tree_num, s_tree_num_dflt);
h.item_experimental(kw_temp_for_trees, help_temp_for_trees);
h.item_experimental(kw_f_ratio, help_f_ratio);
h.item_experimental(kw_doPassiveRoot, help_doPassiveRoot);
h.end();
reg_depth->printHelp(h);
opt->printHelp(h);
ens->printHelp(h);
h.begin(Azforest_config, "AzRgforest", "Info display");
h.item(kw_doTime, help_doTime);
h.item(kw_beVerbose, help_beVerbose);
h.item(kw_mem_policy, help_mem_policy, mp_not_beTight);
h.end();
}
virtual void printHelp_data(AzHelp &h) const {
h.item(kw_channels, "Use this with \"image\". Number of channels of the input data.");
h.item(kw_sz1, "Use this with \"image\". Number of horizontal pixels.");
h.item(kw_sz2, "Use this with \"image\". Number of vertical pixels.");
h.item(kw_data_scale, "Use this with \"image\". Multiply this value to the input data.");
}
/*------------------------------------------------*/
virtual void printHelp(AzHelp &h) const {
h.begin("", "AzDataForTrTree", "Data processing");
h.item(kw_dataproc, help_dataproc, "Auto");
}
/* static */
void AzsSvrg::printHelp(AzHelp &h)
{
h.item_required(kw_ite_num, "Number of iterations (i.e., how many times to go through the training data).", " 30");
h.item_required(kw_svrg_interval, "SVRG interval. E.g., if this value is 2, average gradient is computed after 2 iterations, 4 iterations, and so on. Note: one iteration goes through the entire training data once.");
h.item_required(kw_sgd_ite, "number of initial SGD iterations before starting SVRG.");
h.item_required(kw_eta, "Learning rate.");
h.item_required(kw_lam, "L2 regularization parameter.");
h.item_required(kw_loss, "Loss function. Logistic | Square", " Logistic");
h.item_noquotes("", "\"Logistic\" with >2 classes: multi-class logistic; one vs. all otherwise. Use \"Square\" if the task is regression.");
h.nl();
h.item_experimental(kw_momentum, "Momentum");
h.item(kw_pred_fn, "File to write predictions at the end of training. Optional");
h.item(kw_rseed, "Seed for randomizing the order of training data points.", " 1");
h.item_experimental(kw_with_replacement, "Randomize the order of training data points with replacement.");
h.item(kw_test_interval, "How often to test. E.g., if this value is 2, test is done after 2 iterations, 4 iterations, and so on. It must be a multiple of svrg_interval.",
" once at the end of training");
h.item(kw_do_compact, "When specified, derivatives with previous weights are not saved and recomputed, which consumes a little less memory and slows down the training a little.");
h.item(kw_do_show_loss, "Show training loss (training objective including the regularization term) and test loss when test is done. If \"Regression\" is on, test loss is always shown irrespective of this switch.");
h.item(kw_do_show_timing, "Display time stamps to show progress.");
}
