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A singular Riemannian geometry approach to Deep Neural Networks.
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This is the complete list of members for fc_layer_sm, including all inherited members.
| biases (defined in layer) | layer | protected |
| compute_partial_derivatives_wrt_inputs(Eigen::VectorXd &input) override | fc_layer_sm | virtual |
| compute_partial_derivatives_wrt_weights_biases(Eigen::VectorXd &input) override | fc_layer_sm | virtual |
| exp_max_cut (defined in layer) | layer | protected |
| exp_zero_approx (defined in layer) | layer | protected |
| fc_layer_sm() (defined in fc_layer_sm) | fc_layer_sm | inline |
| fc_layer_sm(const std::vector< std::vector< double >> &weights_, const std::vector< double > &biases_) (defined in fc_layer_sm) | fc_layer_sm | |
| fc_layer_sm(const Eigen::MatrixXd &weights_, const Eigen::VectorXd &biases_) (defined in fc_layer_sm) | fc_layer_sm | |
| fc_layer_sm(const Eigen::MatrixXd &weights_, const std::vector< double > &biases_) (defined in fc_layer_sm) | fc_layer_sm | |
| get_bias(int i) | layer | |
| get_biases() | layer | |
| get_exp_max_cut() | layer | |
| get_exp_zero_approx() | layer | |
| get_input_size() (defined in fc_layer_sm) | fc_layer_sm | |
| get_num_nodes() (defined in fc_layer_sm) | fc_layer_sm | |
| get_type() | layer | |
| get_weight(int i, int j) | layer | |
| get_weights() | layer | |
| get_weights_biases_as_mat() | layer | |
| get_weights_biases_as_vec_col_maj() | layer | |
| get_weights_biases_as_vec_row_maj() | layer | |
| get_weights_cols() | layer | |
| get_weights_rows() | layer | |
| input_dim (defined in layer) | layer | protected |
| layer() | layer | inline |
| layer(const std::vector< std::vector< double >> &weights_, const std::vector< double > &biases_) | layer | |
| layer(const std::vector< std::vector< double >> &weights_) | layer | |
| layer(const Eigen::MatrixXd &weights_) | layer | |
| layer(const Eigen::MatrixXd &weights_, const std::vector< double > &biases_) | layer | |
| layer(const Eigen::MatrixXd &weights_, const Eigen::VectorXd &biases_) | layer | |
| layer(const Eigen::MatrixXd &weights_, const Eigen::VectorXd &biases_, int in_dim, int out_dim) | layer | |
| output_dim (defined in layer) | layer | protected |
| predict(const Eigen::VectorXd &input) override | fc_layer_sm | virtual |
| predict_batch(const std::vector< Eigen::VectorXd > &input) | layer | inlinevirtual |
| set_bias(int i, double bias_) | layer | |
| set_biases(const Eigen::VectorXd &biases_) | layer | |
| set_biases(const std::vector< double > &biases_) | layer | |
| set_exp_max_cut(double exp_max_cut_) | layer | |
| set_exp_zero_approx(double exp_zero_approx_) | layer | |
| set_weight(int i, int j, double weight_) | layer | |
| set_weights(const Eigen::MatrixXd &weights_) | layer | |
| set_weights_biases(Eigen::MatrixXd &source) | layer | |
| set_weights_biases_compact(Eigen::MatrixXd &source) | layer | |
| set_weights_biases_row_maj(Eigen::MatrixXd &source) | layer | |
| transpose_weights() | layer | |
| type (defined in layer) | layer | protected |
| weights (defined in layer) | layer | protected |
| weights_cols (defined in layer) | layer | protected |
| weights_rows (defined in layer) | layer | protected |
| ~fc_layer_sm() (defined in fc_layer_sm) | fc_layer_sm | inline |
| ~layer() | layer | inlinevirtual |
1.8.17