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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 neural_network, including all inherited members.
| accuracy(const std::vector< Eigen::VectorXd > &data, const std::vector< Eigen::VectorXd > &features) (defined in neural_network) | neural_network | |
| add_fc_hidden_layer(int out_dim, std::string type) | neural_network | |
| add_fc_input_layer(int in_dim, int out_dim, std::string type) | neural_network | |
| add_layer_all_parameters(int wrows, int wcols, int in_dim, int out_dim, std::string type) | neural_network | |
| ClassChange_1D(std::string output_file, int steps, double delta, bool invert_build, Eigen::VectorXd ipoint) | neural_network | |
| get_layer(int n) (defined in neural_network) | neural_network | inline |
| get_layer_biases(int num_layer) | neural_network | |
| get_layer_weights(int num_layer) | neural_network | |
| get_num_layers() | neural_network | |
| loss_mse(const std::vector< Eigen::VectorXd > &data, const std::vector< Eigen::VectorXd > &features) (defined in neural_network) | neural_network | |
| network_read(const std::string &filename) | neural_network | |
| network_save(const std::string &filename) | neural_network | |
| neural_network() | neural_network | inline |
| neural_network(const std::vector< layer * > &ls) | neural_network | |
| predict(const Eigen::VectorXd &input) | neural_network | |
| predict(const std::vector< Eigen::VectorXd > &input) | neural_network | |
| predict_to_layer(int tolayer, const Eigen::VectorXd &input) | neural_network | |
| predict_to_layer(int tolayer, const std::vector< Eigen::VectorXd > &input) | neural_network | |
| print_network_detailed_info() | neural_network | |
| print_network_info() | neural_network | |
| set_layer_biases(int num_layer, Eigen::VectorXd &biases_) | neural_network | |
| set_layer_weights(int num_layer, Eigen::MatrixXd &weights_) | neural_network | |
| SiMEC_1D(std::string output_file, int steps, double delta, bool invert_build, Eigen::VectorXd ipoint) | neural_network | |
| SiMEC_1D_norm_proj(std::string output_file, int steps, double delta, bool invert_build, Eigen::VectorXd ipoint, Eigen::VectorXd &H_inf, Eigen::VectorXd &H_sup) | neural_network | |
| SiMEC_1D_stop_boundary(std::string output_file, int steps, double delta, bool invert_build, Eigen::VectorXd ipoint, Eigen::VectorXd &H_inf, Eigen::VectorXd &H_sup) | neural_network | |
| SiMExp_1D(std::string output_file, double delta, double epsilon, int max_steps, bool invert_build, Eigen::VectorXd &ipoint) | neural_network | |
| ~neural_network() | neural_network | inline |
1.8.17