jacobian

commit 7c598661b0d483eb00b84a56aaf12eaaf12ffa6c
parent 0be5798cdcde98abeed8ca818d312cfcde51e410
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Sun, 28 Mar 2021 18:36:17 -0700

Update Python bindings to use optimizers

Diffstat:
M
src/pybind.cpp
 | 
74
+++++++++++++++++++++++++++++++++++++++++---------------------------------

1 file changed, 41 insertions(+), 33 deletions(-)
diff --git a/src/pybind.cpp b/src/pybind.cpp
@@ -7,43 +7,51 @@
 
 #include <pybind11/pybind11.h>
 #include <pybind11/functional.h>
+#include <pybind11/eigen.h>
+
 #include "bpnn.hpp"
 #include "utils.hpp"
 namespace py = pybind11;
 
 PYBIND11_MODULE(_jacobian, m)
 {
-	m.doc() = "Fast machine learning in C++"; // optional module docstring
-	py::enum_<Regularization>(m, "Regularization")
-		.value("L1", Regularization::L1)
-		.value("L2", Regularization::L2)
-		.export_values();
-	py::class_<Network>(m, "Network")
-		.def(py::init<char *, int, float, float, Regularization, float,
-			      float, bool, float>(),
-		     py::arg("path"), py::arg("batch"), py::arg("learn_rate"),
-		     py::arg("bias_rate"), py::arg("regularization"),
-		     py::arg("lambda"), py::arg("ratio"),
-		     py::arg("early_exit") = true, py::arg("cutoff") = 0)
-		.def("add_layer", &Network::add_layer, py::arg("nodes"),
-		     py::arg("name"), py::arg("activation"),
-		     py::arg("activation_deriv"))
-		.def("initialize", &Network::initialize)
-		.def("set_activation", &Network::set_activation,
-		     py::arg("index"), py::arg("custom"),
-		     py::arg("custom_deriv"))
-		.def("feedforward", &Network::feedforward)
-		.def("backpropagate", &Network::backpropagate)
-		.def("list_net", &Network::list_net)
-		.def("cost", &Network::cost)
-		.def("accuracy", &Network::accuracy)
-		.def("train", &Network::train)
-		.def("get_acc", &Network::get_acc)
-		.def("get_cost", &Network::get_cost)
-		.def("get_val_acc", &Network::get_val_acc)
-		.def("get_val_cost", &Network::get_val_cost);
-	m.def("linear", &linear, py::arg("x"));
-	m.def("linear_deriv", &linear_deriv, py::arg("x"));
-	m.def("sigmoid", &sigmoid, py::arg("x"));
-	m.def("sigmoid_deriv", &sigmoid_deriv, py::arg("x"));
+    m.doc() = "Fast machine learning in C++"; // optional module docstring
+    py::enum_<Regularization>(m, "Regularization")
+        .value("L1", Regularization::L1)
+        .value("L2", Regularization::L2)
+        .export_values();
+    py::class_<Layer>(m, "Layer")
+        .def(py::init<int, int>());
+    py::class_<Network>(m, "Network")
+        .def(py::init<char *, int, float, float, Regularization, float,
+                  float, bool, float>(),
+             py::arg("path"), py::arg("batch"), py::arg("learn_rate"),
+             py::arg("bias_rate"), py::arg("regularization"),
+             py::arg("lambda"), py::arg("ratio"),
+             py::arg("early_exit") = true, py::arg("cutoff") = 0)
+        .def("add_layer", &Network::add_layer, py::arg("nodes"),
+             py::arg("activation"), py::arg("activation_deriv"))
+        .def("initialize", &Network::initialize)
+        .def("init_optimizer", &Network::init_optimizer, py::arg("optimizer"))
+        .def("set_activation", &Network::set_activation,
+             py::arg("index"), py::arg("custom"),
+             py::arg("custom_deriv"))
+        .def("feedforward", &Network::feedforward)
+        .def("backpropagate", &Network::backpropagate)
+        .def("list_net", &Network::list_net)
+        .def("cost", &Network::cost)
+        .def("accuracy", &Network::accuracy)
+        .def("train", &Network::train)
+        .def("get_acc", &Network::get_acc)
+        .def("get_cost", &Network::get_cost)
+        .def("get_val_acc", &Network::get_val_acc)
+        .def("get_val_cost", &Network::get_val_cost);
+    m.def("linear", &linear, py::arg("x"));
+    m.def("linear_deriv", &linear_deriv, py::arg("x"));
+    m.def("sigmoid", &sigmoid, py::arg("x"));
+    m.def("sigmoid_deriv", &sigmoid_deriv, py::arg("x"));
+    m.def("momentum", &optimizers::momentum, py::arg("beta"));
+    m.def("demon", &optimizers::demon, py::arg("beta"), py::arg("max_ep"));
+    m.def("adam", &optimizers::adam, py::arg("beta1"), py::arg("beta2"), py::arg("epsilon"));
+    m.def("adamax", &optimizers::adamax, py::arg("beta1"), py::arg("beta2"), py::arg("epsilon"));
 }