commit ea4890b8fb55d9abdda7f0af0f772df753874327
parent 53918879b575719969080636d815b42034e2d036
Author: David Freifeld <freifeld.david@gmail.com>
Date: Mon, 29 Mar 2021 11:37:18 -0700
Merge branch 'master' of https://github.com/richardfeynmanrocks/Jacobian
Diffstat:
| M | src/pybind.cpp | | | 129 | ++++++++++++++++++++++++++++++++++++++++++++++--------------------------------- |
1 file changed, 75 insertions(+), 54 deletions(-)
diff --git a/src/pybind.cpp b/src/pybind.cpp
@@ -16,58 +16,79 @@ 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_<Layer>(m, "Layer")
- .def(py::init<int, int>())
- .def("get_contents", &Layer::get_contents)
- .def("get_weights", &Layer::get_weights)
- .def("get_bias", &Layer::get_bias)
- .def("get_v", &Layer::get_v)
- .def("get_m", &Layer::get_m)
- .def("get_dZ", &Layer::get_dZ)
- .def_readonly("activation", &Layer::activation)
- .def_readonly("activation_deriv", &Layer::activation);
- 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("init_decay", &Network::init_decay, py::arg("decay"))
- .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("next_batch", &Network::interactive_next_batch)
- .def("cost", &Network::cost)
- .def("accuracy", &Network::accuracy)
- .def("train", &Network::train)
- .def("get_cost", &Network::get_cost)
- .def("get_acc", &Network::get_acc)
- .def("get_val_cost", &Network::get_val_cost)
- .def("get_val_acc", &Network::get_val_acc)
- .def_readonly("layers", &Network::layers);
- 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"));
- m.def("step", &decays::step, py::arg("a_0"), py::arg("k"));
- m.def("exponential", &decays::exponential, py::arg("a_0"), py::arg("k"));
- m.def("fractional", &decays::fractional, py::arg("a_0"), py::arg("k"));
- m.def("linear", &decays::linear, py::arg("max_ep"));
+ 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>())
+ .def("get_contents", &Layer::get_contents)
+ .def("get_weights", &Layer::get_weights)
+ .def("get_bias", &Layer::get_bias)
+ .def("get_v", &Layer::get_v)
+ .def("get_m", &Layer::get_m)
+ .def("get_dZ", &Layer::get_dZ)
+ .def_readonly("activation", &Layer::activation)
+ .def_readonly("activation_deriv", &Layer::activation);
+ 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("init_decay", &Network::init_decay, py::arg("decay"))
+ .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("next_batch", &Network::interactive_next_batch)
+ .def("cost", &Network::cost)
+ .def("accuracy", &Network::accuracy)
+ .def("train", &Network::train)
+ .def("get_cost", &Network::get_cost)
+ .def("get_acc", &Network::get_acc)
+ .def("get_val_cost", &Network::get_val_cost)
+ .def("get_val_acc", &Network::get_val_acc)
+ .def_readonly("layers", &Network::layers);
+ auto a = m.def_submodule("activations", "Submodule supplying built-in activation functions.");
+ a.def("linear", &linear, py::arg("x"));
+ a.def("linear_deriv", &linear_deriv, py::arg("x"));
+ a.def("sigmoid", &sigmoid, py::arg("x"));
+ a.def("sigmoid_deriv", &sigmoid_deriv, py::arg("x"));
+ a.def("lecun_tanh", &lecun_tanh, py::arg("x"));
+ a.def("lecun_tanh_deriv", &lecun_tanh_deriv, py::arg("x"));
+ a.def("softplus", &softplus, py::arg("x"));
+ a.def("softplus_deriv", &softplus_deriv, py::arg("x"));
+ a.def("inverse_logit", &inverse_logit, py::arg("x"));
+ a.def("inverse_logit_deriv", &inverse_logit_deriv, py::arg("x"));
+ a.def("cloglog", &cloglog, py::arg("x"));
+ a.def("cloglog_deriv", &cloglog_deriv, py::arg("x"));
+ a.def("bipolar", &bipolar, py::arg("x"));
+ a.def("bipolar_deriv", &bipolar_deriv, py::arg("x"));
+ a.def("step", &step, py::arg("x"));
+ a.def("step_deriv", &step_deriv, py::arg("x"));
+ a.def("hard_tanh", &hard_tanh, py::arg("x"));
+ a.def("hard_tanh_deriv", &hard_tanh_deriv, py::arg("x"));
+ a.def("leaky_relu", &leaky_relu, py::arg("x"));
+ a.def("leaky_relu_deriv", &leaky_relu_deriv, py::arg("x"));
+ a.def("relu", (rectifier(linear)), py::arg("x"));
+ a.def("relu_deriv", (rectifier(linear_deriv)), py::arg("x"));
+ auto o = m.def_submodule("optimizers", "Submodule supplying built-in gradient descent optimizers.");
+ o.def("momentum", &optimizers::momentum, py::arg("beta"));
+ o.def("demon", &optimizers::demon, py::arg("beta"), py::arg("max_ep"));
+ o.def("adam", &optimizers::adam, py::arg("beta1"), py::arg("beta2"), py::arg("epsilon"));
+ o.def("adamax", &optimizers::adamax, py::arg("beta1"), py::arg("beta2"), py::arg("epsilon"));
+ auto d = m.def_submodule("decays", "Submodule supplying built-in learning rate annealing functions.");
+ d.def("step", &decays::step, py::arg("a_0"), py::arg("k"));
+ d.def("exponential", &decays::exponential, py::arg("a_0"), py::arg("k"));
+ d.def("fractional", &decays::fractional, py::arg("a_0"), py::arg("k"));
+ d.def("linear", &decays::linear, py::arg("max_ep"));
}
