TensorFlow.NET/src/TensorFlowNET.Keras/Optimizers/SGD.cs at master · feelsyt/TensorFlow.NET · GitHub

History

73 lines (62 loc) · 2.63 KB

Raw

using System;

using System.Collections.Generic;

using System.Linq;

using Tensorflow.Keras.ArgsDefinition;

namespace Tensorflow.Keras.Optimizers

{

public class SGD : OptimizerV2

{

protected override string _name => "SGD";

#pragma warning disable CS0169 // The field 'SGD.nesterov' is never used

bool nesterov;

#pragma warning restore CS0169 // The field 'SGD.nesterov' is never used

public SGD(float learning_rate,

float momentum = 0.0f,

bool nesterov = false,

float decay = 0.0f) : base(new OptimizerV2Args { })

{

_set_hyper("learning_rate", learning_rate);

_set_hyper("decay", decay);

_momentum = momentum > 0;

if (momentum < 0 || momentum > 1)

throw new ValueError($"momentum must be a number between 0 and 1, got {momentum}.");

_set_hyper("momentum", momentum);

#pragma warning disable CS1717 // Assignment made to same variable

nesterov = nesterov;

#pragma warning restore CS1717 // Assignment made to same variable

}

protected override void _create_slots(IVariableV1[] var_list)

{

if (_momentum)

foreach (var var in var_list)

add_slot(var, "momentum");

}

protected override void _prepare_local(DeviceDType device_dtype,

Dictionary<DeviceDType, Dictionary<string, Tensor>> _apply_state)

{

base._prepare_local(device_dtype, _apply_state);

_apply_state[device_dtype]["momentum"] = array_ops.identity(

_get_hyper("momentum", device_dtype.DType));

}

protected override Operation _resource_apply_dense(IVariableV1 var, Tensor grad, Dictionary<DeviceDType, Dictionary<string, Tensor>> _apply_state)

{

if (_momentum)

{

var momentum_var = get_slot(var, "momentum");

return gen_training_ops.resource_apply_keras_momentum(

var.Handle,

momentum_var.Handle,

_get_hyper("learning_rate", var.dtype),

grad,

_get_hyper("momentum", var.dtype),

use_locking: _use_locking,

use_nesterov: nesterov);

}

var device_dtype = _apply_state.Keys.FirstOrDefault(x => x.Device == var.Device && x.DType == var.dtype.as_base_dtype());

return gen_training_ops.resource_apply_gradient_descent(var.Handle,

_apply_state[device_dtype]["lr_t"],

grad,

use_locking: _use_locking);

}

TensorFlow.NET/src/TensorFlowNET.Keras/Optimizers/SGD.cs at master · feelsyt/TensorFlow.NET · GitHub

Expand file tree

Breadcrumbs

SGD.cs

Latest commit

History

Breadcrumbs

SGD.cs

File metadata and controls