pub struct NNOptimizer { /* private fields */ }Implementations§
Source§impl NNOptimizer
impl NNOptimizer
pub fn learning_rate(&self) -> f32
pub fn set_learning_rate(&self, value: f32)
pub fn gradient_rescale(&self) -> f32
pub fn apply_gradient_clipping(&self) -> bool
pub fn set_apply_gradient_clipping(&self, value: bool)
pub fn gradient_clip_max(&self) -> f32
pub fn gradient_clip_min(&self) -> f32
pub fn regularization_scale(&self) -> f32
Sourcepub fn regularization_type(&self) -> usize
pub fn regularization_type(&self) -> usize
Examples found in repository?
examples/07_optimizer_and_state.rs (line 68)
37fn main() {
38 let device = MetalDevice::system_default().expect("no Metal device available");
39 let queue = device.new_command_queue().expect("command queue");
40
41 let command_buffer = queue.new_command_buffer().expect("command buffer");
42 let temporary_a =
43 State::temporary_with_buffer_size(&command_buffer, 32).expect("temporary state a");
44 let temporary_b =
45 State::temporary_with_buffer_size(&command_buffer, 64).expect("temporary state b");
46 let unique_count =
47 state_batch_increment_read_count(&[&temporary_a, &temporary_a, &temporary_b], 1);
48 assert_eq!(unique_count, 2);
49 assert_eq!(
50 temporary_a.resource_type_at_index(0),
51 state_resource_type::BUFFER
52 );
53 command_buffer.commit();
54 command_buffer.wait_until_completed();
55
56 let resource_list = StateResourceList::new().expect("resource list");
57 resource_list.append_buffer(16);
58 let persistent_state =
59 State::new_with_resource_list(&device, &resource_list).expect("persistent state");
60 assert_eq!(persistent_state.resource_count(), 1);
61 assert_eq!(persistent_state.buffer_size_at_index(0), 16);
62
63 let (gradient_buffer, gradient_vector) = vector_with_values(&device, &[0.1, -0.2]);
64 let (values_buffer, values_vector) = vector_with_values(&device, &[1.0, -1.0]);
65 let (result_buffer, result_vector) = vector_with_values(&device, &[0.0, 0.0]);
66 let optimizer = NNOptimizerStochasticGradientDescent::new(&device, 0.5).expect("sgd");
67 let base = optimizer.as_optimizer().expect("optimizer base");
68 assert_eq!(base.regularization_type(), nn_regularization_type::NONE);
69
70 let command_buffer = queue.new_command_buffer().expect("command buffer");
71 optimizer.encode_vector(
72 &command_buffer,
73 &gradient_vector,
74 &values_vector,
75 None,
76 &result_vector,
77 );
78 command_buffer.commit();
79 command_buffer.wait_until_completed();
80
81 let _ = gradient_buffer;
82 let _ = values_buffer;
83 let output = read_f32_values(&result_buffer, 2);
84 println!("{output:?}");
85}Trait Implementations§
Auto Trait Implementations§
impl Freeze for NNOptimizer
impl RefUnwindSafe for NNOptimizer
impl Unpin for NNOptimizer
impl UnsafeUnpin for NNOptimizer
impl UnwindSafe for NNOptimizer
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more