[−][src]Struct darknet_sys::layer
Fields
type_: LAYER_TYPE
activation: ACTIVATION
cost_type: COST_TYPE
forward: Option<unsafe extern "C" fn(arg1: layer, arg2: network)>
backward: Option<unsafe extern "C" fn(arg1: layer, arg2: network)>
update: Option<unsafe extern "C" fn(arg1: layer, arg2: update_args)>
forward_gpu: Option<unsafe extern "C" fn(arg1: layer, arg2: network)>
backward_gpu: Option<unsafe extern "C" fn(arg1: layer, arg2: network)>
update_gpu: Option<unsafe extern "C" fn(arg1: layer, arg2: update_args)>
batch_normalize: c_int
shortcut: c_int
batch: c_int
forced: c_int
flipped: c_int
inputs: c_int
outputs: c_int
nweights: c_int
nbiases: c_int
extra: c_int
truths: c_int
h: c_int
w: c_int
c: c_int
out_h: c_int
out_w: c_int
out_c: c_int
n: c_int
max_boxes: c_int
groups: c_int
size: c_int
side: c_int
stride: c_int
reverse: c_int
flatten: c_int
spatial: c_int
pad: c_int
sqrt: c_int
flip: c_int
index: c_int
binary: c_int
xnor: c_int
steps: c_int
truth: c_int
smooth: f32
dot: f32
angle: f32
jitter: f32
saturation: f32
exposure: f32
shift: f32
ratio: f32
learning_rate_scale: f32
clip: f32
noloss: c_int
softmax: c_int
classes: c_int
coords: c_int
background: c_int
rescore: c_int
objectness: c_int
joint: c_int
noadjust: c_int
reorg: c_int
log: c_int
tanh: c_int
mask: *mut c_int
total: c_int
alpha: f32
beta: f32
kappa: f32
coord_scale: f32
object_scale: f32
noobject_scale: f32
mask_scale: f32
class_scale: f32
bias_match: c_int
random: c_int
ignore_thresh: f32
truth_thresh: f32
thresh: f32
focus: f32
classfix: c_int
absolute: c_int
onlyforward: c_int
stopbackward: c_int
dontload: c_int
dontsave: c_int
dontloadscales: c_int
numload: c_int
temperature: f32
probability: f32
scale: f32
cweights: *mut c_char
indexes: *mut c_int
input_layers: *mut c_int
input_sizes: *mut c_int
map: *mut c_int
counts: *mut c_int
sums: *mut *mut f32
rand: *mut f32
cost: *mut f32
state: *mut f32
prev_state: *mut f32
forgot_state: *mut f32
forgot_delta: *mut f32
state_delta: *mut f32
combine_cpu: *mut f32
combine_delta_cpu: *mut f32
concat: *mut f32
concat_delta: *mut f32
binary_weights: *mut f32
biases: *mut f32
bias_updates: *mut f32
scales: *mut f32
scale_updates: *mut f32
weights: *mut f32
weight_updates: *mut f32
delta: *mut f32
output: *mut f32
loss: *mut f32
squared: *mut f32
norms: *mut f32
spatial_mean: *mut f32
mean: *mut f32
variance: *mut f32
mean_delta: *mut f32
variance_delta: *mut f32
rolling_mean: *mut f32
rolling_variance: *mut f32
x: *mut f32
x_norm: *mut f32
m: *mut f32
v: *mut f32
bias_m: *mut f32
bias_v: *mut f32
scale_m: *mut f32
scale_v: *mut f32
z_cpu: *mut f32
r_cpu: *mut f32
h_cpu: *mut f32
prev_state_cpu: *mut f32
temp_cpu: *mut f32
temp2_cpu: *mut f32
temp3_cpu: *mut f32
dh_cpu: *mut f32
hh_cpu: *mut f32
prev_cell_cpu: *mut f32
cell_cpu: *mut f32
f_cpu: *mut f32
i_cpu: *mut f32
g_cpu: *mut f32
o_cpu: *mut f32
c_cpu: *mut f32
dc_cpu: *mut f32
binary_input: *mut f32
input_layer: *mut layer
self_layer: *mut layer
output_layer: *mut layer
reset_layer: *mut layer
update_layer: *mut layer
state_layer: *mut layer
input_gate_layer: *mut layer
state_gate_layer: *mut layer
input_save_layer: *mut layer
state_save_layer: *mut layer
input_state_layer: *mut layer
state_state_layer: *mut layer
input_z_layer: *mut layer
state_z_layer: *mut layer
input_r_layer: *mut layer
state_r_layer: *mut layer
input_h_layer: *mut layer
state_h_layer: *mut layer
wz: *mut layer
uz: *mut layer
wr: *mut layer
ur: *mut layer
wh: *mut layer
uh: *mut layer
uo: *mut layer
wo: *mut layer
uf: *mut layer
wf: *mut layer
ui: *mut layer
wi: *mut layer
ug: *mut layer
wg: *mut layer
softmax_tree: *mut tree
workspace_size: usize
Trait Implementations
Auto Trait Implementations
impl RefUnwindSafe for layer
impl !Send for layer
impl !Sync for layer
impl Unpin for layer
impl UnwindSafe for layer
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
fn to_owned(&self) -> T
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fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,