public FullyConnected(uint kernel_, calcMode.type mode_ = calcMode.type.CPU,
batchNormType.type bnorm_ = batchNormType.type.none)
{
kernel = kernel_;
bnorm = new batchNormType(bnorm_);
mode = new calcMode(mode_);
}
public Deconvolution(uint kernel_, calcMode.type mode_ = calcMode.type.CPU,
batchNormType.type bnorm_ = batchNormType.type.none)
{
kernel = kernel_;
mode = new calcMode(mode_);
bnorm = new batchNormType(bnorm_);
}
public float batchNormLr = 0.001F; ///< Learning rate for batch norm coef. Optional parameter [0..)
public Convolution(uint kernel_,
active.type act_ = active.type.relu,
optimizer.type opt_ = optimizer.type.adam,
float dropOut_ = 0.0F,
batchNormType.type bnorm_ = batchNormType.type.none,
uint fWidth_ = 3,
uint fHeight_ = 3,
int padding_ = 0,
uint stride_ = 1,
uint dilate_ = 1,
calcMode.type mode_ = calcMode.type.CPU,
uint gpuDeviceId_ = 0)
{
kernel = kernel_;
act = new active(act_);
opt = new optimizer(opt_);
dropOut = dropOut_;
bnorm = new batchNormType(bnorm_);
fWidth = fWidth_;
fHeight = fHeight_;
padding = padding_;
stride = stride_;
dilate = dilate_;
mode = new calcMode(mode_);
gpuDeviceId = gpuDeviceId_;
}
public Convolution(uint filters_, int padding_ = 0,
batchNormType.type bnorm_ = batchNormType.type.none)
{
filters = filters_;
padding = padding_;
bnorm = new batchNormType(bnorm_);
}
public FullyConnected(uint units_, calcMode.type mode_ = calcMode.type.CPU,
batchNormType.type bnorm_ = batchNormType.type.none)
{
units = units_;
bnorm = new batchNormType(bnorm_);
mode = new calcMode(mode_);
}
public Deconvolution(uint filters_, calcMode.type mode_ = calcMode.type.CPU,
batchNormType.type bnorm_ = batchNormType.type.none)
{
filters = filters_;
mode = new calcMode(mode_);
bnorm = new batchNormType(bnorm_);
}
public Convolution(uint kernel_, int padding_ = 0, calcMode.type mode_ = calcMode.type.CPU,
batchNormType.type bnorm_ = batchNormType.type.none)
{
kernel = kernel_;
padding = padding_;
mode = new calcMode(mode_);
bnorm = new batchNormType(bnorm_);
}
public Convolution(uint filters_, int padding_ = 0, calcMode.type mode_ = calcMode.type.CPU,
batchNormType.type bnorm_ = batchNormType.type.none)
{
filters = filters_;
padding = padding_;
mode = new calcMode(mode_);
bnorm = new batchNormType(bnorm_);
}
public float batchNormLr = 0.001F; ///< Learning rate for batch norm coef. Optional parameter [0..)
public FullyConnected(uint units_,
active.type act_ = active.type.relu,
optimizer.type opt_ = optimizer.type.adam,
float dropOut_ = 0.0f,
batchNormType.type bnorm_ = batchNormType.type.none,
uint gpuDeviceId_ = 0)
{
units = units_;
act = new active(act_);
opt = new optimizer(opt_);
dropOut = dropOut_;
bnorm = new batchNormType(bnorm_);
gpuDeviceId = gpuDeviceId_;
}
public float batchNormLr = 0.001F; ///< Learning rate for batch norm coef. Optional parameter [0..)
public FullyConnected(uint kernel_,
active.type act_ = active.type.relu,
optimizer.type opt_ = optimizer.type.adam,
float dropOut_ = 0.0f,
batchNormType.type bnorm_ = batchNormType.type.none,
calcMode.type mode_ = calcMode.type.CPU,
uint gpuDeviceId_ = 0)
{
kernel = kernel_;
act = new active(act_);
opt = new optimizer(opt_);
dropOut = dropOut_;
bnorm = new batchNormType(bnorm_);
mode = new calcMode(mode_);
gpuDeviceId = gpuDeviceId_;
}
public float batchNormLr = 0.001F; ///< Learning rate for batch norm coef. Optional parameter [0..)
public Deconvolution(uint filters_,
active.type act_ = active.type.relu,
optimizer.type opt_ = optimizer.type.adam,
float dropOut_ = 0.0F,
batchNormType.type bnorm_ = batchNormType.type.none,
uint fWidth_ = 3,
uint fHeight_ = 3,
uint stride_ = 1,
uint gpuDeviceId_ = 0)
{
filters = filters_;
act = new active(act_);
opt = new optimizer(opt_);
dropOut = dropOut_;
bnorm = new batchNormType(bnorm_);
fWidth = fWidth_;
fHeight = fHeight_;
stride = stride_;
gpuDeviceId = gpuDeviceId_;
}
public Deconvolution(uint filters_,
batchNormType.type bnorm_ = batchNormType.type.none)
{
filters = filters_;
bnorm = new batchNormType(bnorm_);
}