/// <summary>
/// Initializes a new instance of the <see cref="Dense" /> class.
/// </summary>
/// <param name="dim">Positive integer, dimensionality of the output space..</param>
/// <param name="activation">
/// Activation function to use. If you don't specify anything, no activation is applied (ie. "linear"
/// activation: a(x) = x). <see cref="SiaNet.Common.OptActivations" />
/// </param>
/// <param name="useBias">Boolean, whether the layer uses a bias vector.</param>
/// <param name="weightInitializer">Initializer for the kernel weights matrix. </param>
/// <param name="biasInitializer">Initializer for the bias vector. </param>
public Dense(
int dim,
ActivationBase activation = null,
bool useBias = false,
InitializerBase weightInitializer = null,
InitializerBase biasInitializer = null)
{
Dim = dim;
Activation = activation;
UseBias = useBias;
WeightInitializer = weightInitializer ?? new Xavier();
BiasInitializer = biasInitializer ?? new Zeros();
}
/// <summary>
/// Initializes a new instance of the <see cref="CudaRNN" /> class.
/// </summary>
/// <param name="hiddenSize">Size of the hidden layer.</param>
/// <param name="numLayers">The number of layers.</param>
/// <param name="bidirectional">If bidirectional RNN</param>
/// <param name="activation">Activation function to use. Supported are ReLU and TanH</param>
/// <param name="weightInitializer">The weight initializer.</param>
public CudaRNN(
uint hiddenSize,
uint numLayers,
bool bidirectional = false,
ActivationBase activation = null,
InitializerBase weightInitializer = null)
{
LayerSize = hiddenSize;
Layers = numLayers;
BiDirectional = bidirectional;
WeightInitializer = weightInitializer ?? new Xavier();
Activation = activation ?? new ReLU();
if (!(Activation is ReLU) && !(Activation is Tanh))
{
throw new NotSupportedException("Supported activation for RNN is ReLU and Tanh");
}
}
/// <summary>
/// Initializes a new instance of the <see cref="LSTM" /> class.
/// </summary>
/// <param name="dim">Positive integer, dimensionality of the output space.</param>
/// <param name="shape">The input shape.</param>
/// <param name="activation">
/// Activation function to use. If you don't specify anything, no activation is applied (ie.
/// "linear" activation: a(x) = x). <see cref="SiaNet.Common.OptActivations" />
/// </param>
/// <param name="useBias">Boolean, whether the layer uses a bias vector.</param>
/// <param name="weightInitializer">
/// Initializer for the kernel weights matrix. <see cref="SiaNet.Common.OptInitializers" />
/// </param>
/// <param name="biasInitializer">Initializer for the bias vector. <see cref="SiaNet.Common.OptInitializers" /></param>
public LSTM(
int dim,
int?cellDim = null,
ActivationBase activation = null,
ActivationBase recurrentActivation = null,
InitializerBase weightInitializer = null,
InitializerBase recurrentInitializer = null,
bool useBias = true,
InitializerBase biasInitializer = null,
bool returnSequence = false)
{
Dim = dim;
CellDim = cellDim;
Activation = activation ?? new Tanh();
RecurrentActivation = recurrentActivation ?? new Sigmoid();
UseBias = useBias;
ReturnSequence = returnSequence;
WeightInitializer = weightInitializer ?? new GlorotUniform();
RecurrentInitializer = recurrentInitializer ?? new GlorotUniform();
BiasInitializer = biasInitializer ?? new Zeros();
}
/// <summary>
/// Initializes a new instance of the <see cref="Conv1D" /> class.
/// </summary>
/// <param name="shape">The 1D input shape.</param>
/// <param name="channels">Integer, the dimensionality of the output space</param>
/// <param name="kernalSize">An integer specifying the length of the 1D convolution window.</param>
/// <param name="strides">An integer specifying the stride length of the convolution.</param>
/// <param name="padding">
/// Boolean, if true results in padding the input such that the output has the same length as the
/// original input.
/// </param>
/// <param name="dialation">
/// An integer specifying the dilation rate to use for dilated convolution. Currently, specifying
/// any dilation_rate value != 1 is incompatible with specifying any strides value != 1.
/// </param>
/// <param name="activation">
/// Activation function to use. If you don't specify anything, no activation is applied (ie.
/// "linear" activation: a(x) = x). <see cref="SiaNet.Common.OptActivations" />
/// </param>
/// <param name="useBias">Boolean, whether the layer uses a bias vector.</param>
/// <param name="weightInitializer">
/// Initializer for the kernel weights matrix. <see cref="SiaNet.Common.OptInitializers" />
/// </param>
/// <param name="biasInitializer">Initializer for the bias vector. <see cref="SiaNet.Common.OptInitializers" /></param>
public Conv1D(
int channels,
int kernalSize,
int strides = 1,
bool padding = true,
int dialation = 1,
ActivationBase activation = null,
bool useBias = false,
InitializerBase weightInitializer = null,
InitializerBase biasInitializer = null)
{
WeightInitializer = weightInitializer ?? new Xavier();
BiasInitializer = biasInitializer ?? new Zeros();
Channels = channels;
KernalSize = kernalSize;
Padding = padding;
Dialation = dialation;
Activation = activation;
UseBias = useBias;
Strides = strides;
}
/// <summary>
/// Initializes a new instance of the <see cref="Conv2D" /> class.
/// </summary>
/// <param name="channels">Integer, the dimensionality of the output space.</param>
/// <param name="kernalSize">
/// A tuple of 2 integers, specifying the width and height of the 2D convolution window. Can be a
/// single integer to specify the same value for all spatial dimensions.
/// </param>
/// <param name="strides">
/// A tuple of 2 integers, specifying the strides of the convolution along the width and height. Can
/// be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is
/// incompatible with specifying any dilation_rate value != 1.
/// </param>
/// <param name="padding">
/// Boolean, if true results in padding the input such that the output has the same length as the
/// original input.
/// </param>
/// <param name="dialation">
/// A tuple of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a
/// single integer to specify the same value for all spatial dimensions. Currently, specifying any dilation_rate value
/// != 1 is incompatible with specifying any stride value != 1.
/// </param>
/// <param name="activation">
/// Activation function to use. If you don't specify anything, no activation is applied (ie.
/// "linear" activation: a(x) = x). <see cref="SiaNet.Common.OptActivations" />
/// </param>
/// <param name="useBias">Boolean, whether the layer uses a bias vector.</param>
/// <param name="weightInitializer">
/// Initializer for the kernel weights matrix. <see cref="SiaNet.Common.OptInitializers" />
/// </param>
/// <param name="biasInitializer">Initializer for the bias vector. <see cref="SiaNet.Common.OptInitializers" /></param>
public Conv2D(
int channels,
Tuple <int, int> kernalSize,
Tuple <int, int> strides = null,
bool padding = true,
Tuple <int, int> dialation = null,
ActivationBase activation = null,
bool useBias = false,
InitializerBase weightInitializer = null,
InitializerBase biasInitializer = null)
{
Channels = channels;
KernalSize = kernalSize;
Strides = strides ?? Tuple.Create(1, 1);
Padding = padding;
Dialation = dialation ?? Tuple.Create(1, 1);
Activation = activation;
UseBias = useBias;
WeightInitializer = weightInitializer ?? new Xavier();
BiasInitializer = biasInitializer ?? new Zeros();
}
