/// <summary>
/// Initializes a new instance of the <see cref="Dense"/> class.
/// </summary>
///
/// <param name="units">Positive integer, dimensionality of the output space.</param>
/// <param name="input_dim">The input dim.</param>
/// <param name="batch_input_shape">The batch input shape.</param>
/// <param name="input_shape">The input shape.</param>
/// <param name="activation">The activation function to use.</param>
/// <param name="use_bias">Whether the layer uses a bias vector.</param>
///
public Dense(int units, IActivationFunction activation = null, bool use_bias = true,
IWeightInitializer kernel_initializer = null, IWeightInitializer bias_initializer = null,
IWeightRegularizer kernel_regularizer = null, IWeightRegularizer bias_regularizer = null, IWeightRegularizer activity_regularizer = null,
IWeightConstraint kernel_constraint = null, IWeightConstraint bias_constraint = null,
int?input_dim = null, int?[] input_shape = null, int?[] batch_input_shape = null)
: base(input_dim: input_dim, input_shape: input_shape, batch_input_shape: batch_input_shape)
{
//if 'input_shape' not in kwargs and 'input_dim' in kwargs:
// kwargs['input_shape'] = (kwargs.pop('input_dim'),)
if (bias_initializer == null)
{
bias_initializer = new Zeros();
}
if (kernel_initializer == null)
{
kernel_initializer = new GlorotUniform();
}
this.units = units;
this.activation = activation;
this.use_bias = use_bias;
this.kernel_initializer = kernel_initializer;
this.bias_initializer = bias_initializer;
this.kernel_regularizer = kernel_regularizer;
this.bias_regularizer = bias_regularizer;
this.activity_regularizer = activity_regularizer;
this.kernel_constraint = kernel_constraint;
this.bias_constraint = bias_constraint;
this.input_spec.Add(new InputSpec(min_ndim: 2));
this.supports_masking = true;
}
public Conv2D(int filters,
int[] kernel_size = null,
int[] strides = null,
PaddingType padding = PaddingType.Valid,
DataFormatType?data_format = null,
int[] dilation_rate = null,
string activation = null,
bool use_bias = true,
IWeightInitializer kernel_initializer = null,
IWeightInitializer bias_initializer = null,
IWeightRegularizer kernel_regularizer = null,
IWeightRegularizer bias_regularizer = null,
IWeightRegularizer activity_regularizer = null,
IWeightConstraint kernel_constraint = null,
IWeightConstraint bias_constraint = null,
int?[] input_shape = null)
: this(filters : filters,
kernel_size : kernel_size,
strides : strides,
padding : padding,
data_format : data_format,
dilation_rate : dilation_rate,
activation : Activation.Create(activation),
use_bias : use_bias,
kernel_initializer : kernel_initializer,
bias_initializer : bias_initializer,
kernel_regularizer : kernel_regularizer,
bias_regularizer : bias_regularizer,
activity_regularizer : activity_regularizer,
kernel_constraint : kernel_constraint,
bias_constraint : bias_constraint,
input_shape : input_shape)
{
}
/// <summary>
/// Initializes a new instance of the <see cref="Dense"/> class.
/// </summary>
///
/// <param name="units">Positive integer, dimensionality of the output space.</param>
/// <param name="input_dim">The input dim.</param>
/// <param name="batch_input_shape">The batch input shape.</param>
/// <param name="input_shape">The input shape.</param>
/// <param name="activation">The activation function to use.</param>
/// <param name="use_bias">Whether the layer uses a bias vector.</param>
///
public Dense(int units, string activation, bool use_bias = true,
IWeightInitializer kernel_initializer = null, IWeightInitializer bias_initializer = null,
IWeightRegularizer kernel_regularizer = null, IWeightRegularizer bias_regularizer = null, IWeightRegularizer activity_regularizer = null,
IWeightConstraint kernel_constraint = null, IWeightConstraint bias_constraint = null,
int?input_dim = null, int?[] input_shape = null, int?[] batch_input_shape = null)
: this(units : units, activation : Activation.Create(activation), use_bias : use_bias,
kernel_initializer : kernel_initializer, bias_initializer : bias_initializer,
kernel_regularizer : kernel_regularizer, bias_regularizer : bias_regularizer, activity_regularizer : activity_regularizer,
kernel_constraint : kernel_constraint, bias_constraint : bias_constraint,
input_dim : input_dim, input_shape : input_shape, batch_input_shape : batch_input_shape)
{
}
public Conv2D(int filters,
int[] kernel_size = null,
int[] strides = null,
PaddingType padding = PaddingType.Valid,
DataFormatType?data_format = null,
int[] dilation_rate = null,
IActivationFunction activation = null,
bool use_bias = true,
IWeightInitializer kernel_initializer = null,
IWeightInitializer bias_initializer = null,
IWeightRegularizer kernel_regularizer = null,
IWeightRegularizer bias_regularizer = null,
IWeightRegularizer activity_regularizer = null,
IWeightConstraint kernel_constraint = null,
IWeightConstraint bias_constraint = null,
int?[] input_shape = null)
: base(rank: 2,
filters: filters,
kernel_size: kernel_size,
strides: strides,
padding: padding,
data_format: data_format,
dilation_rate: dilation_rate,
activation: activation,
use_bias: use_bias,
kernel_initializer: kernel_initializer,
bias_initializer: bias_initializer,
kernel_regularizer: kernel_regularizer,
bias_regularizer: bias_regularizer,
activity_regularizer: activity_regularizer,
kernel_constraint: kernel_constraint,
bias_constraint: bias_constraint,
input_shape: input_shape)
{
this.input_spec = new List <InputSpec> {
new InputSpec(ndim: 4)
};
}
/// <summary>
/// Initializes a new instance of the <see cref="Dense"/> class.
/// </summary>
///
/// <param name="units">Positive integer, dimensionality of the output space.</param>
/// <param name="input_dim">The input dim.</param>
/// <param name="batch_input_shape">The batch input shape.</param>
/// <param name="input_shape">The input shape.</param>
/// <param name="activation">The activation function to use.</param>
/// <param name="use_bias">Whether the layer uses a bias vector.</param>
/// <param name="spectral_norm_iteration">Whether the layer uses a bias vector.</param>
///
public Dense(int units, IActivationFunction activation = null, bool use_bias = true,
IWeightInitializer kernel_initializer = null, IWeightInitializer bias_initializer = null,
IWeightRegularizer kernel_regularizer = null, IWeightRegularizer bias_regularizer = null, IWeightRegularizer activity_regularizer = null,
IWeightConstraint kernel_constraint = null, IWeightConstraint bias_constraint = null,
int?input_dim = null, int?[] input_shape = null, int?[] batch_input_shape = null, int spectral_norm_iteration = 0)
: base(input_dim: input_dim, input_shape: input_shape, batch_input_shape: batch_input_shape)
{
// https://github.com/fchollet/keras/blob/f65a56fb65062c8d14d215c9f4b1015b97cc5bf3/keras/layers/core.py#L791
//if 'input_shape' not in kwargs and 'input_dim' in kwargs:
// kwargs['input_shape'] = (kwargs.pop('input_dim'),)
if (bias_initializer == null)
{
bias_initializer = new Zeros();
}
if (kernel_initializer == null)
{
kernel_initializer = new GlorotUniform();
}
this.units = units;
this.activation = activation;
this.use_bias = use_bias;
this.kernel_initializer = kernel_initializer;
this.bias_initializer = bias_initializer;
this.kernel_regularizer = kernel_regularizer;
this.bias_regularizer = bias_regularizer;
this.activity_regularizer = activity_regularizer;
this.kernel_constraint = kernel_constraint;
this.bias_constraint = bias_constraint;
this.input_spec = new List <InputSpec>();
this.input_spec.Add(new InputSpec(min_ndim: 2));
this.supports_masking = true;
this.spectral_norm_iteration = spectral_norm_iteration;
}
/// <summary>
/// Initializes a new instance of the <see cref="_Conv" /> class.
/// </summary>
/// <param name="rank">rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.</param>
/// <param name="filters">Integer, the dimensionality of the output space (i.e.the number output of filters in the convolution).</param>
/// <param name="kernel_size">An integer or tuple/list of n integers, specifying the dimensions of the convolution window.</param>
/// <param name="strides">An integer or tuple/list of n integers, specifying the strides of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.</param>
/// <param name="padding">One of `"valid"` or `"same"` (case-insensitive).</param>
/// <param name="data_format">A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs.
/// `channels_last` corresponds to inputs with shape `(batch, ..., channels)` while `channels_first` corresponds to inputs with shape
/// `(batch, channels, ...)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`.
/// If you never set it, then it will be "channels_last".</param>
/// <param name="dilation_rate">An integer or tuple/list of n integers, 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 (see[activations](../activations.md)). If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).</param>
/// <param name="use_bias">Boolean, whether the layer uses a bias vector.</param>
/// <param name="kernel_initializer">Initializer for the `kernel` weights matrix (see[initializers](../initializers.md)).</param>
/// <param name="bias_initializer">Initializer for the bias vector (see[initializers](../initializers.md)).</param>
/// <param name="kernel_regularizer">Regularizer function applied to the `kernel` weights matrix (see[regularizer](../regularizers.md)).</param>
/// <param name="bias_regularizer">Regularizer function applied to the bias vector (see[regularizer](../regularizers.md)).</param>
/// <param name="activity_regularizer">Regularizer function applied to the output of the layer(its "activation"). (see[regularizer](../regularizers.md)).</param>
/// <param name="kernel_constraint">Constraint function applied to the kernel matrix (see[constraints](../constraints.md)).</param>
/// <param name="bias_constraint">Constraint function applied to the bias vector (see[constraints](../constraints.md)).</param>
public _Conv(int rank,
int filters,
int[] kernel_size,
int[] strides = null,
PaddingType padding = PaddingType.Valid,
DataFormatType?data_format = null,
int[] dilation_rate = null,
IActivationFunction activation = null,
bool use_bias = true,
IWeightInitializer kernel_initializer = null,
IWeightInitializer bias_initializer = null,
IWeightRegularizer kernel_regularizer = null,
IWeightRegularizer bias_regularizer = null,
IWeightRegularizer activity_regularizer = null,
IWeightConstraint kernel_constraint = null,
IWeightConstraint bias_constraint = null,
int?[] input_shape = null)
: base(input_shape: input_shape)
{
if (kernel_initializer == null)
{
kernel_initializer = new GlorotUniform();
}
if (bias_initializer == null)
{
bias_initializer = new Zeros();
}
if (strides == null)
{
strides = Vector.Create <int>(size: rank, value: 1);
}
if (dilation_rate == null)
{
dilation_rate = Vector.Create <int>(size: rank, value: 1);
}
if (data_format == null)
{
data_format = K.image_data_format();
}
if (kernel_size.Length != rank)
{
throw new ArgumentException("kernel_size");
}
if (strides.Length != rank)
{
throw new ArgumentException("strides");
}
if (dilation_rate.Length != rank)
{
throw new ArgumentException("dilation_rate");
}
// https://github.com/fchollet/keras/blob/f65a56fb65062c8d14d215c9f4b1015b97cc5bf3/keras/layers/convolutional.py#L101
this.rank = rank;
this.filters = filters;
this.kernel_size = kernel_size;
this.strides = strides;
this.padding = padding;
this.data_format = data_format;
this.dilation_rate = dilation_rate;
this.activation = activation;
this.use_bias = use_bias;
this.kernel_initializer = kernel_initializer;
this.bias_initializer = bias_initializer;
this.kernel_regularizer = kernel_regularizer;
this.bias_regularizer = bias_regularizer;
this.activity_regularizer = activity_regularizer;
this.kernel_constraint = kernel_constraint;
this.bias_constraint = bias_constraint;
this.input_spec = new List <InputSpec> {
new InputSpec(ndim: this.rank + 2)
};
}
