public static Tensor conv2d(Tensor inputs,
int filters,
int[] kernel_size,
int[] strides = null,
string padding = "valid",
string data_format = "channels_last",
int[] dilation_rate = null,
bool use_bias = true,
IActivation activation = null,
IInitializer kernel_initializer = null)
{
if (strides == null)
{
strides = new int[] { 1, 1 }
}
;
if (dilation_rate == null)
{
dilation_rate = new int[] { 1, 1 }
}
;
var layer = new Conv2D(filters, kernel_size);
return(layer.apply(inputs));
}
}
}
public static Tensor conv2d(Tensor inputs,
int filters,
int[] kernel_size,
int[] strides = null,
string padding = "valid",
string data_format = "channels_last",
int[] dilation_rate = null,
bool use_bias = true,
IActivation activation = null,
IInitializer kernel_initializer = null,
IInitializer bias_initializer = null,
bool trainable = true,
string name = null)
{
if (strides == null)
{
strides = new int[] { 1, 1 }
}
;
if (dilation_rate == null)
{
dilation_rate = new int[] { 1, 1 }
}
;
if (bias_initializer == null)
{
bias_initializer = tf.zeros_initializer;
}
var layer = new Conv2D(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,
trainable: trainable,
name: name);
return(layer.apply(inputs));
}
}
}