public override List <int?[]> compute_output_shape(List <int?[]> input_shapes)
{
if (input_shapes.Count != 1)
{
throw new Exception("Expected a single input.");
}
int?[] input_shape = input_shapes[0];
// https://github.com/fchollet/keras/blob/f65a56fb65062c8d14d215c9f4b1015b97cc5bf3/keras/layers/convolutional.py#L185
if (this.data_format == DataFormatType.ChannelsLast)
{
var space = input_shape.Get(1, -1);
var new_space = new List <int?>();
for (int i = 0; i < space.Length; i++)
{
int?new_dim = ConvUtils.ConvOutputLength(
space[i],
this.kernel_size[i],
padding: this.padding,
stride: this.strides[i],
dilation: this.dilation_rate[i]);
new_space.Add(new_dim);
}
return(new[] { new[] { input_shape[0] }.Concat(new_space).Concat(new int?[] { this.filters }).ToList().ToArray() }.ToList());
}
else if (this.data_format == DataFormatType.ChannelsFirst)
{
var space = input_shape.Get(2, 0);
var new_space = new List <int?>();
for (int i = 0; i < space.Length; i++)
{
int?new_dim = ConvUtils.ConvOutputLength(
space[i],
this.kernel_size[i],
padding: this.padding,
stride: this.strides[i],
dilation: this.dilation_rate[i]);
new_space.Add(new_dim);
}
return(new[] { new[] { input_shape[0] }.Concat(new int?[] { this.filters }).Concat(new_space).ToList().ToArray() }.ToList());
}
else
{
throw new Exception();
}
}
