Format code

Author: Dobiasd

include/fdeep/layers/batch_normalization_layer.hpp

@@ -48,9 +48,11 @@ class batch_normalization_layer : public layer
     tensors apply_impl(const tensors& inputs) const override
     {
         const auto input = single_tensor_from_tensors(inputs);
+
         std::vector<std::size_t> dims(5, 1);
         dims[rank_aligned_axis_to_absolute_axis(input.shape().rank(), axis_) - 1] = moving_mean_->size();
         const tensor_shape params_shape = create_tensor_shape_from_dims(dims);
+
         return {batch_normalization(
             input,
             broadcast(tensor(params_shape, moving_mean_), input.shape()),

include/fdeep/layers/layer_normalization_layer.hpp

@@ -37,8 +37,9 @@ class layer_normalization_layer : public layer
 
     tensors apply_impl(const tensors& inputs) const override
     {
-        // https://github.com/keras-team/keras/blob/v2.14.0/keras/layers/normalization/layer_normalization.py#L291-L304
         const auto& input = single_tensor_from_tensors(inputs);
+
+        // https://github.com/keras-team/keras/blob/v2.14.0/keras/layers/normalization/layer_normalization.py#L291-L304
         const auto& input_moments = moments(input, axes_);
         const auto& mean = input_moments.first;
         const auto& variance = input_moments.second;
@@ -52,6 +53,7 @@ class layer_normalization_layer : public layer
             dims[pos] = input_shape_dimensions[pos];
         }
         const tensor_shape params_shape = create_tensor_shape_from_dims(dims);
+
         return {batch_normalization(
             input,
             mean,

include/fdeep/tensor.hpp

@@ -814,7 +814,9 @@ inline tensor broadcast(const tensor& t, const tensor_shape& shape)
         (t.shape().width_ == 1 || t.shape().width_ == shape.width_) &&
         (t.shape().depth_ == 1 || t.shape().depth_ == shape.depth_),
         "Invalid shapes for combining tensors.");
+
     tensor out_tensor = tensor(shape, static_cast<float_type>(0));
+
     loop_over_all_dims(out_tensor.shape(), [&](
         std::size_t dim5, std::size_t dim4, std::size_t y, std::size_t x, std::size_t z)
     {
@@ -907,7 +909,6 @@ inline tensor batch_normalization(
             transform_tensor(
                 fplus::add_to(variance_epsilon), variance)),
                 scale);
-
     return add_tensors(
         mult_tensors(x, inv),
         subtract_tensors(