mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-11-22 09:13:01 +00:00
Add todo and comment
This commit is contained in:
parent
2b182214ea
commit
0ee6703c1e
2 changed files with 2 additions and 0 deletions
|
@ -36,6 +36,7 @@ def convnext_inverse(layer_in, depths, dims):
|
|||
def block_upscale(layer_in, block_number, depth, dim):
|
||||
layer_next = layer_in
|
||||
|
||||
# Ref https://machinelearningmastery.com/upsampling-and-transpose-convolution-layers-for-generative-adversarial-networks/ to understand Conv2DTranspose
|
||||
layer_next = tf.keras.layers.Conv2DTranspose(
|
||||
name=f"cns.stage{block_number}.end.convtp",
|
||||
filters=dim,
|
||||
|
|
|
@ -29,6 +29,7 @@ def model_rainfallwater_segmentation(metadata, feature_dim_in, shape_water_out,
|
|||
layer_next = do_convnext_inverse(layer_next, arch_name="convnext_i_tiny")
|
||||
|
||||
# TODO: An attention layer here instead of a dense layer, with a skip connection perhaps?
|
||||
raise Exception("Error: read and implement attention from https://ieeexplore.ieee.org/document/9076883")
|
||||
layer_next = tf.keras.layers.Dense(32)(layer_next)
|
||||
layer_next = tf.keras.layers.Conv2D(1, kernel_size=1, activation="softmax", padding="same")(layer_next)
|
||||
|
||||
|
|
Loading…
Reference in a new issue