sbrl-github/research-rainfallradar
1
0
Fork 0
mirror of https://github.com/sbrl/research-rainfallradar synced 2024-11-22 17:23:01 +00:00
Code Issues Projects Releases Packages Wiki Activity

model shape logging

Browse source
This commit is contained in:
Starbeamrainbowlabs 2022-11-11 19:03:37 +00:00
parent 00917b2698
commit 9da059d738
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
1 changed files with 3 additions and 1 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
aimodel/src/lib/ai/model_rainfallwater_mono.py
View file

@ -38,7 +38,7 @@ def model_rainfallwater_mono(metadata, shape_water_out, model_arch_enc="convnext
arch_name = model_arch_enc arch_name = model_arch_enc
)(layer_input) )(layer_input)
print("ENCODER output_shape", layer_next.shape) print("DEBUG:model ENCODER output_shape", layer_next.shape)
# BOTTLENECK # BOTTLENECK
layer_next = tf.keras.layers.Dense(name="cns.stage.bottleneck.dense2", units=feature_dim)(layer_input) layer_next = tf.keras.layers.Dense(name="cns.stage.bottleneck.dense2", units=feature_dim)(layer_input)
@ -50,6 +50,8 @@ def model_rainfallwater_mono(metadata, shape_water_out, model_arch_enc="convnext
layer_next = LayerStack2Image(target_width=4, target_height=4)(layer_next) layer_next = LayerStack2Image(target_width=4, target_height=4)(layer_next)
# layer_next = tf.keras.layers.Reshape((4, 4, math.floor(feature_dim_in/(4*4))), name="cns.stable_begin.reshape")(layer_next) # layer_next = tf.keras.layers.Reshape((4, 4, math.floor(feature_dim_in/(4*4))), name="cns.stable_begin.reshape")(layer_next)
print("DEBUG:model BOTTLENECK:stack2image output_shape", layer_next.shape)
layer_next = tf.keras.layers.Dense(name="cns.stage.begin.dense2", units=feature_dim)(layer_next) layer_next = tf.keras.layers.Dense(name="cns.stage.begin.dense2", units=feature_dim)(layer_next)
layer_next = tf.keras.layers.Activation(name="cns.stage_begin.relu2", activation="gelu")(layer_next) layer_next = tf.keras.layers.Activation(name="cns.stage_begin.relu2", activation="gelu")(layer_next)
layer_next = tf.keras.layers.LayerNormalization(name="cns.stage_begin.norm2", epsilon=1e-6)(layer_next) layer_next = tf.keras.layers.LayerNormalization(name="cns.stage_begin.norm2", epsilon=1e-6)(layer_next)