train_mono: debug

this commit will generate a large amount of debug output.
This commit is contained in:
Starbeamrainbowlabs 2022-11-28 16:46:17 +00:00
parent f39e4ade70
commit 09f81b0746
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
3 changed files with 49 additions and 2 deletions

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@ -2,7 +2,14 @@ import math
import tensorflow as tf import tensorflow as tf
class LossContrastive(tf.keras.losses.Loss): class LossContrastive(tf.keras.losses.Loss):
"""Implements a contrastive loss function.
@warning: This does not function as it should.
Args:
weight_temperature (integer): The temperature weight (e.g. from LayerCheeseMultipleOut).
batch_size (integer): The batch size.
"""
def __init__(self, weight_temperature, batch_size): def __init__(self, weight_temperature, batch_size):
super(LossContrastive, self).__init__() super(LossContrastive, self).__init__()
self.batch_size = batch_size self.batch_size = batch_size

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@ -0,0 +1,38 @@
import math
import tensorflow as tf
class LossCrossentropy(tf.keras.losses.Loss):
"""Wraps the cross-entropy loss function because it's buggy.
@warning: tf.keras.losses.CategoricalCrossentropy() isn't functioning as intended during training...
Args:
batch_size (integer): The batch size (currently unused).
"""
def __init__(self, batch_size):
super(LossCrossentropy, self).__init__()
self.param_batch_size = batch_size
def call(self, y_true, y_pred):
result = tf.keras.metrics.categorical_crossentropy(y_true, y_pred)
result_reduce = tf.math.reduce_sum(result)
tf.print("DEBUG:TFPRINT:loss BEFORE_REDUCE", result, "AFTER_REDUCE", result_reduce)
return result
def get_config(self):
config = super(LossCrossentropy, self).get_config()
config.update({
"batch_size": self.param_batch_size,
})
return config
if __name__ == "__main__":
weight_temperature = tf.Variable(name="loss_temperature", shape=1, initial_value=tf.constant([
math.log(1 / 0.07)
]))
loss = LossCrossentropy(weight_temperature=weight_temperature, batch_size=64)
tensor_input = tf.random.uniform([64, 2, 512])
print(loss(tf.constant(1), tensor_input))

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@ -6,6 +6,7 @@ import tensorflow as tf
from .components.convnext import make_convnext from .components.convnext import make_convnext
from .components.convnext_inverse import do_convnext_inverse from .components.convnext_inverse import do_convnext_inverse
from .components.LayerStack2Image import LayerStack2Image from .components.LayerStack2Image import LayerStack2Image
from .components.LossCrossentropy import LossCrossentropy
def model_rainfallwater_mono(metadata, shape_water_out, model_arch_enc="convnext_xtiny", model_arch_dec="convnext_i_xtiny", feature_dim=512, batch_size=64, water_bins=2): def model_rainfallwater_mono(metadata, shape_water_out, model_arch_enc="convnext_xtiny", model_arch_dec="convnext_i_xtiny", feature_dim=512, batch_size=64, water_bins=2):
"""Makes a new rainfall / waterdepth mono model. """Makes a new rainfall / waterdepth mono model.
@ -71,7 +72,8 @@ def model_rainfallwater_mono(metadata, shape_water_out, model_arch_enc="convnext
model.compile( model.compile(
optimizer="Adam", optimizer="Adam",
loss=tf.keras.losses.CategoricalCrossentropy(), loss=LossCrossentropy(batch_size=batch_size),
# loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=[tf.keras.metrics.CategoricalAccuracy()] metrics=[tf.keras.metrics.CategoricalAccuracy()]
) )