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train_mono: debug
this commit will generate a large amount of debug output.
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3 changed files with 49 additions and 2 deletions
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@ -2,7 +2,14 @@ import math
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import tensorflow as tf
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class LossContrastive(tf.keras.losses.Loss):
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"""Implements a contrastive loss function.
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@warning: This does not function as it should.
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Args:
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weight_temperature (integer): The temperature weight (e.g. from LayerCheeseMultipleOut).
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batch_size (integer): The batch size.
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"""
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def __init__(self, weight_temperature, batch_size):
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super(LossContrastive, self).__init__()
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self.batch_size = batch_size
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38
aimodel/src/lib/ai/components/LossCrossentropy.py
Normal file
38
aimodel/src/lib/ai/components/LossCrossentropy.py
Normal file
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@ -0,0 +1,38 @@
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import math
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import tensorflow as tf
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class LossCrossentropy(tf.keras.losses.Loss):
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"""Wraps the cross-entropy loss function because it's buggy.
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@warning: tf.keras.losses.CategoricalCrossentropy() isn't functioning as intended during training...
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Args:
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batch_size (integer): The batch size (currently unused).
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"""
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def __init__(self, batch_size):
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super(LossCrossentropy, self).__init__()
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self.param_batch_size = batch_size
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def call(self, y_true, y_pred):
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result = tf.keras.metrics.categorical_crossentropy(y_true, y_pred)
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result_reduce = tf.math.reduce_sum(result)
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tf.print("DEBUG:TFPRINT:loss BEFORE_REDUCE", result, "AFTER_REDUCE", result_reduce)
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return result
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def get_config(self):
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config = super(LossCrossentropy, self).get_config()
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config.update({
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"batch_size": self.param_batch_size,
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})
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return config
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if __name__ == "__main__":
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weight_temperature = tf.Variable(name="loss_temperature", shape=1, initial_value=tf.constant([
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math.log(1 / 0.07)
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]))
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loss = LossCrossentropy(weight_temperature=weight_temperature, batch_size=64)
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tensor_input = tf.random.uniform([64, 2, 512])
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print(loss(tf.constant(1), tensor_input))
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@ -6,6 +6,7 @@ import tensorflow as tf
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from .components.convnext import make_convnext
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from .components.convnext_inverse import do_convnext_inverse
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from .components.LayerStack2Image import LayerStack2Image
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from .components.LossCrossentropy import LossCrossentropy
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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):
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"""Makes a new rainfall / waterdepth mono model.
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@ -71,8 +72,9 @@ def model_rainfallwater_mono(metadata, shape_water_out, model_arch_enc="convnext
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model.compile(
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optimizer="Adam",
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loss=tf.keras.losses.CategoricalCrossentropy(),
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loss=LossCrossentropy(batch_size=batch_size),
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# loss=tf.keras.losses.CategoricalCrossentropy(),
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metrics=[tf.keras.metrics.CategoricalAccuracy()]
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)
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return model
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return model
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