mirror of
https://github.com/sbrl/research-rainfallradar
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39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
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, **kwargs):
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super(LossCrossentropy, self).__init__(**kwargs)
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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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# label_nowater = tf.math.reduce_sum(tf.argmax(y_true, axis=-1))
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# tf.print("DEBUG:TFPRINT:loss LABEL", y_true.shape, y_true, "LABEL_ARGMAX_COUNT_AXIS0", label_nowater, "PREDICT", y_pred.shape, y_pred, "BEFORE_REDUCE", result.shape, result, "AFTER_REDUCE", result_reduce.shape, 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)) |