mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-09-17 20:45:58 +00:00
44 lines
1.9 KiB
Python
44 lines
1.9 KiB
Python
import tensorflow as tf
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class LossContrastive(tf.keras.losses.Loss):
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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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self.weight_temperature = weight_temperature
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def call(self, y_true, y_pred):
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rainfall, water = tf.unstack(y_pred, axis=-2)
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# print("LOSS:call y_true", y_true.shape)
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# print("LOSS:call y_pred", y_pred.shape)
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# print("BEFORE_RESHAPE rainfall", rainfall)
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# print("BEFORE_RESHAPE water", water)
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# # Ensure the shapes are defined
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# rainfall = tf.reshape(rainfall, [self.batch_size, rainfall.shape[1]])
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# water = tf.reshape(water, [self.batch_size, water.shape[1]])
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# normalise features
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rainfall = rainfall / tf.math.l2_normalize(rainfall, axis=1)
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water = water / tf.math.l2_normalize(water, axis=1)
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# logits = tf.linalg.matmul(rainfall, tf.transpose(water)) * tf.clip_by_value(tf.math.exp(self.weight_temperature), 0, 100)
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logits = tf.linalg.matmul(rainfall, tf.transpose(water)) * tf.math.exp(self.weight_temperature)
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# print("LOGITS", logits)
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# labels = tf.eye(self.batch_size, dtype=tf.int32) # we *would* do this if we were using mean squared error...
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labels = tf.range(self.batch_size, dtype=tf.int32) # each row is a different category we think
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loss_rainfall = tf.keras.metrics.sparse_categorical_crossentropy(labels, logits, from_logits=True, axis=0)
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loss_water = tf.keras.metrics.sparse_categorical_crossentropy(labels, logits, from_logits=True, axis=1)
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# loss_rainfall = tf.keras.metrics.binary_crossentropy(labels, logits, from_logits=True, axis=0)
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# loss_water = tf.keras.metrics.binary_crossentropy(labels, logits, from_logits=True, axis=1)
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loss = (loss_rainfall + loss_water) / 2
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# cosine_similarity results in tensor of range -1 - 1, but tf.sparse.eye has range 0 - 1
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# print("LABELS", labels)
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# print("LOSS_rainfall", loss_rainfall)
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# print("LOSS_water", loss_water)
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# print("LOSS", loss)
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return loss
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