research-rainfallradar/aimodel/src/lib/ai/components/LossContrastive.py

44 lines
1.9 KiB
Python
Raw Normal View History

import tensorflow as tf
class LossContrastive(tf.keras.losses.Loss):
def __init__(self, weight_temperature, batch_size):
super(LossContrastive, self).__init__()
self.batch_size = batch_size
self.weight_temperature = weight_temperature
def call(self, y_true, y_pred):
rainfall, water = tf.unstack(y_pred, axis=-2)
# print("LOSS:call y_true", y_true.shape)
# print("LOSS:call y_pred", y_pred.shape)
# print("BEFORE_RESHAPE rainfall", rainfall)
# print("BEFORE_RESHAPE water", water)
# # Ensure the shapes are defined
# rainfall = tf.reshape(rainfall, [self.batch_size, rainfall.shape[1]])
# water = tf.reshape(water, [self.batch_size, water.shape[1]])
# normalise features
rainfall = rainfall / tf.math.l2_normalize(rainfall, axis=1)
2022-10-31 17:18:21 +00:00
water = water / tf.math.l2_normalize(water, axis=1)
# logits = tf.linalg.matmul(rainfall, tf.transpose(water)) * tf.clip_by_value(tf.math.exp(self.weight_temperature), 0, 100)
logits = tf.linalg.matmul(rainfall, tf.transpose(water)) * tf.math.exp(self.weight_temperature)
# print("LOGITS", logits)
# labels = tf.eye(self.batch_size, dtype=tf.int32) # we *would* do this if we were using mean squared error...
labels = tf.range(self.batch_size, dtype=tf.int32) # each row is a different category we think
loss_rainfall = tf.keras.metrics.sparse_categorical_crossentropy(labels, logits, from_logits=True, axis=0)
loss_water = tf.keras.metrics.sparse_categorical_crossentropy(labels, logits, from_logits=True, axis=1)
# loss_rainfall = tf.keras.metrics.binary_crossentropy(labels, logits, from_logits=True, axis=0)
# loss_water = tf.keras.metrics.binary_crossentropy(labels, logits, from_logits=True, axis=1)
loss = (loss_rainfall + loss_water) / 2
# cosine_similarity results in tensor of range -1 - 1, but tf.sparse.eye has range 0 - 1
# print("LABELS", labels)
# print("LOSS_rainfall", loss_rainfall)
# print("LOSS_water", loss_water)
# print("LOSS", loss)
return loss