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Masked loss and metric classes/functions for Tensorflow 2 (Keras)
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import tensorflow as tf | |
import tensorflow.keras.metrics as tfkm | |
from tensorflow.keras import backend as K | |
MASKED_VALUE = -1 | |
def get_1d_mask(y_true, masked_value): | |
"""Get 1D mask by comparing y_true with masked_value. | |
By using `reduce_any`, it masks any item that has more than one y_true that is equal to `MASKED_VALUE`. | |
""" | |
mask_2d = K.not_equal(y_true, masked_value) # (batch, n_class) | |
return K.cast_to_floatx(tf.math.reduce_any(mask_2d, axis=1)) # (batch, ) | |
# losses | |
def masked_binary_crossentropy(y_true, y_pred): | |
mask = K.cast_to_floatx(K.not_equal(y_true, MASKED_VALUE)) | |
return K.binary_crossentropy(y_true * mask, y_pred * mask) | |
def masked_categorical_crossentropy(y_true, y_pred): | |
mask = K.cast_to_floatx(K.not_equal(y_true, MASKED_VALUE)) | |
return K.categorical_crossnetropy(y_true * mask, y_pred * mask) | |
# metrics - when there are parent classes | |
class MaskedRecall(tfkm.Recall): | |
def update_state(self, y_true, y_pred, sample_weight=None): | |
mask = get_1d_mask(y_true, MASKED_VALUE) | |
return super().update_state( | |
tf.boolean_mask(y_true, y_mask), | |
tf.boolean_mask(y_pred, mask), | |
sample_weight, | |
) | |
class MaskedPrecision(tfkm.Precision): | |
def update_state(self, y_true, y_pred, sample_weight=None): | |
mask = get_1d_mask(y_true, MASKED_VALUE) | |
return super().update_state( | |
tf.boolean_mask(y_true, y_mask), | |
tf.boolean_mask(y_pred, mask), | |
sample_weight, | |
) | |
class MaskedAUC(tfkm.AUC): | |
def update_state(self, y_true, y_pred, sample_weight=None): | |
mask = get_1d_mask(y_true, MASKED_VALUE) | |
return super().update_state( | |
tf.boolean_mask(y_true, y_mask), | |
tf.boolean_mask(y_pred, mask), | |
sample_weight, | |
) | |
# Customized metric | |
class MaskedCategoricalAccuracy(tfkm.Metric): | |
def __init__(self, name="masked_categorical_accuracy", **kwargs): | |
super(MaskedCategoricalAccuracy, self).__init__(name=name, **kwargs) | |
self.n_corrects = self.add_weight(name="n_corrects", initializer="zeros") | |
self.n_items = self.add_weight(name="n_items", initializer="zeros") | |
def update_state(self, y_true, y_pred, sample_weight=None): | |
# Note: this implementation ignores sample_weight | |
mask = get_1d_mask(y_true, MASKED_VALUE) # (batch, n_class) | |
y_true = tf.boolean_mask(y_true, mask) # (n_items, n_class) | |
y_pred = tf.boolean_mask(y_pred, mask) | |
n_item = K.int_shape(y_true)[0] | |
if n_item in (0, None): | |
return | |
if_correct = K.equal(K.argmax(y_true, axis=1), K.argmax(y_pred, axis=1)) | |
self.n_items.assign_add(K.cast_to_float(n_item)) | |
self.n_corrects.assign_add(K.sum(K.cast_to_floatx(if_correct))) | |
def result(self): | |
if self.n_items == 0.0: | |
return 0.0 | |
return self.n_corrects / self.n_items | |
def reset_states(self): | |
self.n_corrects.assign(0.0) | |
self.n_items.assign(0.0) |
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