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https://github.com/sbrl/research-rainfallradar
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argmax for sensitivity & specificity too
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8470aec996
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3 changed files with 9 additions and 6 deletions
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@ -15,9 +15,6 @@ def dice_coefficient(y_true, y_pred):
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tf.Tensor: The computed Dice coefficient.
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tf.Tensor: The computed Dice coefficient.
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"""
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"""
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y_true = tf.cast(y_true, dtype=tf.float32)
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y_pred = tf.cast(y_pred, dtype=tf.float32)
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y_pred = tf.math.sigmoid(y_pred)
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y_pred = tf.math.sigmoid(y_pred)
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numerator = 2 * tf.reduce_sum(y_true * y_pred)
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numerator = 2 * tf.reduce_sum(y_true * y_pred)
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denominator = tf.reduce_sum(y_true + y_pred)
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denominator = tf.reduce_sum(y_true + y_pred)
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@ -26,5 +23,8 @@ def dice_coefficient(y_true, y_pred):
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def metric_dice_coefficient(y_true, y_pred):
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def metric_dice_coefficient(y_true, y_pred):
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y_true = tf.cast(y_true, dtype=tf.float32)
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y_pred = tf.cast(y_pred, dtype=tf.float32)
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y_pred = tf.math.argmax(y_pred, axis=-1)
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y_pred = tf.math.argmax(y_pred, axis=-1)
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return dice_coefficient(y_true, y_pred)
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return dice_coefficient(y_true, y_pred)
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@ -3,8 +3,10 @@ import math
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import tensorflow as tf
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import tensorflow as tf
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def sensitivity(y_true, y_pred):
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def sensitivity(y_true, y_pred):
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ground_truth = tf.cast(y_true, dtype=tf.float32)
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y_true = tf.cast(y_true, dtype=tf.float32)
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prediction = tf.cast(y_pred, dtype=tf.float32)
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y_pred = tf.cast(y_pred, dtype=tf.float32)
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y_pred = tf.math.argmax(y_pred, axis=-1)
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recall = tf.keras.metrics.Recall()
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recall = tf.keras.metrics.Recall()
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recall.update_state(y_true, y_pred)
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recall.update_state(y_true, y_pred)
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@ -13,10 +13,11 @@ def specificity(y_pred, y_true):
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Returns:
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Returns:
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Specificity score
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Specificity score
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"""
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"""
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y_true = tf.cast(y_true, dtype=tf.float32)
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y_true = tf.cast(y_true, dtype=tf.float32)
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y_pred = tf.cast(y_pred, dtype=tf.float32)
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y_pred = tf.cast(y_pred, dtype=tf.float32)
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y_pred = tf.math.argmax(y_pred, axis=-1)
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neg_y_true = 1 - y_true
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neg_y_true = 1 - y_true
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neg_y_pred = 1 - y_pred
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neg_y_pred = 1 - y_pred
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fp = K.sum(neg_y_true * y_pred)
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fp = K.sum(neg_y_true * y_pred)
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