def yolo_filter_boxes(boxes, box_confidence, box_class_probs, threshold=.6):
"""Filter YOLO boxes based on object and class confidence."""
box_scores = box_confidence * box_class_probs
box_classes = K.argmax(box_scores, axis=-1)
box_class_scores = K.max(box_scores, axis=-1)
prediction_mask = box_class_scores >= threshold
# TODO: Expose tf.boolean_mask to Keras backend?
boxes = tf.boolean_mask(boxes, prediction_mask)
scores = tf.boolean_mask(box_class_scores, prediction_mask)
classes = tf.boolean_mask(box_classes, prediction_mask)
return boxes, scores, classes
def top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), axis=-1)
def sparse_categorical_accuracy(y_true, y_pred):
return K.cast(
K.equal(K.max(y_true, axis=-1),
K.cast(K.argmax(y_pred, axis=-1), K.floatx())), K.floatx())
def top_k_categorical_accuracy(y_true, y_pred, k=5): return K.mean(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), axis=-1)
def sparse_categorical_accuracy(y_true, y_pred):
return K.equal(
K.max(y_true, axis=-1), K.cast(K.argmax(y_pred, axis=-1), K.floatx()))
def categorical_accuracy(y_true, y_pred): return K.equal(K.argmax(y_true, axis=-1), K.argmax(y_pred, axis=-1))
def categorical_accuracy(y_true, y_pred):
return K.equal(K.argmax(y_true, axis=-1), K.argmax(y_pred, axis=-1))