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 softmax(x):
ndim = K.ndim(x)
if ndim == 2:
return K.softmax(x)
elif ndim == 3:
e = K.exp(x - K.max(x, axis=-1, keepdims=True))
s = K.sum(e, axis=-1, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor '
'that is not 2D or 3D. '
'Here, ndim=' + str(ndim))
def softmax(x, axis=-1):
"""Softmax activation function.
Arguments:
x : Tensor.
axis: Integer, axis along which the softmax normalization is applied.
Returns:
Tensor, output of softmax transformation.
Raises:
ValueError: In case `dim(x) == 1`.
"""
ndim = K.ndim(x)
if ndim == 2:
return K.softmax(x)
elif ndim > 2:
e = K.exp(x - K.max(x, axis=axis, keepdims=True))
s = K.sum(e, axis=axis, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor that is 1D')
def softmax(x, axis=-1):
"""Softmax activation function.
Arguments:
x : Tensor.
axis: Integer, axis along which the softmax normalization is applied.
Returns:
Tensor, output of softmax transformation.
Raises:
ValueError: In case `dim(x) == 1`.
"""
ndim = K.ndim(x)
if ndim == 2:
return K.softmax(x)
elif ndim > 2:
e = K.exp(x - K.max(x, axis=axis, keepdims=True))
s = K.sum(e, axis=axis, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor that is 1D')
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(K.in_top_k(y_pred, K.cast(K.max(y_true, axis=-1), 'int32'),
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 call(self, inputs):
if self.data_format == 'channels_last':
return K.max(inputs, axis=[1, 2, 3])
else:
return K.max(inputs, axis=[2, 3, 4])
def call(self, inputs): return K.max(inputs, axis=1)
def call(self, inputs):
if self.data_format == 'channels_last':
return K.max(inputs, axis=[1, 2, 3])
else:
return K.max(inputs, axis=[2, 3, 4])
def call(self, inputs): return K.max(inputs, 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_hinge(y_true, y_pred):
pos = K.sum(y_true * y_pred, axis=-1)
neg = K.max((1. - y_true) * y_pred, axis=-1)
return K.maximum(neg - pos + 1., 0.)
def categorical_hinge(y_true, y_pred): pos = K.sum(y_true * y_pred, axis=-1) neg = K.max((1. - y_true) * y_pred, axis=-1) return K.maximum(neg - pos + 1., 0.)
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(K.in_top_k(y_pred,
K.cast(K.max(y_true, axis=-1), 'int32'), k), axis=-1)
