def __init__(self, lr=0.01, epsilon=1e-8, decay=0., **kwargs): super(Adagrad, self).__init__(**kwargs) self.lr = K.variable(lr, name='lr') self.epsilon = epsilon self.decay = K.variable(decay, name='decay') self.initial_decay = decay self.iterations = K.variable(0., name='iterations')
def __init__(self, lr=0.01, epsilon=1e-8, decay=0., **kwargs): super(Adagrad, self).__init__(**kwargs) self.lr = K.variable(lr, name='lr') self.epsilon = epsilon self.decay = K.variable(decay, name='decay') self.initial_decay = decay self.iterations = K.variable(0., name='iterations')
def __init__(self, lr=1.0, rho=0.95, epsilon=1e-8, decay=0., **kwargs): super(Adadelta, self).__init__(**kwargs) self.lr = K.variable(lr, name='lr') self.rho = rho self.epsilon = epsilon self.decay = K.variable(decay, name='decay') self.initial_decay = decay self.iterations = K.variable(0., name='iterations')
def __init__(self, lr=0.001, rho=0.9, epsilon=1e-8, decay=0., **kwargs): super(RMSprop, self).__init__(**kwargs) self.lr = K.variable(lr, name='lr') self.rho = K.variable(rho, name='rho') self.epsilon = epsilon self.decay = K.variable(decay, name='decay') self.initial_decay = decay self.iterations = K.variable(0., name='iterations')
def __init__(self, lr=0.01, momentum=0., decay=0., nesterov=False, **kwargs): super(SGD, self).__init__(**kwargs) self.iterations = K.variable(0., name='iterations') self.lr = K.variable(lr, name='lr') self.momentum = K.variable(momentum, name='momentum') self.decay = K.variable(decay, name='decay') self.initial_decay = decay self.nesterov = nesterov
def __init__(self, lr=0.01, momentum=0., decay=0., nesterov=False, **kwargs): super(SGD, self).__init__(**kwargs) self.iterations = K.variable(0., name='iterations') self.lr = K.variable(lr, name='lr') self.momentum = K.variable(momentum, name='momentum') self.decay = K.variable(decay, name='decay') self.initial_decay = decay self.nesterov = nesterov
def __init__(self, lr=1.0, rho=0.95, epsilon=1e-8, decay=0., **kwargs): super(Adadelta, self).__init__(**kwargs) self.lr = K.variable(lr, name='lr') self.rho = rho self.epsilon = epsilon self.decay = K.variable(decay, name='decay') self.initial_decay = decay self.iterations = K.variable(0., name='iterations')
def __init__(self, lr=0.01, epsilon=1e-8, decay=0., **kwargs): super(Adagrad, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.lr = K.variable(lr, name='lr') self.decay = K.variable(decay, name='decay') self.iterations = K.variable(0, dtype='int64', name='iterations') self.epsilon = epsilon self.initial_decay = decay
def __init__(self, lr=0.001, rho=0.9, epsilon=1e-8, decay=0., **kwargs): super(RMSprop, self).__init__(**kwargs) self.lr = K.variable(lr, name='lr') self.rho = K.variable(rho, name='rho') self.epsilon = epsilon self.decay = K.variable(decay, name='decay') self.initial_decay = decay self.iterations = K.variable(0., name='iterations')
def __init__(self, lr=0.01, momentum=0., decay=0., nesterov=False, **kwargs): super(SGD, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.lr = K.variable(lr, name='lr') self.momentum = K.variable(momentum, name='momentum') self.decay = K.variable(decay, name='decay') self.initial_decay = decay self.nesterov = nesterov
def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-8, schedule_decay=0.004, **kwargs): super(Nadam, self).__init__(**kwargs) self.iterations = K.variable(0., name='iterations') self.m_schedule = K.variable(1., name='m_schedule') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.epsilon = epsilon self.schedule_decay = schedule_decay
def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=0., **kwargs): super(Adamax, self).__init__(**kwargs) self.iterations = K.variable(0., name='iterations') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.epsilon = epsilon self.decay = K.variable(decay, name='decay') self.initial_decay = decay
def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=0., **kwargs): super(Adamax, self).__init__(**kwargs) self.iterations = K.variable(0., name='iterations') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.epsilon = epsilon self.decay = K.variable(decay, name='decay') self.initial_decay = decay
def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-8, schedule_decay=0.004, **kwargs): super(Nadam, self).__init__(**kwargs) self.iterations = K.variable(0., name='iterations') self.m_schedule = K.variable(1., name='m_schedule') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.epsilon = epsilon self.schedule_decay = schedule_decay
def yolo_eval(yolo_outputs, image_shape, max_boxes=10, score_threshold=.6, iou_threshold=.5): """Evaluate YOLO model on given input batch and return filtered boxes.""" box_xy, box_wh, box_confidence, box_class_probs = yolo_outputs boxes = yolo_boxes_to_corners(box_xy, box_wh) boxes, scores, classes = yolo_filter_boxes(boxes, box_confidence, box_class_probs, threshold=score_threshold) # Scale boxes back to original image shape. height = image_shape[0] width = image_shape[1] image_dims = K.stack([height, width, height, width]) image_dims = K.reshape(image_dims, [1, 4]) boxes = boxes * image_dims # TODO: Something must be done about this ugly hack! max_boxes_tensor = K.variable(max_boxes, dtype='int32') K.get_session().run(tf.variables_initializer([max_boxes_tensor])) nms_index = tf.image.non_max_suppression(boxes, scores, max_boxes_tensor, iou_threshold=iou_threshold) boxes = K.gather(boxes, nms_index) scores = K.gather(scores, nms_index) classes = K.gather(classes, nms_index) return boxes, scores, classes
def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-8, schedule_decay=0.004, **kwargs): super(Nadam, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.m_schedule = K.variable(1., name='m_schedule') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.epsilon = epsilon self.schedule_decay = schedule_decay
def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=0., **kwargs): super(Adamax, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.decay = K.variable(decay, name='decay') self.epsilon = epsilon self.initial_decay = decay
def call(self, x, mask=None): input_shape = K.int_shape(x) layer_width = input_shape[self.waxis] layer_height = input_shape[self.haxis] img_width = self.img_size[0] img_height = self.img_size[1] # define prior boxes shapes box_widths = [] box_heights = [] for ar in self.aspect_ratios: if ar == 1 and len(box_widths) == 0: box_widths.append(self.min_size) box_heights.append(self.min_size) elif ar == 1 and len(box_widths) > 0: box_widths.append(np.sqrt(self.min_size * self.max_size)) box_heights.append(np.sqrt(self.min_size * self.max_size)) elif ar != 1: box_widths.append(self.min_size * np.sqrt(ar)) box_heights.append(self.min_size / np.sqrt(ar)) box_widths = 0.5 * np.array(box_widths) box_heights = 0.5 * np.array(box_heights) # define centers of prior boxes step_x = img_width / layer_width step_y = img_height / layer_height linx = np.linspace(0.5 * step_x, img_width - 0.5 * step_x, layer_width) liny = np.linspace(0.5 * step_y, img_height - 0.5 * step_y, layer_height) centers_x, centers_y = np.meshgrid(linx, liny) centers_x = centers_x.reshape(-1, 1) centers_y = centers_y.reshape(-1, 1) # define xmin, ymin, xmax, ymax of prior boxes num_priors_ = len(self.aspect_ratios) prior_boxes = np.concatenate((centers_x, centers_y), axis=1) prior_boxes = np.tile(prior_boxes, (1, 2 * num_priors_)) prior_boxes[:, ::4] -= box_widths prior_boxes[:, 1::4] -= box_heights prior_boxes[:, 2::4] += box_widths prior_boxes[:, 3::4] += box_heights prior_boxes[:, ::2] /= img_width prior_boxes[:, 1::2] /= img_height prior_boxes = prior_boxes.reshape(-1, 4) if self.clip: prior_boxes = np.minimum(np.maximum(prior_boxes, 0.0), 1.0) # define variances num_boxes = len(prior_boxes) if len(self.variances) == 1: variances = np.ones((num_boxes, 4)) * self.variances[0] elif len(self.variances) == 4: variances = np.tile(self.variances, (num_boxes, 1)) else: raise Exception('Must provide one or four variances.') prior_boxes = np.concatenate((prior_boxes, variances), axis=1) prior_boxes_tensor = K.expand_dims(K.variable(prior_boxes), 0) pattern = [tf.shape(x)[0], 1, 1] prior_boxes_tensor = tf.tile(prior_boxes_tensor, pattern) return prior_boxes_tensor
def __init__(self, optimizer): # pylint: disable=super-init-not-called self.optimizer = optimizer self.iterations = K.variable(0., name='iterations') self.updates = []
def build(self, input_shape): self.input_spec = [InputSpec(shape=input_shape)] shape = (input_shape[self.axis], ) init_gamma = self.scale * np.ones(shape) self.gamma = K.variable(init_gamma, name='{}_gamma'.format(self.name)) self.trainable_weights = [self.gamma]
def __init__(self, optimizer): # pylint: disable=super-init-not-called self.optimizer = optimizer self.iterations = K.variable(0., name='iterations') self.updates = []
def __init__(self, optimizer): # pylint: disable=super-init-not-called self.optimizer = optimizer with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations')