def __init__(self, x, y, seg_labels, obj_class, keep_dropout, train_phase):
self.logits_class = CNNModels.VGG(x,
keep_dropout,
train_phase,
num_classes=100)
self.loss_class = loss_class(y, self.logits_class)
self.loss_seg = 0
def __init__(self, x, y, seg_labels, obj_class, keep_dropout, train_phase):
self.logits_class, self.logits_seg = CNNModels.VGG(x,
keep_dropout,
train_phase,
num_classes=100,
batch_norm=True,
seg=True,
num_classes_seg=176)
self.loss_class = loss_class(y, self.logits_class)
self.loss_seg = loss_seg(obj_class, self.logits_seg)
def __init__(self, x, y, seg_labels, obj_class, keep_dropout, train_phase):
self.logits_class=CNNModels.alexnet(x, keep_dropout, train_phase)
self.loss_class =loss_class(y,self.logits_class)
self.loss_seg = tf.reduce_mean(self.logits_class)
def __init__(self, x, y, seg_labels, keep_dropout, train_phase):
self.prob_class, self.logits_seg = CNNModels.VGG_SegNet(x, keep_dropout, train_phase, debug=True)
self.loss_seg = loss_seg(seg_labels, self.logits_seg)
one_hot_y = tf.one_hot(y, 100)
self.loss_class = -tf.reduce_mean(tf.reduce_sum(one_hot_y * tf.log(self.prob_class + 1e-8), axis=-1))
self.logits_class = self.prob_class
def __init__(self, x, y, seg_labels, obj_class, keep_dropout, train_phase):
self.logits_class=CNNModels.VGG(x, keep_dropout, train_phase, num_classes=100, batch_norm=True)
self.loss_class =loss_class(y,self.logits_class)
self.loss_seg = tf.reduce_mean(self.logits_class)
def __init__(self, x, y, seg_labels, obj_class, keep_dropout, train_phase):
self.logits_class = CNNModels.VGG16_Simple(x, keep_dropout,
train_phase)
self.loss_class = loss_class(y, logits_class)
self.loss_seg = 0