def __init__(self,
source,
target,
beta=tf.placeholder(tf.float32),
keep_prob=tf.placeholder(tf.float32),
dimension=256,
lamda=tf.placeholder(tf.float32),
skip_layer=['fc8'],
n_class=31,
weight_path="bvlc_alexnet.npy"):
self.source = source
self.target = target
self.n_source = tf.shape(source)[0]
self.n_target = tf.shape(target)[0]
self.beta = beta
AlexNet.__init__(self,
tf.concat([self.source, self.target], axis=0),
keep_prob,
skip_layer,
n_class,
weight_path="bvlc_alexnet.npy")
self.rep_dim = dimension
self.lamda = lamda
self.KEEP_PROB_TRAINING = 0.5
self.KEEP_PROB_VALIDATION = 1.0
#self.create()
self.create_block(2)
def __init__(self,
x,
keep_prob,
dimension,
lamda,
decay_l2=1e-4,
model='./checkpoint/model',
skip_layer=['fc8'],
train_layer=['fc8_new', 'fc_adapt'],
n_class=1000,
weight_path="bvlc_alexnet.npy"):
AlexNet.__init__(self,
x,
keep_prob,
skip_layer,
n_class,
weight_path="bvlc_alexnet.npy")
self.rep_dim = dimension
self.model = model
self.lamda = lamda
self.train_layers = train_layer
self.decay_l2 = decay_l2
self.KEEP_PROB_TRAINING = 0.5
self.KEEP_PROB_VALIDATION = 1.0
