def __init__(self, hidden_size, stddev=None):
'''
hidden_size: int,
'''
self.stddev = stddev
self.hidden_size = hidden_size
self.W = tf.Variable(Randomer.random_normal(
[self.hidden_size, self.hidden_size]),
trainable=True)
self.U = tf.Variable(Randomer.random_normal(
[self.hidden_size, self.hidden_size]),
trainable=True)
self.b = tf.Variable(tf.zeros([1]), trainable=True)
def __init__(self, w_shape, stddev=None, params=None):
'''
:param w_shape: [input_dim, output_dim]
:param stddev: 用于初始化
:param params: 从外界制定参数
'''
if params is None:
self.w = tf.Variable(Randomer.random_normal(w_shape),
trainable=True)
else:
self.w = params['w']
def __init__(self, edim, class_num, stddev=None, params=None):
'''
class_num: class type num.
edim: the input embedding dim.
params = {'wline': wline, 'bline': bline}
'''
self.edim = edim
self.class_num = class_num
# the linear basic_layer for softmax.
if params is None:
self.wline_softmax = tf.Variable(Randomer.random_normal(
[self.edim, self.class_num]),
trainable=True)
self.bline_softmax = tf.Variable(tf.zeros([1, 1]), trainable=True)
else:
self.wline_softmax = params['wline']
self.bline_softmax = params['bline']
def __init__(self, w_shape=None, stddev=None, params=None, active='tanh'):
'''
the initialize function.
w_shape is the shape of the w param. if params is None, need.
staddev is the stddev of the tf.random_normal. if params is None, need.
params = {'wline':wline}, is use to assign the params.
active is the active function.
'''
self.w_shape = w_shape
self.stddev = stddev
if params is None:
self.wline = tf.Variable(
Randomer.random_normal(self.w_shape),
# tf.random_uniform(self.w_shape, -0.0015, 0.035),
trainable=True)
else:
self.wline = params['wline']
self.active = active