def declare_parameters(self):
opts = self.opts
with tf.variable_scope('params') as scope:
self.L_init = tf.get_variable(name="L_init",
initializer=tf.random_normal(
[1, self.opts.d]))
self.C_init = tf.get_variable(name="C_init",
initializer=tf.random_normal(
[1, self.opts.d]))
self.LC_msg = MLP(opts,
opts.d,
repeat_end(opts.d, opts.n_msg_layers, opts.d),
name=("LC_msg"))
self.CL_msg = MLP(opts,
opts.d,
repeat_end(opts.d, opts.n_msg_layers, opts.d),
name=("CL_msg"))
self.L_update = tf.contrib.rnn.LayerNormBasicLSTMCell(
self.opts.d,
activation=decode_transfer_fn(opts.lstm_transfer_fn))
self.C_update = tf.contrib.rnn.LayerNormBasicLSTMCell(
self.opts.d,
activation=decode_transfer_fn(opts.lstm_transfer_fn))
self.L_vote = MLP(opts,
opts.d,
repeat_end(opts.d, opts.n_vote_layers, 1),
name=("L_vote"))
self.vote_bias = tf.get_variable(
name="vote_bias", shape=[], initializer=tf.zeros_initializer())
def __init__(self, opts, d_in, d_outs, name):
(self.ws, self.bs) = init_ws_bs(opts, name, d_in, d_outs)
self.opts = opts
self.name = name
self.transfer_fn = decode_transfer_fn(opts.mlp_transfer_fn)
self.output_size = d_outs[-1]
