def dense(x,
size,
name,
weight_init=None,
bias_init=0,
weight_loss_dict=None,
reuse=None):
with tf.variable_scope(name, reuse=reuse):
assert (len(U.scope_name().split('/')) == 2)
w = tf.get_variable("w", [x.get_shape()[1], size],
initializer=weight_init)
b = tf.get_variable("b", [size],
initializer=tf.constant_initializer(bias_init))
weight_decay_fc = 3e-4
if weight_loss_dict is not None:
weight_decay = tf.multiply(tf.nn.l2_loss(w),
weight_decay_fc,
name='weight_decay_loss')
if weight_loss_dict is not None:
weight_loss_dict[w] = weight_decay_fc
weight_loss_dict[b] = 0.0
tf.add_to_collection(U.scope_name().split('/')[0] + '_' + 'losses',
weight_decay)
return tf.nn.bias_add(tf.matmul(x, w), b)
def dense(x, size, name, weight_init=None, bias_init=0, weight_loss_dict=None, reuse=None):
with tf.variable_scope(name, reuse=reuse):
assert (len(U.scope_name().split('/')) == 2)
w = tf.get_variable("w", [x.get_shape()[1], size], initializer=weight_init)
b = tf.get_variable("b", [size], initializer=tf.constant_initializer(bias_init))
weight_decay_fc = 3e-4
if weight_loss_dict is not None:
weight_decay = tf.multiply(tf.nn.l2_loss(w), weight_decay_fc, name='weight_decay_loss')
if weight_loss_dict is not None:
weight_loss_dict[w] = weight_decay_fc
weight_loss_dict[b] = 0.0
tf.add_to_collection(U.scope_name().split('/')[0] + '_' + 'losses', weight_decay)
return tf.nn.bias_add(tf.matmul(x, w), b)