conv_encoder_utils.py 文件源码

def linear_mapping_weightnorm(inputs, out_dim, in_dim=None, dropout=1.0, var_scope_name="linear_mapping"):
  with tf.variable_scope(var_scope_name):
    input_shape = inputs.get_shape().as_list()    # static shape. may has None
    input_shape_tensor = tf.shape(inputs)    
    # use weight normalization (Salimans & Kingma, 2016)  w = g* v/2-norm(v)
    V = tf.get_variable('V', shape=[int(input_shape[-1]), out_dim], dtype=tf.float32, initializer=tf.random_normal_initializer(mean=0, stddev=tf.sqrt(dropout*1.0/int(input_shape[-1]))), trainable=True)
    V_norm = tf.norm(V.initialized_value(), axis=0)  # V shape is M*N,  V_norm shape is N
    g = tf.get_variable('g', dtype=tf.float32, initializer=V_norm, trainable=True)
    b = tf.get_variable('b', shape=[out_dim], dtype=tf.float32, initializer=tf.zeros_initializer(), trainable=True)   # weightnorm bias is init zero

    assert len(input_shape) == 3
    inputs = tf.reshape(inputs, [-1, input_shape[-1]])
    inputs = tf.matmul(inputs, V)
    inputs = tf.reshape(inputs, [input_shape_tensor[0], -1, out_dim])
    #inputs = tf.matmul(inputs, V)    # x*v

    scaler = tf.div(g, tf.norm(V, axis=0))   # g/2-norm(v)
    inputs = tf.reshape(scaler,[1, out_dim])*inputs + tf.reshape(b,[1, out_dim])   # x*v g/2-norm(v) + b


    return inputs