weights = _variable_with_weight_decay('weights', shape=[dim, 384],
                                      stddev=0.1, wd=0.0)
biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.001))

wx = tf.matmul(reshape, weights)
biases_mul = tf.multiply(biases,0.001)

wx_biases = tf.add(wx,biases_mul)

w_square= tf.square(weights)
w_sum = tf.reduce_sum(w_square,0)
biases_square = tf.square(biases)
w_biases = tf.add(w_sum,biases_square)
w_norm = tf.sqrt(w_biases)

x_square = tf.square(reshape)
x_sum = tf.reduce_sum(x_square,1,keep_dims=True)
biases_send_square = tf.square(0.001)
x_biases = tf.add(x_sum, biases_send_square)
x_norm = tf.sqrt(x_biases)

wx_norm_w = tf.div(wx_biases,w_norm)
wx_norm = tf.div(wx_norm_w,x_norm)