tensorflow 实现arelu激活函数(AReLU: Attention-based-Rectified-Linear-Unit)

import tensorflow as tf


class TfModel(object):

    def __init__(self, max_len=10, vocab_size=500, embedding_size=100, alpha=0.90, beta=2.0):
        self.max_len = max_len
        self.vocab_size = vocab_size
        self.embedding_size = embedding_size
        self.alpha = alpha
        self.beta = beta

        self.build_model()

    def build_model(self):
        self.x = tf.placeholder(tf.float32, [None, self.max_len], name="x")
        self.y = tf.placeholder(tf.float32, [None, self.max_len], name="y")

        hidden_output = tf.layers.dense(self.x, units=10)
        # 进入arelu激活函数
        self.alpha_tensor = tf.Variable([self.alpha], dtype=tf.float32)
        self.beta_tensor = tf.Variable([self.beta], dtype=tf.float32)

        self.alpha_tensor = tf.clip_by_value(self.alpha_tensor, clip_value_min=0.01, clip_value_max=0.99)
        self.beta_tensor = 1 + tf.nn.sigmoid(self.beta_tensor)
        hidden_action = tf.nn.relu(hidden_output) * self.beta_tensor - tf.nn.relu(-hidden_output) * self.alpha_tensor
        # arelu激活函数结束
        logits = tf.layers.dense(hidden_action, units=1)

        self.loss = tf.reduce_mean(tf.losses.mean_squared_error(self.y, logits))

        optimizer = tf.train.AdamOptimizer()
        self.train_op = optimizer.minimize(self.loss)



model = TfModel(max_len=1)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    x = [[1], [2], [3]]
    y = [[14], [25], [36]]

    for i in range(1000):

        _, loss = sess.run([model.train_op, model.loss], {model.x: x, model.y: y})
        print("index: {}, loss: {}".format(i, loss))

        print(sess.run([model.alpha_tensor, model.beta_tensor]))

 

本文地址：https://blog.csdn.net/u010626747/article/details/107062960