basic_autoencoder.py 文件源码

def __init__(self, input_dim, hidden_dim, epoch=250, learning_rate = 0.001):
        self.epoch = epoch
        self.learning_rate = learning_rate

        x = tf.placeholder(dtype=tf.float32, shape=[None, input_dim])

        with tf.name_scope("encode"):
            weights = tf.Variable(tf.random_normal([input_dim, hidden_dim], dtype=tf.float32), name="weights")
            biases = tf.Variable(tf.zeros([hidden_dim]), name="biases")
            encoded = tf.nn.tanh(tf.matmul(x, weights) + biases)

            with tf.name_scope("decode"):
                weights = tf.Variable(tf.random_normal([hidden_dim, input_dim], dtype=tf.float32), name="weights")
                biases = tf.Variable(tf.zeros([input_dim]), name="biases")
                decoded = tf.matmul(encoded, weights) + biases

        self.x = x
        self.encoded = encoded
        self.decoded = decoded

        self.loss = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(self.x, self.decoded))))
        self.train_op = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
        self.saver = tf.train.Saver()