DenoisingAutoencoder.py 文件源码

def __init__(self, n_input, n_hidden, transfer_function=tf.nn.softplus, optimizer=tf.train.AdamOptimizer(),
                 dropout_probability=0.95):
        self.n_input = n_input
        self.n_hidden = n_hidden
        self.transfer = transfer_function
        self.dropout_probability = dropout_probability
        self.keep_prob = tf.placeholder(tf.float32)

        network_weights = self._initialize_weights()
        self.weights = network_weights

        # model
        self.x = tf.placeholder(tf.float32, [None, self.n_input])
        self.hidden = self.transfer(tf.add(tf.matmul(tf.nn.dropout(self.x, self.keep_prob), self.weights['w1']),
                                           self.weights['b1']))
        self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']), self.weights['b2'])

        # cost
        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0))
        self.optimizer = optimizer.minimize(self.cost)

        init = tf.global_variables_initializer()
        self.sess = tf.Session()
        self.sess.run(init)