Question

我正在使用下面的代码来实现自动编码器。如何为自动编码器提供数据以进行培训和测试？

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

class Autoencoder(object):     
def __init__(self, n_input, n_hidden, transfer_function=tf.nn.softplus, optimizer = tf.train.AdamOptimizer()):
    self.n_input = n_input
    self.n_hidden = n_hidden
    self.transfer = transfer_function

    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(self.x, 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)

def _initialize_weights(self):
    all_weights = dict()
    all_weights['w1'] = tf.get_variable("w1", shape=[self.n_input, self.n_hidden],
        initializer=tf.contrib.layers.xavier_initializer())
    all_weights['b1'] = tf.Variable(tf.zeros([self.n_hidden], dtype=tf.float32))
    all_weights['w2'] = tf.Variable(tf.zeros([self.n_hidden, self.n_input], dtype=tf.float32))
    all_weights['b2'] = tf.Variable(tf.zeros([self.n_input], dtype=tf.float32))
    return all_weights

def partial_fit(self, X):
    cost, opt = self.sess.run((self.cost, self.optimizer), feed_dict={self.x: X})
    return cost

def calc_total_cost(self, X):
    return self.sess.run(self.cost, feed_dict = {self.x: X})

def transform(self, X):
    return self.sess.run(self.hidden, feed_dict={self.x: X})

def generate(self, hidden = None):
    if hidden is None:
        hidden = self.sess.run(tf.random_normal([1, self.n_hidden]))
    return self.sess.run(self.reconstruction, feed_dict={self.hidden: hidden})

def reconstruct(self, X):
    return self.sess.run(self.reconstruction, feed_dict={self.x: X})

def getWeights(self):
    return self.sess.run(self.weights['w1'])

def getBiases(self):
    return self.sess.run(self.weights['b1'])

# I instantiate the class autoencoder, 5 is the dimension of a raw input, 
2 is the dimension of the hidden layer   

autoencoder = Autoencoder(5, 2, transfer_function=tf.nn.softplus, optimizer 
= tf.train.AdamOptimizer())  

# I prepare my data**
IRIS_TRAINING = "C:\\Users\\Desktop\\iris_training.csv"

#Feeding data to Autoencoder ???
Train and Test ??

如何使用csv文件数据训练此模型？我想我需要在一个时代循环中运行以下指令_, c = sess.run([optimizer, cost], feed_dict={self.x: batch_ofd_ata})，但我正在努力解决它。

Answer 1

查看Stanford CS20SI的教程。

https://github.com/chiphuyen/tf-stanford-tutorials/blob/master/examples/05_csv_reader.py

如何在自动编码器中输入csv数据

1 个答案: