如何在自動編碼器中輸入csv數據

我正在使用下面的代碼來實現自動編碼器。如何爲自編碼器提供用於培訓和測試的數據？如何在自動編碼器中輸入csv數據

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文件數據來訓練此模型？我認爲我需要在一個時代循環內運行以下指令_, c = sess.run([optimizer, cost], feed_dict={self.x: batch_ofd_ata})，但我正在努力。

來源

2017-04-22 hza

如何在自動編碼器中輸入csv數據

回答

相關問題