-tensorflow-/tf_demo4.py at master · devitcn/-tensorflow- · GitHub

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import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#from tensorflow.contrib.factorization.examples.mnist import fill_feed_dict

def xavier_init(fan_in,fan_out,const=1):
    low = -const * np.sqrt(6.0 / (fan_in + fan_out))
    high = const * np.sqrt(6.0 / (fan_in + fan_out))
    return tf.random_uniform((fan_in, fan_out),
                              minval=low,maxval=high,
                              dtype=tf.float32)
class AdditiveGaussianNoiseAutoencoder(object):
    def __init__(self, n_input, n_hidden,transfer_function=tf.nn.softplus,
                 optimizer=tf.train.AdamOptimizer(),scale=0.1):
        self.n_input = n_input
        self.n_hidden = n_hidden
        self.transfer = transfer_function
        self.scale = tf.placeholder(tf.float32)
        self.training_scale = scale
        network_weights = self._initialize_weights()

        self.weights = network_weights

        self.x = tf.placeholder(tf.float32, [None, self.n_input])
        self.n_hidden = self.transfer(tf.add(tf.matmul(
            self.x + scale * tf.random_normal((n_input,)),
            self.weights['w1']),self.weights['b1']))
        self.reconstruction = tf.add(tf.matmul(self.n_hidden,
                                               self.weights['w2']),self.weights['b2'])
        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.Variable(xavier_init(self.n_input,
                                                     self.n_hidden))
        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,self.scale:self.training_scale})
        return cost

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

    def generate(self,hidden=None):
        if hidden is None:
            hidden = np.random.normal(size=self.weight['b1'])
        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,
                                                            self.scale:self.training_scale
                                                            })
    def getWeights(self):
        return self.sess.run(self.weights['w1'])

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

mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)

def standard_scale(X_train,X_test):
    preprocess = prep.StandardScaler().fit(X_train)
    X_train = preprocess.transform(X_train)
    X_test = preprocess.transform(X_test)
    return X_train,X_test

def get_random_block_from_data(data,batch_size):
    start_index = np.random.randint(0,len(data)-batch_size)
    return data[start_index:(start_index+batch_size)]

X_train,X_test = standard_scale(mnist.train.images, mnist.test.images)


n_samples = int(mnist.train.num_examples)
training_epochs = 20
batch_size = 128
display_step = 1


autoencoder = AdditiveGaussianNoiseAutoencoder(n_input=784,
                                               n_hidden=200,
                                               transfer_function=tf.nn.softplus,
                                               optimizer=tf.train.AdamOptimizer(learning_rate=0.001),
                                               scale=0.01)

for epoch in range(training_epochs):
    avg_cost = 0.
    total_batch=int(n_samples / batch_size)
    for i in range(total_batch):
        batch_xs = get_random_block_from_data(X_train, batch_size)
        cost = autoencoder.partial_fit(batch_xs)
        avg_cost += cost / n_samples*batch_size

    if epoch % display_step == 0:
        print("Epoch:",'%04d' %(epoch+1),"cost = ",
              "{:.9f}".format(avg_cost))


print("Total cost:"+str(autoencoder.calc_total_cost(X_test)))