使用Autoencoder进行降维

#coding=utf-8
import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data #需要自己从网上下载Mnist数据集 mnist = input_data.read_data_sets("D:/MNIST", one_hot=False) learning_rate = 0.01 training_epochs = 10 batch_size = 256 display_step = 1 n_input = 784 X = tf.placeholder("float", [None, n_input]) n_hidden_1 = 128 n_hidden_2 = 64 n_hidden_3 = 10 n_hidden_4 = 2 weights = { 'encoder_h1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1], )), 'encoder_h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2], )), 'encoder_h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3], )), 'encoder_h4': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4], )), 'decoder_h1': tf.Variable(tf.truncated_normal([n_hidden_4, n_hidden_3], )), 'decoder_h2': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_2], )), 'decoder_h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_1], )), 'decoder_h4': tf.Variable(tf.truncated_normal([n_hidden_1, n_input], )), } biases = { 'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), 'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])), 'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])), 'encoder_b4': tf.Variable(tf.random_normal([n_hidden_4])), 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_3])), 'decoder_b2': tf.Variable(tf.random_normal([n_hidden_2])), 'decoder_b3': tf.Variable(tf.random_normal([n_hidden_1])), 'decoder_b4': tf.Variable(tf.random_normal([n_input])), } def encoder(x): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1'])) layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2'])) layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['encoder_h3']), biases['encoder_b3'])) # 为了便于编码层的输出,编码层随后一层不使用激活函数 layer_4 = tf.add(tf.matmul(layer_3, weights['encoder_h4']), biases['encoder_b4']) return layer_4 def decoder(x): layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1'])) layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2'])) layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['decoder_h3']), biases['decoder_b3'])) layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3, weights['decoder_h4']), biases['decoder_b4'])) return layer_4 encoder_op = encoder(X) decoder_op = decoder(encoder_op) y_pred = decoder_op y_true = X #使用平均误差最小化损失函数 cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) total_batch = int(mnist.train.num_examples / batch_size) for epoch in range(training_epochs): for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs}) if epoch % display_step == 0: print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c)) print("Optimization Finished!") encoder_result = sess.run(encoder_op, feed_dict={X: mnist.test.images}) plt.scatter(encoder_result[:, 0], encoder_result[:, 1], c=mnist.test.labels) plt.colorbar() plt.show()

结果:每一种颜色代表一种数字,这里是为了可视化才降到2维的,但是实际降维的时候,肯定不会把维度降到这么低的水平。

使用Autoencoder进行降维

 

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