使用autoencoder进行降维

1 tensorflow的原生API实现

#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("MNIST_data/", 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()

2 keras实现

encoding_dim =2

encoder = keras.models.Sequential([
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(32, activation='relu'),
    keras.layers.Dense(8, activation='relu'),
    keras.layers.Dense(encoding_dim)
])
decoder = keras.models.Sequential([
    keras.layers.Dense(8, activation='relu'),
    keras.layers.Dense(32, activation='relu'),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(748, activation='tanh')
])

AutoEncoder = keras.models.Sequential([
    encoder, 
    decoder
])

AutoEncoder.compile(loss="mse", optimizer="adam")

AutoEncoder.fit(X_train, X_train, epochs=10, batch_size=32)

predict = encoder.predict(X_test)