转载一个特征提取的方法——AE
转载
原理
代码
import tensorflow as tf
import matplotlib.pyplot as pltfrom tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)learning_rate = 0.01
training_epochs = 20
batch_size = 256
display_step = 1
n_input = 784
X = tf.placeholder("float", [None, n_input])#压缩过程，压缩到2个元素
n_hidden_1 = 128
n_hidden_2 = 64
n_hidden_3 = 10
n_hidden_4 = 2weights = {'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，输出的范围是无穷大layer_4 = tf.add(tf.matmul(layer_3, weights['encoder_h4']),biases['encoder_b4'])return layer_4def 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_4encoder_op = encoder(X)
decoder_op = decoder(encoder_op)y_pred = decoder_op
y_true = Xcost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)init=tf.global_variables_initializer()with tf.Session() as sess: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) # max(x) = 1, min(x) = 0_, 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!")#显示解压前的结果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
转载一个特征提取的方法——AE
转载
原理
代码
import tensorflow as tf
import matplotlib.pyplot as pltfrom tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)learning_rate = 0.01
training_epochs = 20
batch_size = 256
display_step = 1
n_input = 784
X = tf.placeholder("float", [None, n_input])#压缩过程，压缩到2个元素
n_hidden_1 = 128
n_hidden_2 = 64
n_hidden_3 = 10
n_hidden_4 = 2weights = {'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，输出的范围是无穷大layer_4 = tf.add(tf.matmul(layer_3, weights['encoder_h4']),biases['encoder_b4'])return layer_4def 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_4encoder_op = encoder(X)
decoder_op = decoder(encoder_op)y_pred = decoder_op
y_true = Xcost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)init=tf.global_variables_initializer()with tf.Session() as sess: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) # max(x) = 1, min(x) = 0_, 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!")#显示解压前的结果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()