2.3AutoEncoder
AutoEncoder是包含一個壓縮和解壓縮的過程，屬於一種無監督學習的降維技術。網絡
神經網絡接受大量信息，有時候接受的數據達到上千萬，能夠經過壓縮dom
提取原圖片最具備表明性的信息，壓縮輸入的信息量，在將縮減後的數據放入神經網絡中學習，如此學習起來變得輕鬆了oop
自編碼在這個時候使用，能夠將自編碼歸爲無監督學習，相似於PCA，自編碼能夠爲屬性降維學習
手寫體識別代碼AutoEncoder
from __future__ import division, print_function, absolute_import import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data', one_hot=False) # Visualize decoder setting # Parameters learning_rate = 0.01 training_epochs = 5 batch_size = 256 display_step = 1 examples_to_show = 10 # Network Parameters n_input = 784 # MNIST data input (img shape: 28*28) # tf Graph input (only pictures) X = tf.placeholder("float", [None, n_input]) # hidden layer settings n_hidden_1 = 256 # 1st layer num features n_hidden_2 = 128 # 2nd layer num features weights = { 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])), 'decoder_h2': tf.Variable(tf.random_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])), 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), 'decoder_b2': tf.Variable(tf.random_normal([n_input])), } # Building the encoder def encoder(x): # Encoder Hidden layer with sigmoid activation #1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1'])) # Decoder Hidden layer with sigmoid activation #2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2'])) return layer_2 # Building the decoder def decoder(x): # Encoder Hidden layer with sigmoid activation #1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1'])) # Decoder Hidden layer with sigmoid activation #2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2'])) return layer_2 """ # Visualize encoder setting # Parameters learning_rate = 0.01 # 0.01 this learning rate will be better! Tested training_epochs = 10 batch_size = 256 display_step = 1 # Network Parameters n_input = 784 # MNIST data input (img shape: 28*28) # tf Graph input (only pictures) X = tf.placeholder("float", [None, n_input]) # hidden layer settings n_hidden_1 = 128 n_hidden_2 = 64 n_hidden_3 = 10 n_hidden_4 = 2 #2D show 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'])) 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 """ # Construct model encoder_op = encoder(X) decoder_op = decoder(encoder_op) # Prediction y_pred = decoder_op # Targets (Labels) are the input data. y_true = X # Define loss and optimizer, minimize the squared error cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) # Launch the graph with tf.Session() as sess: # tf.initialize_all_variables() no long valid from # 2017-03-02 if using tensorflow >= 0.12 if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: init = tf.initialize_all_variables() else: init = tf.global_variables_initializer() sess.run(init) total_batch = int(mnist.train.num_examples/batch_size) # Training cycle for epoch in range(training_epochs): # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # max(x) = 1, min(x) = 0 # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs}) # Display logs per epoch step if epoch % display_step == 0: print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c)) print("Optimization Finished!") # # Applying encode and decode over test set encode_decode = sess.run( y_pred, feed_dict={X: mnist.test.images[:examples_to_show]}) # Compare original images with their reconstructions f, a = plt.subplots(2, 10, figsize=(10, 2)) for i in range(examples_to_show): a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28))) a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) plt.show() # 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()
利用AutoEncoder進行相似於PCA的降維
代碼：ui
from __future__ import division, print_function, absolute_import import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data', one_hot=False) """ # Visualize decoder setting # Parameters learning_rate = 0.01 training_epochs = 5 batch_size = 256 display_step = 1 examples_to_show = 10 # Network Parameters n_input = 784 # MNIST data input (img shape: 28*28) # tf Graph input (only pictures) X = tf.placeholder("float", [None, n_input]) # hidden layer settings n_hidden_1 = 256 # 1st layer num features n_hidden_2 = 128 # 2nd layer num features weights = { 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])), 'decoder_h2': tf.Variable(tf.random_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])), 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), 'decoder_b2': tf.Variable(tf.random_normal([n_input])), } # Building the encoder def encoder(x): # Encoder Hidden layer with sigmoid activation #1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1'])) # Decoder Hidden layer with sigmoid activation #2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2'])) return layer_2 # Building the decoder def decoder(x): # Encoder Hidden layer with sigmoid activation #1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1'])) # Decoder Hidden layer with sigmoid activation #2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2'])) return layer_2 """ # Visualize encoder setting # Parameters learning_rate = 0.01 # 0.01 this learning rate will be better! Tested training_epochs = 10 batch_size = 256 display_step = 1 # Network Parameters n_input = 784 # MNIST data input (img shape: 28*28) # tf Graph input (only pictures) X = tf.placeholder("float", [None, n_input]) # hidden layer settings n_hidden_1 = 128 n_hidden_2 = 64 n_hidden_3 = 10 n_hidden_4 = 2 #2D show 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'])) 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 # Construct model encoder_op = encoder(X) decoder_op = decoder(encoder_op) # Prediction y_pred = decoder_op # Targets (Labels) are the input data. y_true = X # Define loss and optimizer, minimize the squared error cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) # Launch the graph with tf.Session() as sess: # tf.initialize_all_variables() no long valid from # 2017-03-02 if using tensorflow >= 0.12 if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: init = tf.initialize_all_variables() else: init = tf.global_variables_initializer() sess.run(init) total_batch = int(mnist.train.num_examples/batch_size) # Training cycle for epoch in range(training_epochs): # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # max(x) = 1, min(x) = 0 # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs}) # Display logs per epoch step if epoch % display_step == 0: print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c)) print("Optimization Finished!") # # # Applying encode and decode over test set # encode_decode = sess.run( # y_pred, feed_dict={X: mnist.test.images[:examples_to_show]}) # # Compare original images with their reconstructions # f, a = plt.subplots(2, 10, figsize=(10, 2)) # for i in range(examples_to_show): # a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28))) # a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) # plt.show() 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()
顯示以下：this
每日一句
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每一个你不满意的现在，都有一个你没有努力的曾经。
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