第十一节:Tensorflow之自编码 Autoencoder (非监督学习)¶
1.可视化解压前后的数字图片¶
导包¶
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt
import numpy as np
获得数据¶
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
定义相关的Parameter¶
# Parameter
learning_rate = 0.01
training_epochs = 5 # 五组训练
batch_size = 256
display_step = 1
examples_to_show = 10
# Network Parameters
n_input = 784 # MNIST data input (img shape: 28*28)
palceholder hold住数据¶
X = tf.placeholder("float", [None, n_input])
定义两层实现encode与decode¶
'''
encode：784-### 256; 256-### 128
decode: 128-### 256; 256-### 784
'''
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 and decoder
def encoder(x):
'''
上一层的信号（也就是wx+b算出的结果）要作为下一层的输入，
但是这个上一层的信号在输入到下一层之前需要一次激活
f = sigmoid(wx+b)，因为并不是所有的上一层信号
都可以激活下一层，如果所有的上一层信号都可以激活下一层，
那么这一层相当于什么都没有做。
'''
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']))
return layer_2
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']))
return layer_2
利用方法构建模型¶
# Construct model
encoder_op = encoder(X) # 128 Features
decoder_op = decoder(encoder_op) # 784 Features
# Prediction通过decode得到y_pred
y_pred = decoder_op # After
# Targets (Labels) are the input data.
y_true = X # Before
对比原数据与decode后数据的差异,并选择相应的优化器进行优化¶
# 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)
生成图¶
# Launch the graph
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
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()
输出¶
2.可视化聚类图¶
注:改动代码为:
1.weights/biases以及encoder/decoder方法(主要是增加层)
2.将以上的数字图变为聚类散点图
完整代码¶
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt
import numpy as np
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# Parameter
learning_rate = 0.001
training_epochs = 20 # 五组训练
batch_size = 256
display_step = 1
# Network Parameters
n_input = 784 # MNIST data input (img shape: 28*28)
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
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])),
'encoder_h3': tf.Variable(tf.random_normal([n_hidden_2,n_hidden_3])),
'encoder_h4': tf.Variable(tf.random_normal([n_hidden_3,n_hidden_4])),
'decoder_h1': tf.Variable(tf.random_normal([n_hidden_4,n_hidden_3])),
'decoder_h2': tf.Variable(tf.random_normal([n_hidden_3,n_hidden_2])),
'decoder_h3': tf.Variable(tf.random_normal([n_hidden_2,n_hidden_1])),
'decoder_h4': 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])),
'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])),
}
# Building the encoder and decoder
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
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:
sess.run(tf.global_variables_initializer())
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!")
encoder_result = sess.run(encoder_op, feed_dict={X: mnist.test.images})
X=encoder_result[:, 0]
Y=encoder_result[:, 1]
T=np.arctan2(X,Y)
plt.scatter(X, Y, c=T)
ax=plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.show()
输出¶