TF之AE：AE实现TF自带数据集AE的encoder之后decoder之前的非监督学习分类—Jason niu

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("/niu/mnist_data/",one_hot=False)# Parameter
learning_rate = 0.001
training_epochs = 20
batch_size = 256
display_step = 1
examples_to_show = 10# Network Parameters
n_input = 784 # MNIST data input (img shape: 28*28像素即784个特征值)#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 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])),
}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']))
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#定义decoder
def decoder(x):
# Decoder Hidden layer with sigmoid activation #2
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) # 128 Features
decoder_op = decoder(encoder_op) # 784 Features# Prediction
y_pred = decoder_op #After
# Targets (Labels) are the input data.
y_true = X #Beforecost = 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!") encode_result = sess.run(encoder_op,feed_dict={X:mnist.test.images})
plt.scatter(encode_result[:,0],encode_result[:,1],c=mnist.test.labels)
plt.title('Matplotlib,AE,classification--Jason Niu')
plt.show()