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

Posted 2020-10-22 一个处女座的IT

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             #Before


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!")

    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()