TF之AE：AE實作TF自帶資料集AE的encoder之後decoder之前的非監督學習分類

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代碼設計

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個特征值)

#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

#定義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']))

# 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()