TF之AE：AE实现TF自带数据集数字真实值对比AE先encoder后decoder预测数字的精确对比—daidingdaiding

TF之AE：AE实现TF自带数据集数字真实值对比AE先encoder后decoder预测数字的精确对比—daidingdaiding

目录

输出结果

代码设计

输出结果

代码设计

[code]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 = 0.01
training_epochs = 10
batch_size = 256
display_step = 1
examples_to_show = 10

# Network Parameters
n_input = 784

#tf Graph input(only pictures)
X=tf.placeholder("float", [None,n_input])

# hidden layer settings
n_hidden_1 = 256
n_hidden_2 = 128 <br>
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])),
}

#定义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

#定义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

# Construct model
encoder_op = encoder(X)             # 128 Features
decoder_op = decoder(encoder_op)    # 784 Features

# 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:<br>
sess.run(tf.initialize_all_variables())
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))
plt.title('Matplotlib,AE--Jason Niu')
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()

相关文章
TF之AE：AE实现TF自带数据集数字真实值对比AE先encoder后decoder预测数字的精确对比