Question

我试图在深DAE中实现一个RNN层，如图所示：

DRDAE：

我的代码是根据DAE教程修改的，我将一层更改为基本的LSTM RNN层。它不知何故可以工作。不同图片之间输出的噪音似乎在同一个地方。

然而，与仅有一层RNN和DAE教程相比，结构的性能要差得多。并且它需要更多的迭代才能达到更低的成本。

有人可以帮助为什么结构会变得更糟？以下是我的DRDAE代码。

# -*- coding: utf-8 -*-

from __future__ import division, print_function, absolute_import
import tensorflow as tf
from tensorflow.contrib import rnn
import numpy as np
import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

# Parameters
learning_rate = 0.0001
training_epochs = 50001
batch_size = 256
display_step = 500
examples_to_show = 10
total_batch = int(mnist.train.num_examples/batch_size)

# Network Parameters
n_input = 784 # data input
n_hidden_1 = 392 # 1st layer num features
n_hidden_2 = 196 # 2nd layer num features
n_steps = 14

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

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])),
}

def RNN(x, size, weights, biases):

    # Prepare data shape to match `rnn` function requirements
    # Current data input shape: (batch_size, n_steps, n_input)
    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)

    # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)

    x = tf.split(x,n_steps,1)

    # Define a lstm cell with tensorflow
    lstm_cell = rnn.BasicLSTMCell(size, forget_bias=1.0)

    # Get lstm cell output
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)

    # Linear activation, using rnn inner loop last output
    return tf.matmul(outputs[-1], weights) + biases

# Building the 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

# Building the decoder
def decoder(x):
    # Encoder Hidden layer with sigmoid activation #1
    layer_1 = RNN(x, n_hidden_2, 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)
decoder_op = decoder(encoder_op)

# Prediction
y_pred = decoder_op
# Targets (Labels) are the original data.
y_true = Y

# Define loss and optimizer, minimize the squared error
cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)

# Evaluate model
correct_pred = tf.equal(tf.argmax(y_pred,1), tf.argmax(y_true,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
    #with tf.device("/cpu:0"):
    sess.run(init)
    # Training cycle
    for epoch in range(training_epochs):
        # Loop over all batches
        for i in range(total_batch):
            batch, _ = mnist.train.next_batch(batch_size)
            origin = batch
            # Run optimization op (backprop) and cost op (to get loss value)
            sess.run(optimizer, feed_dict={X: batch, Y: origin})
        # Display logs per epoch step
        if epoch % display_step == 0:
            c, acy = sess.run([cost, accuracy], feed_dict={X: batch, Y: origin})
            print("Epoch:", '%05d' % (epoch+1), "cost =", "{:.9f}".format(c), "accuracy =", "{:.3f}".format(acy))
    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)))

在深度DAE中实现一个RNN层似乎性能更差

0 个答案: