Warum funktioniert mein dnn nicht?

Question

import numpy as np 
import tensorflow as tf 
import matplotlib.pyplot as plt 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 

input_dim=784 
layer1_dim=10 
#relu-renorm-layer 650dim 

layer2_dim=100 
layer3_dim=100 
layer4_dim=100 
layer5_dim=10 

learning_rate=0.5 

# train_data=np.loadtxt("g_data.txt",dtype=float).astype("float") 
# train_target=train_data[:,-layer5_dim:] 
# train_feature=train_data[:,0:-layer5_dim] 
# test_data=np.loadtxt("g_data.txt",dtype=float).astype("float") 
# test_target=test_data[:,-layer5_dim:] 
# test_feature=test_data[:,0:-layer5_dim] 

#print(train_target.shape) 

x=tf.placeholder(tf.float32) 
y=tf.placeholder(tf.float32) 

w1=tf.Variable(tf.random_normal([input_dim,layer1_dim])) 
w2=tf.Variable(tf.random_normal([layer1_dim,layer2_dim])) 
w3=tf.Variable(tf.random_normal([layer2_dim,layer3_dim])) 
w4=tf.Variable(tf.random_normal([layer3_dim,layer4_dim])) 
w5=tf.Variable(tf.random_normal([layer4_dim,layer5_dim])) 

b1=tf.Variable(tf.random_normal([1,layer1_dim])) 
b2=tf.Variable(tf.random_normal([1,layer2_dim])) 
b3=tf.Variable(tf.random_normal([1,layer3_dim])) 
b4=tf.Variable(tf.random_normal([1,layer4_dim])) 
b5=tf.Variable(tf.random_normal([1,layer5_dim])) 

layer_1 = tf.nn.softmax(tf.add(tf.matmul(x, w1), b1)) 
layer_2 = tf.nn.softmax(tf.add(tf.matmul(layer_1, w2), b2)) 
layer_3 = tf.nn.softmax(tf.add(tf.matmul(layer_2, w3), b3)) 
layer_4 = tf.nn.softmax(tf.add(tf.matmul(layer_3, w4), b4)) 
layer_5 = tf.nn.softmax(tf.add(tf.matmul(layer_4, w5), b5)) 

#loss=tf.reduce_mean(tf.square(layer_5-y)) 
loss=tf.reduce_mean(-tf.reduce_sum(y * tf.log(layer_1), reduction_indices=[1])) 
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) 

init = tf.global_variables_initializer() 

with tf.Session() as session: 
    session.run(init) 
    # we'll make 5000 gradient descent iteration 
    for i in range(1000): 
     batch_xs, batch_ys = mnist.train.next_batch(100) 
     _,err =session.run([train_op,loss], feed_dict={x: batch_xs, y: batch_ys}) 
     print(err) 
    #print(session.run(layer_5,feed_dict={x:test_feature,y:test_target})) 
    correct_prediction = tf.equal(tf.argmax(layer_5, 1), tf.argmax(y, 1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
    print(session.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})) 

Ich folgte dem Tutorial in Tensorflow. Jedoch kann der DNN meines Codes nicht so gut funktionieren wie der MNIST des Tutorials für ML Anfänger.

Gibt es ob mein Code falsch oder nur DNN, die mit diesem Problem nicht funktionieren kann?

Übrigens ist die Genauigkeit meines Codes 10%, was weniger als 92% des Tutorials ist.

Answer 1

Sie können versuchen, diese zu tun, erhielt ich 91%:

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

input_dim=784 
layer1_dim=10 
learning_rate=0.3 

layer2_dim=100 
layer3_dim=100 
layer4_dim=100 
layer5_dim=10 



x=tf.placeholder(tf.float32, [None, input_dim]) 
y=tf.placeholder(tf.float32, [None, 10]) 

w1=tf.Variable(tf.random_normal([input_dim,layer1_dim])) 
w2=tf.Variable(tf.random_normal([layer1_dim,layer2_dim])) 
w3=tf.Variable(tf.random_normal([layer2_dim,layer3_dim])) 
w4=tf.Variable(tf.random_normal([layer3_dim,layer4_dim])) 
w5=tf.Variable(tf.random_normal([layer4_dim,layer5_dim])) 

b1=tf.Variable(tf.zeros([1,layer1_dim])) 
b2=tf.Variable(tf.zeros([1,layer2_dim])) 
b3=tf.Variable(tf.zeros([1,layer3_dim])) 
b4=tf.Variable(tf.zeros([1,layer4_dim])) 
b5=tf.Variable(tf.zeros([1,layer5_dim])) 

layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, w1), b1)) 
layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, w2), b2)) 
layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, w3), b3)) 
layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3, w4), b4)) 
layer_5 = tf.nn.softmax(tf.add(tf.matmul(layer_4, w5), b5)) 

loss=tf.reduce_mean(-tf.reduce_sum(y * tf.log(layer_5), reduction_indices=[1])) 
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) 

init = tf.global_variables_initializer() 

with tf.Session() as session: 
    session.run(init) 
    # we'll make 5000 gradient descent iteration 
    for i in range(10000): 
     batch_xs, batch_ys = mnist.train.next_batch(128) 
     _,err =session.run([train_op,loss], feed_dict={x: batch_xs, y: batch_ys}) 
     print(err) 
    #print(session.run(layer_5,feed_dict={x:test_feature,y:test_target})) 
    correct_prediction = tf.equal(tf.argmax(layer_5, 1), tf.argmax(y, 1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
    print 'ACCURACY' 
    print(session.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))