Tensorflow：顯示MNIST數據集上的線性分類器的訓練權重

我已經在MNIST數據集上訓練了具有92％準確性的線性分類器。然後，我固定權重並對輸入圖像進行優化，使得8的softmax概率達到最大。但softmax損失不會降低到2.302（-log（1/10））以下，這意味着我的訓練毫無用處。我究竟做錯了什麼？Tensorflow：顯示MNIST數據集上的線性分類器的訓練權重

代碼訓練的權重：

import tensorflow as tf 
import numpy as np 
from tensorflow.examples.tutorials.mnist import input_data 

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 
trX, trY, teX, teY = mnist.train.images, mnist.train.labels,  
mnist.test.images, mnist.test.labels 

X = tf.placeholder("float", [None, 784]) 
Y = tf.placeholder("float", [None, 10]) 

w = tf.Variable(tf.random_normal([784, 10], stddev=0.01)) 
b = tf.Variable(tf.zeros([10])) 

o = tf.nn.sigmoid(tf.matmul(X, w)+b) 

cost= tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=o, labels=Y)) 
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost) 
predict_op = tf.argmax(o, 1) 

sess=tf.Session() 
sess.run(tf.global_variables_initializer()) 
for i in range(100): 
    for start, end in zip(range(0, len(trX), 256), range(256, len(trX)+1, 256)): 
     sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]}) 
    print(i, np.mean(np.argmax(teY, axis=1) == sess.run(predict_op, feed_dict={X: teX})))

代碼用於訓練圖像的固定權數：

#Copy trained weights into W,B and pass them as placeholders to new model 
W=sess.run(w) 
B=sess.run(b) 

X=tf.Variable(tf.random_normal([1, 784], stddev=0.01)) 
Y=tf.constant([0, 0, 0, 0, 0, 0, 0, 0, 1, 0]) 

w=tf.placeholder("float") 
b=tf.placeholder("float") 

o = tf.nn.sigmoid(tf.matmul(X, w)+b) 

cost= tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=o, labels=Y)) 
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost) 
predict_op = tf.argmax(o, 1) 

sess.run(tf.global_variables_initializer()) 
for i in range(1000): 
    sess.run(train_op, feed_dict={w:W, b:B}) 
    if i%50==0: 
    sess.run(cost, feed_dict={w:W, b:B}) 
    print(i, sess.run(predict_op, feed_dict={w:W, b:B}))

來源

2017-05-13 Tejus Gupta