CODEWITHSUNDEEP
machine-learningtensorflowdeep-learningmnist
Tejus Gupta
Tejus Gupta

Reputation: 165

Tensorflow: Visualizing trained weights for linear classifier on MNIST dataset

I have trained a linear classifier on the MNIST dataset with 92% accuracy. Then I fixed the weights and optimized the input image such that softmax probability for 8 was maximized. But the softmax loss doesn't decrease below 2.302 (-log(1/10)) which means that my training has been useless. What am I doing wrong?

Code for training the weights:

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})))

Code for training the image for fixed weights:

#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}))

Upvotes: 1

Views: 655

Answers (1)

P-Gn
P-Gn

Reputation: 24591

You shouldn't call tf.sigmoid on the output of your net. softmax_cross_entropy_with_logits assumes your inputs are logits, i.e. unconstrained real numbers. Using

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

increases your accuracy to 92.8%.

With this modification, your second training works. The cost reaches 0 although the resulting image is anything but appealing.

enter image description here

Upvotes: 1

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