Ricardo Cruz - 1 year ago 235

Python Question

I am trying to adapt this MNIST example to binary classification.

But when changing my

`NLABELS`

`NLABELS=2`

`NLABELS=1`

`from __future__ import absolute_import`

from __future__ import division

from __future__ import print_function

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

# Import data

mnist = input_data.read_data_sets('data', one_hot=True)

NLABELS = 2

sess = tf.InteractiveSession()

# Create the model

x = tf.placeholder(tf.float32, [None, 784], name='x-input')

W = tf.Variable(tf.zeros([784, NLABELS]), name='weights')

b = tf.Variable(tf.zeros([NLABELS], name='bias'))

y = tf.nn.softmax(tf.matmul(x, W) + b)

# Add summary ops to collect data

_ = tf.histogram_summary('weights', W)

_ = tf.histogram_summary('biases', b)

_ = tf.histogram_summary('y', y)

# Define loss and optimizer

y_ = tf.placeholder(tf.float32, [None, NLABELS], name='y-input')

# More name scopes will clean up the graph representation

with tf.name_scope('cross_entropy'):

cross_entropy = -tf.reduce_mean(y_ * tf.log(y))

_ = tf.scalar_summary('cross entropy', cross_entropy)

with tf.name_scope('train'):

train_step = tf.train.GradientDescentOptimizer(10.).minimize(cross_entropy)

with tf.name_scope('test'):

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

_ = tf.scalar_summary('accuracy', accuracy)

# Merge all the summaries and write them out to /tmp/mnist_logs

merged = tf.merge_all_summaries()

writer = tf.train.SummaryWriter('logs', sess.graph_def)

tf.initialize_all_variables().run()

# Train the model, and feed in test data and record summaries every 10 steps

for i in range(1000):

if i % 10 == 0: # Record summary data and the accuracy

labels = mnist.test.labels[:, 0:NLABELS]

feed = {x: mnist.test.images, y_: labels}

result = sess.run([merged, accuracy, cross_entropy], feed_dict=feed)

summary_str = result[0]

acc = result[1]

loss = result[2]

writer.add_summary(summary_str, i)

print('Accuracy at step %s: %s - loss: %f' % (i, acc, loss))

else:

batch_xs, batch_ys = mnist.train.next_batch(100)

batch_ys = batch_ys[:, 0:NLABELS]

feed = {x: batch_xs, y_: batch_ys}

sess.run(train_step, feed_dict=feed)

I have checked the dimensions of both

`batch_ys`

`y`

`_y`

`NLABELS=1`

I actually have got this same problem in a real project, so any help would be appreciated... Thanks!

Answer Source

The original MNIST example uses a one-hot encoding to represent the labels in the data: this means that if there are `NLABELS = 10`

classes (as in MNIST), the target output is `[1 0 0 0 0 0 0 0 0 0]`

for class 0, `[0 1 0 0 0 0 0 0 0 0]`

for class 1, etc. The `tf.nn.softmax()`

operator converts the logits computed by `tf.matmul(x, W) + b`

into a probability distribution across the different output classes, which is then compared to the fed-in value for `y_`

.

If `NLABELS = 1`

, this acts as if there were only a single class, and the `tf.nn.softmax()`

op would compute a probability of `1.0`

for that class, leading to a cross-entropy of `0.0`

, since `tf.log(1.0)`

is `0.0`

for all of the examples.

There are (at least) two approaches you could try for binary classification:

The simplest would be to set

`NLABELS = 2`

for the two possible classes, and encode your training data as`[1 0]`

for label 0 and`[0 1]`

for label 1. This answer has a suggestion for how to do that.You could keep the labels as integers

`0`

and`1`

and use`tf.nn.sparse_softmax_cross_entropy_with_logits()`

, as suggested in this answer.