Ricardo Cruz - 3 years ago 837
Python Question

# TensorFlow for binary classification

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

But when changing my

`NLABELS`
from
`NLABELS=2`
to
`NLABELS=1`
, the loss function always returns 0 (and accuracy 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
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'):

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]
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`
(fed into
`y`
) and
`_y`
and they are both 1xN matrices when
`NLABELS=1`
so the problem seems to be prior to that. Maybe something to do with the matrix multiplication?

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

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.
1. 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.
2. 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.