tensorflow MNIST fully_connected_feed.py fails: range() takes at least 2 arguments (1 given) - tensorflow

Question

I'm having trouble running the example in one of the tensor flow tutorials. The tutorial says to run I just need to type python fully_connected_feed.py. When I do this it gets through fetching the input data, but then fails, like so:
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
Traceback (most recent call last):
File "fully_connected_feed.py", line 225, in <module>
tf.app.run()
File "/Users/me/anaconda/lib/python2.7/site-packages/tensorflow/python/platform/default/_app.py", line 11, in run
sys.exit(main(sys.argv))
File "fully_connected_feed.py", line 221, in main
run_training()
File "fully_connected_feed.py", line 141, in run_training
loss = mnist.loss(logits, labels_placeholder)
File "/Users/me/tftmp/mnist.py", line 96, in loss
indices = tf.expand_dims(tf.range(batch_size), 1)
TypeError: range() takes at least 2 arguments (1 given)
I think this error is caused because there is some problem with session setup and/or tensor evaluation. This is the function in mnist.py causing the problem:
def loss(logits, labels):
"""Calculates the loss from the logits and the labels.
Args:
logits: Logits tensor, float - [batch_size, NUM_CLASSES].
labels: Labels tensor, int32 - [batch_size].
Returns:
loss: Loss tensor of type float.
"""
# Convert from sparse integer labels in the range [0, NUM_CLASSSES)
# to 1-hot dense float vectors (that is we will have batch_size vectors,
# each with NUM_CLASSES values, all of which are 0.0 except there will
# be a 1.0 in the entry corresponding to the label).
batch_size = tf.size(labels)
labels = tf.expand_dims(labels, 1)
indices = tf.expand_dims(tf.range(batch_size), 1)
concated = tf.concat(1, [indices, labels])
onehot_labels = tf.sparse_to_dense(
concated, tf.pack([batch_size, NUM_CLASSES]), 1.0, 0.0)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, onehot_labels,
name='xentropy')
loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
return loss
If I put all the code in the loss function inside a with tf.Session(): block, it gets past this error. However, I get other errors later about uninitialised variables, so I'm guessing something major is going wrong with session setup or initialisation, or something. Being new to tensor flow I'm a little at a loss. Any ideas?
[NB: I havent edited the code at all, just downloaded from the tensorflow tutorials and tried to run as instructed, with python fully_connected_feed.py]

Answer 1

This issue arises because in the latest version of the TensorFlow source on GitHub, tf.range() has been updated to be more permissive with its arguments (previously it required two arguments; now it has the same semantics as Python's range() built-in function), and the fully_connected_feed.py example has been updated to exploit this.
However, if you try to run this version against the binary distribution of TensorFlow, you will get this error because the change to tf.range() has not been incorporated into the binary package.
The easiest solution is to download the old version of mnist.py. Alternatively, you could build from source to use the latest version of the tutorial.

Answer 2

you can right result fix mnist code like this :
indices = tf.expand_dims(tf.range(0,batch_size),1)

Answer 3

TypeError: range() takes at least 2 arguments (1 given)
That's the error.
Looking at the tensorflow docs for range, we can see that range has a function signature of start, limit, delta=1, name='range'. This means that at least two arguments are required for function invocation. Your example only shows one argument provided.
An example can be found in the docs:
# 'start' is 3
# 'limit' is 18
# 'delta' is 3
tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]