# TensorFlow: Is there a way to measure FLOPS for a model?

## Issue

The closest example I can get is found in this issue: https://github.com/tensorflow/tensorflow/issues/899

With this minimum reproducible code:

```
import tensorflow as tf
import tensorflow.python.framework.ops as ops
g = tf.Graph()
with g.as_default():
A = tf.Variable(tf.random_normal( [25,16] ))
B = tf.Variable(tf.random_normal( [16,9] ))
C = tf.matmul(A,B) # shape=[25,9]
for op in g.get_operations():
flops = ops.get_stats_for_node_def(g, op.node_def, 'flops').value
if flops is not None:
print 'Flops should be ~',2*25*16*9
print '25 x 25 x 9 would be',2*25*25*9 # ignores internal dim, repeats first
print 'TF stats gives',flops
```

However, the FLOPS returned is always None. Is there a way to concretely measure FLOPS, especially with a PB file?

## Solution

A little bit late but maybe it helps some visitors in future. For your example I successfully tested the following snippet:

```
g = tf.Graph()
run_meta = tf.RunMetadata()
with g.as_default():
A = tf.Variable(tf.random_normal( [25,16] ))
B = tf.Variable(tf.random_normal( [16,9] ))
C = tf.matmul(A,B) # shape=[25,9]
opts = tf.profiler.ProfileOptionBuilder.float_operation()
flops = tf.profiler.profile(g, run_meta=run_meta, cmd='op', options=opts)
if flops is not None:
print('Flops should be ~',2*25*16*9)
print('25 x 25 x 9 would be',2*25*25*9) # ignores internal dim, repeats first
print('TF stats gives',flops.total_float_ops)
```

It’s also possible to use the profiler in combination with `Keras`

like the following snippet:

```
import tensorflow as tf
import keras.backend as K
from keras.applications.mobilenet import MobileNet
run_meta = tf.RunMetadata()
with tf.Session(graph=tf.Graph()) as sess:
K.set_session(sess)
net = MobileNet(alpha=.75, input_tensor=tf.placeholder('float32', shape=(1,32,32,3)))
opts = tf.profiler.ProfileOptionBuilder.float_operation()
flops = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)
opts = tf.profiler.ProfileOptionBuilder.trainable_variables_parameter()
params = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)
print("{:,} --- {:,}".format(flops.total_float_ops, params.total_parameters))
```

I hope I could help!

Answered By – Tobias Scheck

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