# Difference between np.mean and tf.reduce_mean in Numpy and Tensorflow?

In the MNIST beginner tutorial, there is `accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))`

`tf.cast`

basically changes the type of tensor the object is, but what is the difference between `tf.reduce_mean`

and `np.mean`

?

Here is the doc on `tf.reduce_mean`

:

```
reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None)
input_tensor: The tensor to reduce. Should have numeric type.
reduction_indices: The dimensions to reduce. If `None` (the defaut),
reduces all dimensions.
# 'x' is [[1., 1. ]]
# [2., 2.]]
tf.reduce_mean(x) ==> 1.5
tf.reduce_mean(x, 0) ==> [1.5, 1.5]
tf.reduce_mean(x, 1) ==> [1., 2.]
```

For a 1D vector, it looks like `np.mean == tf.reduce_mean`

but I don't understand what's happening in `tf.reduce_mean(x, 1) ==> [1., 2.]`

. `tf.reduce_mean(x, 0) ==> [1.5, 1.5]`

kind of makes sense, since mean of [1,2] and [1,2] are [1.5,1.5] but what's going on with `tf.reduce_mean(x,1)`

?

The functionality of `numpy.mean`

and `tensorflow.reduce_mean`

are the same. They do the same thing. From the documentation, for numpy and tensorflow, you can see that. Lets look at an example,

```
c = np.array([[3.,4], [5.,6], [6.,7]])
print(np.mean(c,1))
Mean = tf.reduce_mean(c,1)
with tf.Session() as sess:
result = sess.run(Mean)
print(result)
```

Output

```
[ 3.5 5.5 6.5]
[ 3.5 5.5 6.5]
```

Here you can see that when `axis`

(numpy) or `reduction_indices`

(tensorflow) is 1, it computes mean across (3,4) and (5,6) and (6,7), so `1`

defines across which axis the mean is computed. When it is 0, the mean is computed across(3,5,6) and (4,6,7), and so on. I hope you get the idea.

Now what are the differences between them?

You can compute the numpy operation anywhere on python. But in order to do a tensorflow operation, it must be done inside a tensorflow `Session`

. You can read more about it here. So when you need to perform any computation for your tensorflow graph(or structure if you will), it must be done inside a tensorflow `Session`

.

Lets look at another example.

```
npMean = np.mean(c)
print(npMean+1)
tfMean = tf.reduce_mean(c)
Add = tfMean + 1
with tf.Session() as sess:
result = sess.run(Add)
print(result)
```

We could increase mean by `1`

in `numpy`

as you would naturally, but in order to do it in tensorflow, you need to perform that in `Session`

, without using `Session`

you can't do that. In other words, when you are computing `tfMean = tf.reduce_mean(c)`

, tensorflow doesn't compute it then. It only computes that in a `Session`

. But numpy computes that instantly, when you write `np.mean()`

.

I hope it makes sense.

From: stackoverflow.com/q/34236252

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