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Computes the mean of elements across dimensions of a tensor.
tf.compat.v1.reduce_mean(
input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None,
keep_dims=None
)
Reduces input_tensor along the dimensions given in axis.
Unless keepdims is true, the rank of the tensor is reduced by 1 for each
entry in axis. If keepdims is true, the reduced dimensions
are retained with length 1.
If axis is None, all dimensions are reduced, and a
tensor with a single element is returned.
For example:
x = tf.constant([[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.]
Args | |
|---|---|
input_tensor
|
The tensor to reduce. Should have numeric type. |
axis
|
The dimensions to reduce. If None (the default), reduces all
dimensions. Must be in the range [-rank(input_tensor),
rank(input_tensor)).
|
keepdims
|
If true, retains reduced dimensions with length 1. |
name
|
A name for the operation (optional). |
reduction_indices
|
The old (deprecated) name for axis. |
keep_dims
|
Deprecated alias for keepdims.
|
Returns | |
|---|---|
| The reduced tensor. |
Numpy Compatibility
Equivalent to np.mean
Please note that np.mean has a dtype parameter that could be used to
specify the output type. By default this is dtype=float64. On the other
hand, tf.reduce_mean has an aggressive type inference from input_tensor,
for example:
x = tf.constant([1, 0, 1, 0])
tf.reduce_mean(x) # 0
y = tf.constant([1., 0., 1., 0.])
tf.reduce_mean(y) # 0.5