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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
)
Used in the notebooks
| Used in the tutorials |
|---|
Reduces input_tensor along the dimensions given in axis by computing the
mean of elements across the dimensions in axis.
Unless keepdims is true, the rank of the tensor is reduced by 1 for each
the entries in axis, which must be unique. 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)<tf.Tensor: shape=(), dtype=float32, numpy=1.5>tf.reduce_mean(x, 0)<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1.5, 1.5], dtype=float32)>tf.reduce_mean(x, 1)<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>
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)<tf.Tensor: shape=(), dtype=int32, numpy=0>y = tf.constant([1., 0., 1., 0.])tf.reduce_mean(y)<tf.Tensor: shape=(), dtype=float32, numpy=0.5>