def reduce_mean(input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=None):
if reduction_indices is not None:
if axis is not None:
raise ValueError(
"cannot specify both 'axis' and 'reduction_indices'.")
axis = reduction_indices
elif axis is None:
axis = -1 # reduce all
if isinstance(axis, list) or isinstance(axis,
tuple): # reduce continuously
if len(axis) < 1:
raise RuntimeError('reduce axes should at least have one.')
if len(axis) == 1:
return ops.Mean(input_tensor, axis=axis[0], keep_dims=keep_dims)
else:
ret = ops.Mean(input_tensor, axis=axis[0], keep_dims=True)
for i in xrange(1, len(axis) - 1):
ret = ops.Mean(ret, axis=axis[i], keep_dims=True)
return ops.Mean(ret, axis=axis[len(axis) - 1], keep_dims=keep_dims)
else:
return ops.Mean(input_tensor, axis=axis, keep_dims=keep_dims)
def reduce_mean(input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=None):
"""
Computes the mean of elements across dimensions of a tensor.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
entry in `axis`. If `keep_dims` is true, the reduced dimensions
are retained with length 1.
If `axis` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
For example:
```python
# '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.]
```
Args:
input_tensor: The tensor to reduce. Should have numeric type.
axis: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
reduction_indices: The old (deprecated) name for axis.
Returns:
The reduced tensor.
"""
if reduction_indices is not None:
if axis is not None:
raise ValueError(
"cannot specify both 'axis' and 'reduction_indices'.")
axis = reduction_indices
elif axis is None:
axis = -1 # reduce all
if isinstance(axis, list) or isinstance(axis,
tuple): # reduce continuously
if len(axis) < 1:
raise RuntimeError('reduce axes should at least have one.')
if len(axis) == 1:
return ops.Mean(input_tensor, axis=axis[0], keep_dims=keep_dims)
else:
ret = ops.Mean(input_tensor, axis=axis[0], keep_dims=True)
for i in xrange(1, len(axis) - 1):
ret = ops.Mean(ret, axis=axis[i], keep_dims=True)
return ops.Mean(ret, axis=axis[len(axis) - 1], keep_dims=keep_dims)
else:
return ops.Mean(input_tensor, axis=axis, keep_dims=keep_dims)
def reduce_mean(
input_tensor,
axis=None,
keep_dims=False,
name=None,
reduction_indices=None,
):
if reduction_indices is not None:
if axis is not None:
raise ValueError(
"cannot specify both 'axis' and 'reduction_indices'.")
axis = reduction_indices
return _ops.Mean(
input_tensor,
axes=axis,
keep_dims=keep_dims,
name=name,
)
def mean(input, axis=None, keepdims=False, **kwargs):
"""Compute the mean along the given axis.
Parameters
----------
input : Tensor
The input tensor.
axis : int
The axis to compute. Default is ``None`` (Along all axes).
keep_dims : boolean
Whether to keep dims after computing.
Returns
-------
Tensor
The mean result.
"""
if axis is None: axis = -1
return ops.Mean(input, axis=axis, keep_dims=keepdims)
