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tf.compat.v1.reduce_mean

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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 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 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) 
<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>