tf.compat.v1.reduce_mean

View source on GitHub

Computes the mean of elements across dimensions of a tensor.

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 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)>

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.

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>