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# tf.math.reduce_mean

Computes the mean of elements across dimensions of a tensor.

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.]

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).

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
[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Não contém as informações de que eu preciso" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Muito complicado / etapas demais" },{ "type": "thumb-down", "id": "outOfDate", "label":"Desatualizado" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Outro" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Fácil de entender" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Meu problema foi resolvido" },{ "type": "thumb-up", "id": "otherUp", "label":"Outro" }]