tf.math.reduce_mean(
input_tensor, axis=None, keepdims=False, name=None
)
对tf.reduce_mean的理解就是求平均值，reduce指的是一串数据求平均值后维数降低了，可不是吗，一串向量变成了一个数，维数自然降低了
API URL
https://tensorflow.google.cn/api_docs/python/tf/math/reduce_mean?hl=en
Used in the notebooks
tf.math.reduce_mean(
input_tensor, axis=None, keepdims=False, name=None
)
Used in the notebooks
| Used in the guide |
Used in the tutorials |
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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.]
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). 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) # 0
y = tf.constant([1., 0., 1., 0.])
tf.reduce_mean(y) # 0.5
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Reduces input_tensor along the dimensions given in axis
