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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
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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 | | | 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) # 1.5tf.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, has an aggressive type inference from input_tensor , for example: x = tf.constant([1, 0, 1, 0])tf.reduce_mean(x) # 0y = 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
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