meridian.backend.reduce_max

    Computes tf.math.maximum of elements across dimensions of a tensor.

      meridian 
 . 
 backend 
 . 
 reduce_max 
 ( 
 input_tensor 
 , 
 axis 
 = 
 None 
 , 
 keepdims 
 = 
 False 
 , 
 name 
 = 
 None 
 )

    This is the reduction operation for the elementwise tf.math.maximum op.

    Reduces input_tensor along the dimensions given in axis . Unless keepdims is true, the rank of the tensor is reduced by 1 for each of the entries in axis , which must be unique. 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.

    ```

    x = tf.constant([5, 1, 2, 4]) tf.reduce_max(x) x = tf.constant([-5, -1, -2, -4]) tf.reduce_max(x) x = tf.constant([4, float('nan')]) tf.reduce_max(x) x = tf.constant([float('nan'), float('nan')]) tf.reduce_max(x) x = tf.constant([float('-inf'), float('inf')]) tf.reduce_max(x) ```

    See the numpy docs for np.amax and np.nanmax behavior.

    input_tensor
    The tensor to reduce. Should have real 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.
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