My question is in two connected parts:
How do I calculate the max along a certain axis of a tensor? For example, if I have
x = tf.constant([[1,220,55],[4,3,-1]])
I want something like
x_max = tf.max(x, axis=1) print sess.run(x_max) output: [220,4]
I know there is a tf.argmax
and a tf.maximum
, but neither give the maximum value along an axis of a single tensor. For now I have a workaround:
x_max = tf.slice(x, begin=[0,0], size=[-1,1]) for a in range(1,2): x_max = tf.maximum(x_max , tf.slice(x, begin=[0,a], size=[-1,1]))
But it looks less than optimal. Is there a better way to do this?
Given the indices of an argmax
of a tensor, how do I index into another tensor using those indices? Using the example of x
above, how do I do something like the following:
ind_max = tf.argmax(x, dimension=1) #output is [1,0] y = tf.constant([[1,2,3], [6,5,4]) y_ = y[:, ind_max] #y_ should be [2,6]
I know slicing, like the last line, does not exist in TensorFlow yet (#206).
My question is: what is the best workaround for my specific case (maybe using other methods like gather, select, etc.)?
Additional information: I know x
and y
are going to be two dimensional tensors only!
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.
argmax() is a method present in tensorflow math module. This method is used to find the maximum value across the axes. Syntax: tensorflow. math. argmax( input,axes,output_type,name ) Arguments: 1.
expand_dims() is used to insert an addition dimension in input Tensor. Parameters: input: It is the input Tensor. axis: It defines the index at which dimension should be inserted.
The tf.reduce_max()
operator provides exactly this functionality. By default it computes the global maximum of the given tensor, but you can specify a list of reduction_indices
, which has the same meaning as axis
in NumPy. To complete your example:
x = tf.constant([[1, 220, 55], [4, 3, -1]]) x_max = tf.reduce_max(x, reduction_indices=[1]) print sess.run(x_max) # ==> "array([220, 4], dtype=int32)"
If you compute the argmax using tf.argmax()
, you could obtain the the values from a different tensor y
by flattening y
using tf.reshape()
, converting the argmax indices into vector indices as follows, and using tf.gather()
to extract the appropriate values:
ind_max = tf.argmax(x, dimension=1) y = tf.constant([[1, 2, 3], [6, 5, 4]]) flat_y = tf.reshape(y, [-1]) # Reshape to a vector. # N.B. Handles 2-D case only. flat_ind_max = ind_max + tf.cast(tf.range(tf.shape(y)[0]) * tf.shape(y)[1], tf.int64) y_ = tf.gather(flat_y, flat_ind_max) print sess.run(y_) # ==> "array([2, 6], dtype=int32)"
As of TensorFlow 1.10.0-dev20180626, tf.reduce_max
accepts axis
and keepdims
keyword arguments offering the similar functionality of numpy.max
.
In [55]: x = tf.constant([[1,220,55],[4,3,-1]]) In [56]: tf.reduce_max(x, axis=1).eval() Out[56]: array([220, 4], dtype=int32)
To have a resultant tensor of the same dimension as the input tensor, use keepdims=True
In [57]: tf.reduce_max(x, axis=1, keepdims=True).eval()Out[57]: array([[220], [ 4]], dtype=int32)
If the axis
argument is not explicitly specified then the tensor level maximum element is returned (i.e. all axes are reduced).
In [58]: tf.reduce_max(x).eval() Out[58]: 220
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