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)"
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