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官方API定义
tf.argmax(input, axis=None, name=None, dimension=None)
Returns the index with the largest value across axes of a tensor.
Args:
- input: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half.
- axis: A Tensor. Must be one of the following types: int32, int64. int32, 0 <= axis < rank(input). Describes which axis of the input Tensor to reduce across. For vectors, use axis = 0.
- name: A name for the operation (optional).
Returns:
- A Tensor of type int64.
关于axis
定义中的axis与numpy中的axis是一致的，下面通过代码进行解释
import numpy as np
import tensorflow as tf
sess = tf.session()
m = sess.run(tf.truncated_normal((5,10), stddev = 0.1) )
print type(m)
print m
-------------------------------------------------------------------------------
<type 'numpy.ndarray'>
[[ 0.09957541 -0.0965599 0.06064715 -0.03011306 0.05533558 0.17263047
-0.02660419 0.08313394 -0.07225946 0.04916157]
[ 0.11304571 0.02099175 0.03591062 0.01287777 -0.11302195 0.04822164
-0.06853487 0.0800944 -0.1155676 -0.01168544]
[ 0.15760773 0.05613248 0.04839646 -0.0218203 0.02233066 0.00929849
-0.0942843 -0.05943 0.08726917 -0.059653 ]
[ 0.02553608 0.07298559 -0.06958302 0.02948747 0.00232073 0.11875584
-0.08325859 -0.06616175 0.15124641 0.09522969]
[-0.04616683 0.01816062 -0.10866459 -0.12478453 0.01195056 0.0580056
-0.08500613 0.00635608 -0.00108647 0.12054099]]
m是一个5行10列的矩阵，类型为numpy.ndarray
#使用tensorflow中的tf.argmax()
col_max = sess.run(tf.argmax(m, 0) ) #当axis=0时返回每一列的最大值的位置索引
print col_max
row_max = sess.run(tf.argmax(m, 1) ) #当axis=1时返回每一行中的最大值的位置索引
print row_max
array([2, 3, 0, 3, 0, 0, 0, 0, 3, 4])
array([5, 0, 0, 8, 9])
-------------------------------------------------------------------------------
#使用numpy中的numpy.argmax
row_max = m.argmax(0)
print row_max
col_max = m.argmax(1)
print col_max
array([2, 3, 0, 3, 0, 0, 0, 0, 3, 4])
array([5, 0, 0, 8, 9])
可以看到tf.argmax()与numpy.argmax()方法的用法是一致的
- axis = 0的时候返回每一列最大值的位置索引
- axis = 1的时候返回每一行最大值的位置索引
- axis = 2、3、4...，即为多维张量时，同理推断
