If you need to shield some elements in a tensor, tf.boolean_mask() may be a good choice. In this tutorial, we will use some examples to show you how to use it correctly.
Syntax
tf.boolean_mask() is defined as:
tf.boolean_mask(
tensor,
mask,
name='boolean_mask',
axis=None
)
where:
tensor:N-D tensor.
mask: K-D boolean tensor or numpy.ndarray, K <= N and K must be known statically. It is very important, we will use it to remove some elements from tensor.
name: A name for this operation (optional).
axis: A 0-D int Tensor representing the axis in tensor to mask from.
Here we will write some examples to show how to use this function.
Remove an element from a tensor on axis = 0
import tensorflow as tf import numpy as np x = np.array([[2,2,3],[6,7,2],[1,2,2]], dtype = np.float32) #remove the second element from tensor x mask = np.array([True, False, True])
Here mask is a numpy.ndarray, the second value is False, which means we will remove the second element from tensor x.
x2 = tf.boolean_mask(x, mask, axis = 0)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(sess.run(x2))
Run this code, we will get x2:
[[2. 2. 3.] [1. 2. 2.]]
We also can make mask be a tensor to remove data from tensor.
mask = np.array([True, False, True]) maskx = tf.convert_to_tensor(mask, dtype = tf.bool) x2 = tf.boolean_mask(x, maskx, axis = 0)
Run this code, x2 also is:
[[2. 2. 3.] [1. 2. 2.]]