A tensor mask can be very useful when developing a Deep Learning Model. It can be used to turn on/off activations deterministically or randomly.
We will make a mask of 0 s and 1s:
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[[1,1,1,1,0,0,0,0],
[1,1,1,0,0,0,0,0],
[1,1,0,0,0,0,0,0],
[1,0,0,0,0,0,0,0],
]
Make a 4x8 matrix where each row contains the length repeated 8 times: lengths=[4,3,5,2]:
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length_transposed = tf.expand_dims(lengths, 1)
Make a 4x8 matrix where each row contains [0, 1, …7]
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range = tf.range(0, 8 , 1)
range_row = tf.expand_dims(range, 0)
Use the logical operations to create a mask:
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mask = tf.less(range_row, lengths_transposed)
#use the select operation to select between 1 or 0 for each value:
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result = tf.select(mask, tf.ones([4,8]), tf.zeros(4,8]))
Check
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tf.sequence(lengths, max(en=None, dtype=tf.bool, name=None))