Converting a tf.Tensor from one int value to another via lookup-CodePudding

I have the following labels: [15, 76, 34]. I am trying to map them to be [0, 1, 2] inside of a tf.data.Dataset using the map function.

So I need a function that can do the following:

def relabel(label: tf.Tensor) -> tf.Tensor:
    # TODO: convert 15 --> 0, 76 --> 1, 34 --> 2
    return new_label

dataset: tf.data.Dataset
dataset = dataset.map(lambda x, y: x, relabel(y))

I am having a tough time working with tf.Tensor, can anyone complete this implementation?

CodePudding user response：

You can create a lookup table that assigns the old labels to new labels:

label_tensor = tf.constant([15, 76, 34], tf.int32)
new_label_tensor = tf.constant([0, 1, 2])
table = tf.lookup.StaticHashTable(
    tf.lookup.KeyValueTensorInitializer(label_tensor, new_label_tensor, key_dtype=tf.int32,value_dtype=tf.int32), -1)

checking for inputs:

X = tf.constant([0.1, 0.2, 0.3], dtype=tf.float32)
Y = tf.constant([15, 76, 34], dtype=tf.int32)
dataset = tf.data.Dataset.from_tensor_slices((X, Y))

relabeling can be done by,

def relabel(x, y):
    return x, table.lookup(y)
dataset = dataset.map(relabel)

Outputs,

 for x, y in dataset:
    print(x.numpy(), y.numpy())
 #outputs
 0.1 0
 0.2 1
 0.3 2