@keras_export('keras.preprocessing.sequence.pad_sequences')
def pad_sequences(sequences, maxlen=None, dtype='int32',
padding='pre', truncating='pre', value=0.):
return sequence.pad_sequences(
sequences, maxlen=maxlen, dtype=dtype,
padding=padding, truncating=truncating, value=value)
I want to transform this code to javascript.
It works like this:
sequence = [[1], [2, 3], [4, 5, 6]]
tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=2)
array =
0,1
2,3
5,6
You can truncate and pad your sequences with Javascript like this:
const sequence = [[1], [2, 3], [4, 5, 6]];
var new_sequence = sequence.map(function(e) {
const max_length = 2;
const row_length = e.length
if (row_length > max_length){ // truncate
return e.slice(row_length - max_length, row_length)
}
else if (row_length < max_length){ // pad
return Array(max_length - row_length).fill(0).concat(e);
}
return e;
});
console.log('Before truncating and paddig: ',sequence)
console.log('After truncating and paddig: ', new_sequence)
// "Before truncating and paddig: ", [[1], [2, 3], [4, 5, 6]]
// "After truncating and paddig: ", [[0, 1], [2, 3], [5, 6]]
which is equivalent to the following Python code with Tensorflow:
import tensorflow as tf
def truncate_and_pad(row):
row_length = tf.shape(row)[0]
if tf.greater(row_length, max_length): # truncate
return row[row_length-max_length:]
elif tf.less(row_length, max_length): # pad
padding = tf.constant([[max_length-row_length.numpy(), 0]])
return tf.pad(row, padding, "CONSTANT")
else: return row
max_length = 2
sequence = tf.ragged.constant([[1], [2, 3], [4, 5, 6]])
Y = tf.map_fn(truncate_and_pad, sequence)
but you actually do not need any fancy functions.