neuro-lab8/loss.py

import tensorflow as tf
from tensorflow import keras

def CTCLoss(y_true, y_pred):
    batch_len = tf.cast(tf.shape(y_true)[0], dtype = "int64")
    input_length = tf.cast(tf.shape(y_pred)[1], dtype = "int64")
    label_length = tf.cast(tf.shape(y_true)[1], dtype = "int64")

    input_length = input_length * tf.ones(shape = (batch_len, 1), dtype = "int64")
    label_length = label_length * tf.ones(shape = (batch_len, 1), dtype = "int64")

    #y_pred = tf.math.log_softmax(y_pred)

    #y_true = tf.cast(y_true, dtype = "int64")
    #y_true_sparse = tf.keras.backend.ctc_label_dense_to_sparse(y_true, label_length)

    #print(y_true_sparse)

    loss = keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)
    #loss = tf.nn.ctc_loss(y_true_sparse, y_pred, input_length, label_length)
    return loss