28 lines
1018 B
Python
28 lines
1018 B
Python
import tensorflow as tf
|
|
from tensorflow import keras
|
|
from tensorflow.keras import layers
|
|
|
|
|
|
# see https://keras.io/guides/making_new_layers_and_models_via_subclassing/
|
|
class CTCLayer(layers.Layer):
|
|
|
|
def __init__(self, name=None):
|
|
super().__init__(name=name)
|
|
self.loss_fn = keras.backend.ctc_batch_cost
|
|
|
|
def call(self, y_true, y_pred):
|
|
# Compute the training-time loss value and add it
|
|
# to the layer using `self.add_loss()`.
|
|
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")
|
|
|
|
loss = self.loss_fn(y_true, y_pred, input_length, label_length)
|
|
self.add_loss(loss)
|
|
|
|
# At test time, just return the computed predictions
|
|
return y_pred
|