17e198bcc50b15e0d521c71f25dd76f1cc840754,python/eight_mile/tf/layers.py,LSTMEncoderWithState,call,#LSTMEncoderWithState#Any#Any#,358

Before Change

    def call(self, inputs, training=False):

        inputs, hidden = inputs
        rnnout, hidden = tf.nn.dynamic_rnn(self.rnn, inputs, initial_state=hidden, dtype=tf.float32)
        return self.output_fn(rnnout, hidden)


// Mapped

After Change

    def call(self, inputs, training=False):

        unary, lengths = inputs
        if training:
            return tf.contrib.crf.crf_log_norm(unary, lengths, self.transitions)
        else:
            return self.decode(unary, lengths)

    def decode(self, unary, lengths):
        Do Viterbi decode on a batch.

        :param unary: torch.FloatTensor: [T, B, N] or [B, T, N]

Instances