de00082780be884fc90e0113d323bfd63006ffba,main.py,Model,build,#Model#,249

Before Change

                        hparams.MAX_N_SIGNAL,
                        -1, hparams.FEATURE_SIZE])

                s_separated_signals_valid = tf.expand_dims(tf.complex(
                    tf.cos(s_mixed_signals_phase),
                    tf.sin(s_mixed_signals_phase)), 1)
                s_separated_signals *= tf.expm1(s_separated_signals_log_valid)
                s_valid_snr = tf.reduce_mean(ops.batch_snr(
                    s_src_signals, s_separated_signals_valid, is_complex=True))


        // ===============
        // prepare summary
        // TODO add impl & summary for word error rate

        // FIXME gan_loss summary is broken
        with tf.name_scope("train_summary"):
            s_loss_summary_t = tf.summary.scalar("loss", s_train_loss)
            s_snr_summary_t = tf.summary.scalar("SNR", s_train_snr)

        with tf.name_scope("valid_summary"):
            s_loss_summary_v = tf.summary.scalar("loss", s_valid_loss)
            s_snr_summary_v = tf.summary.scalar("SNR", s_valid_snr)

        // apply optimizer
        ozer = hparams.get_optimizer()(
            learn_rate=hparams.LR, lr_decay=hparams.LR_DECAY)

        v_params_li = tf.trainable_variables()

        op_sgd_step = ozer.minimize(
            s_train_loss, var_list=v_params_li)
        self.op_init_params = tf.variables_initializer(v_params_li)
        self.op_init_states = tf.variables_initializer(
            list(self.s_states_di.values()))

        self.train_feed_keys = [
            s_src_signals, s_dropout_keep]
        train_summary = tf.summary.merge(
            [s_loss_summary_t, s_snr_summary_t])
        self.train_fetches = [
            train_summary,
            dict(loss=s_train_loss, SNR=s_train_snr),
            op_sgd_step]

        self.valid_feed_keys = self.train_feed_keys
        valid_summary = tf.summary.merge([s_loss_summary_v, s_snr_summary_v])
        self.valid_fetches = [
            valid_summary,
            dict(loss=s_valid_loss, SNR=s_valid_snr)]

After Change

            s_separated_signals_pwr_infer = tf.expm1(
                s_separated_signals_log_valid)

            s_separated_signals_valid = tf.complex(
                tf.cos(s_mixed_signals_phase) * s_separated_signals_pwr_valid,
                tf.sin(s_mixed_signals_phase) * s_separated_signals_pwr_valid)
            s_separated_signals_infer = tf.complex(
                tf.cos(s_mixed_signals_phase) * s_separated_signals_pwr_infer,
                tf.sin(s_mixed_signals_phase) * s_separated_signals_pwr_infer)
            s_valid_snr = tf.reduce_mean(ops.batch_snr(
                s_src_signals, s_separated_signals_valid, is_complex=True))


        // ===============
        // prepare summary
        // TODO add impl & summary for word error rate

        // FIXME gan_loss summary is broken
        with tf.name_scope("train_summary"):
            s_loss_summary_t = tf.summary.scalar("loss", s_train_loss)
            s_snr_summary_t = tf.summary.scalar("SNR", s_train_snr)

        with tf.name_scope("valid_summary"):
            s_loss_summary_v = tf.summary.scalar("loss", s_valid_loss)
            s_snr_summary_v = tf.summary.scalar("SNR", s_valid_snr)

        // apply optimizer
        ozer = hparams.get_optimizer()(
            learn_rate=hparams.LR, lr_decay=hparams.LR_DECAY)

        v_params_li = tf.trainable_variables()

        op_sgd_step = ozer.minimize(
            s_train_loss, var_list=v_params_li)
        self.op_init_params = tf.variables_initializer(v_params_li)
        self.op_init_states = tf.variables_initializer(
            list(self.s_states_di.values()))

        self.train_feed_keys = [
            s_src_signals, s_dropout_keep]
        train_summary = tf.summary.merge(
            [s_loss_summary_t, s_snr_summary_t])
        self.train_fetches = [
            train_summary,
            dict(loss=s_train_loss, SNR=s_train_snr),
            op_sgd_step]

        self.valid_feed_keys = self.train_feed_keys
        valid_summary = tf.summary.merge([s_loss_summary_v, s_snr_summary_v])
        self.valid_fetches = [
            valid_summary,
            dict(loss=s_valid_loss, SNR=s_valid_snr)]

Instances