(x, xhat, hparams)
return total_loss
MSE loss function.

  Args:
    x: Input data tensor.
    xhat: Reconstruction tensor.
    hparams: Hyperparameters.

  Returns:
    total_loss: MSE loss scalar.
MSE loss function.
MSE loss function .
def compute_mse_loss(x, xhat, hparams):
  """MSE loss function.

  Args:
    x: Input data tensor.
    xhat: Reconstruction tensor.
    hparams: Hyperparameters.

  Returns:
    total_loss: MSE loss scalar.
  """
  with tf.name_scope("Losses"):
    if hparams.raw_audio:
      total_loss = tf.reduce_mean((x - xhat)**2)
    else:
      # Magnitude
      m = x[:, :, :, 0] if hparams.cost_phase_mask else 1.0
      fm = utils.frequency_weighted_cost_mask(
          hparams.fw_loss_coeff,
          hz_flat=hparams.fw_loss_cutoff,
          n_fft=hparams.n_fft)
      mag_loss = tf.reduce_mean(fm * (x[:, :, :, 0] - xhat[:, :, :, 0])**2)
      if hparams.mag_only:
        total_loss = mag_loss
      else:
        # Phase
        if hparams.dphase:
          phase_loss = tf.reduce_mean(fm * m *
                                      (x[:, :, :, 1] - xhat[:, :, :, 1])**2)
        else:
          # Von Mises Distribution "Circular Normal"
          # Added constant to keep positive (Same Probability) range [0, 2]
          phase_loss = 1 - tf.reduce_mean(fm * m * tf.cos(
              (x[:, :, :, 1] - xhat[:, :, :, 1]) * np.pi))
        total_loss = mag_loss + hparams.phase_loss_coeff * phase_loss
        tf.summary.scalar("Loss/Mag", mag_loss)
        tf.summary.scalar("Loss/Phase", phase_loss)
    tf.summary.scalar("Loss/Total", total_loss)
  return total_loss
def compute_mse_loss ( x , xhat , hparams ) : with tf . name_scope ( "Losses" ) : if hparams . raw_audio : total_loss = tf . reduce_mean ( ( x - xhat ) ** 2 ) else : # Magnitude m = x [ : , : , : , 0 ] if hparams . cost_phase_mask else 1.0 fm = utils . frequency_weighted_cost_mask ( hparams . fw_loss_coeff , hz_flat = hparams . fw_loss_cutoff , n_fft = hparams . n_fft ) mag_loss = tf . reduce_mean ( fm * ( x [ : , : , : , 0 ] - xhat [ : , : , : , 0 ] ) ** 2 ) if hparams . mag_only : total_loss = mag_loss else : # Phase if hparams . dphase : phase_loss = tf . reduce_mean ( fm * m * ( x [ : , : , : , 1 ] - xhat [ : , : , : , 1 ] ) ** 2 ) else : # Von Mises Distribution "Circular Normal" # Added constant to keep positive (Same Probability) range [0, 2] phase_loss = 1 - tf . reduce_mean ( fm * m * tf . cos ( ( x [ : , : , : , 1 ] - xhat [ : , : , : , 1 ] ) * np . pi ) ) total_loss = mag_loss + hparams . phase_loss_coeff * phase_loss tf . summary . scalar ( "Loss/Mag" , mag_loss ) tf . summary . scalar ( "Loss/Phase" , phase_loss ) tf . summary . scalar ( "Loss/Total" , total_loss ) return total_loss