def fourier_l2(y_true, y_pred):
y_true_fourier = utils.convert_tensor_to_frequency_domain(
y_true)
y_pred_fourier = utils.convert_tensor_to_frequency_domain(
y_pred)
y_true = utils.join_reim_tensor(y_true_fourier)
y_pred = utils.join_reim_tensor(y_pred_fourier)
return K.mean(K.pow(K.abs(y_true - y_pred), 2))
def image_l1(y_true, y_pred):
y_true_fourier = utils.convert_tensor_to_image_domain(
tf.signal.ifftshift(y_true, axes=(1, 2)))
y_pred_fourier = utils.convert_tensor_to_image_domain(
tf.signal.ifftshift(y_pred, axes=(1, 2)))
return K.mean(
K.abs(
K.abs(utils.join_reim_tensor(y_true_fourier)) -
K.abs(utils.join_reim_tensor(y_pred_fourier))))
def join_reim_mag_output(tensor):
"""
Args:
tensor: Tensor of shape (batch_size, n, n, 2)
Returns:
Tensor of shape (batch_size, n, n) with joined real and imag parts
"""
return tf.expand_dims(K.abs(utils.join_reim_tensor(tensor)), -1)
def image_l1(y_true, y_pred):
return K.mean(
K.abs(
K.abs(utils.join_reim_tensor(y_true)) -
K.abs(utils.join_reim_tensor(y_pred))))
