Tensor indexing in custom loss function

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Basically, I want my custom loss function to alternate between usual MSE and a custom MSE that subtract values from different indexes.

To clarify, let's say I have a y_pred tensor that is [1, 2, 4, 5] and a y_true tensor that is [2, 5, 1, 3]. In usual MSE, we should get:

return K.mean(K.squared(y_pred - y_true))

That would do the following:

[1, 2, 4, 5] - [2, 5, 1, 3] = [-1, -3, 3, 2]

[-1, -3, 3, 2]² = [1, 9, 9, 4]

mean([1, 9, 9, 4]) = 5.75

I need my custom loss function to select the minimum value between this mean and other that switches indexes 1 and 3 from the y_pred tensor, i.e.:

[1, 5, 4, 2] - [2, 5, 1, 3] = [-1, 0, 3, 1]

[-1, 0, 3, 1]² = [1, 0, 9, 1]

mean([1, 0, 9, 1]) = 2.75

So, my custom loss would return 2.75, which is the minimum value between both means. To do this, I tried to transform y_true and y_pred tensors in numpy arrays, doing all math related as following:

def new_mse(y_true, y_pred):
    sess = tf.Session()

    with sess.as_default():
        np_y_true = y_true.eval()
        np_y_pred = y_pred.eval()

        np_err_mse = np.empty(np_y_true.shape)
        np_err_mse = np.square(np_y_pred - np_y_true)

        np_err_new_mse = np.empty(np_y_true.shape)
        l0 = np.square(np_y_pred[:, 2] - np_y_true[:, 0])   
        l1 = np.square(np_y_pred[:, 3] - np_y_true[:, 1])
        l2 = np.square(np_y_pred[:, 0] - np_y_true[:, 2])
        l3 = np.square(np_y_pred[:, 1] - np_y_true[:, 3])   
        l4 = np.square(np_y_pred[:, 4] - np_y_true[:, 4])
        l5 = np.square(np_y_pred[:, 5] - np_y_true[:, 5])
        np_err_new_mse = np.transpose(np.vstack(l0, l1, l2, l3, l4, l5))

        np_err_mse = np.mean(np_err_mse)
        np_err_new_mse = np.mean(np_err_new_mse)

        return np.amin([np_err_mse, np_err_new_mse])

Problem is that I can't use eval() method with y_true and y_pred tensors, not sure why. Finally, my questions are:

  1. Is there an easier method to work with indexing inside tensors and loss functions? I'm newbie to Tensorflow and Keras in general and I strongly believe transforming everything in numpy arrays isn't the optimal method at all.
  2. Not totally related to question, but when I tried to print y_true tensor's shape with K.shape(y_true), I got "Tensor("Shape_1:0", shape=(2,), dtype=int32)". That confuses me, since I'm working with a y.shape equal to (7032, 6), that is 7032 images with 6 labels each. Probably may be some misinterpretation related to my y and y_pred used by loss function.
keras tensorflow neural-network
2 Answers

If using Keras 2 you should just use the K.gather function to perform indexing.

Daniel Möller's answer becomes:

from keras.losses import mean_square_error

def reindex(t, perm):
    K.concatenate([K.gather(t, i) for i in perm])

def my_loss(y_true,y_pred):

    #losses
    regularLoss = mean_squared_error(y_true, y_pred)
    swappedLoss = mean_squared_error(y_true, reindex(y_pred, [0,3,2,1]))

    #concat them for taking a min value
    concat = K.concatenate([regularLoss,swappedLoss])

    #take the minimum
    return K.min(concat)
user_58381090
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