Custom loss function in keras? Correntropy. Math/implementation issues

Question

Im working with a paper where it implements an autoencoder with a custom loss function to work with vibration signals.

Im having trouble implementing it on keras. They implement "Maximum Correntropy" as the loss function to avoid issues with background noise on a signal.

This is the description:

Gaussian kernel is the most popular Mercer kernel in correntropy, which is defined as

where r is the kernel size. Then, the new autoencoder loss function can be designed by maximizing the following function:

Since i never implemented a custom loss function im having issues with the math in python. The kernel is used on the loss function that i need to implement. This is what i have:

dataset.npz

file = np.load('./data/CWRU_48k_load_1_CNN_data.npz')  # Numpy Array 

data = file['data'].reshape(len(file['data']), 1024)
labels = file['labels']
category_labels = np.unique(labels)
labels = pd.Categorical(labels, categories = category_labels).codes

train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size = int(data.shape[0]*0.2), random_state = 100, stratify = labels)

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

# Data shape. Sample Len: 1024. Outputs/Classifications: 10
print(train_data.shape, train_labels.shape, test_data.shape, test_labels.shape)
#(3680, 1024) (3680, 10) (920, 1024) (920, 10)

act_func = 'relu'
out_func = 'softmax'
k_inic = 'glorot_uniform'  

def create_model(shape=[512, 100], loss_func='mse'):
    model = Sequential()

    for shape_size in shape:
        model.add(Dense(shape_size, activation=act_func, kernel_initializer=k_inic))

    model.add(Dense(10, activation=out_func, kernel_initializer=k_inic))
    model.compile(loss=loss_func, optimizer=keras.optimizers.Adam(), metrics=["accuracy"])
    model.build(input_shape=(None, 1024))

    return model

BATCH_SIZE = 45
EPOCHS = 200
VALIDATION_SPLIT = 0.05

# Design Mercer Kernel
def kernel(x, sigma=1):
    return (1/(K.sqrt(2*np.pi)*sigma))*K.exp((-(x*x)/(2*sigma*sigma)))

# Use Mercer Kernel on Maximum Correntropy for loss function
def correntropy(y_true, y_pred):
    sum_score = 0.0
    for i in range(len(y_true)):
        sum_score = kernel(y_true[i] - y_pred[i])
    sum_score = sum_score/len(y_true)
    return -sum_score

# Create AutoEncoder model with my custom loss function
model = create_model(shape=[512, 100], loss_func=correntropy)
history = model.fit(train_data, train_labels, epochs = EPOCHS, batch_size = BATCH_SIZE, validation_data=(test_data, test_labels), 
                        callbacks = callbacks.callbacks, verbose = 0)

res = model.evaluate(test_data, test_labels, batch_size = BATCH_SIZE, verbose = 0)[1]

But i have this error:

AttributeError: in user code:

    /home/user/.local/lib/python3.8/site-packages/keras/engine/training.py:853 train_function  *
        return step_function(self, iterator)
    /tmp/ipykernel_95935/2003563015.py:26 correntropy  *
        sum_score = kernel(y_true[i] - y_pred[i])
    /tmp/ipykernel_95935/2239884018.py:20 kernel  *
        return (1/(K.sqrt(2*np.pi)*sigma))*K.exp((-(x*x)/(2*sigma*sigma)))
    /home/user/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper  **
        return target(*args, **kwargs)
    /home/user/.local/lib/python3.8/site-packages/keras/backend.py:2539 sqrt
        zero = _constant_to_tensor(0., x.dtype.base_dtype)

    AttributeError: 'float' object has no attribute 'dtype'

The error seems to be on the kernel, but how do i fix to work with tensors?

print(y_true)
print(y_pred)
>> Tensor("IteratorGetNext:1", shape=(None, 10), dtype=float32)
>> Tensor("sequential_161/dense_491/Softmax:0", shape=(None, 10), dtype=float32)