@tf.function
def train_step(batch):

# Record all of our operations 
with tf.GradientTape() as tape:     
    # Get anchor and positive/negative image
    X = batch[:2]
    # Get label
    y = batch[2]
    
    # Forward pass
    yhat = siamese_model(X, training=True)
    # Calculate loss
    loss = binary_cross_loss(y, yhat)
    
# Calculate gradients
grad = tape.gradient(loss, siamese_model.trainable_variables)

# Calculate updated weights and apply to siamese model
opt.apply_gradients(zip(grad, siamese_model.trainable_variables))

def train(data, EPOCHS):
# Loop through epochs
for epoch in range(1, EPOCHS+1):
print(‘n Epoch {}/{}’.format(epoch, EPOCHS))
progbar = tf.keras.utils.Progbar(len(data))

    # Creating a metric object 
    r = Recall()
    p = Precision()
    
    # Loop through each batch
    for idx, batch in enumerate(data):
        # Run train step here
        loss = train_step(batch)
        yhat = siamese_model.predict(batch[:2])
        r.update_state(batch[2], yhat)
        p.update_state(batch[2], yhat) 
        progbar.update(idx+1)
    print(loss.numpy(), r.result().numpy(), p.result().numpy())
    
    # Save checkpoints
    if epoch % 10 == 0: 
        checkpoint.save(file_prefix=checkpoint_prefix)

EPOCHS = 50
train(train_data,EPOCHS)

I tried changing the loss functions but it didnt work and i am having the same problem