loss-function

If I correctly understood the significance of the loss function to the model, it directs the model to be trained based on minimizing the loss value. So for example, if I want my model to be trained in order to have the least mean absolute error, i should use the MAE as the loss function. Why is it, for example, sometimes you see someone wanting to achieve the best accuracy possible, but building the model to minimize another completely different function? For example: model.compile(loss='mean_squared_error', optimizer='sgd', metrics='acc') How come the model above is trained to give us the

I have a feature vector size of 1x4098. Each feature vector corresponds to a float number (temperature). In training, I have 10.000 samples. Hence, I have training set size of 10000x4098 and the label of 10000x1. I want to use linear regression model to predict temperature from training data. i am using 3 hidden layers (512, 128, 32) with MSE loss. However, I only got 80% accuracy using tensorflow. Could you suggest to me others loss function to get better performance? Let me give a rather theoretical explanation on the choice of loss function. As you may guess, it all depends on the data. MSE

I'm trying to combine two outputs that are produced by the same network that makes predictions on a 4 class task and a 10 class task. Then I look to combine these outputs to give a length 14 array which I use as my end target. While this seems to work actively the predictions are always for one class so it produces a probability dist which is only concerned with selecting 1 out of the 14 options instead of 2. What I actually need it to do is to provide 2 predictions, one for each class. I want this all to be produced by the same model. input = Input(shape=(100, 100), name='input') lstm = LSTM

Introduction I would like to implement a custom loss function to Keras. I want to do this, because I am not happy with the current result for my dataset. I think the reason for this is because currently the built-in loss functions focuses on the whole dataset. And I just want to focus on the top values in my dataset. That is why I came up with the following idea for a custom loss function: Custom Loss Function Idea The custom loss function should take the top 4 predictions with the highest value and subtract it with the corresponding true value. Then take the absolute value from this

I'm new to Keras (and ML in general) and I'm trying to train a binary classifier. I'm using weighted binary cross entropy as a loss function but I am unsure how I can test if my implementation is correct. Is this an accurate implementation of weighted binary cross entropy? How could I test if it is? def weighted_binary_crossentropy(self, y_true, y_pred): logloss = -(y_true * K.log(y_pred) * self.weights[0] + \ (1 - y_true) * K.log(1 - y_pred) * self.weights[1]) return K.mean(logloss, axis=-1) Atop true vs pred loss, Keras train and val loss includes regularization losses. A simple testing