I want to compare the result of my prediction with that of another person's prediction. In the article, the author says 'The relative percentage of root mean square (RMS%) was used to evaluate the ...

Is there a method in numpy for calculating the Mean Squared Error between two matrices? I've tried searching but found none. Is it under a different name? If there isn't, how do you overcome this...

I am using pytorch to train my CNN network. I want to plot my training and validation loss curves to visulize the model performance. How can I plot two curves? I have below code # create a function...

For a very simple classification problem where I have a target vector [0,0,0,....0] and a prediction vector [0,0.1,0.2,....1] would cross-entropy loss converge better/faster or would MSE loss? When...

In general, the mean_squared_error is the smaller the better. When I am using the sklearn metrics package, it says in the document pages: http://scikit-learn.org/stable/modules/model_evaluation.h...

I am trying to solve a simple binary classification problem using LSTM. I am trying to figure out the correct loss function for the network. The issue is, when I use the binary cross-entropy as los...

I have 2 tensors with .size of torch.Size([2272, 161]). I want to get mean-squared-error between them. However, I want it along each of the 161 channels, so that my error tensor has a .size of torc...

I have produced a linear data set and have used lm() to fit a model to that dataset. I am now trying to find the MSE using mse() I know the formula for MSE but I'm trying to use this function. Wha...

How is Accuracy defined when the loss function is mean square error? Is it mean absolute percentage error? The model I use has output activation linear and is compiled with loss= mean_squared_er...

I am currently implementing a custom loss layer and in the process, I stumbled upon the implementation of mean squared error in the objectives.py file [1]. I know I'm missing something in my unders...

Hello I have a network that produces logits / outputs like this: logits = tf.placeholder(tf.float32, [None, 128, 64, 64]) // outputs y = tf.placeholder(tf.float32, [None, 128, 64, 64]) // gr...

Although both of the above methods provide a better score for the better closeness of prediction, still cross-entropy is preferred. Is it in every case or there are some peculiar scenarios wh...