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Multivariate Gaussian Negative LogLikelihood Loss Keras
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| import keras.backend as K | |
| import numpy as np | |
| def gaussian_nll(ytrue, ypreds): | |
| """Keras implmementation of multivariate Gaussian negative loglikelihood loss function. | |
| This implementation implies diagonal covariance matrix. | |
| Parameters | |
| ---------- | |
| ytrue: tf.tensor of shape [n_samples, n_dims] | |
| ground truth values | |
| ypreds: tf.tensor of shape [n_samples, n_dims*2] | |
| predicted mu and logsigma values (e.g. by your neural network) | |
| Returns | |
| ------- | |
| neg_log_likelihood: float | |
| negative loglikelihood averaged over samples | |
| This loss can then be used as a target loss for any keras model, e.g.: | |
| model.compile(loss=gaussian_nll, optimizer='Adam') | |
| """ | |
| n_dims = int(int(ypreds.shape[1])/2) | |
| mu = ypreds[:, 0:n_dims] | |
| logsigma = ypreds[:, n_dims:] | |
| mse = -0.5*K.sum(K.square((ytrue-mu)/K.exp(logsigma)),axis=1) | |
| sigma_trace = -K.sum(logsigma, axis=1) | |
| log2pi = -0.5*n_dims*np.log(2*np.pi) | |
| log_likelihood = mse+sigma_trace+log2pi | |
| return K.mean(-log_likelihood) |
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Hello, I'd like to ask if the variance value you get here is the logarithm of the variance directly obtained. Then how do you design your variance prediction network? Do you take the logarithmic variance in the code after the prediction?