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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) |
Hi, may I know how to solve this error??
"ValueError: Dimensions must be equal, but are 128 and 64 for '{{node gaussian_nll/sub}} = Sub[T=DT_FLOAT](Cast, gaussian_nll/strided_slice)' with input shapes: [?,128,128,3], [?,64,128,3]."
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Hi,
Why do you use sum in this piece of code
sigma_trace = -K.sum(logsigma, axis=1)
?