In machine learning and information theory, the cross entropy is a measure of distance (inverse similarity) between two probability distributions over the same underlying set of events.
In the following TensorFlow function, we must feed the activation of artificial neurons in the final layer. That I understand. …
tensorflow machine-learning neural-network deep-learning cross-entropyI recently came across tf.nn.sparse_softmax_cross_entropy_with_logits and I can not figure out what the …
neural-network tensorflow softmax cross-entropyI have the following expression: log = np.sum(np.nan_to_num(-y*np.log(a+ 1e-7)-(1-y)*np.…
python numpy math cross-entropyI know that there are a lot of explanations of what cross-entropy is, but I'm still confused. Is it only …
machine-learning cross-entropyClassification problems, such as logistic regression or multinomial logistic regression, optimize a cross-entropy loss. Normally, the cross-entropy layer follows the …
python tensorflow neural-network logistic-regression cross-entropyI am learning the neural network and I want to write a function cross_entropy in python. Where it is …
python machine-learning neural-network cross-entropyAlthough both of the above methods provide a better score for the better closeness of prediction, still cross-entropy is preferred. …
machine-learning neural-network backpropagation mean-square-error cross-entropyWhen trying to get cross-entropy with sigmoid activation function, there is a difference between loss1 = -tf.reduce_sum(p*tf.…
machine-learning tensorflow classification cross-entropy sigmoidI am having a hard time with calculating cross entropy in tensorflow. In particular, I am using the function: tf.…
python machine-learning tensorflow cross-entropyI've got a 40k image dataset of images from four different countries. The images contain diverse subjects: outdoor scenes, city …
tensorflow deep-learning convolution tensorboard cross-entropy