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We introduce a parametric family of entropy regularizers, which includes label smoothing as a special case, and use it to gain a better understanding of the relationship between the entropy of a model and its performance on language generation tasks. We also find that variance in model ...
entropy regularizer (β) tuning To speed up the training procedures for all three methods, we set the horizon length to 500 for two discrete action environments and to 200 for six continuous action environments. 6.3. Hyper-parameter tuning procedure TRPO has one hyper-parameter (δ), ERO-TRPO...
In addition, it naturally avoids the choice on the parameter m due to such a maximum-entropy regularizer. The experiments on real-world data sets show the feasibility and effectiveness of the proposed method with encouraging results.doi:10.1016/j.knosys.2012.05.016Xuesong Yin a...
Minimum entropy regularizers have been used in other contexts to encode learnability priors. Input-Dependent Regularization When the model is regularized (e.g. with weight decay), the conditional entropy is prevented from being too small close to the decision surface. This will favor putting the ...
The minimum entropy regularizer can be applied to both local and global classifiers. As a result, it can improve over manifold learning when the dimensionality of data is effectively high, that is, when data do not lie on a low-dimensional manifold. Also, our experiments suggest that the ...
hidden_units=10000l2_sparsity=5e-7l1_sparsity=1e-8mod=Sequential([Dense(hidden_units,input_shape=(1000,),activation="relu",kernel_regularizer=l1_l2(l1=l1_sparsity,l2=l2_sparsity),),Dense(hidden_units,activation="relu",kernel_regularizer=l1_l2(l1=l1_sparsity,l2=l2_sparsity),),Dense(1000,...
add(Flatten()) clf.add(Dense(300, kernel_regularizer=regularizers.l2(w_decay))) clf.add(BatchNormalization()) clf.add(Activation('relu')) clf.add(Dense(num_class, activation='softmax')) clf.compile(loss=categorical_crossentropy, optimizer=Adam(lr=lr), metrics=['accuracy']) return clf ...
private static double regularizer = 0.1; /// /// Based on the formula for total information entropy given here - https://en.wikipedia.org/wiki/Dirichlet_distribution. /// /// alpha: The parameters of the Dirichlet distribution. These correspond to a histogram with counts. /// <returns...
clf.add(Dense(300, kernel_regularizer=regularizers.l2(w_decay))) clf.add(BatchNormalization()) clf.add(Activation('relu')) clf.add(Dense(num_class, activation='softmax')) clf.compile(loss=categorical_crossentropy, optimizer=Adam(lr=lr), metrics=['accuracy'])returnclf ...