On the other hand, most studies use categorical cross-entropy loss function, which is not optimal for the ordinal regression problem, to train the deep learning models. In this study, we propose a novel loss function called class distance weighted cross-entropy (CDW-CE) that respects the ...
lossAll = lasagne.objectives.categorical_crossentropy(prediction, Y)#loss functionloss = lossAll.mean() loss = loss + l2_penalty accuracy = T.mean(T.eq(T.argmax(prediction, axis=1), Y), dtype=theano.config.floatX) match = T.eq(T.argmax(prediction, axis=1), Y) params = lasa...
Because the problem is multi-class, we will use the categorical cross entropy loss function to optimize the model and the efficient Adam flavor of stochastic gradient descent. 1 2 3 4 5 # define model model = Sequential() model.add(Dense(25, input_dim=2, activation='relu'))...
model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer={{choice(['rmsprop','adam','adadelta','adagrad'])}}) earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1) model.fit(other_train, Y_train, batch_size=32, nb_epoch=25, validation_...
The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned ba
The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned ba
14 Microaggregation for categorical variables: a median based approach Torra, V [121] PSDP Conference on privacy in statistical databases (PSD 2004) 56 2004 15 Soft computing-based aggregation methods for human resource management Canos, L; Liern, V [122] EJOR 3rd Biannual conference on operat...
During training, the binary cross-entropy loss of the predicted outputs for a single batch is calculated as follows: $$J\left(w\right)=-\frac{1}{N}\sum_{n=1}^{N}[{y}^{n}log\left(f\left({x}_{i}^{n};w\right)\right){\widehat{y}}^{n}+(1-{y}^{n})\mathrm{log}(1-...
The proposed method enables cooperative training of image denoising and lung nodule classification by utilizing self-supervised loss and cross-entropy loss. According to the experiments, the simultaneous training of image denoising and lung nodule classification increases the performance significantly. Lei ...
These selected factors involve both continuous and categorical data, of which the former may increase the computational amount much and subsequently lead to complex data processing. To remedy this, these factors were discretized into several categories with the same attribute interval (Figure 7). A ...