blend.py 文件源码

def fit(self, X, y, eval_set=None, class_weight=None, show_accuracy=True):


        if self.loss == 'categorical_crossentropy':
            y = np_utils.to_categorical(y)

        if eval_set != None and self.loss == 'categorical_crossentropy':
            eval_set = (eval_set[0], np_utils.to_categorical(eval_set[1]))

        self.model = self._build_model(self.input_dim,self.output_dim,self.hidden_units,self.activation,
                                       self.dropout, self.loss, self.optimizer, self.class_mode)

        if eval_set !=None:
            early_stopping = EarlyStopping(monitor='val_loss', patience=self.esr, verbose=1, mode='min')
            logs = self.model.fit(X, y, self.batch_size, self.nb_epoch, self.verbose, validation_data=eval_set, callbacks=[early_stopping], show_accuracy=True, shuffle=True)
        else:
            logs = self.model.fit(X, y, self.batch_size, self.nb_epoch, self.verbose, show_accuracy=True, shuffle=True)

        return logs