nn_model.py 文件源码

def fit(self, X_trains, y_train):
        X_train1, X_train2, X_train3 = X_trains
        main_target, X1_vid = y_train
        early_stopping = EarlyStopping(monitor='val_loss', patience=2)
        print(X_train1.shape)
        print(X1_vid.shape)
        print(main_target.shape)
        self.model.fit({'X1': X_train1, 'X2': X_train2, 'X3': X_train3},
                       {'main_output': main_target, 'aux_output': X1_vid},
                       batch_size=self.batch_size, nb_epoch=self.nb_epoch, verbose=1,
                       validation_data=([X_train1, X_train2, X_train3], y_train), callbacks=[early_stopping])
        y_target = np.argmax(X1_vid, axis=1)
        y_predict = np.argmax(self.vision_model.predict(X_train1, verbose=0), axis=1)
        conf_mat = confusion_matrix(y_target, y_predict)
        print('Test accuracy:')
        n_correct = np.sum(np.diag(conf_mat))
        print('# correct:', n_correct, 'out of', len(y_target), ', acc=', float(n_correct) / len(y_target))