predict_emotion.py 文件源码

def __init__(self, x_data, y_data, feature, initmodel, gpu = -1):
        self.N = 5000
        self.N_test = 766
        self.total = self.N + self.N_test
        self.emotion_weight = {0: self.total / 716, 1: self.total / 325, 2: self.total / 1383, 3: self.total / 743, 4: self.total / 2066, 5: self.total / 74, 6: self.total / 17, 7: self.total / 35, 8: self.total / 404,  9: self.total / 3}
        self.x_data = x_data.astype(np.float32)
        self.x_data = np.vstack((self.x_data, self.x_data))
        self.y_data = y_data.astype(np.int32)
        self.y_data = np.vstack((self.y_data, self.y_data))
        if feature == "IS2009":
            self.input_layer = 384
        elif feature == "IS2010":
            self.input_layer = 1582
        self.n_units = 256
        self.output_layer = 10
        self.model = L.Classifier(net.EmotionRecognitionVoice(self.input_layer, self.n_units, self.output_layer))
        self.gpu = gpu
        self.__set_cpu_or_gpu()
        self.emotion = {0: "Anger", 1: "Happiness", 2: "Excited", 3: "Sadness", 4: "Frustration", 5: "Fear", 6: "Surprise", 7: "Other", 8: "Neutral state", 9: "Disgust"}
        # Init/Resume
        serializers.load_hdf5(initmodel, self.model)