Python &引用；“进程停止”；在使用vgg模型计算模型后的预测期间

我目前面临着我的

Tensorflow

管道的问题。 不知道它是特定于

Tensorflow

还是Python

在我编译的

vgg16

模型之后，我试图做一个混淆矩阵。 因此，我使用拟合方法得到的模型对象，并尝试预测相同的特征来计算我的CM

但英文中出现了“Processus arrêté”或process stopped的消息，脚本停止工作

以下是输出：

    Using TensorFlow backend.
    
    Load audio features and labels : 100%  Time: 0:00:50 528.41  B/s
   VGG16 model with last layer changed
    Number of label: 17322
    Model: "sequential"

---

我已经找到了为什么会这样

---

只是因为记忆。我的内存RAM不够大，无法计算我拥有的数据总量

我发现了为什么会出现这种情况

只是因为记忆。我的内存RAM不够大，无法计算我拥有的数据总量

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
vgg16 (Functional)           (None, 4, 13, 512)        14713536  
_________________________________________________________________
flatten (Flatten)            (None, 26624)             0         
_________________________________________________________________
dense (Dense)                (None, 256)               6816000   
_________________________________________________________________
dropout (Dropout)            (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 257       
=================================================================
Total params: 21,529,793
Trainable params: 13,895,681
Non-trainable params: 7,634,112
_________________________________________________________________
2772/2772 [==============================] - 121s 44ms/step - loss: 0.2315 - acc: 0.9407 - val_loss: 0.0829 - val_acc: 0.9948
Processus arrêté

Here is the model : 
   

     def launch2(self):
    
            print("VGG16 model with last layer changed")
            x = np.array(self.getFeatures())[...,np.newaxis]
            print("Number of label: " + str(len(self.getLabels())))
    
            vgg_conv=VGG16(weights=None, include_top=False, input_shape=(128, 431, 1))
    
            #Freeze the layers except the last 4 layers
            for layer in vgg_conv.layers[:-4]:
                layer.trainable = False
    
            #Create the model
            model = tensorflow.keras.Sequential()
    
            #Add the vgg convolutional base model
            model.add(vgg_conv)
    
            opt = Adam(lr=1e-4)
    
            model.add(Flatten())
            model.add(Dense(256, activation='relu'))
            model.add(Dropout(0.5))
            model.add(Dense(1, activation="sigmoid"))
    
            model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['acc'])
            model.summary()
    
            model.fit(x=x,y=self.getLabels(),shuffle=True,batch_size=5,epochs=1, validation_split=0.2, verbose=1)
    
            model.save('vggModelLastLayer.h5')
    
            self.testModel(model,x)

Here is the function which allow me to compute the CM : 


    def testModel(self, model,x):
    
            print("Informations about model still processing. Last step is long")
            y_labels = [int(i) for i in self.getLabels().tolist()]
    
            classes = model.predict_classes(x)
    
            predicted_classes = np.argmax(results, axis=1)
            # Call model info (true labels, predited labels)
    
            #self.modelInfo(y_labels, predicted_classes)
            from sklearn.metrics import classification_report
            from sklearn.metrics import confusion_matrix
            cm=confusion_matrix(y_labels,predicted_classes)
            target_names=["Bulls","No bulls"]
            print(classification_report(y_labels,predicted_classes, target_names=target_names))
            print(cm)


How could I fix this ? Is this a memory leak or something ? 
Thank you in advance