如何在python中使局部变量成为全局变量

如何解决如何在python中使局部变量成为全局变量

模型运行良好，但我想绘制结果并且似乎无法使其正常工作，有什么帮助吗？我相信我必须使局部变量成为全局变量以确保准确性、损失、val_accuracy、val_loss 但我不知道在这种情况下如何做到这一点。我真的需要帮助解决这个问题，因为它造成了很大的压力哈哈

bin_labels = {1:'eosinOPHIL',2:'LYMPHOCYTE',3:'MONOCYTE',4:'NEUTROPHIL'}

def CNN(imgs,img_labels,test_imgs,test_labels,stride):

    #Number of classes (2)
    num_classes = len(img_labels[0])

    #Size of image
    img_rows,img_cols=imgs.shape[1],imgs.shape[2]
    input_shape = (img_rows,img_cols,3)

    #Creating the model
    model = Sequential()

    #First convolution layer
    model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=input_shape,strides=stride))

    #First maxpooling layer
    model.add(MaxPooling2D(pool_size=(2,2)))

    #Second convolution layer
    model.add(Conv2D(64,(3,activation='relu'))

    #Second maxpooling layer
    model.add(MaxPooling2D(pool_size=(2,2)))

    #Third convolution layer
    model.add(Conv2D(128,activation='relu'))

    #Third maxpooling layer
    model.add(MaxPooling2D(pool_size=(2,2)))

    #Convert the matrix to a fully connected layer
    model.add(Flatten())

    #Dense function to convert FCL to 128 values
    model.add(Dense(128,activation='relu'))

    #Final dense layer on which softmax function is performed
    model.add(Dense(num_classes,activation='softmax'))

    #Model parameters
    model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

    #Evaluate the model on the test data before training your model
    score = model.evaluate(test_imgs,verbose=1)

    print('\nKeras CNN binary accuracy:',score[1],'\n')

    #The model details
    history = model.fit(imgs,shuffle = True,epochs=3,validation_data = (test_imgs,test_labels))

    #Evaluate the model on the test data after training your model
    score = model.evaluate(test_imgs,verbose=1)
    print('\nKeras CNN binary accuracy:','\n')

    #Predict the labels from test data
    y_pred = model.predict(test_imgs)
    Y_pred_classes = np.argmax(y_pred,axis=1) 
    Y_true = np.argmax(test_labels,axis=1)

    #Correct labels
    for i in range(len(Y_true)):
        if(Y_pred_classes[i] == Y_true[i]):
            print("The predicted class is : ",Y_pred_classes[i])
            print("The real class is : ",Y_true[i])
            break
        
    #The confusion matrix made from the real Y values and the predicted Y values
    confusion_mtx = [Y_true,Y_pred_classes]

    #Summary of the model
    model.summary()

    return model,confusion_mtx

model,conf_mat = CNN(X_train,y_trainHot,X_test,y_testHot,1);





def plot_accuracy_loss_chart(history):
    epochs = [i for i in range(10)]
    fig,ax = plt.subplots(1,2)
    train_acc = model.history.history['accuracy']
    train_loss = model.history.history['loss']
    val_acc = model.history.history['val_accuracy']
    val_loss = model.history.history['val_loss']
    fig.set_size_inches(20,10)
    ax[0].plot(epochs,train_acc,'go-',label = 'Training Accuracy')
    ax[0].plot(epochs,val_acc,'ro-',label = 'Validation Accuracy')
    ax[0].set_title('Training & Validation Accuracy')
    ax[0].legend()
    ax[0].set_xlabel("Epochs")
    ax[0].set_ylabel("Accuracy")

    ax[1].plot(epochs,train_loss,'g-o',label = 'Training Loss')
    ax[1].plot(epochs,val_loss,'r-o',label = 'Validation Loss')
    ax[1].set_title('Training & Validation Loss')
    ax[1].legend()
    ax[1].set_xlabel("Epochs")
    ax[1].set_ylabel("Training & Validation Loss")
    plt.show()
plot_accuracy_loss_chart(model.history)

这是我收到的错误

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-136-ef9e8c8a1775> in <module>
     21     ax[1].set_ylabel("Training & Validation Loss")
     22     plt.show()
---> 23 plot_accuracy_loss_chart(model.history)

<ipython-input-136-ef9e8c8a1775> in plot_accuracy_loss_chart(history)
      2     epochs = [i for i in range(10)]
      3     fig,2)
----> 4     train_acc = model.history.history['accuracy']
      5     train_loss = model.history.history['loss']
      6     val_acc = model.history.history['val_accuracy']

KeyError: 'accuracy'