如何 plot 這個 Keras CNN model 的准確度和損失？

Question

下面的代碼適用於我的 CNN model，我想 plot 的准確性和損失，任何幫助將不勝感激。 我希望使用 matplotlib 繪制 output 所以需要任何建議，因為我不確定如何處理這個問題。 兩個具有訓練和驗證精度的圖和另一個具有訓練和驗證損失的 plot。

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, 3), activation='relu'))

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

    #Third convolution layer
    model.add(Conv2D(128, (3, 3), 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,test_labels, verbose=1)

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

    #The model details
    history = model.fit(imgs,img_labels,
                        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,test_labels, verbose=1)
    print('\nKeras CNN binary accuracy:', score[1],'\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);

Answer 1

在 CNN model 上工作時，這對我有用：

import matplotlib.pyplot as plt

# summarize history for accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()

你可以在這里看到 plot 的圖像