代码:

import keras.datasets.fashion_mnist as fashion_mnistimport kerasimport matplotlib.pyplot as pltfrom keras.utils import np_utilsfrom sklearn.model_selection import train_test_split(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() xtrain, xvalid, ytrain, yvalid = train_test_split(train_images, train_labels, test_size=0.33, shuffle= True)xtrain = xtrain / 255.0xvalid =  xvalid/255.0ytrain = np_utils.to_categorical(ytrain )yvalid = np_utils.to_categorical(yvalid)history_dict = history.historyprint(history_dict.keys())history=model1.fit(train_images, train_labels, epochs=30, batch_size=64)loss = history.history['loss']val_loss = history.history['val_loss']accuracy = history.history['binary_accuracy']val_accuracy = history.history['val_accuracy']plt.plot(history.history['accuracy'])  plt.plot(history.history['val_accuracy'])plt.plot(history.history['loss'])plt.plot(history.history['val_loss'])plt.show()

我得到了以下结果:KeyError: 'val_accuracy‘, 我使用了 google.colab. dict_keys(['loss', 'accuracy']), 只有两个变量可用。如何获取val_accuracy和val_loss?

任何建议都将不胜感激

回答：

要获取val_accuracy和val_loss, 你需要在model.fit中提供验证数据。试试这个:

history = model1.fit(train_images, train_labels,                   validation_data = (xvalid,yvalid),                   epochs=30, batch_size=64)