如何在keras中验证预测

我正在经历 Kaggle Digit Recognizer Tutorial 我想知道这一切是如何运作的。我想验证 预测 价值。基本上,我有一个错误的预测,但我想看看这个预测的实际价值是什么。我想我还差得远:

...

df = pd.read_csv('data/train.csv')

labels = df['label'].values
x_train = df.drop(columns=['label']).values / 255
# trying to produce a crappy dataset for train/test
x_train, x_test, y_train, y_test = train_test_split(x_train, labels, test_size=0.95)


# Purposely trying to get a crappy model so I can learn about validation
model = tf.keras.models.Sequential()
# model.add(tf.keras.layers.Flatten())
# model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train, epochs=1)
predictions = model.predict([x_test])


index_to_predict = 0
print('Prediction: ', np.argmax(predictions[index_to_predict]))
print('Actual:     ', predictions.argmax(axis=-1)[index_to_predict])
print(predictions.shape)
vals = x_test[index_to_predict].reshape(28, 28)
plt.imshow(vals)

这将产生以下结果:

我怎样才能得到一个真实的“这里是预测”和“这里是实际”的分解?我对得到实际情况的逻辑肯定是错误的。