如何在keras中训练和实现信心

import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(image_width, image_height, num_channels)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

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

from keras.datasets import mnist
from keras.utils import to_categorical

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, image_width, image_height, num_channels)
x_test = x_test.reshape(-1, image_width, image_height, num_channels)
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

model.fit(x_train, y_train, batch_size=batch_size, epochs=num_epochs, validation_data=(x_test, y_test))

loss, accuracy = model.evaluate(x_test, y_test)

predictions = model.predict(x_test)