可以参考：
官方Keras函数式编程
如何使用Keras函數式API進行深度學習

使用tf.kears sequential的实例

import matplotlib as pl
import matplotlib.pyplot as plt
%matplotlib inline
import scikitplot as skplt
import numpy as np
import sklearn
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras

from sklearn.metrics import confusion_matrix


print(tf.__version__)
print(sys.version_info)
for module in np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)

# 导入mnist数据
fashion_mnist = keras.datasets.fashion_mnist
(x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()
x_valid, x_train = x_train_all[:5000], x_train_all[5000:]
y_valid, y_train = y_train_all[:5000], y_train_all[5000:]

print(x_valid.shape, y_valid.shape)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)

# 标准化
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
# x_train: [None, 28, 28] -> [None, 784]
x_train_scaled = scaler.fit_transform(
    x_train.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28)
# 使用训练集fit好的值标准化验证集和测试集
x_valid_scaled = scaler.transform(
    x_valid.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28)
x_test_scaled = scaler.transform(
    x_test.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28)

# tf.keras.models.Sequential()
model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape=[28, 28]))
for _ in range(5):
    model.add(keras.layers.Dense(100, activation="relu"))
    # BatchNormalization
    model.add(keras.layers.BatchNormalization())

model.add(keras.layers.Dense(10, activation="softmax"))

model.compile(loss="sparse_categorical_crossentropy",
              optimizer = keras.optimizers.SGD(0.001),
              metrics = ["accuracy"])
model.summary()
keras.utils.plot_model(model, "my_first_model_with_shape_info.png", show_shapes=True)

logdir = './saved'
if not os.path.exists(logdir):
    os.mkdir(logdir)
output_model_file = os.path.join(logdir, "fashion_mnist_model.h5")
# 添加callbacks: Tensorboard, earlystopping, ModelCheckpoint
callbacks = [
    keras.callbacks.TensorBoard(logdir),
    keras.callbacks.ModelCheckpoint(output_model_file, save_best_only = True),
    keras.callbacks.EarlyStopping('val_loss', patience=5, min_delta=1e-3)]
history = model.fit(x_train_scaled, y_train, epochs=10,
                    validation_data=(x_valid_scaled, y_valid),
                    callbacks = callbacks)

test_scores = model.evaluate(x_test, y_test, verbose=2)
print("Test loss:", test_scores[0])
print("Test accuracy:", test_scores[1])

# predict probabilities for test set
y_probs = model.predict(x_test, verbose=0)
ypred_classes = np.argmax(y_probs, axis=1)

# save and plot confusion matrix
matrix = confusion_matrix(ypred_classes, y_test)
print(matrix)
skplt.metrics.plot_confusion_matrix(ypred_classes, y_test, normalize=True)
plt.savefig('./saved/confusion_matrix.png', format='png')
plt.show()