TensorFlow 2.0 中手写数字识别：`resize_with_pad` 方法错误修复

这个错误是因为resize_with_pad方法在TensorFlow 2.0中已经被移除了，可以使用tf.image.resize_with_pad方法代替。修改代码如下：

# 获取手写数字数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# 训练集数据维度的调整：N H W C
train_images = np.reshape(train_images, (train_images.shape[0], train_images.shape[1], train_images.shape[2], 1))
# 测试集数据维度的调整：N H W C
test_images = np.reshape(test_images, (test_images.shape[0], test_images.shape[1], test_images.shape[2], 1))

def get_train(size):
    # 随机生成要抽样的样本的索引
    index = np.random.randint(0, np.shape(train_images)[0], size)
    # 将这些数据resize成224*224大小
    resized_images = tf.image.resize_with_pad(train_images[index], 224, 224)
    # 返回抽取的
    return resized_images.numpy(), train_labels[index]

# 获取测试集数据
def get_test(size):
    # 随机生成要抽样的样本的索引
    index = np.random.randint(0, np.shape(test_images)[0], size)
    # 将这些数据resize成224*224大小
    resized_images = tf.image.resize_with_pad(test_images[index], 224, 224)
    # 返回抽样的测试样本
    return resized_images.numpy(), test_labels[index]

# 获取训练样本和测试样本
train_images, train_labels = get_train(256)
test_images, test_labels = get_test(128)

# 指定优化器，损失函数和评价指标
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.0)
net.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 模型训练：指定训练数据，batchsize,epoch,验证集
net.fit(train_images, train_labels, batch_size=128, epochs=3, verbose=1, validation_split=0.1)

# 指定测试数据
net.evaluate(test_images, test_labels, verbose=1)

这样修改后应该可以正常运行了。