入层的节点数、隐藏层的节点数、输出层的节点数、激活函数的选择、损失函数的选择等。然后使用tensorflow或者pytorch等深度学习框架搭建神经网络模型，进行训练和测试。

以下是一个示例代码，用于实现手写体数字识别：

import gzip
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

# 加载数据
def load_data():
    with gzip.open('mnist_data.gz', 'rb') as f:
        data = np.frombuffer(f.read(), np.uint8, offset=16)
    data = data.reshape(-1, 28, 28)
    return data

# 可视化图像
def visualize_data(data):
    plt.figure(figsize=(10, 10))
    for i in range(25):
        plt.subplot(5, 5, i+1)
        plt.imshow(data[i], cmap='gray')
        plt.axis('off')
    plt.show()

# 数据预处理
def preprocess_data(data):
    data = data.astype('float32') / 255.0
    data = (data - np.mean(data)) / np.std(data)
    return data

# 打包数据
def pack_data(data):
    packed_data = []
    for i in range(len(data)):
        packed_data.append((data[i], i % 10))  # 假设标签就是数字本身
    return packed_data

# 构建神经网络模型
def build_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(10, activation='softmax')
    ])
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    return model

# 训练模型
def train_model(model, data):
    x_train = np.array([sample[0] for sample in data])
    y_train = np.array([sample[1] for sample in data])
    model.fit(x_train, y_train, epochs=10, batch_size=32)

# 测试模型
def test_model(model, data):
    x_test = np.array([sample[0] for sample in data])
    y_test = np.array([sample[1] for sample in data])
    loss, accuracy = model.evaluate(x_test, y_test)
    print('Test loss:', loss)
    print('Test accuracy:', accuracy)

# 主函数
def main():
    data = load_data()
    visualize_data(data)
    preprocessed_data = preprocess_data(data)
    packed_data = pack_data(preprocessed_data)
    model = build_model()
    train_model(model, packed_data)
    test_model(model, packed_data)

if __name__ == '__main__':
    main()

请注意，上述代码仅为示例，实际应用中可能需要根据具体情况进行调整和优化。