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：

“`python
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。