选择一个神经网络模型，实现MNIST手写体识别数据库的训练与测试。开发环境为python

好的，我可以为您介绍一个常用的神经网络模型，即卷积神经网络（Convolutional Neural Network，CNN）。

首先，您需要安装一些必要的Python库，如TensorFlow、Keras和NumPy。可以使用以下命令进行安装：

pip install tensorflow
pip install keras
pip install numpy

接下来，您可以使用以下代码来加载MNIST手写数字数据集并将其分为训练集和测试集：

from keras.datasets import mnist

# load the MNIST dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# reshape the data to be a 4D tensor - (batch_size, rows, columns, channels)
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)

# convert the data to float32 and normalize the values to the range [0, 1]
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255

# convert the labels to one-hot encoded vectors
num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

接下来，您可以使用以下代码来定义CNN模型：

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

# define the model architecture
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

# compile the model
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

最后，您可以使用以下代码来训练和测试模型：

# train the model
model.fit(X_train, y_train,
          batch_size=128,
          epochs=12,
          verbose=1,
          validation_data=(X_test, y_test))

# evaluate the model on the test set
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

希望这可以帮助您实现MNIST手写体识别数据库的训练与测试！