请给出一个用TensorFlow搭建的简易图像分类识别的卷积神经网络模型代码

以下是一个简单的TensorFlow卷积神经网络模型，用于图像分类识别：

import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Flatten, Dense, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 将数据转换为浮点数并归一化
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# 将标签进行One-Hot编码
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

# 构建卷积神经网络模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
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(10, activation='softmax'))

# 定义优化器和损失函数
optimizer = Adam()
loss_fn = tf.keras.losses.CategoricalCrossentropy()

# 编译模型
model.compile(loss=loss_fn, optimizer=optimizer, metrics=['accuracy'])

# 训练模型
model.fit(x_train.reshape((-1, 28, 28, 1)), y_train, batch_size=128, epochs=10, validation_data=(x_test.reshape((-1, 28, 28, 1)), y_test))

# 评估模型
model.evaluate(x_test.reshape((-1, 28, 28, 1)), y_test)

这个模型使用了一个卷积层、一个最大池化层、一个Dropout层和两个全连接层。使用Adam优化器和CategoricalCrossentropy损失函数进行训练，并在训练结束后评估模型的准确性。