基于TensorFlow搭建卷积神经网络进行花卉图像分类；

基于TensorFlow搭建卷积神经网络进行花卉图像分类的步骤如下：

1. 导入所需的库和模块：

import tensorflow as tf
from tensorflow.keras import datasets, layers, models

2. 加载花卉数据集：

(train_images, train_labels), (test_images, test_labels) = datasets.load_dataset('flower_photos')

3. 对数据进行预处理：

train_images = train_images / 255.0
test_images = test_images / 255.0

4. 构建卷积神经网络模型：

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(5))  # 输出层的神经元个数等于花卉类别数

5. 编译模型：

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

6. 训练模型：

model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

7. 对新的花卉图像进行分类预测：

predictions = model.predict(test_images)

8. 输出预测结果：

print(predictions)