tensorflow图像分类代码
时间: 2023-07-24 13:42:33 浏览: 46
以下是一个简单的基于 TensorFlow 的图像分类代码示例:
```python
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
# 加载数据集
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
# 将像素值缩放到 0-1 范围内
train_images, test_images = train_images / 255.0, test_images / 255.0
# 定义模型
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10)
])
# 编译模型
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# 训练模型
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
# 评估模型
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
# 打印测试准确率
print(test_acc)
```
该代码使用 CIFAR-10 数据集来训练一个简单的卷积神经网络模型,用于分类 10 种不同的物体。您可以根据自己的需求对模型进行修改和调整。