写一个用识别1000×16×16的卷积神经网络的代码,实现三分类
时间: 2023-04-04 21:01:54 浏览: 58
很高兴回答您的问题。以下是一个用于识别100×16×16的卷积神经网络的代码,实现三分类的示例:
```python
import tensorflow as tf
from tensorflow.keras import layers
# 定义模型
model = tf.keras.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(16, 16, 1)),
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(3)
])
# 编译模型
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# 训练模型
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)
print('\nTest accuracy:', test_acc)
```
请注意,此代码仅用于示例目的。您需要根据您的数据和问题进行适当的修改和调整。