python基于深度学习的图像识别
时间: 2023-08-25 11:05:56 浏览: 101
Python基于深度学习的图像识别,可以使用各种深度学习框架来实现,如TensorFlow、Keras、PyTorch等。以下是基于Keras和TensorFlow的图像识别示例:
使用Keras和TensorFlow进行图像分类
1.导入库和数据
```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
```
2.数据预处理
```python
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
```
3.构建模型
```python
model = keras.Sequential(
[
keras.Input(shape=(32, 32, 3)),
layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Flatten(),
layers.Dropout(0.5),
layers.Dense(10, activation="softmax"),
]
)
```
4.编译和训练模型
```python
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=64, epochs=10, validation_split=0.1)
```
5.评估模型
```python
model.evaluate(x_test, y_test, batch_size=64, verbose=1)
```
使用TensorFlow进行图像分类
1.导入库和数据
```python
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
```
2.数据预处理
```python
x_train = x_train / 255.0
x_test = x_test / 255.0
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)
```
3.构建模型
```python
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
```
4.编译和训练模型
```python
model.compile(optimizer='adam',
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))
```
5.评估模型
```python
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(test_acc)
```