cnn分类python

下面是使用CNN进行图像分类的Python示例代码：

首先，导入必要的库：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

接下来，加载和预处理数据集：

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

定义CNN模型：

model = keras.Sequential(
    [
        keras.Input(shape=(32, 32, 3)),
        layers.Conv2D(32, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(128, 3, padding="same", activation="relu"),
        layers.Flatten(),
        layers.Dense(64, activation="relu"),
        layers.Dense(10),
    ]
)

编译模型：

model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(lr=3e-4),
    metrics=["accuracy"],
)

训练模型：

model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=2)

评估模型：

model.evaluate(x_test, y_test, batch_size=64, verbose=2)

完整的代码示例：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

model = keras.Sequential(
    [
        keras.Input(shape=(32, 32, 3)),
        layers.Conv2D(32, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, 3, padding="same", activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(128, 3, padding="same", activation="relu"),
        layers.Flatten(),
        layers.Dense(64, activation="relu"),
        layers.Dense(10),
    ]
)

model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(lr=3e-4),
    metrics=["accuracy"],
)

model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=2)

model.evaluate(x_test, y_test, batch_size=64, verbose=2)