TensorFlow と Keras を使用して深層学習モデルを構築する

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import cifar10

# 加载CIFAR-10数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# 将像素值缩放到0到1之间
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

# 将标签从整数转换为one-hot编码
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

model = keras.Sequential(
    [
        # 第一个卷积层
        layers.Conv2D(32, (3, 3), activation="relu", input_shape=(32, 32, 3)),
        layers.MaxPooling2D((2, 2)),
        # 第二个卷积层
        layers.Conv2D(64, (3, 3), activation="relu"),
        layers.MaxPooling2D((2, 2)),
        # 第三个卷积层
        layers.Conv2D(128, (3, 3), activation="relu"),
        layers.MaxPooling2D((2, 2)),
        # 展平层
        layers.Flatten(),
        # 全连接层
       layers.Dense(128, activation="relu"),
        layers.Dense(10, activation="softmax"),
    ]
)

# 编译模型
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])

# 训练模型
history = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# 在测试集上评估模型
test_loss, test_acc = model.evaluate(x_test, y_test)

print("Test loss:", test_loss)
print("Test accuracy:", test_acc)

import matplotlib.pyplot as plt

# 绘制训练和验证损失
plt.plot(history.history["loss"], label="Training loss")
plt.plot(history.history["val_loss"], label="Validation loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()

plt.show()

# 绘制训练和验证准确率
plt.plot(history.history["accuracy"], label="Training accuracy")
plt.plot(history.history["val_accuracy"], label="Validation accuracy")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()

plt.show()