实训

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
import matplotlib.pyplot as plt

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

x_train = x_train / 255.0
x_test = x_test / 255.0

y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

print("训练集图像形状：", x_train.shape)
print("训练集标签形状：", y_train.shape)
print("测试集图像形状：", x_test.shape)
print("测试集标签形状：", y_test.shape)

class_names = ['飞机', '汽车', '鸟', '猫', '鹿',
'狗', '青蛙', '马', '船', '卡车']
plt.figure(figsize=(10, 10))
for i in range(25):
plt.subplot(5, 5, i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(x_train[i])
plt.xlabel(class_names[y_train[i].argmax()])
plt.show()

model = Sequential([
Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)),
MaxPooling2D((2, 2)),
Dropout(0.25),

Conv2D(64, (3, 3), activation='relu', padding='same'),
MaxPooling2D((2, 2)),
Dropout(0.25),

Conv2D(128, (3, 3), activation='relu', padding='same'),
MaxPooling2D((2, 2)),
Dropout(0.25),

Flatten(),

Dense(128, activation='relu'),
Dropout(0.5),

Dense(10, activation='softmax')

])

model.summary()

model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy']
)

history = model.fit(
x_train, y_train,
epochs=30,
batch_size=64,
validation_data=(x_test, y_test)
)

plt.figure(figsize=(12, 4))

plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='训练准确率')
plt.plot(history.history['val_accuracy'], label='测试准确率')
plt.xlabel('轮数')
plt.ylabel('准确率')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='训练损失')
plt.plot(history.history['val_loss'], label='测试损失')
plt.xlabel('轮数')
plt.ylabel('损失')
plt.legend()

plt.show()

test_loss, test_acc = model.evaluate(x_test, y_test)
print(f"测试集损失：{test_loss:.4f}")
print(f"测试集准确率：{test_acc:.4f}")

import numpy as np
predictions = model.predict(x_test[:5])
for i in range(5):
print(f"预测类别：{class_names[np.argmax(predictions[i])]}")
print(f"真实类别：{class_names[np.argmax(y_test[i])]}")
plt.imshow(x_test[i])
plt.show()