编写GoogLeNet 网络，实现 CIFAR-10数据集的分类任务。

GoogLeNet是一个深度卷积神经网络，它在2014年ImageNet大规模视觉识别竞赛（ILSVRC）上获得了冠军。以下是一个使用GoogLeNet进行CIFAR-10分类任务的示例代码。

首先，我们需要导入必要的库：TensorFlow和Keras。

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

然后，我们可以定义GoogLeNet网络。GoogLeNet由多个“Inception”模块组成，每个模块都有多个分支，包含不同大小的卷积和池化操作。下面是一个包含3个Inception模块的简单实现。

def create_googlenet():
    input_shape = (32, 32, 3)

    # Input layer
    inputs = keras.Input(shape=input_shape)

    # First convolutional layer
    x = layers.Conv2D(filters=64, kernel_size=7, strides=2, padding="same", activation="relu")(inputs)
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding="same")(x)

    # First Inception module
    x = inception_module(x, filters=[64, 96, 128, 16, 32, 32])

    # Second Inception module
    x = inception_module(x, filters=[128, 128, 192, 32, 96, 64])
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding="same")(x)

    # Third Inception module
    x = inception_module(x, filters=[192, 96, 208, 16, 48, 64])

    # Output layer
    x = layers.GlobalAveragePooling2D()(x)
    outputs = layers.Dense(units=10, activation="softmax")(x)

    # Create model
    model = keras.Model(inputs=inputs, outputs=outputs, name="googlenet")
    return model

Inception模块可以使用以下代码实现：

def inception_module(inputs, filters):
    f1, f3r, f3, f5r, f5, fp = filters

    # 1x1 convolution branch
    conv1 = layers.Conv2D(filters=f1, kernel_size=1, activation="relu")(inputs)

    # 3x3 convolution branch
    conv3r = layers.Conv2D(filters=f3r, kernel_size=1, activation="relu")(inputs)
    conv3 = layers.Conv2D(filters=f3, kernel_size=3, padding="same", activation="relu")(conv3r)

    # 5x5 convolution branch
    conv5r = layers.Conv2D(filters=f5r, kernel_size=1, activation="relu")(inputs)
    conv5 = layers.Conv2D(filters=f5, kernel_size=5, padding="same", activation="relu")(conv5r)

    # Max pooling branch
    pool = layers.MaxPooling2D(pool_size=3, strides=1, padding="same")(inputs)
    pool_conv = layers.Conv2D(filters=fp, kernel_size=1, activation="relu")(pool)

    # Concatenate branches
    outputs = layers.Concatenate()([conv1, conv3, conv5, pool_conv])
    return outputs

最后，我们可以编译和训练模型。

# Load CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()

# Normalize pixel values
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

# Convert labels to one-hot vectors
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

# Create model
model = create_googlenet()

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

# Train model
model.fit(x_train, y_train, batch_size=128, epochs=50, validation_data=(x_test, y_test))

在训练完成后，我们可以使用以下代码评估模型的性能。

# Evaluate model
test_loss, test_acc = model.evaluate(x_test, y_test)
print("Test accuracy:", test_acc)