在天池实验室基于cifar10数据集建立alexnet结构并用d2l中的d2l.train_ch6函数显示

好的，我可以回答你的问题并提供相应的Python代码。

首先，我们需要导入所需的Python库和模块：

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.utils import to_categorical
import numpy as np
import matplotlib.pyplot as plt
import d2l

然后，我们需要加载CIFAR-10数据集，并将其归一化：

(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()
train_images = train_images.astype('float32') / 255
test_images = test_images.astype('float32') / 255

接下来，我们需要对标签进行独热编码：

num_classes = 10
train_labels = to_categorical(train_labels, num_classes)
test_labels = to_categorical(test_labels, num_classes)

然后，我们可以建立AlexNet模型：

model = Sequential([
    Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), activation='relu', input_shape=(32,32,3)),
    MaxPooling2D(pool_size=(3,3), strides=(2,2)),
    Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), activation='relu', padding="same"),
    MaxPooling2D(pool_size=(3,3), strides=(2,2)),
    Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
    Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
    Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
    MaxPooling2D(pool_size=(3,3), strides=(2,2)),
    Flatten(),
    Dense(units=4096, activation='relu'),
    Dropout(0.5),
    Dense(units=4096, activation='relu'),
    Dropout(0.5),
    Dense(units=num_classes, activation='softmax')
])

接下来，我们需要定义优化器、损失函数和评估指标：

lr, num_epochs, batch_size = 0.01, 10, 256
optimizer = SGD(learning_rate=lr)
loss = 'categorical_crossentropy'
metric = 'accuracy'
model.compile(optimizer=optimizer, loss=loss, metrics=[metric])

然后，我们可以使用d2l中的`d2l.train_ch6`函数来训练模型：

train_iter = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).batch(batch_size)
test_iter = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size)
history = model.fit(train_iter, epochs=num_epochs, validation_data=test_iter)

最后，我们可以使用d2l中的`d2l.plot_history`函数来绘制训练和验证精度随时间的变化情况：

d2l.plot_history(history, ('accuracy', 'val_accuracy'))

完整代码如下所示：

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.utils import to_categorical
import numpy as np
import matplotlib.pyplot as plt
import d2l

(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()
train_images = train_images.astype('float32') / 255
test_images = test_images.astype('float32') / 255

num_classes = 10
train_labels = to_categorical(train_labels, num_classes)
test_labels = to_categorical(test_labels, num_classes)

model = Sequential([
    Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), activation='relu', input_shape=(32,32,3)),
    MaxPooling2D(pool_size=(3,3), strides=(2,2)),
    Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), activation='relu', padding="same"),
    MaxPooling2D(pool_size=(3,3), strides=(2,2)),
    Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
    Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
    Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
    MaxPooling2D(pool_size=(3,3), strides=(2,2)),
    Flatten(),
    Dense(units=4096, activation='relu'),
    Dropout(0.5),
    Dense(units=4096, activation='relu'),
    Dropout(0.5),
    Dense(units=num_classes, activation='softmax')
])

lr, num_epochs, batch_size = 0.01, 10, 256
optimizer = SGD(learning_rate=lr)
loss = 'categorical_crossentropy'
metric = 'accuracy'
model.compile(optimizer=optimizer, loss=loss, metrics=[metric])

train_iter = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).batch(batch_size)
test_iter = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size)
history = model.fit(train_iter, epochs=num_epochs, validation_data=test_iter)

d2l.plot_history(history, ('accuracy', 'val_accuracy'))

希望能帮助到你！