搭建VGG13解决CIFAR10数据集上的图片分类，画出这两种网络模型在该数据集上的随epoch的精度变化曲线 tensorflow实现 optimizers 使用adam

好的，我明白了您的问题。

下面是使用 TensorFlow 搭建 VGG13 模型在 CIFAR10 数据集上进行图片分类的代码：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# 定义 VGG13 模型
model = tf.keras.Sequential([
    Conv2D(64, (3,3), padding='same', activation='relu', input_shape=(32,32,3)),
    Conv2D(64, (3,3), padding='same', activation='relu'),
    MaxPooling2D((2,2)),
    Conv2D(128, (3,3), padding='same', activation='relu'),
    Conv2D(128, (3,3), padding='same', activation='relu'),
    MaxPooling2D((2,2)),
    Conv2D(256, (3,3), padding='same', activation='relu'),
    Conv2D(256, (3,3), padding='same', activation='relu'),
    Conv2D(256, (3,3), padding='same', activation='relu'),
    MaxPooling2D((2,2)),
    Conv2D(512, (3,3), padding='same', activation='relu'),
    Conv2D(512, (3,3), padding='same', activation='relu'),
    Conv2D(512, (3,3), padding='same', activation='relu'),
    MaxPooling2D((2,2)),
    Conv2D(512, (3,3), padding='same', activation='relu'),
    Conv2D(512, (3,3), padding='same', activation='relu'),
    Conv2D(512, (3,3), padding='same', activation='relu'),
    MaxPooling2D((2,2)),
    Flatten(),
    Dense(4096, activation='relu'),
    Dense(4096, activation='relu'),
    Dense(10, activation='softmax')
])

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

# 加载数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()

# 数据预处理
x_train = x_train / 255.0
x_test = x_test / 255.0
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

# 训练模型
history = model.fit(x_train, y_train, epochs=50, validation_data=(x_test, y_test))

同时，下面是画出模型在 CIFAR10 数据集上的随 epoch 的精度变化曲线的代码：

import matplotlib.pyplot as plt

# 绘制训练集和验证集的精度曲线
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()

希望这个回答对您有帮助！