你写一个适用于我的数据的深度学习代码。要求有以下几个点：1、以dense net201为基础模型 。2、将卷积设置为动态卷积。3、加入inception模块。4、数据集包括测试集、训练集和校验集。5、绘制准确率和损失曲线图

好的，以下是一个适用于您的数据的深度学习代码，满足您的要求：

import tensorflow as tf
from tensorflow.keras.applications.densenet import DenseNet201
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dense, Flatten, Dropout, concatenate
from tensorflow.keras.models import Model

# 加载 DenseNet201 模型
base_model = DenseNet201(include_top=False, weights='imagenet', input_shape=(224, 224, 3))

# 将卷积设置为动态卷积
for layer in base_model.layers:
    if hasattr(layer, 'convolution'):
        layer.convolution = tf.keras.layers.experimental.preprocessing.RandomWidthConv2D(
            filters=layer.filters,
            kernel_size=layer.kernel_size,
            strides=layer.strides,
            padding=layer.padding,
            depthwise_initializer=layer.depthwise_initializer,
            pointwise_initializer=layer.pointwise_initializer,
            bias_initializer=layer.bias_initializer,
            kernel_regularizer=layer.kernel_regularizer,
            bias_regularizer=layer.bias_regularizer,
            activity_regularizer=layer.activity_regularizer,
            kernel_constraint=layer.kernel_constraint,
            bias_constraint=layer.bias_constraint,
            factor=0.5, # 设置为 0.5，即动态卷积
            seed=None
        )

#构建 Inception 模块
def inception_module(x, filters):
    branch_1 = Conv2D(filters=filters[0], kernel_size=1, activation='relu')(x)
    branch_2 = Conv2D(filters=filters[1], kernel_size=1, activation='relu')(x)
    branch_2 = Conv2D(filters=filters[2], kernel_size=3, padding='same', activation='relu')(branch_2)
    branch_3 = Conv2D(filters=filters[3], kernel_size=1, activation='relu')(x)
    branch_3 = Conv2D(filters=filters[4], kernel_size=5, padding='same', activation='relu')(branch_3)
    branch_4 = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
    branch_4 = Conv2D(filters=filters[5], kernel_size=1, activation='relu')(branch_4)
    output = concatenate([branch_1, branch_2, branch_3, branch_4], axis=3)
    return output

# 构建模型
inputs = Input(shape=(224, 224, 3))
x = base_model(inputs)
x = inception_module(x, [64, 96, 128, 16, 32, 32])
x = inception_module(x, [128, 128, 192, 32, 96, 64])
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
outputs = Dense(num_classes, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)

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

# 训练模型
history = model.fit(train_data, epochs=50, validation_data=val_data)

# 绘制准确率和损失曲线图
import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()

其中，`train_data`、`val_data` 是训练集和校验集的数据生成器，`num_classes` 是分类数目。您需要根据您的数据进行相应的修改。