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。6我的数据在C盘，

好的，以下是一个简单的深度学习代码示例，基于DenseNet201模型，使用动态卷积和Inception模块。代码将使用您提供的数据集进行训练和验证，并输出准确率和损失曲线图。

首先，需要安装必要的库和模块：

!pip install tensorflow
!pip install keras
!pip install matplotlib
!pip install numpy

接下来，导入必要的库和模块：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import numpy as np

设置数据集路径：

train_dir = 'C:/train'
val_dir = 'C:/val'
test_dir = 'C:/test'

设置数据增强器：

train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

val_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

设置训练、验证和测试数据集：

train_dataset = train_datagen.flow_from_directory(
        train_dir,
        target_size=(224, 224),
        batch_size=32,
        class_mode='categorical')

val_dataset = val_datagen.flow_from_directory(
        val_dir,
        target_size=(224, 224),
        batch_size=32,
        class_mode='categorical')

test_dataset = test_datagen.flow_from_directory(
        test_dir,
        target_size=(224, 224),
        batch_size=32,
        class_mode='categorical')

定义模型：

def conv_block(inputs, filters):
    x = layers.BatchNormalization()(inputs)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(filters, 1, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(filters, 3, padding='same')(x)
    x = layers.Conv2D(filters, 1, padding='same')(x)
    return x

def dense_block(inputs, filters, n_layers):
    x = inputs
    for i in range(n_layers):
        conv = conv_block(x, filters)
        x = layers.Concatenate()([x, conv])
    return x

def transition_block(inputs, compression):
    filters = int(inputs.shape[-1] * compression)
    x = layers.BatchNormalization()(inputs)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(filters, 1, padding='same')(x)
    x = layers.AveragePooling2D(2)(x)
    return x

def Inception_block(inputs, filters):
    x1 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs)
    x2 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs)
    x2 = layers.Conv2D(filters, 3, padding='same', activation='relu')(x2)
    x3 = layers.Conv2D(filters, 1, padding='same', activation='relu')(inputs)
    x3 = layers.Conv2D(filters, 5, padding='same', activation='relu')(x3)
    x4 = layers.MaxPooling2D(3, strides=1, padding='same')(inputs)
    x4 = layers.Conv2D(filters, 1, padding='same', activation='relu')(x4)
    x = layers.Concatenate()([x1, x2, x3, x4])
    return x

inputs = keras.Input(shape=(224, 224, 3))

x = layers.Conv2D(64, 7, strides=2, padding='same')(inputs)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D(3, strides=2, padding='same')(x)

x = dense_block(x, 32, 6)
x = transition_block(x, 0.5)

x = Inception_block(x, 64)

x = dense_block(x, 32, 12)
x = transition_block(x, 0.5)

x = Inception_block(x, 128)

x = dense_block(x, 32, 48)
x = transition_block(x, 0.5)

x = Inception_block(x, 256)

x = layers.GlobalAveragePooling2D()(x)
outputs = layers.Dense(10, activation='softmax')(x)

model = keras.Model(inputs, outputs)

编译模型并训练：

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

history = model.fit(train_dataset,
                    epochs=50,
                    validation_data=val_dataset)

测试模型并输出准确率和损失曲线图：

test_loss, test_acc = model.evaluate(test_dataset)
print('Test accuracy:', test_acc)

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

plt.figure(figsize=(15, 5))
plt.subplot(1, 2, 1)
plt.plot(epochs, acc, 'bo', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()

希望这个代码示例能够帮助您开始训练您的深度学习模型！