将C盘不规则图片整形为224×224的深度学习模型输入，并引入动态卷积到densenet 结合inception实现四分类代码

以下是将C盘不规则图片整形为224×224的深度学习模型输入，并引入动态卷积到DenseNet结合Inception实现四分类的代码：

import tensorflow as tf
from tensorflow.keras.applications.densenet import DenseNet201
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dropout, Dense, concatenate
from tensorflow.keras.models import Model

# 定义输入形状
input_shape = (224, 224, 3)

# 定义输入层
input_layer = Input(shape=input_shape)

# 对输入层进行动态卷积处理
dynamic_conv = Conv2D(32, (3, 3), strides=(1, 1), padding='same', activation='relu')(input_layer)
pool = MaxPooling2D(pool_size=(2, 2))(dynamic_conv)

dynamic_conv = Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu')(pool)
pool = MaxPooling2D(pool_size=(2, 2))(dynamic_conv)

dynamic_conv = Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')(pool)
pool = MaxPooling2D(pool_size=(2, 2))(dynamic_conv)

dynamic_conv = Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')(pool)
pool = MaxPooling2D(pool_size=(2, 2))(dynamic_conv)

# 加载DenseNet201模型
densenet_model = DenseNet201(input_tensor=pool, include_top=False, weights='imagenet')

# 获取DenseNet201模型的输出层
densenet_output = densenet_model.output

# 对DenseNet201模型的输出层进行Inception操作
inception_conv1 = Conv2D(32, (1, 1), padding='same', activation='relu')(densenet_output)
inception_conv3 = Conv2D(64, (3, 3), padding='same', activation='relu')(densenet_output)
inception_conv5 = Conv2D(16, (5, 5), padding='same', activation='relu')(densenet_output)
inception_pool = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(densenet_output)

# 将Inception操作的结果拼接起来
inception_output = concatenate([inception_conv1, inception_conv3, inception_conv5, inception_pool], axis=-1)

# 对Inception操作的结果进行全局平均池化
inception_output = GlobalAveragePooling2D()(inception_output)

# 对Inception操作的结果进行Dropout
inception_output = Dropout(0.5)(inception_output)

# 对Inception操作的结果进行全连接层
inception_output = Dense(512, activation='relu')(inception_output)

# 对Inception操作的结果进行Dropout
inception_output = Dropout(0.5)(inception_output)

# 最后的输出层，4个节点，softmax激活函数
predictions = Dense(4, activation='softmax')(inception_output)

# 构建模型
model = Model(inputs=input_layer, outputs=predictions)

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

这段代码使用了TensorFlow的Keras API实现了一个深度学习模型，包括了动态卷积、DenseNet、Inception等操作，最终实现了一个四分类的模型。