resnet 用keras实现 输入50*50

以下是使用Keras实现ResNet50网络并输入大小为50x50的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, Add, GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model

def resnet_block(inputs, filters, strides=1):
    x = Conv2D(filters, kernel_size=3, strides=strides, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters, kernel_size=3, padding='same')(x)
    x = BatchNormalization()(x)
    
    if strides != 1 or inputs.shape[-1] != filters:
        shortcut = Conv2D(filters, kernel_size=1, strides=strides, padding='same')(inputs)
        shortcut = BatchNormalization()(shortcut)
    else:
        shortcut = inputs
    
    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

def ResNet50(input_shape=(50,50, 3), num_classes=1000):
    inputs = Input(shape=input_shape)
    
    x = Conv2D(64, kernel_size=7, strides=2, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
    
    x = resnet_block(x, filters=64)
    x = resnet_block(x, filters=64)
    x = resnet_block(x, filters=64)
    
    x = resnet_block(x, filters=128, strides=2)
    x = resnet_block(x, filters=128)
    x = resnet_block(x, filters=128)
    x = resnet_block(x, filters=128)
    
    x = resnet_block(x, filters=256, strides=2)
    x = resnet_block(x, filters=256)
    x = resnet_block(x, filters=256)
    x = resnet_block(x, filters=256)
    x = resnet_block(x, filters=256)
    x = resnet_block(x, filters=256)
    
    x = resnet_block(x, filters=512, strides=2)
    x = resnet_block(x, filters=512)
    x = resnet_block(x, filters=512)
    
    x = GlobalAveragePooling2D()(x)
    x = Dense(num_classes, activation='softmax')(x)
    
    model = Model(inputs=inputs, outputs=x)
    return model

model = ResNet50(input_shape=(50, 50, 3), num_classes=1000)
model.summary()

这段代码定义了一个ResNet50网络模型，输入大小为50x50的彩色图像。模型的结构包括卷积层、批归一化层、激活函数层、池化层、残差块、全局平均池化层和全连接层。最后使用`model.summary()`打印出模型的结构信息。