写一个resnet50的网络

很高兴为你提供帮助！ResNet50是一个非常流行的深度神经网络，常用于图像分类和目标检测等任务。以下是一个简单的实现代码：

import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv2D, BatchNormalization, Activation, Add, Flatten, AveragePooling2D

def residual_block(x, filters, downsample=False):
    shortcut = x
    stride = 1
    if downsample:
        stride = 2
        shortcut = Conv2D(filters, 1, strides=2)(shortcut)
        shortcut = BatchNormalization()(shortcut)
    x = Conv2D(filters, 3, strides=stride, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters, 3, padding='same')(x)
    x = BatchNormalization()(x)
    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

def ResNet50(input_shape=(224, 224, 3), num_classes=1000):
    inputs = tf.keras.Input(shape=input_shape)
    x = Conv2D(64, 7, strides=2, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
    
    filters = 64
    for stage in range(3):
        for block in range(4):
            downsample = False
            if block == 0 and stage > 0:
                downsample = True
            x = residual_block(x, filters, downsample)
        filters *= 2
    
    x = AveragePooling2D()(x)
    x = Flatten()(x)
    x = Dense(num_classes, activation='softmax')(x)
    
    model = tf.keras.Model(inputs=inputs, outputs=x)
    
return model

希望这能为你提供帮助！如果你有任何其他问题，请随时问我。