请帮我写出基于keras实现resnet34的代码

好的，下面是基于 Keras 实现 ResNet34 的代码：

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

def conv_bn_relu(inputs, filters, kernel_size, strides):
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    return x

def identity_block(inputs, filters):
    x = conv_bn_relu(inputs, filters=filters, kernel_size=3, strides=1)
    x = conv_bn_relu(x, filters=filters, kernel_size=3, strides=1)
    x = Add()([x, inputs])
    x = Activation('relu')(x)
    return x

def conv_block(inputs, filters, strides):
    x = conv_bn_relu(inputs, filters=filters, kernel_size=3, strides=strides)
    x = conv_bn_relu(x, filters=filters, kernel_size=3, strides=1)
    shortcut = Conv2D(filters=filters, kernel_size=1, strides=strides)(inputs)
    shortcut = BatchNormalization()(shortcut)
    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

def resnet34(input_shape, num_classes):
    inputs = Input(shape=input_shape)
    x = conv_bn_relu(inputs, filters=64, kernel_size=7, strides=2)
    x = MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    x = conv_block(x, filters=64, strides=1)
    x = identity_block(x, filters=64)
    x = identity_block(x, filters=64)

    x = conv_block(x, filters=128, strides=2)
    x = identity_block(x, filters=128)
    x = identity_block(x, filters=128)
    x = identity_block(x, filters=128)

    x = conv_block(x, filters=256, strides=2)
    x = identity_block(x, filters=256)
    x = identity_block(x, filters=256)
    x = identity_block(x, filters=256)
    x = identity_block(x, filters=256)
    x = identity_block(x, filters=256)

    x = conv_block(x, filters=512, strides=2)
    x = identity_block(x, filters=512)
    x = identity_block(x, filters=512)

    x = Flatten()(x)
    outputs = Dense(num_classes, activation='softmax')(x)

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

这个代码实现了基于 Keras 的 ResNet34 模型。其中，我们定义了 `conv_bn_relu` 函数来方便地实现卷积层、BN 层和 ReLU 激活函数的组合。`identity_block` 函数实现了 ResNet 的残差块，`conv_block` 函数实现了 ResNet 的卷积块。最后，我们使用这些函数来搭建 ResNet34 模型。