请帮我写出基于keras实现ResNet实现图像二分类的代码

好的，以下是基于Keras实现ResNet实现图像二分类的代码：

import tensorflow as tf
from tensorflow import keras

def resnet_block(inputs, num_filters=16, kernel_size=3, strides=1, activation='relu', batch_normalization=True, conv_first=True):
    conv = keras.layers.Conv2D(num_filters, kernel_size=kernel_size, strides=strides, padding='same', kernel_initializer='he_normal', kernel_regularizer=keras.regularizers.l2(1e-4))
    x = inputs
    if conv_first:
        x = conv(x)
        if batch_normalization:
            x = keras.layers.BatchNormalization()(x)
        if activation is not None:
            x = keras.layers.Activation(activation)(x)
    else:
        if batch_normalization:
            x = keras.layers.BatchNormalization()(x)
        if activation is not None:
            x = keras.layers.Activation(activation)(x)
        x = conv(x)
    return x

def resnet_v1(input_shape, depth, num_classes=2):
    if (depth - 2) % 6 != 0:
        raise ValueError('Depth must be 6n + 2 (e.g. 20, 32, 44).')

    num_filters = 16
    num_res_blocks = int((depth - 2) / 6)

    inputs = keras.Input(shape=input_shape)
    x = resnet_block(inputs=inputs)
    for stack in range(3):
        for res_block in range(num_res_blocks):
            strides = 1
            if stack > 0 and res_block == 0:
                strides = 2
            y = resnet_block(inputs=x, num_filters=num_filters, strides=strides)
            y = resnet_block(inputs=y, num_filters=num_filters, activation=None)

            if stack > 0 and res_block == 0:
                x = resnet_block(inputs=x, num_filters=num_filters, kernel_size=1, strides=strides, activation=None, batch_normalization=False)
            x = keras.layers.add([x, y])
            x = keras.layers.Activation('relu')(x)

        num_filters *= 2

    x = keras.layers.AveragePooling2D(pool_size=8)(x)
    y = keras.layers.Flatten()(x)
    outputs = keras.layers.Dense(num_classes, activation='softmax', kernel_initializer='he_normal')(y)

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

input_shape = (32, 32, 3)
depth = 20

model = resnet_v1(input_shape=input_shape, depth=depth, num_classes=2)
model.compile(loss='categorical_crossentropy',
              optimizer=keras.optimizers.Adam(),
              metrics=['accuracy'])

这里采用了ResNet20的结构，输入图片大小为32x32，输出为二分类。你可以根据自己的需求进行修改。同时，需要注意数据集的准备和预处理，以及模型的训练和评估。