请帮我写出基于keras实现ResNet实现图像二分类的代码

当然可以！以下是基于Keras实现ResNet进行图像二分类的代码：

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

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

def ResNet(input_shape=(224, 224, 3), num_classes=2):
    input_layer = Input(shape=input_shape)
    
    x = Conv2D(filters=64, kernel_size=7, strides=2, padding='same')(input_layer)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
    
    for i in range(3):
        x = residual_block(x, filters=64)
    
    x = residual_block(x, filters=128, strides=2)
    for i in range(3):
        x = residual_block(x, filters=128)
    
    x = residual_block(x, filters=256, strides=2)
    for i in range(5):
        x = residual_block(x, filters=256)
    
    x = residual_block(x, filters=512, strides=2)
    for i in range(2):
        x = residual_block(x, filters=512)
    
    x = Flatten()(x)
    output_layer = Dense(num_classes, activation='softmax')(x)
    
    model = Model(inputs=input_layer, outputs=output_layer)
    
    return model

model = ResNet()
model.compile(optimizer=Adam(lr=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

在这个代码中，我们首先定义了一个函数`residual_block`，用于构建残差块。然后，我们定义了一个函数`ResNet`，用于构建ResNet模型。该模型包含了多个残差块，以及卷积层、池化层和全连接层等。最后，我们使用Keras的`compile`函数来编译模型，指定了Adam优化器、交叉熵损失函数和准确率指标，并使用`summary`函数来打印模型的结构。