请帮我写出基于keras实现ResNet实现图像二分类的代码并且构造自己的图像数据集，且有训练集、验证集和测试集

好的，以下是基于Keras实现ResNet进行图像二分类的代码，并且构造自己的图像数据集，包括训练集、验证集和测试集：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, BatchNormalization, ReLU, GlobalAveragePooling2D, Dense, Add
from tensorflow.keras.models import Model
from tensorflow.keras.utils import plot_model
from tensorflow.keras.preprocessing.image import ImageDataGenerator

def residual_block(x, filters, strides=(1, 1), downsample=False):
    identity = x
    if downsample:
        identity = Conv2D(filters, (1, 1), strides=strides, padding='same', kernel_initializer='he_normal')(identity)
        identity = BatchNormalization()(identity)

    x = Conv2D(filters, (3, 3), strides=strides, padding='same', kernel_initializer='he_normal')(x)
    x = BatchNormalization()(x)
    x = ReLU()(x)

    x = Conv2D(filters, (3, 3), strides=(1, 1), padding='same', kernel_initializer='he_normal')(x)
    x = BatchNormalization()(x)

    x = Add()([x, identity])
    x = ReLU()(x)

    return x

def resnet(input_shape, num_classes):
    inputs = Input(shape=input_shape)

    x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', kernel_initializer='he_normal')(inputs)
    x = BatchNormalization()(x)
    x = ReLU()(x)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

    x = residual_block(x, filters=64)
    x = residual_block(x, filters=64)
    x = residual_block(x, filters=64)

    x = residual_block(x, filters=128, strides=(2, 2), downsample=True)
    x = residual_block(x, filters=128)
    x = residual_block(x, filters=128)
    x = residual_block(x, filters=128)

    x = residual_block(x, filters=256, strides=(2, 2), downsample=True)
    x = residual_block(x, filters=256)
    x = residual_block(x, filters=256)
    x = residual_block(x, filters=256)
    x = residual_block(x, filters=256)
    x = residual_block(x, filters=256)

    x = residual_block(x, filters=512, strides=(2, 2), downsample=True)
    x = residual_block(x, filters=512)
    x = residual_block(x, filters=512)

    x = GlobalAveragePooling2D()(x)
    outputs = Dense(num_classes, activation='softmax')(x)

    model = Model(inputs, outputs)
    return model

train_datagen = ImageDataGenerator(rescale=1./255, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)
valid_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory('train', target_size=(224, 224), batch_size=32, class_mode='categorical')
valid_generator = valid_datagen.flow_from_directory('valid', target_size=(224, 224), batch_size=32, class_mode='categorical')
test_generator = test_datagen.flow_from_directory('test', target_size=(224, 224), batch_size=32, class_mode='categorical')

input_shape = train_generator.image_shape
num_classes = train_generator.num_classes

model = resnet(input_shape, num_classes)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

plot_model(model, to_file='resnet.png', show_shapes=True)

model.fit(train_generator, epochs=100, validation_data=valid_generator)
model.evaluate(test_generator)

这个代码实现了ResNet在自己构造的图像数据集上的图像二分类任务，其中包括ResNet的模型定义，训练数据、验证数据和测试数据的处理，以及模型的编译和训练。需要注意的是，这里的图像数据集需要按照以下格式进行组织：

train/
    class1/
        image1.jpg
        image2.jpg
        ...
    class2/
        image1.jpg
        image2.jpg
        ...
    ...
valid/
    class1/
        image1.jpg
        image2.jpg
        ...
    class2/
        image1.jpg
        image2.jpg
        ...
    ...
test/
    class1/
        image1.jpg
        image2.jpg
        ...
    class2/
        image1.jpg
        image2.jpg
        ...
    ...

其中，train、valid和test分别表示训练集、验证集和测试集的文件夹，class1、class2等表示类别的文件夹，image1.jpg、image2.jpg等表示图像文件。需要注意的是，每个图像文件的文件名应当以类别名称作为前缀，比如class1_image1.jpg、class1_image2.jpg等。同时，还需要指定图像的大小和批量大小，这里设置为224*224和32，可以根据需要进行调整。