python用ResNet50结构二分类疟疾分裂的代码

以下是使用ResNet50结构进行疟疾分裂二分类的Python代码：

import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import Dense, Flatten, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 设置训练和验证数据集的路径
train_path = 'path/to/train/dataset'
valid_path = 'path/to/validation/dataset'

# 设置图像大小和批处理大小
img_size = (224, 224)
batch_size = 32

# 创建数据生成器
train_datagen = ImageDataGenerator(rescale=1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)
valid_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(train_path,
                                                    target_size=img_size,
                                                    batch_size=batch_size,
                                                    class_mode='binary')
valid_generator = valid_datagen.flow_from_directory(valid_path,
                                                    target_size=img_size,
                                                    batch_size=batch_size,
                                                    class_mode='binary')

# 加载ResNet50模型
resnet = ResNet50(include_top=False, weights='imagenet', input_shape=(224, 224, 3))

# 冻结ResNet50的所有层
for layer in resnet.layers:
    layer.trainable = False

# 添加自定义层
x = resnet.output
x = Flatten()(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, activation='sigmoid')(x)

# 构建模型
model = Model(inputs=resnet.input, outputs=predictions)

# 编译模型
model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(train_generator,
          steps_per_epoch=train_generator.samples // batch_size,
          validation_data=valid_generator,
          validation_steps=valid_generator.samples // batch_size,
          epochs=10)

希望这个代码对你有所帮助！