眼科疾病分类python代码

以下是一个简单的眼科疾病分类的Python代码示例，使用了Keras深度学习框架和眼科疾病图像数据集：

import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# 设置GPU内存增长
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)

# 数据路径和参数
data_path = 'path/to/dataset/'
img_height = 256
img_width = 256
batch_size = 32

# 数据预处理
train_ds = keras.preprocessing.image_dataset_from_directory(
    data_path,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

val_ds = keras.preprocessing.image_dataset_from_directory(
    data_path,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

class_names = train_ds.class_names
num_classes = len(class_names)

# 数据增强
data_augmentation = keras.Sequential(
  [
    layers.experimental.preprocessing.RandomFlip("horizontal", 
                                                 input_shape=(img_height, 
                                                              img_width,
                                                              3)),
    layers.experimental.preprocessing.RandomRotation(0.1),
    layers.experimental.preprocessing.RandomZoom(0.1),
  ]
)

# 构建模型
model = keras.Sequential([
    data_augmentation,
    layers.experimental.preprocessing.Rescaling(1./255),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(128, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(num_classes)
])

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 训练模型
epochs = 10
model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs
)

# 评估模型
test_ds = keras.preprocessing.image_dataset_from_directory(
    data_path,
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

loss, accuracy = model.evaluate(test_ds)
print("Accuracy", accuracy)

这个示例代码使用了卷积神经网络（CNN）来训练眼科疾病分类模型。数据集使用了眼科图像数据集，预处理包括数据增强和归一化。在训练过程中，使用了验证集进行模型性能评估，并在测试集上进行最终评估。