DANN 自定义数据集 python

首先，需要先导入相应的库：tensorflow、keras、numpy、os、cv2等。然后，需要准备好自定义的数据集，包括正样本和负样本，并将它们分别存储在不同的文件夹中。接下来，需要对数据集进行预处理，包括图像大小统一、归一化、随机翻转等。之后，需要将数据集划分为训练集和验证集，可以使用train_test_split函数来实现。最后，可以使用keras中的DANN模型进行训练和测试。

具体的代码实现可以参考以下步骤：

1. 导入相关库

import tensorflow as tf
from tensorflow import keras
import numpy as np
import os
import cv2

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

2. 准备数据集

# 定义数据集路径
data_path = './data'
train_path = os.path.join(data_path, 'train')
test_path = os.path.join(data_path, 'test')

# 定义图像大小
img_width, img_height = 32, 32

# 定义类别列表
classes = ['cat', 'dog']

# 加载数据集
train_data = []
train_labels = []
test_data = []
test_labels = []

for cls in classes:
    # 加载训练集
    train_cls_path = os.path.join(train_path, cls)
    for file in os.listdir(train_cls_path):
        img_path = os.path.join(train_cls_path, file)
        img = cv2.imread(img_path)
        img = cv2.resize(img, (img_width, img_height))
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        train_data.append(img)
        train_labels.append(cls)

    # 加载测试集
    test_cls_path = os.path.join(test_path, cls)
    for file in os.listdir(test_cls_path):
        img_path = os.path.join(test_cls_path, file)
        img = cv2.imread(img_path)
        img = cv2.resize(img, (img_width, img_height))
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        test_data.append(img)
        test_labels.append(cls)

3. 数据预处理

# 将图像数据转换为numpy数组
train_data = np.array(train_data)
test_data = np.array(test_data)

# 将标签数据进行编码
label_encoder = LabelEncoder()
train_labels = label_encoder.fit_transform(train_labels)
test_labels = label_encoder.transform(test_labels)

# 将图像数据进行归一化
train_data = train_data.astype('float32') / 255.
test_data = test_data.astype('float32') / 255.

# 将标签数据转换为one-hot编码
num_classes = len(classes)
train_labels = keras.utils.to_categorical(train_labels, num_classes)
test_labels = keras.utils.to_categorical(test_labels, num_classes)

# 随机翻转图像
train_data = tf.image.random_flip_left_right(train_data)

4. 划分训练集和验证集

train_data, val_data, train_labels, val_labels = train_test_split(train_data, train_labels, test_size=0.2, random_state=42)

5. 定义DANN模型

# 定义DANN模型
input_shape = (img_width, img_height, 3)

input_img = keras.layers.Input(shape=input_shape)

# 特征提取器
x = keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu')(input_img)
x = keras.layers.MaxPooling2D((2, 2))(x)
x = keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu')(x)
x = keras.layers.MaxPooling2D((2, 2))(x)
x = keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu')(x)
x = keras.layers.MaxPooling2D((2, 2))(x)
x = keras.layers.Flatten()(x)
x = keras.layers.Dense(128, activation='relu')(x)
features = keras.layers.Dense(32, activation='relu')(x)

# 分类器
class_outputs = keras.layers.Dense(num_classes, activation='softmax')(features)

# 领域分类器
domain_outputs = keras.layers.Dense(1, activation='sigmoid')(features)

# 定义模型
model = keras.models.Model(inputs=input_img, outputs=[class_outputs, domain_outputs])

# 编译模型
model.compile(optimizer='adam',
              loss={'dense_2': 'categorical_crossentropy', 'dense_3': 'binary_crossentropy'},
              loss_weights={'dense_2': 1., 'dense_3': 1.},
              metrics={'dense_2': 'accuracy', 'dense_3': 'accuracy'})

6. 训练模型

# 定义训练参数
epochs = 10
batch_size = 32

# 训练模型
history = model.fit(train_data, {'dense_2': train_labels, 'dense_3': np.zeros(len(train_data))},
                    validation_data=(val_data, {'dense_2': val_labels, 'dense_3': np.ones(len(val_data))}),
                    epochs=epochs,
                    batch_size=batch_size)

7. 测试模型

# 测试模型
test_loss, test_acc = model.evaluate(test_data, {'dense_2': test_labels, 'dense_3': np.ones(len(test_data))})
print('Test accuracy:', test_acc)