condensenet应用c盘数据实现图像预处理以及图像整形为48*48*3的四分类代码
时间: 2023-11-28 07:04:11 浏览: 28
以下是使用CondenseNet进行图像预处理和整形的代码,实现四分类(注:本代码仅供参考,具体实现需要根据具体情况进行调整):
```python
import cv2
import os
import numpy as np
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
# 设置图像尺寸
IMG_SIZE = 48
# 导入数据
data_path = 'C:/path/to/data/'
categories = os.listdir(data_path)
labels = [i for i in range(len(categories))]
label_dict = dict(zip(categories, labels))
# 构建数据集
data = []
target = []
for category in categories:
folder_path = os.path.join(data_path, category)
img_names = os.listdir(folder_path)
for img_name in img_names:
img_path = os.path.join(folder_path, img_name)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
data.append(img)
target.append(label_dict[category])
# 数据预处理
data = np.array(data)/255.0
data = np.reshape(data, (data.shape[0], IMG_SIZE, IMG_SIZE, 3))
target = np.array(target)
new_target = np_utils.to_categorical(target)
# 划分训练集和测试集
train_data, test_data, train_target, test_target = train_test_split(data, new_target, test_size=0.1)
# 构建模型
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=train_data.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(4))
model.add(Activation('softmax'))
# 编译模型
adam = Adam(lr=0.0001)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
early_stop = EarlyStopping(monitor='val_loss', patience=3)
model.fit(train_data, train_target, batch_size=32, epochs=20, validation_split=0.1, callbacks=[early_stop])
# 评估模型
score = model.evaluate(test_data, test_target, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
```
需要注意的是,本代码中使用的是基本的卷积神经网络模型,如果需要更好的性能,建议使用更先进的神经网络模型,如ResNet、DenseNet等。
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