def title(y_pred, y_test, target_names, i): pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1] true_name = target_names[y_test[i]].rsplit(' ', 1)[-1] return 'predicted: %s\ntrue: %s' % (pred_name, true_name)
时间: 2024-04-27 13:23:11 浏览: 122
这段代码定义了一个名为title()的函数,该函数用于生成一个图像的标题,该标题包括该图像的真实标签和预测标签。该函数接受以下参数作为输入:
1. y_pred:预测结果向量数组。
2. y_test:测试集的标签向量数组。
3. target_names:标签名称数组。
4. i:要生成标题的图像索引。
该函数返回一个字符串,作为该图像的标题。
具体的操作包括:
1. 从目标名称中提取预测标签和真实标签的名称。
2. 格式化字符串并返回结果。
需要注意的是,该函数假定标签名称遵循一定的命名规则,即标签名称包含一个空格,空格之后是标签的名称。
相关问题
import pandas as pd from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.feature_selection import SelectKBest, f_classif from sklearn.decomposition import PCA from sklearn.metrics import accuracy_score, classification_report from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC data = load_wine() # 导入数据集 X = pd.DataFrame(data.data, columns=data.feature_names) y = pd.Series(data.target) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # 构建分类模型 model = LogisticRegression() model.fit(X_train, y_train) # 预测测试集结果 y_pred = model.predict(X_test) #评估模型性能 accuracy = accuracy_score(y_test, y_pred) report = classification_report(y_test, y_pred) print('准确率:', accuracy) # 特征选择 selector = SelectKBest(f_classif, k=6) X_new = selector.fit_transform(X, y) print('所选特征:', selector.get_support()) # 模型降维 pca = PCA(n_components=2) X_new = pca.fit_transform(X_new) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.2, random_state=0) def Sf(model,X_train, X_test, y_train, y_test,modelname): mode = model() mode.fit(X_train, y_train) y_pred = mode.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(modelname, accuracy) importance = mode.feature_importances_ print(importance) def Sf1(model,X_train, X_test, y_train, y_test,modelname): mode = model() mode.fit(X_train, y_train) y_pred = mode.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(modelname, accuracy) modelname='支持向量机' Sf1(SVC,X_train, X_test, y_train, y_test,modelname) modelname='逻辑回归' Sf1(LogisticRegression,X_train, X_test, y_train, y_test,modelname) modelname='高斯朴素贝叶斯算法训练分类器' Sf1(GaussianNB,X_train, X_test, y_train, y_test,modelname) modelname='K近邻分类' Sf1(KNeighborsClassifier,X_train, X_test, y_train, y_test,modelname) modelname='决策树分类' Sf(DecisionTreeClassifier,X_train, X_test, y_train, y_test,modelname) modelname='随机森林分类' Sf(RandomForestClassifier,X_train, X_test, y_train, y_test,modelname)加一个画图展示
抱歉,由于我是一个纯文本AI,无法进行画图展示。但是,你可以使用Python中的matplotlib库进行数据可视化。例如,你可以使用以下代码绘制特征重要性条形图:
import matplotlib.pyplot as plt
importance = mode.feature_importances_
features = list(X.columns)
plt.barh(features, importance)
plt.title('Feature Importance')
plt.xlabel('Importance')
plt.show()
这段代码可以在Sf函数中的print(importance)下方添加,并在运行时展示特征重要性条形图。
我要完成船舶图像的二分类任务。我现在只有图片信息,那你可以帮我给出基于tensorflow2下vgg13,每个文件所需要的代码吗。里面还要有test文件,以及draw文件。draw文件用来画出损失函数随epoch的变化曲线。并教我怎么把本地的船舶图片加载到代码中
好的,我可以帮你提供一些基于TensorFlow 2.x下的VGG13实现的代码,以及相关的test和draw文件。同时,我也会给你一些加载本地船舶图片的代码示例。请注意,这份代码仅供参考,你需要根据自己的实际情况进行修改和调整。
首先,我们需要安装TensorFlow 2.x和相关的依赖库,可以通过以下命令进行安装:
```
pip install tensorflow
pip install matplotlib
pip install numpy
```
然后,我们来看一下基于VGG13的图片分类模型的实现代码。代码如下:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.models import Model
def VGG13(input_shape=(224, 224, 3), num_classes=2):
# 定义输入层
input_layer = Input(shape=input_shape)
# 第一段卷积层
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(input_layer)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# 第二段卷积层
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# 第三段卷积层
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# 第四段卷积层
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# 第五段卷积层
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
# 全连接层
x = Flatten()(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dropout(0.5)(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dropout(0.5)(x)
output_layer = Dense(num_classes, activation='softmax', name='output')(x)
# 定义模型
model = Model(inputs=input_layer, outputs=output_layer, name='vgg13')
# 打印模型结构
model.summary()
return model
```
接下来,我们来看一下测试代码。测试代码需要读入测试数据集,然后对测试集进行预测并计算准确率等评价指标。代码如下:
```python
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report
# 加载测试数据集
test_dir = './test_data'
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(224, 224),
batch_size=32,
class_mode='binary')
# 加载模型
model = tf.keras.models.load_model('./vgg13.h5')
# 对测试集进行预测
y_pred = model.predict(test_generator)
y_pred = np.argmax(y_pred, axis=1)
# 计算评价指标
target_names = ['class 0', 'class 1']
print(classification_report(test_generator.classes, y_pred, target_names=target_names))
```
最后,我们来看一下绘制损失函数随epoch的变化曲线的代码。代码如下:
```python
import matplotlib.pyplot as plt
import numpy as np
# 加载训练日志文件
log_file = './training.log'
with open(log_file, 'r') as f:
lines = f.readlines()
# 解析训练日志文件
epochs = []
losses = []
val_losses = []
for line in lines:
if line.startswith('Epoch'):
epoch = int(line.split()[1])
epochs.append(epoch)
elif line.startswith('loss:'):
loss = float(line.split()[1])
val_loss = float(line.split()[3])
losses.append(loss)
val_losses.append(val_loss)
# 绘制损失函数随epoch的变化曲线
plt.plot(epochs, losses, 'r', label='Training Loss')
plt.plot(epochs, val_losses, 'b', label='Validation Loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()
```
至于如何将本地的船舶图片加载到代码中,你可以使用TensorFlow提供的ImageDataGenerator类来读取本地图片,具体代码如下:
```python
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# 加载本地船舶图片
data_dir = './ship_data'
batch_size = 32
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
data_dir+'/train',
target_size=(224, 224),
batch_size=batch_size,
class_mode='binary')
val_datagen = ImageDataGenerator(rescale=1./255)
val_generator = val_datagen.flow_from_directory(
data_dir+'/val',
target_size=(224, 224),
batch_size=batch_size,
class_mode='binary')
```
希望这份代码能够帮助到你,祝你在船舶图像二分类任务中取得好成绩!