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我有一个名为 train 的文件夹，其中有三个单独的文件夹，分别是“Covid”、“Pneumonia”、“Healthy”。我使用迁移学习创建了 VGG19 模型。我需要帮助为我的测试数据创建混淆矩阵。我已将我的火车数据拆分为 75%、25%。我已经尝试了一切来创建混淆矩阵并计算 F-1 分数。如果有人向我提供代码，那就太好了，因为我是新手。

#import needed packages
import numpy as np
import tensorflow as tf
import keras
from keras import backend as K
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.layers import Dense,GlobalAveragePooling2D,Dropout,SeparableConv2D,BatchNormalization, Activation, Dense
from keras.applications.vgg19 import VGG19
from keras.applications.resnet import ResNet101
from keras.optimizers import Adam
from keras.layers import Input, Lambda, Dense, Flatten
import matplotlib.pyplot as plt

# dataset has 3 classes
num_class = 3

# Base model without Fully connected Layers
base_model = VGG19(include_top=False, weights='imagenet', input_shape=(224,224,3))

# don't train existing weights
for layer in base_model.layers:
  layer.trainable = False
  
  
x=base_model.output
x = Flatten()(base_model.output)
##3 classes ,COVID, PNEUNOMIA, NORMAL


preds=Dense(num_class, activation='softmax')(x) #final layer with softmax activation

model=Model(inputs=base_model.input,outputs=preds)
##check the model
model.summary()


# tell the model what cost and optimization method to use
model.compile(
  loss='categorical_crossentropy',
  optimizer='adam',
  metrics=['accuracy']
)


##Preparing Data 
train_datagen=ImageDataGenerator(preprocessing_function=keras.applications.vgg16.preprocess_input,
                                 validation_split=0.25)

train_generator=train_datagen.flow_from_directory('C:/Users/prani/OneDrive/Desktop/Masters project/chest_xray/Train-250/',
                                                 target_size=(224,224),
                                                 batch_size=64,
                                                 class_mode='categorical',
                                                 subset='training')


validation_generator = train_datagen.flow_from_directory(
                                                'C:/Users/prani/OneDrive/Desktop/Masters project/chest_xray/Train-250/', # same directory as training data
                                                target_size=(224,224),
                                                batch_size=64,
                                                class_mode='categorical',
                                                subset='validation') # set as validation data



##Training
##SEtting hyper parameter
epochs = 50
learning_rate = 0.0005
decay_rate = learning_rate / epochs
opt = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, decay=decay_rate, amsgrad=False)
model.compile(optimizer=opt,loss='categorical_crossentropy',metrics=['accuracy'])


##Train
step_size_train = train_generator.n/train_generator.batch_size
step_size_val = validation_generator.samples // validation_generator.batch_size
r = model.fit_generator(generator=train_generator,
                   steps_per_epoch=step_size_train,
                   validation_data = validation_generator, 
                   validation_steps =step_size_val,
                   epochs=50)


##Confusion Matrix  (Sample code for confusion matrix but this is not working, it doesn't produce error neither results)
from sklearn.metrics import classification_report, confusion_matrix
batch_size=64
Y_pred = model.predict_generator(validation_generator,186)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(validation_generator.classes, y_pred))
print('Classification Report')
target_names = ['COVID','NORMAL','PNEUMONIA']
print(classification_report(validation_generator.classes, y_pred, target_names=target_names))
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1 回答 1

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您可以查看此页面以获取有关如何编写混淆矩阵和其他指标的信息： https ://www.tensorflow.org/tutorials/structured_data/imbalanced_data

于 2020-10-27T17:08:20.660 回答