使用SkLearning使用Kera数据生成器绘制混淆矩阵
问题描述
SkLearning清楚地定义了如何使用其自己的分类模型绘制混淆矩阵1。 但将其与使用数据生成器的Kera模型一起使用又如何呢?让我们看一看示例代码: 首先,我们需要训练模型。import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusion_matrix
#Start
train_data_path = 'F://data//Train'
test_data_path = 'F://data//Validation'
img_rows = 150
img_cols = 150
epochs = 30
batch_size = 32
num_of_train_samples = 3000
num_of_test_samples = 600
#Image Generator
train_datagen = ImageDataGenerator(rescale=1. / 255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(train_data_path,
target_size=(img_rows, img_cols),
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(test_data_path,
target_size=(img_rows, img_cols),
batch_size=batch_size,
class_mode='categorical')
# Build model
model = Sequential()
model.add(Convolution2D(32, (3, 3), input_shape=(img_rows, img_cols, 3), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, (3, 3), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, (3, 3), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(5))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
#Train
model.fit_generator(train_generator,
steps_per_epoch=num_of_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=num_of_test_samples // batch_size)
现在,在对模型进行训练后,让我们构建一个混淆矩阵。
#Confution Matrix and Classification Report
Y_pred = model.predict_generator(validation_generator, num_of_test_samples // batch_size+1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(validation_generator.classes, y_pred))
print('Classification Report')
target_names = ['Cats', 'Dogs', 'Horse']
print(classification_report(validation_generator.classes, y_pred, target_names=target_names))
到目前为止,这个方法运行得很好。但是,如何将其保存为PNG格式,并采用与上面的sknowledge示例相同的布局?
任何想法都会受到高度赞赏。 提前感谢
解决方案
这样(另见ConfusionMatrixDisplay
和confusion_matrix
):
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
y_pred = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2])
y_test = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2])
labels = ["Cats", "Dogs", "Horses"]
cm = confusion_matrix(y_test, y_pred)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)
disp.plot(cmap=plt.cm.Blues)
plt.show()
结果:
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