Keras多标签分类"to_categorical"错误 [英] Keras Multi-Label Classification 'to_categorical' Error
问题描述
接收
IndexError:索引3超出了尺寸1为3的轴1的边界
IndexError: index 3 is out of bounds for axis 1 with size 3
尝试在输出向量上使用Keras to_categorical创建单点编码时. Y.shape = (178,1)
.请帮助(:
when trying to create one-hot encoding using Keras to_categorical on output vectors. Y.shape = (178,1)
. Please help (:
import keras
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
# number of wine classes
classifications = 3
# load dataset
dataset = np.loadtxt('wine.csv', delimiter=",")
X = dataset[:,1:14]
Y = dataset[:,0:1]
# convert output values to one-hot
Y = keras.utils.to_categorical(Y, classifications)
# creating model
model = Sequential()
model.add(Dense(10, input_dim=13, activation='relu'))
model.add(Dense(15, activation='relu'))
model.add(Dense(20, activation='relu'))
model.add(Dense(classifications, activation='softmax'))
# compile and fit model
model.compile(loss="categorical_crossentropy", optimizer="adam",
metrics=['accuracy'])
model.fit(X, Y, batch_size=10, epochs=10)
推荐答案
好吧,问题在于wine
标签来自范围[1, 3]
,而to_categorical
则索引了来自0
的类.当将3
标记为to_categorical
将该索引视为实际的第4类时,这会产生错误-这与您提供的类数不一致.最简单的解决方法是枚举标签,以0
开头:
Well, the problem lies in the fact that wine
labels are from range [1, 3]
and to_categorical
indexes classes from 0
. This makes an error when labeling 3
as to_categorical
treats this index as an actual 4th class - what is inconsistent with the number of classes you provided. The easiest fix is to enumerate labels to start from 0
by:
Y = Y - 1
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