Question

我是keras的新手，

我尝试使用我的数据集，按照多层感知器（MLP）的Keras教程进行多类softmax分类。我的数据有3个类，只有一个功能，但我不明白为什么结果总是只显示精度为0.3，模型将所有训练数据预测为头等。那么混淆矩阵是这样的。

编码：

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
import pandas as pd
import numpy as np



# Importing the dataset
dataset = pd.read_csv('StatusAll.csv')
X = dataset.iloc[:, 1:].values
y = dataset.iloc[:, 0:1].values


# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

from keras.utils import to_categorical
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

model = Sequential()

# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
model.add(Dense(64, activation='tanh', input_dim=1))
model.add(Dropout(0.5))
model.add(Dense(64, activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(4, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])

history = model.fit(x_train, y_train,
          epochs=100,
          batch_size=128)
score = model.evaluate(x_test, y_test, batch_size=128)
print('Test score:', score[0])
print('Test accuracy:', score[1])


from sklearn import metrics

prediction = model.predict(x_test)
prediction = np.around(prediction)

y_test_non_category = [ np.argmax(t) for t in y_test ]
y_predict_non_category = [ np.argmax(t) for t in prediction ]

from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test_non_category, y_predict_non_category)

print (conf_mat)

我希望我能得到一些建议，谢谢。

x_train示例 x_train

y_train before converted to categorical

enter image description here

Answer 1

你的最终Dense图层有4个输出，看起来你正在分类4而不是3。

{{1}}

从x_train和y_train查看示例数据以确保预处理正确将会很有帮助。因为你只有1个功能，所以MLP可能有点过分。除非您想要尝试使用MLP，否则decision tree会更简单。

MLP classifier_for多类

1 个答案: