Question

我已经使用keras和tensorflow创建了一个项目。我使用了NSL KDD数据集，并用python编码了我的项目。我还使用了SGD优化器。

我想拟合一个模型，然后对其进行评估，然后检查其准确性。（因此我可以将其与机器学习的结果进行比较。）

这是我下面的完整代码，请仔细阅读。

import tensorflow as tf
from keras import backend as K

from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants, signature_constants, signature_def_utils_impl

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np
sess = tf.Session()
K.set_session(sess)
K.set_learning_phase(0)
model_version = "2"


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('KDD_Dataset.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 41:42].values
# Encoding categorical data X
from sklearn.preprocessing import LabelEncoder
labelencoder_X = LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:,0])
X[:,1] = labelencoder_X.fit_transform(X[:,1])
X[:,2] = labelencoder_X.fit_transform(X[:,2])
#
from sklearn.preprocessing import OneHotEncoder
onehotencoder_0 = OneHotEncoder(categorical_features=[0])
onehotencoder_1 = OneHotEncoder(categorical_features=[1])
onehotencoder_2 = OneHotEncoder(categorical_features=[2])
X = onehotencoder_0.fit_transform(X).toarray()
X = onehotencoder_1.fit_transform(X).toarray()
X = onehotencoder_2.fit_transform(X).toarray()

# Encoding categorical data y
from sklearn.preprocessing import LabelEncoder
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)
max(y)

# Splitting the dataset into the Training set and Test set
#from sklearn.cross_validation import train_test_split
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)
# create the model
model = Sequential()
model.add(Dense(41, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(20, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# compile the model

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

model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=200, batch_size=5, verbose=0)

Answer 1

请参阅密集（41，input_dim = 8，init ='统一'，激活='relu'）您定义的模型具有8个功能，但是您的输入具有45个功能。它们不匹配。您必须使模型具有45个要素以匹配输入，或者将输入要素的长度减少到8个以匹配模型。

Answer 2

更改行

model.add（密集（41，input_dim = 8，init ='统一'，激活='relu'））

到

model.add（Dense（42，input_dim = 42，init ='uniform'，activation ='relu'））

和

optimizer = sgd转换为optimizer ='sgd'

Keras神经网络预测不起作用

2 个答案: