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- I have created with keras tensorflow.I use NSL KDD dataset.
- 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)
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