69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
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import numpy as np
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from keras.utils import np_utils
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import dataset
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import models
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def main():
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# parameter
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innerCNNFilters = 512
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innerCNNKernelSize = 2
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cnnDropout = 0.5
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cnnHiddenDims = 1024
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domainFeatures = 512
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flowFeatures = 3
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numCiscoFeatures = 30
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windowSize = 10
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maxLen = 40
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embeddingSize = 100
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kernel_size = 2
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drop_out = 0.5
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filters = 2
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hidden_dims = 100
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vocabSize = 40
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threshold = 3
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minFlowsPerUser = 10
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numEpochs = 100
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timesNeg = -1
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char_dict = dataset.get_character_dict()
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user_flow_df = dataset.get_user_flow_data()
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print("create training dataset")
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(X_tr, y_tr, hits_tr, names_tr) = dataset.create_dataset_from_flows(
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user_flow_df, char_dict,
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maxLen=maxLen, threshold=threshold, windowSize=windowSize)
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pos_idx = np.where(y_tr == 1.0)[0]
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neg_idx = np.where(y_tr == 0.0)[0]
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use_idx = np.concatenate((pos_idx, neg_idx))
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y_tr = y_tr[use_idx]
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# hits_tr = hits_tr[use_idx]
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# names_tr = names_tr[use_idx]
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for i in range(len(X_tr)):
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X_tr[i] = X_tr[i][use_idx]
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# TODO: WTF? I don't get it...
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shared_cnn = models.get_shared_cnn(len(char_dict) + 1, embeddingSize, maxLen,
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domainFeatures, kernel_size, domainFeatures, 0.5)
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model = models.get_top_cnn(shared_cnn, flowFeatures, maxLen, windowSize, domainFeatures, filters, kernel_size,
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cnnHiddenDims, cnnDropout)
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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epochNumber = 0
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y_tr = np_utils.to_categorical(y_tr, 2)
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model.fit(x=X_tr, y=y_tr, batch_size=128,
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epochs=epochNumber + 1, shuffle=True, initial_epoch=epochNumber) # ,
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# validation_data=(testData,testLabel))
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if __name__ == "__main__":
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main()
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