133 lines
4.3 KiB
Python
133 lines
4.3 KiB
Python
import argparse
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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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parser = argparse.ArgumentParser()
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parser.add_argument("--modes", action="store", dest="modes", nargs="+")
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# parser.add_argument("--data", action="store", dest="data",
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# default="data/")
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#
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# parser.add_argument("--h5data", action="store", dest="h5data",
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# default="")
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#
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# parser.add_argument("--model", action="store", dest="model",
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# default="model_x")
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#
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# parser.add_argument("--pred", action="store", dest="pred",
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# default="")
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#
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# parser.add_argument("--type", action="store", dest="model_type",
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# default="simple_conv")
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#
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parser.add_argument("--batch", action="store", dest="batch_size",
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default=64, type=int)
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parser.add_argument("--epochs", action="store", dest="epochs",
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default=10, type=int)
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# parser.add_argument("--samples", action="store", dest="samples",
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# default=100000, type=int)
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#
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# parser.add_argument("--samples_val", action="store", dest="samples_val",
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# default=10000, type=int)
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#
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# parser.add_argument("--area", action="store", dest="area_size",
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# default=25, type=int)
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#
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# parser.add_argument("--queue", action="store", dest="queue_size",
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# default=50, type=int)
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#
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# parser.add_argument("--p", action="store", dest="p_train",
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# default=0.5, type=float)
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#
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# parser.add_argument("--p_val", action="store", dest="p_val",
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# default=0.01, type=float)
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#
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# parser.add_argument("--gpu", action="store", dest="gpu",
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# default=0, type=int)
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#
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# parser.add_argument("--tmp", action="store_true", dest="tmp")
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#
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# parser.add_argument("--test", action="store", dest="test_image",
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# default=6, choices=range(7), type=int)
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args = parser.parse_args()
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# config = tf.ConfigProto(log_device_placement=True)
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# config.gpu_options.per_process_gpu_memory_fraction = 0.5
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# config.gpu_options.allow_growth = True
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# session = tf.Session(config=config)
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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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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, hits_tr, names_tr, server_tr, trusted_hits_tr) = dataset.create_dataset_from_flows(
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user_flow_df, char_dict,
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max_len=maxLen, window_size=windowSize)
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# make client labels discrete with 4 different values
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# TODO: use trusted_hits_tr for client classification too
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client_labels = np.apply_along_axis(lambda x: dataset.discretize_label(x, 3), 0, np.atleast_2d(hits_tr))
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# select only 1.0 and 0.0 from training data
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pos_idx = np.where(client_labels == 1.0)[0]
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neg_idx = np.where(client_labels == 0.0)[0]
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idx = np.concatenate((pos_idx, neg_idx))
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# select labels for prediction
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client_labels = client_labels[idx]
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server_labels = server_tr[idx]
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# TODO: remove when features are flattened
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for i in range(len(X_tr)):
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X_tr[i] = X_tr[i][idx]
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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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client_labels = np_utils.to_categorical(client_labels, 2)
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server_labels = np_utils.to_categorical(server_labels, 2)
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model.fit(X_tr,
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[client_labels, server_labels],
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batch_size=args.batch_size,
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epochs=args.epochs,
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shuffle=True)
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# TODO: for validation we use future data -> validation_data=(testData,testLabel))
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if __name__ == "__main__":
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main()
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