move utils functions to new file
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@ -29,7 +29,7 @@ def encode_char(c):
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encode_char = np.vectorize(encode_char)
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# TODO: refactor
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# TODO: ask for correct refactoring
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def get_user_chunks(user_flow, window=10):
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# TODO: what is maxLengthInSeconds for?!?
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# maxMilliSeconds = maxLengthInSeconds * 1000
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@ -149,7 +149,6 @@ def create_dataset_from_lists(chunks, vocab, max_len):
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:param max_len:
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:return:
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"""
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def get_domain_features_reduced(d):
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return get_domain_features(d[0], vocab, max_len)
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18
main.py
18
main.py
@ -17,6 +17,7 @@ import models
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# create logger
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import visualize
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from dataset import load_or_generate_h5data
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from utils import exists_or_make_path
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logger = logging.getLogger('logger')
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logger.setLevel(logging.DEBUG)
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@ -55,11 +56,6 @@ if args.gpu:
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session = tf.Session(config=config)
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def exists_or_make_path(p):
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if not os.path.exists(p):
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os.makedirs(p)
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def main_paul_best():
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char_dict = dataset.get_character_dict()
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pauls_best_params = models.pauls_networks.best_config
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@ -80,15 +76,15 @@ def main_hyperband():
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"flow_features": [3],
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"input_length": [40],
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# model params
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"embedding_size": [16, 32, 64, 128, 256, 512],
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"filter_embedding": [16, 32, 64, 128, 256, 512],
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"embedding_size": [8, 16, 32, 64, 128, 256],
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"filter_embedding": [8, 16, 32, 64, 128, 256],
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"kernel_embedding": [1, 3, 5, 7, 9],
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"hidden_embedding": [16, 32, 64, 128, 256, 512],
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"hidden_embedding": [8, 16, 32, 64, 128, 256],
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"dropout": [0.5],
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"domain_features": [16, 32, 64, 128, 256, 512],
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"filter_main": [16, 32, 64, 128, 256, 512],
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"domain_features": [8, 16, 32, 64, 128, 256],
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"filter_main": [8, 16, 32, 64, 128, 256],
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"kernels_main": [1, 3, 5, 7, 9],
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"dense_main": [16, 32, 64, 128, 256, 512],
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"dense_main": [8, 16, 32, 64, 128, 256],
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}
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logger.info("create training dataset")
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