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39 rivejä
1.3 KiB
39 rivejä
1.3 KiB
from math import ceil
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from typing import Optional
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import logging
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from argparse import ArgumentParser
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import sys
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import os
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class Config:
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def __init__(self):
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self.__logger: Optional[logging.Logger] = None
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self.set_defaults()
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def set_defaults(self):
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self.NUM_TRAIN_EPOCHS = 100
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self.SAVE_EVERY_EPOCHS = 5
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self.TRAIN_BATCH_SIZE = 64
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# model hyper-params
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self.categories = 10
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# self.learning_rate=0.001
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# self.decay_rate=0.9
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self.path_vocab_size = 27500
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self.token_vocab_size = 1500
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self.MAX_CONTEXTS = 200
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self.MAX_TOKEN_VOCAB_SIZE = 1301136
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self.MAX_PATH_VOCAB_SIZE = 911417
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self.DEFAULT_EMBEDDINGS_SIZE = 64
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self.TOKEN_EMBEDDINGS_SIZE = self.DEFAULT_EMBEDDINGS_SIZE
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self.PATH_EMBEDDINGS_SIZE = self.DEFAULT_EMBEDDINGS_SIZE
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self.CODE_VECTOR_SIZE = self.context_vector_size
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self.TARGET_EMBEDDINGS_SIZE = self.CODE_VECTOR_SIZE
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self.DROPOUT_KEEP_RATE = 0.5
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@property
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def context_vector_size(self) -> int:
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# The context vector is actually a concatenation of the embedded
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# source & target vectors and the embedded path vector.
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return self.PATH_EMBEDDINGS_SIZE + 2 * self.TOKEN_EMBEDDINGS_SIZE
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