parameters.py

from tensorflow.keras.layers import LSTM

# max number of words in each sentence
SEQUENCE_LENGTH = 300
# N-Dimensional GloVe embedding vectors
EMBEDDING_SIZE = 300
# number of words to use, discarding the rest
N_WORDS = 10000
# out of vocabulary token
OOV_TOKEN = None
# 30% testing set, 70% training set
TEST_SIZE = 0.3
# number of CELL layers
N_LAYERS = 1
# the RNN cell to use, LSTM in this case
RNN_CELL = LSTM
# whether it's a bidirectional RNN
IS_BIDIRECTIONAL = False
# number of units (RNN_CELL ,nodes) in each layer
UNITS = 128
# dropout rate
DROPOUT = 0.4
### Training parameters
LOSS = "categorical_crossentropy"
OPTIMIZER = "adam"
BATCH_SIZE = 64
EPOCHS = 6

def get_model_name(dataset_name):
    # construct the unique model name
    model_name = f"{dataset_name}-{RNN_CELL.__name__}-seq-{SEQUENCE_LENGTH}-em-{EMBEDDING_SIZE}-w-{N_WORDS}-layers-{N_LAYERS}-units-{UNITS}-opt-{OPTIMIZER}-BS-{BATCH_SIZE}-d-{DROPOUT}"
    if IS_BIDIRECTIONAL:
        # add 'bid' str if bidirectional
        model_name = "bid-" + model_name
    if OOV_TOKEN:
        # add 'oov' str if OOV token is specified
        model_name += "-oov"
    return model_name