'--inference',
action="store_true",
help='Whether to run inference or simply train the network')
parser.add_argument('--pretrained_path', help='Path to Pre-trained Weights')
args = parser.parse_args()
assert args.dataset.endswith('csv'), "Dataset File needs to be in CSV format"
assert 0. <= args.train_val_split < 1., "Train-vs-Validation Split need to be between [0, 1)"
latent_dim = args.latent_dim
# Reading and Preparing Training/Validation Dataset
reader = ReadData(args.dataset, args.train_val_split, args.language_1,
args.language_2)
(X_train, y_train), (X_val, y_val) = reader.prep_data()
train_samples = len(X_train)
val_samples = len(X_val)
num_encoder_tokens = reader.num_encoder_tokens
num_decoder_tokens = reader.num_decoder_tokens
# Loading Embedding Matrix
lang1_embedding = Word2Vec.load(args.lang1_embedding)
lang1_tok = Tokenizer()
lang1_tok.fit_on_texts(reader.language_1_text)
encoder_embedding_matrix = np.zeros((num_encoder_tokens, latent_dim))
for word, i in lang1_tok.word_index.items():
try:
embedding_vector = lang1_embedding[word]
if embedding_vector is not None:
