default=None,
help='initialize with pretrained model weights',
type=str)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
embed_dict = get_embed_dict(embedding_path)
# print(dataset1.columns)
datasets = Datasets(root_dir)
X, Y, tokenizer = datasets.get_tokenized_data(
max_sentence_len=max_sentence_len)
embedding_matrix, word_num = make_embedding(tokenizer, embed_dict)
model = Network(word_num=word_num,
embedding_matrix=embedding_matrix,
maxlen=max_sentence_len)
# if args.weights is not None:
# model.load_weights(args.weights)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=[
'accuracy',
])
filepath = "weights\weights2-{epoch:02d}-{val_acc:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath,
