output=Dense(3, activation='softmax', name="output"),
params=params_spc,
attention=ATTENTION)
model.build()
model_sent.build()
#model.model().load_weights("/data/sent_nosent07_F1_0.8949_catAcc_0.8921_trainable_LSTM_300X300_ATT_DROPOUT_0.2X0.5_120__.hdf5") # old
model.model().load_weights(
"spc_07_F1_0.9506_catAcc_0.9176_trainable_LSTM_256X256_ATT_DROPOUT_0.3X0.5_None__.hdf5"
)
model_sent.model().load_weights(
"/data/spc_02_F1_0.9540_catAcc_0.9496_trainable_LSTM_300X300_ATT_DROPOUT_0.4X0.4_None__.hdf5"
)
model.compile()
model_sent.compile()
# old
#X_val = [value for value in tqdm(sem_eval_dataset_global.iterate_test_x(max_len=MAX_LEN, one_hot=True))]
#X_val_text = [value for value in tqdm(sem_eval_dataset_global.iterate_test_x(max_len=MAX_LEN, one_hot=False))]
X = [
value for value in tqdm(
sem_eval_dataset.iterate_train_x(max_len=MAX_LEN, one_hot=True))
]
X_text = [
value for value in tqdm(
sem_eval_dataset.iterate_train_x(max_len=MAX_LEN, one_hot=False))
]
X_val_spc = [
features: List[Feature] = [manual_features, glove_features, elmo_features]
params = RNNModelParams(layers_size=LAYERS_SIZE,
spatial_dropout=SPATIAL_DROPOUT,
recurrent_dropout=RECURRENT_DROPOUT,
dropout_dense=DROPOUT_DENSE,
dense_encoder_size=DENSE)
model = RNNModel(inputs=[input, text_input],
features=features,
output=Dense(3, activation='softmax', name="output"),
params=params,
attention=ATTENTION)
model.build()
model.compile()
X = [
value for value in tqdm(
sem_eval_dataset.iterate_train_x(max_len=MAX_LEN, one_hot=True))
]
X_text = [
value for value in tqdm(
sem_eval_dataset.iterate_train_x(max_len=MAX_LEN, one_hot=False))
]
X_val = [
value for value in tqdm(
sem_eval_dataset_dev.iterate_x(max_len=MAX_LEN, one_hot=True))
]
X_val_text = [
