h_train.shape, h_val.shape, h_test.shape,
y_train.shape, y_val.shape, y_test.shape)
if USE_HOLIDAY == 'no_use':
X_train[:, :, [1]] = 0
# X_val[:, :, [1]] = 0
X_test[:, :, [1]] = 0
model = RNNModel(
lookback=LOOKBACK, lookahead=LOOKAHEAD, input_dim=2, hid_dim=40,
device='cuda', data_dir=DATA_DIR, task=USE_HOLIDAY
)
model.fit(
X_train=X_train, y_train=y_train,
X_val=X_test, y_val=y_test,
metric='mae', max_epoch=MAX_EPOCH, patience=3000000,
batch_size=128, lr=1e-3, weight_decay=1e-3
)
forecast = model.eval(X_test=X_test)
elif USE_HOLIDAY == 'feature':
model = RNNModel(
lookback=LOOKBACK, lookahead=LOOKAHEAD, input_dim=2, hid_dim=40,
device='cuda', data_dir=DATA_DIR, task=USE_HOLIDAY
)
model.fit(
X_train=X_train, y_train=y_train,
X_val=X_test, y_val=y_test,
metric='mae', max_epoch=MAX_EPOCH, patience=3000000,
batch_size=128, lr=1e-3, weight_decay=1e-3
filepath = "_{}_{}_{}_{}_{}_{}_".format(
'LSTM' if RNN_TYPE == LSTM else 'GRU',
'X'.join(str(x) for x in LAYERS_SIZE), 'ATT' if ATTENTION else 'NO_ATT',
'DROPOUT_' + 'X'.join(str(x) for x in SPATIAL_DROPOUT),
str(DENSE) if DENSE != 0 else 'NO_DENSE',
str() if DROPOUT_DENSE else '')
filepath = "/data/spc_{epoch:02d}_F1_{f1_micro_score:.4f}_catAcc_{val_categorical_accuracy:.4f}_trainable" + filepath + ".hdf5"
# model.model().predict((([np.array(X_val), np.array(X_val_text)], np.array(Y_val))[0]), batch_size=BATCH_SIZE)
model.fit(x=[np.array(X), np.array(X_text)],
y=np.array(Y),
validation_data=val_data,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
callbacks=[
MicroF1ModelCheckpoint(filepath=filepath,
verbose=1,
monitor='categorical_accuracy',
save_weights_only=True,
mode='auto')
])
predictions = []
# predictions = model.model().predict(np.array([np.array(X_val), np.array(X_val_text)]), batch_size=BATCH_SIZE)
predictions = predictions.argmax(axis=1)
label2emotion = {0: "others", 1: "happy", 2: "sad", 3: "angry"}
with open('/data/result/test.txt', "w", encoding="utf8") as fout:
fout.write('\t'.join(["id", "turn1", "turn2", "turn3", "label"]) + '\n')
with open(env.DEV_FILE_PATH, encoding="utf8") as fin:
fin.readline()
for lineNum, line in enumerate(fin):
