parser = get_parser()
p = parser.parse_args()
# Parameters
BATCH_SIZE = p.batch_size
EPOCH = p.n_epochs
DROPOUT = p.dropout
LAYERS = p.layers
PATIENCE = p.patience
HIDDEN_SIZE = p.n_hidden
PREFIX = 'exp/'+p.prefix + '/'
os.system('mkdir -p '+PREFIX)
FOOTPRINT = 'H' + str(HIDDEN_SIZE) + '_L' + str(LAYERS) + '_D' + str(DROPOUT)
### get data
X_tr, Y_tr, X_val, Y_val, X_test,Y_test = prepare_ass()
print('data loaded...')
DIM = X_tr[0][0].shape[2]
N_tr = len(X_tr)
N_val = len(X_val)
print('building model...')
model = Sequential()
model.add(Dense(HIDDEN_SIZE, input_shape = (DIM,),W_regularizer= l1l2(l1 = 0.00001, l2 = 0.00001)))
model.add(Activation('relu'))
for layer in xrange(LAYERS-1):
model.add(Dense(HIDDEN_SIZE,W_regularizer= l1l2(l1 = 0.00001, l2 = 0.00001)))
model.add(Activation('relu'))
model.add(Dropout(DROPOUT))
# Parameters
BATCH_SIZE = p.batch_size
EPOCH = p.n_epochs
DROPOUT = p.dropout
LAYERS = p.layers
PATIENCE = p.patience
HIDDEN_SIZE = p.n_hidden
FINETUNE = p.finetune
PREFIX = 'exp/'+p.prefix + '/'
os.system('mkdir -p '+PREFIX)
FOOTPRINT = 'M' + str(p.model) + '_U' + str(p.unit) + '_H' + str(HIDDEN_SIZE) + '_L' + str(LAYERS) + '_D' + str(DROPOUT) + '_TR' + p.train + '_FT' + str(FINETUNE)
### get data
[X_tr_q,X_tr_a], Y_tr, [X_val_q,X_val_a], Y_val, [X_test_q,X_test_a],Y_test, [tr_length_a,val_length_a,test_length_a], [word_idx,idx_word], embedding_weights = prepare_ass(train = TR[p.train], mini_batch = True, fp = PREFIX + FOOTPRINT, finetune = FINETUNE)
b_X_tr, b_Y_tr = distribute_buckets(tr_length_a, [X_tr_q, X_tr_a], [Y_tr], step_size = 20, x_set = set([1]), y_set = set())
print('data loaded...')
if FINETUNE:
DIM = 0
else:
DIM = X_tr_q.shape[2]
MAX_Q = X_tr_q.shape[1]
MAX_A = X_tr_a.shape[1]
print('building model...')
parser = get_parser()
p = parser.parse_args()
# Parameters
BATCH_SIZE = p.batch_size
EPOCH = p.n_epochs
DROPOUT = p.dropout
LAYERS = p.layers
PATIENCE = p.patience
HIDDEN_SIZE = p.n_hidden
PREFIX = 'exp/' + p.prefix + '/'
os.system('mkdir -p ' + PREFIX)
FOOTPRINT = 'H' + str(HIDDEN_SIZE) + '_L' + str(LAYERS) + '_D' + str(DROPOUT)
### get data
X_tr, Y_tr, X_val, Y_val, X_test, Y_test = prepare_ass()
print('data loaded...')
DIM = X_tr[0][0].shape[2]
N_tr = len(X_tr)
N_val = len(X_val)
print('building model...')
model = Sequential()
model.add(
Dense(HIDDEN_SIZE,
input_shape=(DIM, ),
W_regularizer=l1l2(l1=0.00001, l2=0.00001)))
model.add(Activation('relu'))
for layer in xrange(LAYERS - 1):
model.add(Dense(HIDDEN_SIZE, W_regularizer=l1l2(l1=0.00001, l2=0.00001)))
HIDDEN_SIZE = p.n_hidden
FINETUNE = p.finetune
PREFIX = 'exp/' + p.prefix + '/'
os.system('mkdir -p ' + PREFIX)
FOOTPRINT = 'M' + str(p.model) + '_U' + str(
p.unit) + '_H' + str(HIDDEN_SIZE) + '_L' + str(LAYERS) + '_D' + str(
DROPOUT) + '_TR' + p.train + '_FT' + str(FINETUNE)
### get data
[X_tr_q,
X_tr_a], Y_tr, [X_val_q, X_val_a], Y_val, [X_test_q, X_test_a], Y_test, [
tr_length_a, val_length_a, test_length_a
], [word_idx,
idx_word], embedding_weights = prepare_ass(train=TR[p.train],
mini_batch=True,
fp=PREFIX + FOOTPRINT,
finetune=FINETUNE)
b_X_tr, b_Y_tr = distribute_buckets(tr_length_a, [X_tr_q, X_tr_a], [Y_tr],
step_size=20,
x_set=set([1]),
y_set=set())
print('data loaded...')
if FINETUNE:
DIM = 0
else:
DIM = X_tr_q.shape[2]
MAX_Q = X_tr_q.shape[1]
