num_label=label_dim,
num_hidden_proj=num_hidden_proj)
data_names = ['data'] + state_names
label_names = ['softmax_label']
return (sym, data_names, label_names)
module = mx.mod.BucketingModule(
sym_gen,
default_bucket_key=data_train.default_bucket_key,
context=contexts)
do_training(training_method, args, module, data_train, data_val)
elif training_method == METHOD_TBPTT:
truncate_len = args.config.getint('train', 'truncate_len')
data_train = TruncatedSentenceIter(train_sets,
batch_size,
init_states,
truncate_len=truncate_len,
feat_dim=feat_dim)
data_val = TruncatedSentenceIter(dev_sets,
batch_size,
init_states,
truncate_len=truncate_len,
feat_dim=feat_dim,
do_shuffling=False,
pad_zeros=True)
sym = lstm_unroll(num_lstm_layer,
truncate_len,
feat_dim,
num_hidden=num_hidden,
num_label=label_dim,
output_states=True,
num_hidden_proj=num_hidden_proj)
data_names = ['data'] + state_names
label_names = []
return (sym, data_names, label_names)
module = mx.mod.BucketingModule(
sym_gen,
default_bucket_key=data_test.default_bucket_key,
context=contexts)
else:
truncate_len = 20
data_test = TruncatedSentenceIter(test_sets,
batch_size,
init_states,
truncate_len,
feat_dim=feat_dim,
do_shuffling=False,
pad_zeros=True,
has_label=True)
sym = lstm_unroll(num_lstm_layer,
truncate_len,
feat_dim,
num_hidden=num_hidden,
num_label=label_dim,
output_states=True,
num_hidden_proj=num_hidden_proj)
data_names = [x[0] for x in data_test.provide_data]
label_names = ['softmax_label']
module = mx.mod.Module(sym,
context=contexts,
init_h = [('l%d_init_h' % l, (batch_size, num_hidden_lstm))
for l in range(num_lstm_layer)]
init_states = init_c + init_h
state_names = [x[0] for x in init_states]
data_names = [data_name] + state_names
data_test = TruncatedSentenceIter(test_sets,
batch_size,
init_states,
truncate_len=truncate_len,
delay=10,
feat_dim=feat_dim,
label_dim=label_dim,
data_name='data',
label_name='linear_label',
do_shuffling=False,
pad_zeros=False)
data_names = [x[0] for x in data_test.provide_data]
label_names = [x[0] for x in data_test.provide_label]
sym, arg_params, aux_params = mx.model.load_checkpoint(
test_prefix, load_epoch_num)
mod = mx.mod.Module(sym,
context=contexts,
data_names=data_names,
label_names=label_names)