def sym_gen(seq_len):
sym = lstm_unroll(num_lstm_layer,
seq_len,
feat_dim,
num_hidden=num_hidden,
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)
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,
data_names=data_names,
label_names=label_names)
# set the parameters
module.bind(data_shapes=data_test.provide_data,
label_shapes=None,
for_training=False)
module.set_params(arg_params=arg_params, aux_params=aux_params)
def sym_gen(seq_len):
sym = lstm_unroll(num_lstm_layer, seq_len, feat_dim, num_hidden=num_hidden,
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)
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 = [x[0] for x in data_train.provide_data]
label_names = [x[0] for x in data_train.provide_label]
module = mx.mod.Module(sym, context=contexts, data_names=data_names,
label_names=label_names)
do_training(training_method, args, module, data_train, data_val)
else:
raise RuntimeError('Unknown training method: %s' % training_method)
print("="*80)
print("Finished Training")
print("="*80)
args.config.write(sys.stdout)
