def get_model(args):
if args.model_type == 'baseline':
m = BaselineModel(num_layers=args.num_layers,
num_channels=args.num_channels)
elif args.model_type == 'parallel':
m = ParallelDilationModel(num_channels=args.num_channels)
elif args.model_type == 'parallel_aggregate':
m = ParallelAggregationModel(num_channels=args.num_channels)
elif args.model_type == 'parallel_aggregate_test':
m = ParallelAggregationModelTest(num_channels=args.num_channels)
elif args.model_type == 'parallel_aggregate_4_test':
m = ParallelAggregationModel4ColTest(num_channels=args.num_channels)
elif args.model_type == 'parallel_aggregate_3_test':
m = ParallelAggregationModel3ColTest(num_channels=args.num_channels)
elif args.model_type == 'parallel_aggregate_2_test':
m = ParallelAggregationModel2ColTest(num_channels=args.num_channels)
elif args.model_type == 'parallel_aggregate_1_test':
m = ParallelAggregationModel1ColTest(num_channels=args.num_channels)
elif args.model_type == 'parallel_noaggregate_test':
m = ParallelNoAggregationModelTest(num_channels=args.num_channels)
elif args.model_type == 'parallel_noaggregate_4_test':
m = ParallelNoAggregationModel4ColTest(num_channels=args.num_channels)
elif args.model_type == 'parallel_noaggregate_3_test':
m = ParallelNoAggregationModel3ColTest(num_channels=args.num_channels)
elif args.model_type == 'parallel_noaggregate_2_test':
m = ParallelNoAggregationModel2ColTest(num_channels=args.num_channels)
elif args.model_type == 'parallel_noaggregate_1_test':
m = ParallelNoAggregationModel1ColTest(num_channels=args.num_channels)
else:
raise NameError('unknown model type: {}'.format(args.model_type))
return m
def get_network(network_type):
'''
returns an instance of a model with randomly initialized weights
'''
dataset_dict = data_loader_factory.get_dataset_dictionary(config.DATALOADER_TYPE)
if network_type == ModelType.BASELINE:
return BaselineModel(dataset_dict)
elif network_type == ModelType.RELATION_NETWORK:
return RelNet(dataset_dict, config.RELATION_NETWORK_DICTIONARY)
elif network_type == ModelType.FILM:
return FiLM(dataset_dict)
elif network_type == ModelType.MOD_FILM:
return mod_FiLM(dataset_dict)
elif network_type == ModelType.BN_MOD_FILM:
return bn_mod_FiLM(dataset_dict)
elif network_type == ModelType.STACKED_CO_ATTENTION:
pass
elif network_type == ModelType.MEMORY_NETWORK:
pass
elif network_type == ModelType.RELATION_GROUP_ATTENTION_STANDARD:
return RelNetGroupAttentionStandard(dataset_dict, config.RELATION_GROUP_ATTENTION_NETWORK_DICTIONARY)
elif network_type == ModelType.RELATION_GROUP_ATTENTION_ALTERNATE:
return RelNetGroupAttentionAlternate(dataset_dict, config.RELATION_GROUP_ATTENTION_NETWORK_DICTIONARY)
elif network_type == ModelType.RELATION_GROUP_ATTENTION_SELF:
return RelNetGroupAttentionSelf(dataset_dict, config.RELATION_GROUP_ATTENTION_NETWORK_DICTIONARY)
elif network_type == ModelType.RELATION_NETWORK_BATCH_NORM:
return RelNetBatchNorm(dataset_dict, config.RELATION_NETWORK_DICTIONARY)
elif network_type == ModelType.RELATION_NETWORK_CONV_ATTENTION:
return RelNetConvAttention(dataset_dict, config.RELATION_NETWORK_DICTIONARY)
def baseline(percent):
X, Y, X_train, Y_train, answers = split_data()
reg = BaselineModel(X, X_train, Y_train)
preds_val = reg.predict(X)
ids = list(range(1, 190))
pairs = []
for i in ids:
pairs.append((i, preds_val[i - 1]))
roomsNotDone = [x for x in pairs if x[0] not in elizabeth_known]
length = len(roomsNotDone)
percentage = percent / 100.0
threshold = sorted(roomsNotDone, key=lambda x: x[1])[int(
(length - 1) * percentage)][1]
preds = []
for i in sorted(pairs, key=lambda x: x[0]):
if i[1] >= threshold:
preds.append(1)
else:
preds.append(0)
roomsDone = [x for x in pairs if x[0] in elizabeth_known]
known_preds = []
limited_answers = []
for i in sorted(roomsDone, key=lambda x: x[0]):
limited_answers.append(Y[i[0] - 1])
known_preds.append(i[1])
rms = sqrt(mean_squared_error(limited_answers, known_preds))
print("rms: ", rms)
mae = mean_absolute_error(limited_answers, known_preds)
print("mae: ", mae)
stats = check_answers(preds, answers)
window_size = args.window_size # in ms
step_size = args.step_size
n_input = int(1e-3 * window_size * 16000 / 2 + 1)
n_output = n_input
if args.model_type == 'residual':
model = ResidualModel(n_input, args.num_blocks, args.num_hidden,
args.num_layers_per_block).cuda()
elif args.model_type == 'highway':
model = HighwayModel(n_input, args.num_blocks, args.num_hidden,
args.num_layers_per_block).cuda()
elif args.model_type == 'masking':
model = MaskingModel(n_input, args.num_blocks, args.num_hidden,
args.num_layers_per_block).cuda()
elif args.model_type == 'baseline':
model = BaselineModel(n_input, args.num_hidden,
args.num_layers_per_block).cuda()
else:
raise ValueError(
'model_type has to be either "residual", "highway", or "baseline"'
)
print(model)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
if args.data_type == 'reverb':
print('Loading reverb dataset')
G_train, G_val = load_data(window_size, step_size, args.use_log)
elif args.data_type == 'noisy':
print('Loading noisy dataset')
def test(args):
label_name = [
'not related or not informative', 'other useful information',
'donations and volunteering', 'affected individuals',
'sympathy and support', 'infrastructure and utilities damage',
'caution and advice'
]
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
print('load best model...')
model = BaselineModel.load(args['--model-path'], device)
model.to(device)
model.eval()
df_test = pd.read_csv(args['--test'], index_col=0)
cn_loss = torch.load('loss_func')
sents = [text.split(' ') for text in df_test.ProcessedText]
with torch.no_grad():
pre_softmax = model(sents)
loss = cn_loss(
pre_softmax,
torch.tensor(df_test.InformationType_label.values,
dtype=torch.long,
device=device))
softmax = torch.nn.Softmax(dim=1)
prob = softmax(pre_softmax)
prediction = [t.item() for t in list(torch.argmax(prob, dim=1))]
pickle.dump([label_name[i] for i in prediction],
open('test_prediction', 'wb'))
accuracy = accuracy_score(df_test.InformationType_label.values, prediction)
matthews = matthews_corrcoef(df_test.InformationType_label.values,
prediction)
precisions = {}
recalls = {}
f1s = {}
aucrocs = {}
for i in range(len(label_name)):
prediction_ = [1 if pred == i else 0 for pred in prediction]
true_ = [
1 if label == i else 0
for label in df_test.InformationType_label.values
]
f1s.update({label_name[i]: f1_score(true_, prediction_)})
precisions.update({label_name[i]: precision_score(true_, prediction_)})
recalls.update({label_name[i]: recall_score(true_, prediction_)})
aucrocs.update({
label_name[i]:
roc_auc_score(true_, list(t.item() for t in prob[:, i]))
})
metrics_dict = {
'loss': loss,
'accuracy': accuracy,
'matthews coef': matthews,
'precision': precisions,
'recall': recalls,
'f1': f1s,
'aucroc': aucrocs
}
pickle.dump(metrics_dict, open('evaluation_metrics', 'wb'))
cm = plot_confusion_matrix(list(df_test.InformationType_label.values),
prediction,
label_name,
normalize=False,
path='test_confusion_matrix',
title='confusion matrix for test dataset')
plt.savefig('test_confusion_matrix', format='png')
cm_norm = plot_confusion_matrix(
list(df_test.InformationType_label.values),
prediction,
label_name,
normalize=True,
path='test normalized_confusion_matrix',
title='normalized confusion matrix for test dataset')
plt.savefig('test_normalized_confusion_matrix', format='png')
if args['--verbose']:
print('loss: %.2f' % loss)
print('accuracy: %.2f' % accuracy)
print('matthews coef: %.2f' % matthews)
for i in range(len(label_name)):
print('precision score for %s: %.2f' %
(label_name[i], precisions[label_name[i]]))
print('recall score for %s: %.2f' %
(label_name[i], recalls[label_name[i]]))
print('f1 score for %s: %.2f' %
(label_name[i], f1s[label_name[i]]))
print('auc roc score for %s: %.2f' %
(label_name[i], aucrocs[label_name[i]]))
def train(args):
label_name = ['0', '1']
device = torch.device("cuda:0" if args['--cuda'] else "cpu")
start_time = time.time()
print('Initializing Glove vocab and embeddings...', file=sys.stderr)
glove_word2id = pickle.load(open(args['--vocab'], 'rb'))
glove_word2id.update({'<unk>': len(glove_word2id)})
glove_word2id.update({'<pad>': len(glove_word2id)})
vocab = VocabEntry(glove_word2id)
embedding_matrix = np.load(open(args['--embeddings'], 'rb'))
embedding_matrix = np.vstack(
(embedding_matrix,
np.random.uniform(embedding_matrix.min(), embedding_matrix.max(),
(2, embedding_matrix.shape[1]))))
glove_embeddings = torch.tensor(embedding_matrix,
dtype=torch.float,
device=device)
print('Done! time elapsed %.2f sec' % (time.time() - start_time),
file=sys.stderr)
print('-' * 80, file=sys.stderr)
start_time = time.time()
print('Importing data...', file=sys.stderr)
df_train = pd.read_csv(args['--train'], index_col=0)
df_val = pd.read_csv(args['--dev'], index_col=0)
train_label = dict(df_train.InformationType_label.value_counts())
label_max = float(max(train_label.values()))
train_label_weight = torch.tensor(
[label_max / train_label[i] for i in range(len(train_label))],
device=device)
print('Done! time elapsed %.2f sec' % (time.time() - start_time),
file=sys.stderr)
print('-' * 80, file=sys.stderr)
start_time = time.time()
print('Set up model...', file=sys.stderr)
model = BaselineModel(hidden_size=int(args['--hidden-size']),
embedding=glove_embeddings,
vocab=vocab,
n_class=len(label_name),
dropout_rate=float(args['--dropout']))
model = model.to(device)
print('Use device: %s' % device, file=sys.stderr)
print('Done! time elapsed %.2f sec' % (time.time() - start_time),
file=sys.stderr)
print('-' * 80, file=sys.stderr)
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=float(args['--lr']))
cn_loss = torch.nn.CrossEntropyLoss(weight=train_label_weight.float(),
reduction='mean')
torch.save(cn_loss, 'loss_func') # for later testing
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
num_trial = 0
train_iter = patience = cum_loss = report_loss = 0
cum_examples = report_examples = epoch = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print('Begin Maximum Likelihood training...')
while True:
epoch += 1
for sents, targets in batch_iter(df_train,
batch_size=train_batch_size,
shuffle=True): # for each epoch
train_iter += 1
optimizer.zero_grad()
batch_size = len(sents)
pre_softmax = model(sents)
print(type(targets[0]))
loss = cn_loss(
pre_softmax,
torch.tensor(targets, dtype=torch.long, device=device))
loss.backward()
# clip gradient
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
clip_grad)
optimizer.step()
batch_losses_val = loss.item() * batch_size
report_loss += batch_losses_val
cum_loss += batch_losses_val
report_examples += batch_size
cum_examples += batch_size
if train_iter % log_every == 0:
print('epoch %d, iter %d, avg. loss %.2f, '
'cum. examples %d, speed %.2f examples/sec, '
'time elapsed %.2f sec' %
(epoch, train_iter, report_loss / report_examples,
cum_examples, report_examples /
(time.time() - train_time), time.time() - begin_time),
file=sys.stderr)
train_time = time.time()
report_loss = report_examples = 0.
# perform validation
if train_iter % valid_niter == 0:
print(
'epoch %d, iter %d, cum. loss %.2f, cum. examples %d' %
(epoch, train_iter, cum_loss / cum_examples, cum_examples),
file=sys.stderr)
cum_loss = cum_examples = 0.
print('begin validation ...', file=sys.stderr)
validation_loss = validation(
model, df_val, cn_loss,
device) # dev batch size can be a bit larger
print('validation: iter %d, loss %f' %
(train_iter, validation_loss),
file=sys.stderr)
is_better = len(
hist_valid_scores
) == 0 or validation_loss < min(hist_valid_scores)
hist_valid_scores.append(validation_loss)
if is_better:
patience = 0
print('save currently the best model to [%s]' %
model_save_path,
file=sys.stderr)
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(),
model_save_path + '.optim')
elif patience < int(args['--patience']):
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == int(args['--patience']):
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == int(args['--max-num-trial']):
print('early stop!', file=sys.stderr)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * float(
args['--lr-decay'])
print(
'load previously best model and decay learning rate to %f'
% lr,
file=sys.stderr)
# load model
params = torch.load(
model_save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
print('restore parameters of the optimizers',
file=sys.stderr)
optimizer.load_state_dict(
torch.load(model_save_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
if epoch == int(args['--max-epoch']):
print('reached maximum number of epochs!', file=sys.stderr)
exit(0)
def mainFunc(argv):
def printUsage():
print('main.py -n <num_cores> -x <experiment>')
print(
'num_cores = Number of cores requested from the cluster. Set to -1 to leave unset'
)
print(
'experiment = experiment setup that should be executed. e.g \'baseline\' or \'attention\''
)
print(
'tag = optional tag or name to distinguish the runs, e.g. \'bidirect3layers\' '
)
num_cores = -1
experiment = ""
tag = None
# Command line argument handling
try:
opts, args = getopt.getopt(argv, "n:x:t:",
["num_cores=", "experiment=", "tag="])
except getopt.GetoptError:
printUsage()
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
printUsage()
sys.exit()
elif opt in ("-n", "--num_cores"):
num_cores = int(arg)
elif opt in ("-x", "--experiment"):
if arg in ("baseline", "attention"):
experiment = arg
else:
printUsage()
sys.exit(2)
elif opt in ("-t", "--tag"):
tag = arg
print("Executing experiment {} with {} CPU cores".format(
experiment, num_cores))
if num_cores != -1:
# We set the op_parallelism_threads in the ConfigProto and pass it to the TensorFlow session
configProto = tf.ConfigProto(inter_op_parallelism_threads=num_cores,
intra_op_parallelism_threads=num_cores)
else:
configProto = tf.ConfigProto()
print("Initializing model")
model = None
if experiment == "baseline":
model = BaselineModel(vocab_size=conf.vocabulary_size,
embedding_size=conf.word_embedding_size,
bidirectional=conf.bidirectional_encoder,
attention=False,
dropout=conf.use_dropout,
num_layers=conf.num_layers,
is_training=True)
elif experiment == "attention":
model = BaselineModel(vocab_size=conf.vocabulary_size,
embedding_size=conf.word_embedding_size,
bidirectional=conf.bidirectional_encoder,
attention=True,
dropout=conf.use_dropout,
num_layers=conf.num_layers,
is_training=True)
assert model != None
print("=== GETTING DATA BY TYPE = TRAIN ===")
enc_inputs, dec_inputs, word_2_index, index_2_word = get_data_by_type(
'train')
print("***********")
print("Encoder inputs length {}".format(len(enc_inputs)))
print("Decoder inputs length {}".format(len(dec_inputs)))
print("***********")
# Materialize validation data
print("=== GETTING DATA BY TYPE = EVAL ===")
validation_enc_inputs, validation_dec_inputs, _, _ = get_data_by_type(
'eval')
validation_data = list(
bucket_by_sequence_length(validation_enc_inputs,
validation_dec_inputs,
conf.batch_size,
filter_long_sent=False))
print("Starting TensorFlow session")
with tf.Session(config=configProto) as sess:
global_step = 1
saver = tf.train.Saver(max_to_keep=3, keep_checkpoint_every_n_hours=4)
# Init Tensorboard summaries. This will save Tensorboard information into a different folder at each run.
timestamp = '{0:%Y-%m-%d_%H-%M-%S}'.format(datetime.datetime.now())
tag_string = ""
if tag is not None:
tag_string = "-" + tag
train_logfolderPath = os.path.join(
conf.log_directory,
"{}{}-training-{}".format(experiment, tag_string, timestamp))
train_writer = tf.summary.FileWriter(train_logfolderPath,
graph=tf.get_default_graph())
validation_writer = tf.summary.FileWriter(
"{}{}{}-validation-{}".format(conf.log_directory, experiment,
tag_string, timestamp),
graph=tf.get_default_graph())
copy_config(train_logfolderPath
) # Copies the current config.py to the log directory
sess.run(tf.global_variables_initializer())
if conf.use_word2vec:
print("Using word2vec embeddings")
if not os.path.isfile(conf.word2vec_path):
train_sentences = TRAINING_FILEPATH
train_embeddings(save_to_path=conf.word2vec_path,
embedding_size=conf.word_embedding_size,
minimal_frequency=conf.word2vec_min_word_freq,
train_tuples_path=train_sentences,
validation_path=None,
num_workers=conf.word2vec_workers_count)
print("Loading word2vec embeddings")
load_embedding(sess, get_or_create_vocabulary(),
model.embedding_matrix, conf.word2vec_path,
conf.word_embedding_size, conf.vocabulary_size)
sess.graph.finalize()
print("Starting training")
for i in range(conf.num_epochs):
print("Training epoch {}".format(i))
for data_batch, data_sentence_lengths, label_inputs_batch, label_targets_batch, label_sentence_lengths in tqdm(
bucket_by_sequence_length(enc_inputs, dec_inputs,
conf.batch_size),
total=ceil(len(enc_inputs) / conf.batch_size)):
feed_dict = model.make_train_inputs(data_batch,
data_sentence_lengths,
label_inputs_batch,
label_targets_batch,
label_sentence_lengths)
run_options = None
run_metadata = None
if global_step % conf.trace_frequency == 0:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
_, train_summary = sess.run([model.train_op, model.summary_op],
feed_dict,
options=run_options,
run_metadata=run_metadata)
if global_step % conf.trace_frequency == 0:
train_writer.add_run_metadata(run_metadata,
"step{}".format(global_step))
train_writer.add_summary(train_summary, global_step)
if global_step % conf.validation_summary_frequency == 0: #
# Randomly choose a batch from the validation dataset and use it for loss calculation
vali_data_batch, vali_data_sentence_lengths, vali_label_inputs_batch, vali_label_targets_batch, vali_label_sentence_lengths = choice(
validation_data)
validation_feed_dict = model.make_train_inputs(
vali_data_batch,
vali_data_sentence_lengths,
vali_label_inputs_batch,
vali_label_targets_batch,
vali_label_sentence_lengths,
keep_prob=1.0)
validation_summary = sess.run(model.validation_summary_op,
validation_feed_dict)
validation_writer.add_summary(validation_summary,
global_step)
if global_step % conf.checkpoint_frequency == 0:
saver.save(sess,
os.path.join(
train_logfolderPath,
"{}{}-{}-ep{}.ckpt".format(
experiment, tag_string, timestamp, i)),
global_step=global_step)
global_step += 1
saver.save(
sess,
os.path.join(
train_logfolderPath,
"{}{}-{}-ep{}-final.ckpt".format(experiment, tag_string,
timestamp, conf.num_epochs)))
print("Done with training for {} epochs".format(conf.num_epochs))
if args.load:
model = torch.load(args.load, map_location=device)
vocab = model.vocab
test_source, _test_target = read_data(args.test)
test_source = index_data(test_source, vocab)
for i, source in enumerate(batchify_data(test_source)):
output = model.decode(source)
for words in output:
print(' '.join(words))
exit(0)
if args.model == 'baseline':
model = BaselineModel(vocab).to(device)
elif args.model == 'transformer':
model = TransformerModel(vocab).to(device)
else:
print('error: invalid model or model not specified (--model)',
file=sys.stderr)
sys.exit()
for p in model.parameters():
if p.dim() > 1:
torch.nn.init.xavier_uniform_(p)
criterion = torch.nn.CrossEntropyLoss(ignore_index=pad_id)
lr = 5 # learning rate
optimizer = torch.optim.SGD(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
def mainFunc(argv):
def printUsage():
print('perplexity.py -n <num_cores> -x <experiment> -i <input file> -c <checkpoint>')
print('num_cores = Number of cores requested from the cluster. Set to -1 to leave unset')
print('experiment = experiment setup that should be executed. e.g \'baseline\'')
print('input = what dialogs to predict from. e.g \'./Dialog_Triples.txt\'')
print('checkpoint = Path to the checkpoint to load parameters from. e.g. \'./logs/baseline-ep4-500\'')
def maptoword(sentence):
return " ".join(map(lambda x: index_2_word[x], sentence)) + '\n'
num_cores = -1
experiment = ""
checkpoint_filepath = ""
input_filepath = ""
# Command line argument handling
try:
opts, args = getopt.getopt(argv, "n:x:c:i:", ["num_cores=", "experiment=", "checkpoint=", "input="])
except getopt.GetoptError:
printUsage()
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
printUsage()
sys.exit()
elif opt in ("-n", "--num_cores"):
num_cores = int(arg)
elif opt in ("-x", "--experiment"):
if arg in ("baseline", "attention"):
experiment = arg
elif arg in ("attention"):
experiment = arg
else:
printUsage()
sys.exit(2)
elif opt in ("-i", "--input"):
if arg != "":
input_filepath = arg
else:
printUsage()
sys.exit(2)
elif opt in ("-c", "--checkpoint"):
if arg != "":
checkpoint_filepath = arg
else:
printUsage()
sys.exit(2)
if num_cores != -1:
# We set the op_parallelism_threads in the ConfigProto and pass it to the TensorFlow session
configProto = tf.ConfigProto(inter_op_parallelism_threads=num_cores,
intra_op_parallelism_threads=num_cores)
else:
configProto = tf.ConfigProto()
model = None
if experiment == "baseline":
model = BaselineModel(vocab_size=conf.vocabulary_size,
embedding_size=conf.word_embedding_size,
bidirectional=conf.bidirectional_encoder,
attention=False,
dropout=conf.use_dropout,
num_layers=conf.num_layers,
is_training=False)
elif experiment == "attention":
model = BaselineModel(vocab_size=conf.vocabulary_size,
embedding_size=conf.word_embedding_size,
bidirectional=conf.bidirectional_encoder,
attention=True,
dropout=conf.use_dropout,
num_layers=conf.num_layers,
is_training=False)
assert model != None
with tf.Session(config=configProto) as sess:
global_step = 1
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess, checkpoint_filepath)
triples_to_tuples(input_filepath, testing_path)
w2i, _ = get_w2i_i2w_dicts()
vocabulary = get_vocabulary()
enc_inputs, dec_inputs = apply_w2i_to_corpus_tuples(load_testing_tuples(), vocabulary, w2i)
validation_input_lengths = set(map(lambda x: len(x), enc_inputs))
lm_logits_dict = construct_lm_logits(sess, model, validation_input_lengths)
is_first_tuple = True
pplf = open("perplexities_attention_antilm.out", 'w')
for data_batch, data_sentence_lengths, label_inputs_batch, label_targets_batch, label_sentence_lengths in bucket_by_sequence_length(enc_inputs, dec_inputs, conf.batch_size, sort_data=False, shuffle_batches=False, filter_long_sent=False):
lm_logits_batch = construct_lm_logits_batch(lm_logits_dict, data_sentence_lengths)
feed_dict = model.make_inference_inputs(data_batch, data_sentence_lengths, lm_logits_batch)
softmax_predictions = sess.run(model.decoder_softmax_prediction, feed_dict)
# Perplexity calculation
for sentID in range(len(label_sentence_lengths)): # Loop
word_probs = []
# As long as we havent reached either end of the target or predicted sentences
word_index = 0
while word_index < label_sentence_lengths[sentID] and word_index < softmax_predictions.shape[1]:
ground_truth_word_index = label_targets_batch[sentID, word_index]
prob = softmax_predictions[sentID, word_index,ground_truth_word_index]
word_probs.append(prob)
word_index += 1
log_probs = np.log(word_probs)
perplexity = 2**(-1.0*log_probs.mean())
if is_first_tuple:
print(perplexity, end=' ')
print(perplexity, end=' ', file=pplf)
is_first_tuple = False
else:
print(perplexity)
print(perplexity, file=pplf)
is_first_tuple = True
global_step += 1
def best_cell_and_baseline_hyperparameters(train_dataloader, validate_dataloader, test_dataloader, embedding):
configs = []
RNN_config = {}
RNN_config["model"] = "LSTM"
RNN_config["hidden_size"] = 30
RNN_config["num_layers"] = 3
RNN_config["dropout"] = 0.9
RNN_config["bidirectional"] = True
RNN_config["fc1_width"] = "//"
RNN_config["fc2_width"] = "//"
RNN_config["vocab_size"] = -1
RNN_config["lr"] = 0.0001
RNN_config["optimizer"] = torch.optim.Adam
baseline_config = {}
baseline_config["model"] = "Baseline"
baseline_config["hidden_size"] = "//"
baseline_config["num_layers"] = "//"
baseline_config["dropout"] = "//"
baseline_config["bidirectional"] = "//"
baseline_config["fc1_width"] = 150
baseline_config["fc2_width"] = 150
baseline_config["vocab_size"] = -1
baseline_config["lr"] = 0.0001
baseline_config["optimizer"] = torch.optim.Adam
hyperparameters = {}
hyperparameters["vocab_size"] = [50, 1000, 10000]
hyperparameters["lr"] = [0.0001, 0.001, 0.01, 0.1]
hyperparameters["dropout"] = [0, 0.2, 0.4, 0.6, 0.8, 1]
hyperparameters["num_layers"] = [1, 3, 6]
hyperparameters["hidden_size"] = [30, 100, 150, 200]
hyperparameters["optimizer"] = [torch.optim.Adam, torch.optim.SGD, torch.optim.RMSprop]
supports = {}
supports["vocab_size"] = [BaselineModel, RNN.RecurrentModel]
supports["lr"] = [BaselineModel, RNN.RecurrentModel]
supports["dropout"] = [RNN.RecurrentModel]
supports["num_layers"] = [RNN.RecurrentModel]
supports["hidden_size"] = [RNN.RecurrentModel]
supports["optimizer"] = [BaselineModel, RNN.RecurrentModel]
initial_config = {}
initial_config["clip"] = args.clip
initial_config["epochs"] = args.epochs
initial_config["input_width"] = 300
initial_config["output_width"] = 1
models = [BaselineModel, RNN.RecurrentModel]
criterion = nn.BCEWithLogitsLoss()
for model_type in models:
for (key, values) in hyperparameters.items():
# Skip this hyperparameter testing if the model does not support it
if model_type not in supports[key]:
continue
for value in values:
start = time.time()
config = {}
if model_type == RNN.RecurrentModel:
config.update(RNN_config)
train = RNN.train
evaluate = RNN.evaluate
model = RNN.RecurrentModel(config["model"], initial_config["input_width"],
config["hidden_size"],
initial_config["output_width"], config["num_layers"],
config["bidirectional"], config["dropout"])
else:
config.update(baseline_config)
train = baseline.train
evaluate = baseline.evaluate
model = BaselineModel(initial_config["input_width"], config["fc1_width"],
config["fc2_width"], initial_config["output_width"])
config.update(initial_config)
config[key] = value
print(config)
optimizer = config["optimizer"](model.parameters(), lr=config["lr"])
for epoch in range(args.epochs):
print(f'\nEpoch: {epoch}')
train(model, train_dataloader, optimizer, criterion, embedding, args.clip)
evaluate(model, validate_dataloader, criterion, embedding)
accuracy, f1, confusion_matrix = evaluate(model, test_dataloader, criterion, embedding)
config["accuracy"] = accuracy.item()
config["f1"] = f1.item()
config["TP"] = confusion_matrix[0, 0].item()
config["FP"] = confusion_matrix[0, 1].item()
config["FN"] = confusion_matrix[1, 0].item()
config["TN"] = confusion_matrix[1, 1].item()
end = time.time()
config["time"] = end - start
config["optimizer"] = method_to_string(config["optimizer"])
configs.append(config)
print_to_file("5_final.xls", "RNN baseline hyperparameters", configs)
def best_cell_and_baseline_with_and_without_pretrained(train_dataset, train_dataloader, validate_dataloader,
test_dataloader, clip):
configs = []
RNN_config = {}
RNN_config["model"] = "LSTM"
RNN_config["hidden_size"] = 30
RNN_config["num_layers"] = 3
RNN_config["dropout"] = 0.9
RNN_config["bidirectional"] = True
RNN_config["fc1_width"] = "//"
RNN_config["fc2_width"] = "//"
baseline_config = {}
baseline_config["model"] = "Baseline"
baseline_config["hidden_size"] = "//"
baseline_config["num_layers"] = "//"
baseline_config["dropout"] = "//"
baseline_config["bidirectional"] = "//"
baseline_config["fc1_width"] = 150
baseline_config["fc2_width"] = 150
initial_config = {}
initial_config["clip"] = args.clip
initial_config["epochs"] = args.epochs
initial_config["input_width"] = 300
initial_config["output_width"] = 1
lstm = RNN.RecurrentModel(RNN_config["model"], initial_config["input_width"], RNN_config["hidden_size"],
initial_config["output_width"], RNN_config["num_layers"],
RNN_config["bidirectional"], RNN_config["dropout"])
base = BaselineModel(initial_config["input_width"], baseline_config["fc1_width"], baseline_config["fc2_width"],
initial_config["output_width"])
models = [base, lstm]
criterion = nn.BCEWithLogitsLoss()
use_embeddings = [False, True]
for use_embedding in use_embeddings:
if use_embedding:
file_path = util.config["glove_file_path"]
else:
file_path = None
embedding_matrix = util.embedding(train_dataset.text_vocab, file_path)
use_freeze = util.config["glove_file_path"] is not None
embedding = torch.nn.Embedding.from_pretrained(embedding_matrix, padding_idx=0, freeze=use_freeze)
for model in models:
start = time.time()
config = {}
if type(model) == RNN.RecurrentModel:
config.update(RNN_config)
train = RNN.train
evaluate = RNN.evaluate
else:
config.update(baseline_config)
train = baseline.train
evaluate = baseline.evaluate
config.update(initial_config)
config["pretrained"] = use_embedding
print(config)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
for epoch in range(args.epochs):
print(f'----------------------------\nEpoch: {epoch}')
train(model, train_dataloader, optimizer, criterion, embedding, clip.clip)
evaluate(model, validate_dataloader, criterion, embedding)
accuracy, f1, confusion_matrix = evaluate(model, test_dataloader, criterion, embedding)
config["accuracy"] = accuracy.item()
config["f1"] = f1.item()
config["TP"] = confusion_matrix[0, 0].item()
config["FP"] = confusion_matrix[0, 1].item()
config["FN"] = confusion_matrix[1, 0].item()
config["TN"] = confusion_matrix[1, 1].item()
end = time.time()
config["time"] = end - start
configs.append(config)
print_to_file("4a_pretrained.xls", "Best RNN and baseline", configs)
def mainFunc(argv):
def printUsage():
print('predict.py -n <num_cores> -x <experiment> -o <output file> -c <checkpoint>')
print('num_cores = Number of cores requested from the cluster. Set to -1 to leave unset')
print('experiment = experiment setup that should be executed. e.g \'baseline\'')
print('checkpoint = Path to the checkpoint to load parameters from. e.g. \'./logs/baseline-ep4-500\'')
print('output = where to write the prediction outputs to. e.g \'./predictions.out\'')
def maptoword(sentence):
return " ".join(map(lambda x: index_2_word[x], sentence))
num_cores = -1
experiment = ""
checkpoint_filepath = ""
output_filepath = ""
# Command line argument handling
try:
opts, args = getopt.getopt(argv, "n:x:c:o:", ["num_cores=", "experiment=", "checkpoint=", "output="])
except getopt.GetoptError:
printUsage()
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
printUsage()
sys.exit()
elif opt in ("-n", "--num_cores"):
num_cores = int(arg)
elif opt in ("-x", "--experiment"):
if arg in ("baseline", "attention"):
experiment = arg
else:
printUsage()
sys.exit(2)
elif opt in ("-o", "--output"):
if arg != "":
output_filepath = arg
else:
printUsage()
sys.exit(2)
elif opt in ("-c", "--checkpoint"):
if arg != "":
checkpoint_filepath = arg
else:
printUsage()
sys.exit(2)
print("Executing experiment {} with {} CPU cores".format(experiment, num_cores))
print("Loading checkpoint from {}".format(checkpoint_filepath))
if num_cores != -1:
# We set the op_parallelism_threads in the ConfigProto and pass it to the TensorFlow session
configProto = tf.ConfigProto(inter_op_parallelism_threads=num_cores,
intra_op_parallelism_threads=num_cores)
else:
configProto = tf.ConfigProto()
print("Initializing model")
model = None
if experiment == "baseline":
model = BaselineModel(vocab_size=conf.vocabulary_size,
embedding_size=conf.word_embedding_size,
bidirectional=conf.bidirectional_encoder,
attention=False,
dropout=conf.use_dropout,
num_layers=conf.num_layers,
is_training=False)
elif experiment == "attention":
model = BaselineModel(vocab_size=conf.vocabulary_size,
embedding_size=conf.word_embedding_size,
bidirectional=conf.bidirectional_encoder,
attention=True,
dropout=conf.use_dropout,
num_layers=conf.num_layers,
is_training=False)
assert model != None
# Materialize validation data
validation_enc_inputs, validation_dec_inputs, word_2_index, index_2_word = get_data_by_type('eval')
validation_input_lengths = set(map(lambda x: len(x), validation_enc_inputs))
with tf.Session(config=configProto) as sess:
global_step = 1
sent_count = 1
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess, checkpoint_filepath)
print("Constructing language model")
lm_logits_dict = construct_lm_logits(sess, model, validation_input_lengths)
print("Using network to predict sentences..")
with open(output_filepath, 'w') as out:
for data_batch, data_sentence_lengths, _, label_targets_batch, label_sentence_lengths in tqdm(
bucket_by_sequence_length(validation_enc_inputs, validation_dec_inputs, conf.batch_size, sort_data=False, shuffle_batches=False, filter_long_sent=False),
total=ceil(len(validation_enc_inputs) / conf.batch_size)):
lm_logits_batch = construct_lm_logits_batch(lm_logits_dict, data_sentence_lengths)
feed_dict = model.make_inference_inputs(data_batch, data_sentence_lengths, lm_logits_batch)
predictions = sess.run(model.decoder_prediction_inference, feed_dict)
reversed_truncated_data = truncate_after_eos(data_batch)
truncated_data = undo_input_reversal(reversed_truncated_data)
truncated_labels = truncate_after_eos(label_targets_batch)
truncated_predictions = truncate_after_eos(predictions)
for enc, target, pred in zip(map(maptoword, truncated_data), map(maptoword, truncated_labels), map(maptoword, truncated_predictions)):
print("{}. Input: {}".format(sent_count, enc), file=out)
print("{}. Ground Truth: {}".format(sent_count, target), file=out)
print("{}. Prediction: {}".format(sent_count, pred), file=out)
sent_count += 1
global_step += 1
