def main():
start_time = time.time()
parser = argparse.ArgumentParser(
prog='trainMemNN.py',
description='Train MemmNN model for visual question answering')
parser.add_argument('--mlp-hidden-units',
type=int,
default=1024,
metavar='<mlp-hidden-units>')
parser.add_argument('--mlp-hidden-layers',
type=int,
default=3,
metavar='<mlp-hidden-layers>')
parser.add_argument('--mlp-activation',
type=str,
default='tanh',
metavar='<activation-function>')
parser.add_argument('--emb-dimension',
type=int,
default=50,
metavar='<embedding-dimension>')
parser.add_argument('--batch-size',
type=int,
default=128,
metavar='<batch-size>')
parser.add_argument('--hops', type=int, default=3, metavar='<memnet-hops>')
#parser.add_argument('--model-path', type=str, required=True, metavar='<model-path>')
parser.add_argument('--weight-path',
type=str,
required=True,
metavar='<weight-path>')
parser.add_argument('--output-path',
type=str,
required=True,
metavar='<output-path>')
args = parser.parse_args()
word_vec_dim = 300
img_dim = 300
max_len = 30
img_feature_num = 125
######################
# Load Data #
######################
data_dir = '/home/mlds/data/0.05_val/'
print('Loading data...')
#dev_q_ids, dev_image_ids = LoadIds('dev', data_dir)
test_q_ids, test_image_ids = LoadIds('test', data_dir)
#dev_questions = LoadQuestions('dev', data_dir)
test_questions = LoadQuestions('test', data_dir)
#dev_choices = LoadChoices('dev', data_dir)
test_choices = LoadChoices('test', data_dir)
caption_map = LoadCaptions('test')
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
######################
# Model Descriptions #
######################
print('Loading and compiling model...')
model = CreateGraph(args.emb_dimension, args.hops, args.mlp_activation,
args.mlp_hidden_units, args.mlp_hidden_layers,
word_vec_dim, img_dim, img_feature_num)
#model = model_from_json(open(args.model_path,'r').read())
# loss and optimizer
model.compile(loss={'output': Loss}, optimizer='rmsprop')
model.load_weights(args.weight_path)
print('Model and weights loaded.')
print('Time: %f s' % (time.time() - start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
'''
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time()-start_time))
'''
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# validation batches
# dev_question_batches = [ b for b in MakeBatches(dev_questions, args.batch_size, fillvalue=dev_questions[-1]) ]
# dev_answer_batches = [ b for b in MakeBatches(dev_answers['labs'], args.batch_size, fillvalue=dev_answers['labs'][-1]) ]
# dev_choice_batches = [ b for b in MakeBatches(dev_choices, args.batch_size, fillvalue=dev_choices[-1]) ]
# dev_image_batches = [ b for b in MakeBatches(dev_image_ids, args.batch_size, fillvalue=dev_image_ids[-1]) ]
# testing batches
test_question_batches = [
b for b in MakeBatches(
test_questions, args.batch_size, fillvalue=test_questions[-1])
]
test_choice_batches = [
b for b in MakeBatches(
test_choices, args.batch_size, fillvalue=test_choices[-1])
]
test_image_batches = [
b for b in MakeBatches(
test_image_ids, args.batch_size, fillvalue=test_image_ids[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Testing #
######################
# start predicting
pbar = generic_utils.Progbar(len(test_question_batches) * args.batch_size)
predictions = []
# feed forward
for i in range(len(test_question_batches)):
X_question_batch = GetQuestionsTensor(test_question_batches[i],
word_embedding, word_map)
X_caption_batch = GetCaptionsTensor2(test_image_batches[i],
word_embedding, word_map,
caption_map)
prob = model.predict_on_batch({
'question': X_question_batch,
'image': X_caption_batch
})
prob = prob[0]
# get word vecs of choices
choice_feats = GetChoicesTensor(test_choice_batches[i], word_embedding,
word_map)
similarity = np.zeros((5, args.batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
predictions.extend(pred)
pbar.add(args.batch_size)
SavePredictions(args.output_path, predictions, test_q_ids)
print('Testing finished.')
print('Time: %f s' % (time.time() - start_time))
def main():
start_time = time.time()
#signal.signal(signal.SIGINT, InterruptHandler)
#signal.signal(signal.SIGKILL, InterruptHandler)
#signal.signal(signal.SIGTERM, InterruptHandler)
parser = argparse.ArgumentParser(
prog='testLSTM.py',
description='Test LSTM model for visual question answering')
parser.add_argument('--model',
type=str,
required=True,
metavar='<model-path>')
parser.add_argument('--weights',
type=str,
required=True,
metavar='<weights-path>')
parser.add_argument('--output',
type=str,
required=True,
metavar='<prediction-path>')
args = parser.parse_args()
word_vec_dim = 300
batch_size = 128
#######################
# Load Model #
#######################
print('Loading model and weights...')
model = model_from_json(open(args.model, 'r').read())
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.load_weights(args.weights)
print('Model and weights loaded.')
print('Time: %f s' % (time.time() - start_time))
######################
# Load Data #
######################
print('Loading data...')
dev_id_pairs, dev_image_ids = LoadIds('dev')
#test_id_pairs, test_image_ids = LoadIds('test')
dev_questions = LoadQuestions('dev')
#test_questions = LoadQuestions('test')
dev_choices = LoadChoices('dev')
#test_choices = LoadChoices('test')
dev_answers = LoadAnswers('dev')
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
#######################
# Load Word Vectors #
#######################
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# validation batches
dev_question_batches = [
b for b in MakeBatches(
dev_questions, batch_size, fillvalue=dev_questions[-1])
]
dev_answer_batches = [
b for b in MakeBatches(
dev_answers['labs'], batch_size, fillvalue=dev_answers['labs'][-1])
]
dev_choice_batches = [
b for b in MakeBatches(
dev_choices, batch_size, fillvalue=dev_choices[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Testing #
######################
# evaluate on dev set
widgets = [
'Evaluating ',
Percentage(), ' ',
Bar(marker='#', left='[', right=']'), ' ',
ETA()
]
pbar = ProgressBar(widgets=widgets)
dev_correct = 0
predictions = []
for i in pbar(range(len(dev_question_batches))):
# feed forward
X_question_batch = GetQuestionsTensor(dev_question_batches[i],
word_embedding, word_map)
prob = model.predict_proba(X_question_batch, batch_size, verbose=0)
# get word vecs of choices
choice_feats = GetChoicesTensor(dev_choice_batches[i], word_embedding,
word_map)
similarity = np.zeros((5, batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
predictions.extend(pred.tolist())
dev_correct += np.count_nonzero(dev_answer_batches[i] == pred)
dev_acc = float(dev_correct) / len(dev_questions)
print('Validation Accuracy: %f' % dev_acc)
print('Validation Accuracy: %f' % dev_acc, file=sys.stderr)
SavePredictions(args.output, predictions, dev_id_pairs)
print('Time: %f s' % (time.time() - start_time))
print('Time: %f s' % (time.time() - start_time), file=sys.stderr)
print('Testing finished.')
print('Testing finished.', file=sys.stderr)
def main():
start_time = time.time()
parser = argparse.ArgumentParser(prog='trainLSTM_MLP.py',
description='Train LSTM-MLP model for visual question answering')
parser.add_argument('--mlp-hidden-units', type=int, default=1024, metavar='<mlp-hidden-units>')
parser.add_argument('--lstm-hidden-units', type=int, default=512, metavar='<lstm-hidden-units>')
parser.add_argument('--mlp-hidden-layers', type=int, default=3, metavar='<mlp-hidden-layers>')
parser.add_argument('--lstm-hidden-layers', type=int, default=1, metavar='<lstm-hidden-layers>')
parser.add_argument('--dropout', type=float, default=0.5, metavar='<dropout-rate>')
parser.add_argument('--mlp-activation', type=str, default='tanh', metavar='<activation-function>')
parser.add_argument('--num-epochs', type=int, default=100, metavar='<num-epochs>')
parser.add_argument('--batch-size', type=int, default=128, metavar='<batch-size>')
parser.add_argument('--learning-rate', type=float, default=0.001, metavar='<learning-rate>')
parser.add_argument('--dev-accuracy-path', type=str, required=True, metavar='<accuracy-path>')
args = parser.parse_args()
word_vec_dim = 300
img_dim = 4096
max_len = 30
######################
# Load Data #
######################
data_dir = '/home/mlds/data/0.05_val/'
print('Loading data...')
train_id_pairs, train_image_ids = LoadIds('train', data_dir)
dev_id_pairs, dev_image_ids = LoadIds('dev', data_dir)
train_questions = LoadQuestions('train', data_dir)
dev_questions = LoadQuestions('dev', data_dir)
train_choices = LoadChoices('train', data_dir)
dev_choices = LoadChoices('dev', data_dir)
train_answers = LoadAnswers('train', data_dir)
dev_answers = LoadAnswers('dev', data_dir)
print('Finished loading data.')
print('Time: %f s' % (time.time()-start_time))
######################
# Model Descriptions #
######################
print('Generating and compiling model...')
# image model (CNN features)
image_model = Sequential()
image_model.add(Reshape(
input_shape=(img_dim,), dims=(img_dim,)
))
# language model (LSTM)
language_model = Sequential()
if args.lstm_hidden_layers == 1:
language_model.add(LSTM(
output_dim=args.lstm_hidden_units, return_sequences=False, input_shape=(max_len, word_vec_dim)
))
else:
language_model.add(LSTM(
output_dim=args.lstm_hidden_units, return_sequences=True, input_shape=(max_len, word_vec_dim)
))
for i in range(args.lstm_hidden_layers-2):
language_model.add(LSTM(
output_dim=args.lstm_hidden_units, return_sequences=True
))
language_model.add(LSTM(
output_dim=args.lstm_hidden_units, return_sequences=False
))
# feedforward model (MLP)
model = Sequential()
model.add(Merge(
[language_model, image_model], mode='concat', concat_axis=1
))
for i in range(args.mlp_hidden_layers):
model.add(Dense(
args.mlp_hidden_units, init='uniform'
))
model.add(Activation(args.mlp_activation))
model.add(Dropout(args.dropout))
model.add(Dense(word_vec_dim))
#model.add(Activation('softmax'))
json_string = model.to_json()
model_filename = 'models/vgg_lstm_units_%i_layers_%i_mlp_units_%i_layers_%i_%s_lr%.1e_dropout%.2f' % (args.lstm_hidden_units, args.lstm_hidden_layers, args.mlp_hidden_units, args.mlp_hidden_layers, args.mlp_activation, args.learning_rate, args.dropout)
#model_filename = 'models/vgg_lstm_units_%i_layers_%i_mlp_units_%i_layers_%i_%s_lr%.1e_dropout%.2f_loss_cosine' % (args.lstm_hidden_units, args.lstm_hidden_layers, args.mlp_hidden_units, args.mlp_hidden_layers, args.mlp_activation, args.learning_rate, args.dropout)
open(model_filename + '.json', 'w').write(json_string)
# loss and optimizer
rmsprop = RMSprop(lr=args.learning_rate)
#model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
model.compile(loss=Loss, optimizer=rmsprop)
print('Compilation finished.')
print('Time: %f s' % (time.time()-start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time()-start_time))
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time()-start_time))
######################
# Make Batches #
######################
print('Making batches...')
# training batches
train_question_batches = [ b for b in MakeBatches(train_questions, args.batch_size, fillvalue=train_questions[-1]) ]
train_answer_batches = [ b for b in MakeBatches(train_answers['toks'], args.batch_size, fillvalue=train_answers['toks'][-1]) ]
train_image_batches = [ b for b in MakeBatches(train_image_ids, args.batch_size, fillvalue=train_image_ids[-1]) ]
train_indices = list(range(len(train_question_batches)))
# validation batches
dev_question_batches = [ b for b in MakeBatches(dev_questions, args.batch_size, fillvalue=dev_questions[-1]) ]
dev_answer_batches = [ b for b in MakeBatches(dev_answers['labs'], args.batch_size, fillvalue=dev_answers['labs'][-1]) ]
dev_choice_batches = [ b for b in MakeBatches(dev_choices, args.batch_size, fillvalue=dev_choices[-1]) ]
dev_image_batches = [ b for b in MakeBatches(dev_image_ids, args.batch_size, fillvalue=dev_image_ids[-1]) ]
print('Finished making batches.')
print('Time: %f s' % (time.time()-start_time))
######################
# Training #
######################
acc_file = open(args.dev_accuracy_path, 'w')
dev_accs = []
max_acc = -1
max_acc_epoch = -1
# define interrupt handler
def PrintDevAcc():
print('Max validation accuracy epoch: %i' % max_acc_epoch)
print(dev_accs)
def InterruptHandler(sig, frame):
print(str(sig))
PrintDevAcc()
sys.exit(-1)
signal.signal(signal.SIGINT, InterruptHandler)
signal.signal(signal.SIGTERM, InterruptHandler)
# print training information
print('-'*80)
print('Training Information')
print('# of LSTM hidden units: %i' % args.lstm_hidden_units)
print('# of LSTM hidden layers: %i' % args.lstm_hidden_layers)
print('# of MLP hidden units: %i' % args.mlp_hidden_units)
print('# of MLP hidden layers: %i' % args.mlp_hidden_layers)
print('Dropout: %f' % args.dropout)
print('MLP activation function: %s' % args.mlp_activation)
print('# of training epochs: %i' % args.num_epochs)
print('Batch size: %i' % args.batch_size)
print('Learning rate: %f' % args.learning_rate)
print('# of train questions: %i' % len(train_questions))
print('# of dev questions: %i' % len(dev_questions))
print('-'*80)
acc_file.write('-'*80 + '\n')
acc_file.write('Training Information\n')
acc_file.write('# of LSTM hidden units: %i\n' % args.lstm_hidden_units)
acc_file.write('# of LSTM hidden layers: %i\n' % args.lstm_hidden_layers)
acc_file.write('# of MLP hidden units: %i\n' % args.mlp_hidden_units)
acc_file.write('# of MLP hidden layers: %i\n' % args.mlp_hidden_layers)
acc_file.write('Dropout: %f\n' % args.dropout)
acc_file.write('MLP activation function: %s\n' % args.mlp_activation)
acc_file.write('# of training epochs: %i\n' % args.num_epochs)
acc_file.write('Batch size: %i\n' % args.batch_size)
acc_file.write('Learning rate: %f\n' % args.learning_rate)
acc_file.write('# of train questions: %i\n' % len(train_questions))
acc_file.write('# of dev questions: %i\n' % len(dev_questions))
acc_file.write('-'*80 + '\n')
# start training
print('Training started...')
for k in range(args.num_epochs):
print('-'*80)
print('Epoch %i' % (k+1))
progbar = generic_utils.Progbar(len(train_indices)*args.batch_size)
# shuffle batch indices
random.shuffle(train_indices)
for i in train_indices:
X_question_batch = GetQuestionsTensor(train_question_batches[i], word_embedding, word_map)
X_image_batch = GetImagesMatrix(train_image_batches[i], img_map, VGG_features)
Y_answer_batch = GetAnswersMatrix(train_answer_batches[i], word_embedding, word_map)
loss = model.train_on_batch([X_question_batch, X_image_batch], Y_answer_batch)
loss = loss[0].tolist()
progbar.add(args.batch_size, values=[('train loss', loss)])
print('Time: %f s' % (time.time()-start_time))
# evaluate on dev set
pbar = generic_utils.Progbar(len(dev_question_batches)*args.batch_size)
dev_correct = 0
# feed forward
for i in range(len(dev_question_batches)):
X_question_batch = GetQuestionsTensor(dev_question_batches[i], word_embedding, word_map)
X_image_batch = GetImagesMatrix(dev_image_batches[i], img_map, VGG_features)
prob = model.predict_proba([X_question_batch, X_image_batch], args.batch_size, verbose=0)
# get word vecs of choices
choice_feats = GetChoicesTensor(dev_choice_batches[i], word_embedding, word_map)
similarity = np.zeros((5, args.batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
if i != (len(dev_question_batches)-1):
dev_correct += np.count_nonzero(dev_answer_batches[i]==pred)
else:
num_padding = args.batch_size * len(dev_question_batches) - len(dev_questions)
last_idx = args.batch_size - num_padding
dev_correct += np.count_nonzero(dev_answer_batches[:last_idx]==pred[:last_idx])
pbar.add(args.batch_size)
dev_acc = float(dev_correct)/len(dev_questions)
dev_accs.append(dev_acc)
print('Validation Accuracy: %f' % dev_acc)
print('Time: %f s' % (time.time()-start_time))
if dev_acc > max_acc:
max_acc = dev_acc
max_acc_epoch = k
model.save_weights(model_filename + '_best.hdf5', overwrite=True)
#model.save_weights(model_filename + '_epoch_{:03d}.hdf5'.format(k+1))
print(dev_accs)
for acc in dev_accs:
acc_file.write('%f\n' % acc)
print('Best validation accuracy: %f; epoch#%i' % (max_acc,(max_acc_epoch+1)))
acc_file.write('Best validation accuracy: %f; epoch#%i\n' % (max_acc,(max_acc_epoch+1)))
print('Training finished.')
acc_file.write('Training finished.\n')
print('Time: %f s' % (time.time()-start_time))
acc_file.write('Time: %f s\n' % (time.time()-start_time))
acc_file.close()
def main():
start_time = time.time()
# argument parser
parser = argparse.ArgumentParser(
prog='train_sent.py',
description='Train LSTM-NN model for ABSA sentiment classification')
parser.add_argument('--lstm-hidden-units',
type=int,
default=512,
metavar='<lstm-hidden-units>')
parser.add_argument('--lstm-hidden-layers',
type=int,
default=2,
metavar='<lstm-hidden-layers>')
parser.add_argument('--mlp-hidden-units',
type=int,
default=512,
metavar='<mlp-hidden-units>')
parser.add_argument('--mlp-hidden-layers',
type=int,
default=2,
metavar='<mlp-hidden-layers>')
parser.add_argument('--dropout',
type=float,
default=0.3,
metavar='<dropout-rate>')
parser.add_argument('--mlp-activation',
type=str,
default='relu',
metavar='<activation-function>')
parser.add_argument('--num-epochs',
type=int,
default=100,
metavar='<num-epochs>')
parser.add_argument('--batch-size',
type=int,
default=128,
metavar='<batch-size>')
parser.add_argument('--learning-rate',
type=float,
default=0.0001,
metavar='<learning-rate>')
parser.add_argument('--aspects',
type=int,
required=True,
metavar='<number of aspects>')
parser.add_argument('--domain',
type=str,
required=True,
choices=['rest', 'lapt'],
metavar='<domain>')
parser.add_argument('--cross-val-index',
type=int,
required=True,
choices=range(0, 10),
metavar='<cross-validation-index>')
args = parser.parse_args()
sent_vec_dim = 300
word_vec_dim = 300
aspect_dim = args.aspects
polarity_num = 3
max_len = 40
######################
# Model Descriptions #
######################
print('Generating and compiling model...')
# sentence input
sent_model = Sequential()
if args.lstm_hidden_layers == 1:
sent_model.add(
LSTM(output_dim=args.lstm_hidden_units,
return_sequences=False,
input_shape=(max_len, word_vec_dim)))
else:
sent_model.add(
LSTM(output_dim=args.lstm_hidden_units,
return_sequences=True,
input_shape=(max_len, word_vec_dim)))
for i in range(args.lstm_hidden_layers - 2):
sent_model.add(
LSTM(output_dim=args.lstm_hidden_units, return_sequences=True))
sent_model.add(
LSTM(output_dim=args.lstm_hidden_units, return_sequences=False))
# aspect input
aspect_model = Sequential()
aspect_model.add(Reshape(input_shape=(aspect_dim, ), dims=(aspect_dim, )))
# feedforward model (MLP)
model = Sequential()
model.add(Merge([sent_model, aspect_model], mode='concat', concat_axis=1))
for i in range(args.mlp_hidden_layers):
model.add(Dense(args.mlp_hidden_units, init='uniform'))
model.add(Activation(args.mlp_activation))
model.add(Dropout(args.dropout))
model.add(Dense(polarity_num))
model.add(Activation('softmax'))
# save model configuration
json_string = model.to_json()
model_filename = 'models/%s.lstmNN.lstm_units_%i_layers_%i.mlp_units_%i_layers_%i_%s.lr%.1e.dropout%.1f.%i' % (
args.domain, args.lstm_hidden_units, args.lstm_hidden_layers,
args.mlp_hidden_units, args.mlp_hidden_layers, args.mlp_activation,
args.learning_rate, args.dropout, args.cross_val_index)
open(model_filename + '.json', 'w').write(json_string)
# loss and optimizer
rmsprop = RMSprop(lr=args.learning_rate)
model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
print('Compilation finished.')
print('Time: %f s' % (time.time() - start_time))
######################
# Load Data #
######################
print('Loading data...')
# aspect mapping
asp_map = LoadAspectMap(args.domain)
# sentences
train_sents, dev_sents = LoadSentences(args.domain, 'train',
args.cross_val_index)
# aspects
train_asps, dev_asps = LoadAspects(args.domain, 'train',
args.cross_val_index, asp_map)
# labels
train_labs, dev_labs = LoadLabels(args.domain, 'train',
args.cross_val_index)
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
#####################
# GloVe #
#####################
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
#####################
# Encoders #
#####################
asp_encoder = GetAspectEncoder(asp_map)
lab_encoder = GetLabelEncoder(args.domain, args.cross_val_index)
joblib.dump(
lab_encoder, 'models/' + args.domain + '_labelencoder_' +
str(args.cross_val_index) + '.pkl')
######################
# Make Batches #
######################
print('Making batches...')
# training batches
train_sent_batches = [
b for b in MakeBatches(
train_sents, args.batch_size, fillvalue=train_sents[-1])
]
train_asp_batches = [
b for b in MakeBatches(
train_asps, args.batch_size, fillvalue=train_asps[-1])
]
train_lab_batches = [
b for b in MakeBatches(
train_labs, args.batch_size, fillvalue=train_labs[-1])
]
train_indices = list(range(len(train_sent_batches)))
# validation batches
dev_sent_batches = [
b for b in MakeBatches(
dev_sents, args.batch_size, fillvalue=dev_sents[-1])
]
dev_asp_batches = [
b
for b in MakeBatches(dev_asps, args.batch_size, fillvalue=dev_asps[-1])
]
dev_lab_batches = [
b
for b in MakeBatches(dev_labs, args.batch_size, fillvalue=dev_labs[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Training #
######################
dev_accs = []
max_acc = -1
max_acc_epoch = -1
# define interrupt handler
def PrintDevAcc():
print('Max validation accuracy epoch: %i' % max_acc_epoch)
print(dev_accs)
def InterruptHandler(sig, frame):
print(str(sig))
PrintDevAcc()
sys.exit(-1)
signal.signal(signal.SIGINT, InterruptHandler)
signal.signal(signal.SIGTERM, InterruptHandler)
# print training information
print('-' * 80)
print('Training Information')
print('# of MLP hidden units: %i' % args.mlp_hidden_units)
print('# of MLP hidden layers: %i' % args.mlp_hidden_layers)
print('Dropout: %f' % args.dropout)
print('MLP activation function: %s' % args.mlp_activation)
print('# of training epochs: %i' % args.num_epochs)
print('Batch size: %i' % args.batch_size)
print('Learning rate: %f' % args.learning_rate)
print('-' * 80)
# start training
print('Training started...')
for k in range(args.num_epochs):
print('-' * 80)
print('Epoch %i' % (k + 1))
progbar = generic_utils.Progbar(len(train_indices) * args.batch_size)
# shuffle batch indices
random.shuffle(train_indices)
for i in train_indices:
X_sent_batch = GetSentenceTensor(train_sent_batches[i],
word_embedding, word_map)
X_asp_batch = GetAspectFeatures(train_asp_batches[i], asp_encoder)
Y_lab_batch = GetLabelEncoding(train_lab_batches[i], lab_encoder)
loss = model.train_on_batch([X_sent_batch, X_asp_batch],
Y_lab_batch)
loss = loss[0].tolist()
progbar.add(args.batch_size, values=[('train loss', loss)])
print('Time: %f s' % (time.time() - start_time))
# evaluate on dev set
pbar = generic_utils.Progbar(len(dev_sent_batches) * args.batch_size)
# validation feedforward
dev_correct = 0
for i in range(len(dev_sent_batches)):
X_sent_batch = GetSentenceTensor(dev_sent_batches[i],
word_embedding, word_map)
X_asp_batch = GetAspectFeatures(dev_asp_batches[i], asp_encoder)
Y_lab_batch = GetLabelEncoding(dev_lab_batches[i], lab_encoder)
pred = model.predict_classes([X_sent_batch, X_asp_batch],
args.batch_size,
verbose=0)
print(pred)
if i != (len(dev_sent_batches) - 1):
dev_correct += np.count_nonzero(
np.argmax(Y_lab_batch, axis=1) == pred)
else:
num_padding = args.batch_size * len(dev_sent_batches) - len(
dev_sents)
last_idx = args.batch_size - num_padding
dev_correct += np.count_nonzero(
np.argmax(Y_lab_batch[:last_idx], axis=1) ==
pred[:last_idx])
pbar.add(args.batch_size)
# calculate validation accuracy
dev_acc = float(dev_correct) / len(dev_sents)
dev_accs.append(dev_acc)
print('Validation Accuracy: %f' % dev_acc)
print('Time: %f s' % (time.time() - start_time))
# save best weights
if dev_acc > max_acc:
max_acc = dev_acc
max_acc_epoch = k
model.save_weights(model_filename + '_best.hdf5', overwrite=True)
print(dev_accs)
print('Best validation accuracy: %f; epoch#%i' % (max_acc,
(max_acc_epoch + 1)))
print('Training finished.')
print('Time: %f s' % (time.time() - start_time))
feat = np.zeros((300), float)
for word in sent:
feat += GetWordFeature(word, emb, vocab_map)
return feat
def writeSent(sent, file):
for i in range(len(sent)):
word = sent[i]
if i == len(sent) - 1:
file.write(word + '\n')
else:
file.write(word + ' ')
emb, vocab_map = LoadGloVe()
base_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
toefl_manual_dir = os.path.join(base_dir, 'data', 'toefl', 'manual_trans')
toefl_ASR_dir = os.path.join(base_dir, 'data', 'toefl', 'ASR_trans')
dir_ls = list()
for sub_dir in ['train', 'dev', 'test']:
dir_ls.append(os.path.join(toefl_manual_dir, sub_dir))
# dir_ls.append(os.path.join(toefl_ASR_dir,'test'))
frac = float(sys.argv[1])
for file_dir in dir_ls:
with open(os.path.join(file_dir,'num_sent'), 'r') as nsent_file, \
open(os.path.join(file_dir,'sents.toks'), 'r') as sent_file, \
def main():
start_time = time.time()
parser = argparse.ArgumentParser(
prog='testLSTM_MLP.py',
description='Test LSTM-MLP model for visual question answering')
parser.add_argument('--model',
type=str,
required=True,
metavar='<model-path>')
parser.add_argument('--weights',
type=str,
required=True,
metavar='<weights-path>')
parser.add_argument('--output',
type=str,
required=True,
metavar='<prediction-path>')
args = parser.parse_args()
word_vec_dim = 300
batch_size = 128
#######################
# Load Model #
#######################
print('Loading model and weights...')
model = model_from_json(open(args.model, 'r').read())
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.load_weights(args.weights)
print('Model and weights loaded.')
print('Time: %f s' % (time.time() - start_time))
######################
# Load Data #
######################
print('Loading data...')
#dev_id_pairs, dev_image_ids = LoadIds('dev')
test_id_pairs, test_image_ids = LoadIds('test')
#dev_questions = LoadQuestions('dev')
test_questions = LoadQuestions('test')
#dev_choices = LoadChoices('dev')
test_choices = LoadChoices('test')
#dev_answers = LoadAnswers('dev')
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time() - start_time))
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# validation batches
#dev_question_batches = [ b for b in MakeBatches(dev_questions, batch_size, fillvalue=dev_questions[-1]) ]
#dev_answer_batches = [ b for b in MakeBatches(dev_answers['labs'], batch_size, fillvalue=dev_answers['labs'][-1]) ]
#dev_choice_batches = [ b for b in MakeBatches(dev_choices, batch_size, fillvalue=dev_choices[-1]) ]
#dev_image_batches = [ b for b in MakeBatches(dev_image_ids, batch_size, fillvalue=dev_image_ids[-1]) ]
# testing batches
test_question_batches = [
b for b in MakeBatches(
test_questions, batch_size, fillvalue=test_questions[-1])
]
test_choice_batches = [
b for b in MakeBatches(
test_choices, batch_size, fillvalue=test_choices[-1])
]
test_image_batches = [
b for b in MakeBatches(
test_image_ids, batch_size, fillvalue=test_image_ids[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Testing #
######################
# evaluate on dev set
pbar = generic_utils.Progbar(len(test_question_batches) * batch_size)
#dev_correct = 0
predictions = []
for i in range(len(test_question_batches)):
# feed forward
X_question_batch = GetQuestionsTensor(test_question_batches[i],
word_embedding, word_map)
X_image_batch = GetImagesMatrix(test_image_batches[i], img_map,
VGG_features)
prob = model.predict_proba([X_question_batch, X_image_batch],
batch_size,
verbose=0)
# get word vecs of choices
choice_feats = GetChoicesTensor(test_choice_batches[i], word_embedding,
word_map)
similarity = np.zeros((5, batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
predictions.extend(pred.tolist())
pbar.add(batch_size)
#dev_correct += np.count_nonzero(dev_answer_batches[i]==pred)
SavePredictions(args.output, predictions, test_q_ids)
print('Time: %f s' % (time.time() - start_time))
print('Testing finished.')
def main():
start_time = time.time()
parser = argparse.ArgumentParser(prog='valLSTM_MLP.py',
description='Test LSTM-MLP model for visual question answering')
parser.add_argument('--model-vgg', type=str, required=True, metavar='<model-path>')
parser.add_argument('--weights-vgg', type=str, required=True, metavar='<weights-path>')
parser.add_argument('--model-inc', type=str, required=True, metavar='<model-path>')
parser.add_argument('--weights-inc', type=str, required=True, metavar='<weights-path>')
args = parser.parse_args()
word_vec_dim = 300
batch_size = 128
vgg_weight = 0.25
inc_weight = 1 - vgg_weight
#######################
# Load Models #
#######################
print('Loading models and weights...')
model_vgg = model_from_json(open(args.model_vgg,'r').read())
model_vgg.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model_vgg.load_weights(args.weights_vgg)
model_inc = model_from_json(open(args.model_inc,'r').read())
model_inc.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model_inc.load_weights(args.weights_inc)
print('Models and weights loaded.')
print('Time: %f s' % (time.time()-start_time))
######################
# Load Data #
######################
print('Loading data...')
train_id_pairs, train_image_ids = LoadIds('train')
dev_id_pairs, dev_image_ids = LoadIds('dev')
#test_id_pairs, test_image_ids = LoadIds('test')
train_questions = LoadQuestions('train')
dev_questions = LoadQuestions('dev')
#test_questions = LoadQuestions('test')
train_choices = LoadChoices('train')
dev_choices = LoadChoices('dev')
#test_choices = LoadChoices('test')
train_answers = LoadAnswers('train')
dev_answers = LoadAnswers('dev')
print('Finished loading data.')
print('Time: %f s' % (time.time()-start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
print('Loading VGG features...')
VGG_features, vgg_img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time()-start_time))
# load Inception features
print('Loading Inception features...')
INC_features, inc_img_map = LoadInceptionFeatures()
print('Inception features loaded')
print('Time: %f s' % (time.time()-start_time))
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time()-start_time))
######################
# Make Batches #
######################
print('Making batches...')
# train batches
train_question_batches = [ b for b in MakeBatches(train_questions, batch_size, fillvalue=train_questions[-1]) ]
train_answer_batches = [ b for b in MakeBatches(train_answers['labs'], batch_size, fillvalue=train_answers['labs'][-1]) ]
train_choice_batches = [ b for b in MakeBatches(train_choices, batch_size, fillvalue=train_choices[-1]) ]
train_image_batches = [ b for b in MakeBatches(train_image_ids, batch_size, fillvalue=train_image_ids[-1]) ]
# validation batches
dev_question_batches = [ b for b in MakeBatches(dev_questions, batch_size, fillvalue=dev_questions[-1]) ]
dev_answer_batches = [ b for b in MakeBatches(dev_answers['labs'], batch_size, fillvalue=dev_answers['labs'][-1]) ]
dev_choice_batches = [ b for b in MakeBatches(dev_choices, batch_size, fillvalue=dev_choices[-1]) ]
dev_image_batches = [ b for b in MakeBatches(dev_image_ids, batch_size, fillvalue=dev_image_ids[-1]) ]
# testing batches
#test_question_batches = [ b for b in MakeBatches(test_questions, batch_size, fillvalue=test_questions[-1]) ]
#test_choice_batches = [ b for b in MakeBatches(test_choices, batch_size, fillvalue=test_choices[-1]) ]
#test_image_batches = [ b for b in MakeBatches(test_image_ids, batch_size, fillvalue=test_image_ids[-1]) ]
print('Finished making batches.')
print('Time: %f s' % (time.time()-start_time))
######################
# Testing #
######################
# evaluate on dev set
pbar = generic_utils.Progbar(len(dev_question_batches)*batch_size)
dev_correct = 0
for i in range(len(dev_question_batches)):
# feed forward
X_question_batch = GetQuestionsTensor(dev_question_batches[i], word_embedding, word_map)
X_vgg_image_batch = GetImagesMatrix(dev_image_batches[i], vgg_img_map, VGG_features)
X_inc_image_batch = GetImagesMatrix(dev_image_batches[i], inc_img_map, INC_features)
prob_vgg = model_vgg.predict_proba([X_question_batch, X_vgg_image_batch], batch_size, verbose=0)
prob_inc = model_inc.predict_proba([X_question_batch, X_inc_image_batch], batch_size, verbose=0)
prob = (vgg_weight*prob_vgg + inc_weight*prob_inc)
# get word vecs of choices
choice_feats = GetChoicesTensor(dev_choice_batches[i], word_embedding, word_map)
similarity = np.zeros((5, batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
#predictions.extend(pred.tolist())
if i != (len(dev_question_batches)-1):
dev_correct += np.count_nonzero(dev_answer_batches[i]==pred)
else:
num_padding = batch_size * len(dev_question_batches) - len(dev_questions)
last_idx = batch_size - num_padding
dev_correct += np.count_nonzero(dev_answer_batches[:last_idx]==pred[:last_idx])
#dev_correct += np.count_nonzero(dev_answer_batches[i]==pred)
pbar.add(batch_size)
print('Validation accuracy: %f' % (float(dev_correct)/len(dev_questions)))
'''
train_correct = 0
pbar = generic_utils.Progbar(len(train_question_batches)*batch_size)
for i in range(len(train_question_batches)):
# feed forward
X_question_batch = GetQuestionsTensor(train_question_batches[i], word_embedding, word_map)
X_vgg_image_batch = GetImagesMatrix(train_image_batches[i], vgg_img_map, VGG_features)
X_inc_image_batch = GetImagesMatrix(train_image_batches[i], inc_img_map, INC_features)
prob_vgg = model_vgg.predict_proba([X_question_batch, X_vgg_image_batch], batch_size, verbose=0)
prob_inc = model_inc.predict_proba([X_question_batch, X_inc_image_batch], batch_size, verbose=0)
prob = (vgg_weight*prob_vgg + inc_weight*prob_inc)
# get word vecs of choices
choice_feats = GetChoicesTensor(train_choice_batches[i], word_embedding, word_map)
similarity = np.zeros((5, batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
#predictions.extend(pred.tolist())
if i != (len(dev_question_batches)-1):
train_correct += np.count_nonzero(train_answer_batches[i]==pred)
else:
num_padding = batch_size * len(train_question_batches) - len(train_questions)
last_idx = batch_size - num_padding
train_correct += np.count_nonzero(train_answer_batches[:last_idx]==pred[:last_idx])
#train_correct += np.count_nonzero(train_answer_batches[i]==pred)
pbar.add(batch_size)
print('Training accuracy: %f' % (float(train_correct)/len(train_questions)))
'''
print('Time: %f s' % (time.time()-start_time))
print('Testing finished.')
def main():
start_time = time.time()
signal.signal(signal.SIGINT, InterruptHandler)
#signal.signal(signal.SIGKILL, InterruptHandler)
signal.signal(signal.SIGTERM, InterruptHandler)
parser = argparse.ArgumentParser(
prog='trainLSTM.py',
description='Train LSTM model for visual question answering')
parser.add_argument('--lstm-hidden-units',
type=int,
default=512,
metavar='<lstm-hidden-units>')
parser.add_argument('--lstm-hidden-layers',
type=int,
default=1,
metavar='<lstm-hidden-layers>')
#parser.add_argument('--dropout', type=float, default=0.5, metavar='<dropout-rate>')
parser.add_argument('--num-epochs',
type=int,
default=100,
metavar='<num-epochs>')
#parser.add_argument('--model-save-interval', type=int, default=5, metavar='<interval>')
parser.add_argument('--batch-size',
type=int,
default=128,
metavar='<batch-size>')
args = parser.parse_args()
Inc_features_dim = 2048
word_vec_dim = 300
max_len = 30
data_dir = '/home/mlds/data/0.2_val/'
######################
# Load Data #
######################
print('Loading data...')
train_id_pairs, train_image_ids = LoadIds('train', data_dir)
dev_id_pairs, dev_image_ids = LoadIds('dev', data_dir)
train_questions = LoadQuestions('train', data_dir)
dev_questions = LoadQuestions('dev', data_dir)
train_choices = LoadChoices('train', data_dir)
dev_choices = LoadChoices('dev', data_dir)
train_answers = LoadAnswers('train', data_dir)
dev_answers = LoadAnswers('dev', data_dir)
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
print('-' * 100, file=sys.stderr)
print('Training Information', file=sys.stderr)
print('# of LSTM hidden units: %i' % args.lstm_hidden_units,
file=sys.stderr)
print('# of LSTM hidden layers: %i' % args.lstm_hidden_layers,
file=sys.stderr)
#print('Dropout: %f' % args.dropout, file=sys.stderr)
print('# of training epochs: %i' % args.num_epochs, file=sys.stderr)
print('Batch size: %i' % args.batch_size, file=sys.stderr)
print('# of train questions: %i' % len(train_questions), file=sys.stderr)
print('# of dev questions: %i' % len(dev_questions), file=sys.stderr)
print('-' * 100, file=sys.stderr)
######################
# Model Descriptions #
######################
# LSTM model
model = Sequential()
model.add(
LSTM(output_dim=args.lstm_hidden_units,
return_sequences=True,
input_shape=(max_len, Inc_features_dim + word_vec_dim)))
for i in range(args.lstm_hidden_layers - 2):
model.add(
LSTM(output_dim=args.lstm_hidden_units, return_sequences=True))
model.add(LSTM(output_dim=word_vec_dim, return_sequences=False))
model.add(Activation('softmax'))
json_string = model.to_json()
model_filename = 'models/lstm_units_%i_layers_%i' % (
args.lstm_hidden_units, args.lstm_hidden_layers)
open(model_filename + '.json', 'w').write(json_string)
# loss and optimizer
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print('Compilation finished.')
print('Time: %f s' % (time.time() - start_time))
##############################################
# Load Word Vectors and Inception Features #
##############################################
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
print('Loading Inception features...')
Inc_features, img_map = LoadInceptionFeatures()
print('Inception features loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# training batches
train_question_batches = [
b for b in MakeBatches(
train_questions, args.batch_size, fillvalue=train_questions[-1])
]
train_answer_batches = [
b for b in MakeBatches(train_answers['toks'],
args.batch_size,
fillvalue=train_answers['toks'][-1])
]
train_image_batches = [
b for b in MakeBatches(
train_image_ids, args.batch_size, fillvalue=train_image_ids[-1])
]
train_indices = list(range(len(train_question_batches)))
# validation batches
dev_question_batches = [
b for b in MakeBatches(
dev_questions, args.batch_size, fillvalue=dev_questions[-1])
]
dev_answer_batches = [
b for b in MakeBatches(dev_answers['labs'],
args.batch_size,
fillvalue=dev_answers['labs'][-1])
]
dev_image_batches = [
b for b in MakeBatches(
dev_image_ids, args.batch_size, fillvalue=dev_image_ids[-1])
]
dev_choice_batches = [
b for b in MakeBatches(
dev_choices, args.batch_size, fillvalue=dev_choices[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Training #
######################
dev_accs = []
max_acc = -1
max_acc_epoch = -1
print('Training started...')
for k in range(args.num_epochs):
print('Epoch %i' % (k + 1), file=sys.stderr)
print('-' * 80)
print('Epoch %i' % (k + 1))
progbar = generic_utils.Progbar(len(train_indices) * args.batch_size)
# shuffle batch indices
random.shuffle(train_indices)
for i in train_indices:
X_imgquestion_batch = GetImgQuestionsTensor(
train_image_batches[i], Inc_features, img_map,
train_question_batches[i], word_embedding, word_map)
Y_answer_batch = GetAnswersMatrix(train_answer_batches[i],
word_embedding, word_map)
loss = model.train_on_batch(X_imgquestion_batch, Y_answer_batch)
loss = loss[0].tolist()
progbar.add(args.batch_size, values=[('train loss', loss)])
#if k % args.model_save_interval == 0:
#model.save_weights(model_filename + '_epoch_{:03d}.hdf5'.format(k+1), overwrite=True)
# evaluate on dev set
progbar = generic_utils.Progbar(
len(dev_question_batches) * args.batch_size)
dev_correct = 0
for i in range(len(dev_question_batches)):
# feed forward
X_imgquestion_batch = GetImgQuestionsTensor(
dev_image_batches[i], Inc_features, img_map,
dev_question_batches[i], word_embedding, word_map)
prob = model.predict_proba(X_imgquestion_batch,
args.batch_size,
verbose=0)
# get word vecs of choices
choice_feats = GetChoicesTensor(dev_choice_batches[i],
word_embedding, word_map)
similarity = np.zeros((5, args.batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(
cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
dev_correct += np.count_nonzero(dev_answer_batches[i] == pred)
progbar.add(args.batch_size)
dev_acc = float(dev_correct) / len(dev_questions)
dev_accs.append(dev_acc)
print('Validation Accuracy: %f' % dev_acc)
print('Validation Accuracy: %f' % dev_acc, file=sys.stderr)
print('Time: %f s' % (time.time() - start_time))
print('Time: %f s' % (time.time() - start_time), file=sys.stderr)
if dev_acc > max_acc:
max_acc = dev_acc
max_acc_epoch = k
model.save_weights(model_filename + '_best.hdf5', overwrite=True)
#model.save_weights(model_filename + '_epoch_{:03d}.hdf5'.format(k+1))
print(dev_accs, file=sys.stderr)
print('Best validation accuracy: epoch#%i' % max_acc_epoch)
print('Training finished.')
print('Training finished.', file=sys.stderr)
print('Time: %f s' % (time.time() - start_time))
print('Time: %f s' % (time.time() - start_time), file=sys.stderr)
def main():
start_time = time.time()
parser = argparse.ArgumentParser(
prog='valLSTM_MLP.py',
description='Test LSTM-MLP model for visual question answering')
parser.add_argument('--model',
type=str,
required=True,
metavar='<model-path>')
parser.add_argument('--weights',
type=str,
required=True,
metavar='<weights-path>')
args = parser.parse_args()
word_vec_dim = 300
batch_size = 128
#######################
# Load Model #
#######################
print('Loading model and weights...')
model = model_from_json(open(args.model, 'r').read())
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.load_weights(args.weights)
print('Model and weights loaded.')
print('Time: %f s' % (time.time() - start_time))
######################
# Load Data #
######################
print('Loading data...')
train_id_pairs, train_image_ids = LoadIds('train')
dev_id_pairs, dev_image_ids = LoadIds('dev')
#test_id_pairs, test_image_ids = LoadIds('test')
train_questions = LoadQuestions('train')
dev_questions = LoadQuestions('dev')
#test_questions = LoadQuestions('test')
train_choices = LoadChoices('train')
dev_choices = LoadChoices('dev')
#test_choices = LoadChoices('test')
train_answers = LoadAnswers('train')
dev_answers = LoadAnswers('dev')
train_qa_type, train_qtype_count, train_atype_count = LoadQAType('train')
dev_qa_type, dev_qtype_count, dev_atype_count = LoadQAType('dev')
train_total_qtype_count, train_total_atype_count = LoadTotalQAType('train')
dev_total_qtype_count, dev_total_atype_count = LoadTotalQAType('dev')
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time() - start_time))
# load VGG features
print('Loading Inception features...')
INC_features, inc_img_map = LoadInceptionFeatures()
print('Inception features loaded')
print('Time: %f s' % (time.time() - start_time))
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# train batches
train_question_batches = [
b for b in MakeBatches(
train_questions, batch_size, fillvalue=train_questions[-1])
]
train_answer_batches = [
b for b in MakeBatches(train_answers['labs'],
batch_size,
fillvalue=train_answers['labs'][-1])
]
train_choice_batches = [
b for b in MakeBatches(
train_choices, batch_size, fillvalue=train_choices[-1])
]
train_image_batches = [
b for b in MakeBatches(
train_image_ids, batch_size, fillvalue=train_image_ids[-1])
]
train_qatype_batches = [
b for b in MakeBatches(
train_qa_type, batch_size, fillvalue=train_id_pairs[-1])
]
# validation batches
dev_question_batches = [
b for b in MakeBatches(
dev_questions, batch_size, fillvalue=dev_questions[-1])
]
dev_answer_batches = [
b for b in MakeBatches(
dev_answers['labs'], batch_size, fillvalue=dev_answers['labs'][-1])
]
dev_choice_batches = [
b for b in MakeBatches(
dev_choices, batch_size, fillvalue=dev_choices[-1])
]
dev_image_batches = [
b for b in MakeBatches(
dev_image_ids, batch_size, fillvalue=dev_image_ids[-1])
]
dev_qatype_batches = [
b for b in MakeBatches(
dev_qa_type, batch_size, fillvalue=dev_id_pairs[-1])
]
# testing batches
#test_question_batches = [ b for b in MakeBatches(test_questions, batch_size, fillvalue=test_questions[-1]) ]
#test_choice_batches = [ b for b in MakeBatches(test_choices, batch_size, fillvalue=test_choices[-1]) ]
#test_image_batches = [ b for b in MakeBatches(test_image_ids, batch_size, fillvalue=test_image_ids[-1]) ]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Testing #
######################
# evaluate on dev set
pbar = generic_utils.Progbar(len(dev_question_batches) * batch_size)
dev_correct = 0
for i in range(len(dev_question_batches)):
# feed forward
X_question_batch = GetQuestionsTensor(dev_question_batches[i],
word_embedding, word_map)
X_image_batch = GetImagesMatrix(dev_image_batches[i], inc_img_map,
INC_features)
prob = model.predict_proba([X_question_batch, X_image_batch],
batch_size,
verbose=0)
# get word vecs of choices
choice_feats = GetChoicesTensor(dev_choice_batches[i], word_embedding,
word_map)
similarity = np.zeros((5, batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
#predictions.extend(pred.tolist())
dev_correct += np.count_nonzero(dev_answer_batches[i] == pred)
# count the incorrect question and answer types
incorrect = np.nonzero(dev_answer_batches[i] != pred)[0].tolist()
#idpair_batch = dev_idpair_batches[i]
qatype_batch = dev_qatype_batches[i]
for idx in incorrect:
q_type, a_type = qatype_batch[idx]
dev_qtype_count[q_type] += 1
dev_atype_count[a_type] += 1
pbar.add(batch_size)
print('Validation accuracy: %f' %
(float(dev_correct) / len(dev_questions)))
train_correct = 0
pbar = generic_utils.Progbar(len(train_question_batches) * batch_size)
for i in range(len(train_question_batches)):
# feed forward
X_question_batch = GetQuestionsTensor(train_question_batches[i],
word_embedding, word_map)
X_image_batch = GetImagesMatrix(train_image_batches[i], inc_img_map,
INC_features)
prob = model.predict_proba([X_question_batch, X_image_batch],
batch_size,
verbose=0)
# get word vecs of choices
choice_feats = GetChoicesTensor(train_choice_batches[i],
word_embedding, word_map)
similarity = np.zeros((5, batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
#predictions.extend(pred.tolist())
train_correct += np.count_nonzero(train_answer_batches[i] == pred)
# count the incorrect question and answer types
incorrect = np.nonzero(train_answer_batches[i] != pred)[0].tolist()
qatype_batch = train_qatype_batches[i]
for idx in incorrect:
q_type, a_type = qatype_batch[idx]
train_qtype_count[q_type] += 1
train_atype_count[a_type] += 1
pbar.add(batch_size)
print('Training accuracy: %f' %
(float(train_correct) / len(train_questions)))
print('Validation QA types:')
print(dev_qtype_count)
print(dev_atype_count)
print('Training QA types:')
print(train_qtype_count)
print(train_atype_count)
print('Total QA types:')
print(train_total_qtype_count)
print(train_total_atype_count)
print(dev_total_qtype_count)
print(dev_total_atype_count)
print('Time: %f s' % (time.time() - start_time))
print('Testing finished.')
def main():
start_time = time.time()
# argument parser
parser = argparse.ArgumentParser(prog='test_sent.py',
description='Test MemNN-wordvec model for ABSA sentiment classification')
parser.add_argument('--mlp-hidden-units', type=int, default=256, metavar='<mlp-hidden-units>')
parser.add_argument('--mlp-hidden-layers', type=int, default=2, metavar='<mlp-hidden-layers>')
parser.add_argument('--dropout', type=float, default=0.3, metavar='<dropout-rate>')
parser.add_argument('--mlp-activation', type=str, default='relu', metavar='<activation-function>')
parser.add_argument('--batch-size', type=int, default=32, metavar='<batch-size>')
parser.add_argument('--learning-rate', type=float, default=0.001, metavar='<learning-rate>')
parser.add_argument('--aspects', type=int, required=True, metavar='<number of aspects>')
parser.add_argument('--domain', type=str, required=True, choices=['rest','lapt'], metavar='<domain>')
parser.add_argument('--cross-val-index', type=int, required=True, choices=range(0,10), metavar='<cross-validation-index>')
parser.add_argument('--weights', type=str, required=True, metavar='<weights-path>')
parser.add_argument('--output', type=str, required=True, metavar='<prediction-path>')
args = parser.parse_args()
args = parser.parse_args()
word_vec_dim = 300
aspect_dim = args.aspects
polarity_num = 3
emb_dim = 75
emb_size = 100
img_dim = word_vec_dim
hops = 2
######################
# Model Descriptions #
######################
print('Generating and compiling model...')
model = CreateGraph(emb_dim, hops, 'relu', args.mlp_hidden_units, args.mlp_hidden_layers, word_vec_dim, aspect_dim, img_dim, emb_size, polarity_num)
# loss and optimizer
adagrad = Adagrad(lr=args.learning_rate)
model.compile(loss={'output':'categorical_crossentropy'}, optimizer=adagrad)
model.load_weights(args.weights)
print('Compilation finished.')
print('Time: %f s' % (time.time()-start_time))
######################
# Load Data #
######################
print('Loading data...')
# aspect mapping
asp_map = LoadAspectMap(args.domain)
# sentences
te_sents = LoadSentences(args.domain, 'te', args.cross_val_index)
# aspects
te_asps = LoadAspects(args.domain, 'te', args.cross_val_index, asp_map)
print('Finished loading data.')
print('Time: %f s' % (time.time()-start_time))
#####################
# GloVe #
#####################
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time()-start_time))
#####################
# Encoders #
#####################
asp_encoder = GetAspectEncoder(asp_map)
lab_encoder = joblib.load('models/'+args.domain+'_labelencoder_'+str(args.cross_val_index)+'.pkl')
######################
# Make Batches #
######################
print('Making batches...')
# validation batches
te_sent_batches = [ b for b in MakeBatches(te_sents, args.batch_size, fillvalue=te_sents[-1]) ]
te_asp_batches = [ b for b in MakeBatches(te_asps, args.batch_size, fillvalue=te_asps[-1]) ]
print('Finished making batches.')
print('Time: %f s' % (time.time()-start_time))
######################
# Testing #
######################
# start testing
print('Testing started...')
pbar = generic_utils.Progbar(len(te_sent_batches)*args.batch_size)
predictions = []
# testing feedforward
for i in range(len(te_sent_batches)):
X_sent_batch = GetSentenceTensor(te_sent_batches[i], word_embedding, word_map)
X_asp_batch = GetAspectFeatures(te_asp_batches[i], asp_encoder)
pred = model.predict_on_batch({'sentence': X_sent_batch, 'aspect': X_asp_batch})
pred = pred[0]
pred = np.argmax(pred, axis=1)
pol = lab_encoder.inverse_transform(pred).tolist()
predictions.extend(pol)
pbar.add(args.batch_size)
SavePredictions(args.output, predictions, len(te_sents))
print('Testing finished.')
print('Time: %f s' % (time.time()-start_time))
def main():
start_time = time.time()
signal.signal(signal.SIGINT, InterruptHandler)
#signal.signal(signal.SIGKILL, InterruptHandler)
signal.signal(signal.SIGTERM, InterruptHandler)
parser = argparse.ArgumentParser(
prog='trainMLP.py',
description='Train MLP model for visual question answering')
parser.add_argument('--mlp-hidden-units',
type=int,
default=1024,
metavar='<mlp-hidden-units>')
parser.add_argument('--mlp-hidden-layers',
type=int,
default=3,
metavar='<mlp-hidden-layers>')
parser.add_argument('--dropout',
type=float,
default=0.5,
metavar='<dropout-rate>')
parser.add_argument('--mlp-activation',
type=str,
default='relu',
metavar='<activation-function>')
parser.add_argument('--num-epochs',
type=int,
default=100,
metavar='<num-epochs>')
parser.add_argument('--model-save-interval',
type=int,
default=5,
metavar='<interval>')
parser.add_argument('--batch-size',
type=int,
default=128,
metavar='<batch-size>')
args = parser.parse_args()
word_vec_dim = 300
img_dim = 4096
######################
# Load Data #
######################
print('Loading data...')
train_id_pairs, train_image_ids = LoadIds('train')
dev_id_pairs, dev_image_ids = LoadIds('dev')
train_choices = LoadChoices('train')
dev_choices = LoadChoices('dev')
train_answers = LoadAnswers('train')
dev_answers = LoadAnswers('dev')
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
print('-' * 100, file=sys.stderr)
print('Training Information', file=sys.stderr)
print('# of MLP hidden units: %i' % args.mlp_hidden_units, file=sys.stderr)
print('# of MLP hidden layers: %i' % args.mlp_hidden_layers,
file=sys.stderr)
print('Dropout: %f' % args.dropout, file=sys.stderr)
print('MLP activation function: %s' % args.mlp_activation, file=sys.stderr)
print('# of training epochs: %i' % args.num_epochs, file=sys.stderr)
print('Batch size: %i' % args.batch_size, file=sys.stderr)
print('# of train images: %i' % len(train_image_ids), file=sys.stderr)
print('# of dev images: %i' % len(dev_image_ids), file=sys.stderr)
print('-' * 100, file=sys.stderr)
######################
# Model Descriptions #
######################
# MLP model
model = Sequential()
model.add(Dense(output_dim=args.mlp_hidden_units, input_dim=img_dim))
model.add(Activation(args.mlp_activation))
model.add(Dropout(args.dropout))
for i in range(args.mlp_hidden_layers - 1):
model.add(Dense(args.mlp_hidden_units))
model.add(Activation(args.mlp_activation))
model.add(Dropout(args.dropout))
model.add(Dense(word_vec_dim))
model.add(Activation('softmax'))
json_string = model.to_json()
model_filename = 'models/mlp_units_%i_layers_%i' % (args.mlp_hidden_units,
args.mlp_hidden_layers)
open(model_filename + '.json', 'w').write(json_string)
# loss and optimizer
model.compile(loss='categorical_crossentropy', optimizer='adagrad')
print('Compilation finished.')
print('Time: %f s' % (time.time() - start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time() - start_time))
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# training batches
train_answer_batches = [
b for b in MakeBatches(train_answers['toks'],
args.batch_size,
fillvalue=train_answers['toks'][-1])
]
train_image_batches = [
b for b in MakeBatches(
train_image_ids, args.batch_size, fillvalue=train_image_ids[-1])
]
train_indices = list(range(len(train_image_ids)))
# validation batches
dev_answer_batches = [
b for b in MakeBatches(dev_answers['labs'],
args.batch_size,
fillvalue=dev_answers['labs'][-1])
]
dev_choice_batches = [
b for b in MakeBatches(
dev_choices, args.batch_size, fillvalue=dev_choices[-1])
]
dev_image_batches = [
b for b in MakeBatches(
dev_image_ids, args.batch_size, fillvalue=dev_image_ids[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Training #
######################
dev_accs = []
max_acc = -1
max_acc_epoch = -1
print('Training started...')
for k in range(args.num_epochs):
print('Epoch %i' % (k + 1), file=sys.stderr)
print('-' * 80)
print('Epoch %i' % (k + 1))
progbar = generic_utils.Progbar(len(train_indices) * args.batch_size)
# shuffle batch indices
random.shuffle(train_indices)
for i in train_indices:
X_image_batch = GetImagesMatrix(train_image_batches[i], img_map,
VGG_features)
Y_answer_batch = GetAnswersMatrix(train_answer_batches[i],
word_embedding, word_map)
loss = model.train_on_batch(X_image_batch, Y_answer_batch)
loss = loss[0].tolist()
progbar.add(args.batch_size, values=[('train loss', loss)])
if k % args.model_save_interval == 0:
model.save_weights(model_filename +
'_epoch_{:03d}.hdf5'.format(k + 1),
overwrite=True)
# evaluate on dev set
widgets = [
'Evaluating ',
Percentage(), ' ',
Bar(marker='#', left='[', right=']'), ' ',
ETA()
]
pbar = ProgressBar(widgets=widgets, redirect_stdout=True)
dev_correct = 0
for i in pbar(range(len(dev_image_batches))):
# feed forward
X_image_batch = GetImagesMatrix(dev_image_batches[i], img_map,
VGG_features)
prob = model.predict_proba(X_image_batch,
args.batch_size,
verbose=0)
# get word vecs of choices
choice_feats = GetChoicesTensor(dev_choice_batches[i],
word_embedding, word_map)
similarity = np.zeros((5, args.batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(
cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
dev_correct += np.count_nonzero(dev_answer_batches[i] == pred)
dev_acc = float(dev_correct) / len(dev_image_ids)
dev_accs.append(dev_acc)
print('Validation Accuracy: %f' % dev_acc)
print('Validation Accuracy: %f' % dev_acc, file=sys.stderr)
print('Time: %f s' % (time.time() - start_time))
print('Time: %f s' % (time.time() - start_time), file=sys.stderr)
if dev_acc > max_acc:
max_acc = dev_acc
max_acc_epoch = k
model.save_weights(model_filename + '_best.hdf5', overwrite=True)
model.save_weights(model_filename + '_epoch_{:03d}.hdf5'.format(k + 1))
print(dev_accs, file=sys.stderr)
print('Best validation accuracy: epoch#%i' % max_acc_epoch)
print('Training finished.')
print('Training finished.', file=sys.stderr)
print('Time: %f s' % (time.time() - start_time))
print('Time: %f s' % (time.time() - start_time), file=sys.stderr)
def main():
start_time = time.time()
parser = argparse.ArgumentParser(
prog='trainMemNN.py',
description='Train MemmNN model for visual question answering')
parser.add_argument('--mlp-hidden-units',
type=int,
default=1024,
metavar='<mlp-hidden-units>')
parser.add_argument('--mlp-hidden-layers',
type=int,
default=3,
metavar='<mlp-hidden-layers>')
parser.add_argument('--mlp-activation',
type=str,
default='tanh',
metavar='<activation-function>')
parser.add_argument('--emb-dimension',
type=int,
default=50,
metavar='<embedding-dimension>')
parser.add_argument('--num-epochs',
type=int,
default=100,
metavar='<num-epochs>')
parser.add_argument('--batch-size',
type=int,
default=128,
metavar='<batch-size>')
parser.add_argument('--hops', type=int, default=3, metavar='<memnet-hops>')
parser.add_argument('--learning-rate',
type=float,
default=0.001,
metavar='<learning-rate>')
parser.add_argument('--dropout',
type=float,
default=0.2,
metavar='<dropout-rate>')
parser.add_argument('--dev-accuracy-path',
type=str,
required=True,
metavar='<accuracy-path>')
args = parser.parse_args()
word_vec_dim = 300
img_dim = 300
max_len = 30
img_feature_num = 125
######################
# Load Data #
######################
data_dir = '/home/mlds/data/0.05_val/'
print('Loading data...')
train_q_ids, train_image_ids = LoadIds('train', data_dir)
dev_q_ids, dev_image_ids = LoadIds('dev', data_dir)
#test_q_ids,test_image_ids = LoadIds('test', data_dir)
train_questions = LoadQuestions('train', data_dir)
dev_questions = LoadQuestions('dev', data_dir)
train_choices = LoadChoices('train', data_dir)
dev_choices = LoadChoices('dev', data_dir)
train_answers = LoadAnswers('train', data_dir)
dev_answers = LoadAnswers('dev', data_dir)
caption_map = LoadCaptions('train')
'''
caption_map_test = LoadCaptions('test')
maxtrain=-1
maxdev=-1
maxtest=-1
for img_id in train_image_ids:
sent = caption_map[img_id]
if len(sent) > maxtrain:
maxtrain = len(sent)
for img_id in dev_image_ids:
sent = caption_map[img_id]
if len(sent) > maxdev:
maxdev = len(sent)
for img_id in test_image_ids:
sent = caption_map_test[img_id]
if len(sent) > maxtest:
maxtest = len(sent)
print(maxtrain)
print(maxdev)
print(maxtest)
sys.exit()
'''
print('Finished loading data.')
print('Time: %f s' % (time.time() - start_time))
######################
# Model Descriptions #
######################
print('Generating and compiling model...')
model = CreateGraph(args.emb_dimension, args.hops, args.mlp_activation,
args.mlp_hidden_units, args.mlp_hidden_layers,
word_vec_dim, img_dim, img_feature_num, args.dropout)
json_string = model.to_json()
model_filename = 'models/memNN.mlp_units_%i_layers_%i_%s.emb_dim_%i.hops_%i.lr%.1e.dropout_%.1e' % (
args.mlp_hidden_units, args.mlp_hidden_layers, args.mlp_activation,
args.emb_dimension, args.hops, args.learning_rate, args.dropout)
open(model_filename + '.json', 'w').write(json_string)
# loss and optimizer
rmsprop = RMSprop(lr=args.learning_rate)
#model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
model.compile(loss={'output': Loss}, optimizer=rmsprop)
print('Compilation finished.')
print('Time: %f s' % (time.time() - start_time))
########################################
# Load CNN Features and Word Vectors #
########################################
# load VGG features
'''
print('Loading VGG features...')
VGG_features, img_map = LoadVGGFeatures()
print('VGG features loaded')
print('Time: %f s' % (time.time()-start_time))
'''
# load GloVe vectors
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time() - start_time))
######################
# Make Batches #
######################
print('Making batches...')
# training batches
train_question_batches = [
b for b in MakeBatches(
train_questions, args.batch_size, fillvalue=train_questions[-1])
]
train_answer_batches = [
b for b in MakeBatches(train_answers['toks'],
args.batch_size,
fillvalue=train_answers['toks'][-1])
]
train_image_batches = [
b for b in MakeBatches(
train_image_ids, args.batch_size, fillvalue=train_image_ids[-1])
]
train_indices = list(range(len(train_question_batches)))
# validation batches
dev_question_batches = [
b for b in MakeBatches(
dev_questions, args.batch_size, fillvalue=dev_questions[-1])
]
dev_answer_batches = [
b for b in MakeBatches(dev_answers['labs'],
args.batch_size,
fillvalue=dev_answers['labs'][-1])
]
dev_choice_batches = [
b for b in MakeBatches(
dev_choices, args.batch_size, fillvalue=dev_choices[-1])
]
dev_image_batches = [
b for b in MakeBatches(
dev_image_ids, args.batch_size, fillvalue=dev_image_ids[-1])
]
print('Finished making batches.')
print('Time: %f s' % (time.time() - start_time))
######################
# Training #
######################
acc_file = open(args.dev_accuracy_path, 'w')
dev_accs = []
max_acc = -1
max_acc_epoch = -1
# define interrupt handler
def PrintDevAcc():
print('Max validation accuracy epoch: %i' % max_acc_epoch)
print(dev_accs)
def InterruptHandler(sig, frame):
print(str(sig))
PrintDevAcc()
sys.exit(-1)
signal.signal(signal.SIGINT, InterruptHandler)
signal.signal(signal.SIGTERM, InterruptHandler)
# print training information
print('-' * 80)
print('Training Information')
print('# of MLP hidden units: %i' % args.mlp_hidden_units)
print('# of MLP hidden layers: %i' % args.mlp_hidden_layers)
print('MLP activation function: %s' % args.mlp_activation)
print('# of training epochs: %i' % args.num_epochs)
print('Batch size: %i' % args.batch_size)
print('Learning rate: %f' % args.learning_rate)
print('# of train questions: %i' % len(train_questions))
print('# of dev questions: %i' % len(dev_questions))
print('-' * 80)
acc_file.write('-' * 80 + '\n')
acc_file.write('Training Information\n')
acc_file.write('# of MLP hidden units: %i\n' % args.mlp_hidden_units)
acc_file.write('# of MLP hidden layers: %i\n' % args.mlp_hidden_layers)
acc_file.write('MLP activation function: %s\n' % args.mlp_activation)
acc_file.write('# of training epochs: %i\n' % args.num_epochs)
acc_file.write('Batch size: %i\n' % args.batch_size)
acc_file.write('Learning rate: %f\n' % args.learning_rate)
acc_file.write('# of train questions: %i\n' % len(train_questions))
acc_file.write('# of dev questions: %i\n' % len(dev_questions))
acc_file.write('-' * 80 + '\n')
acc_file.close()
# start training
print('Training started...')
for k in range(args.num_epochs):
print('-' * 80)
print('Epoch %i' % (k + 1))
progbar = generic_utils.Progbar(len(train_indices) * args.batch_size)
# shuffle batch indices
random.shuffle(train_indices)
for i in train_indices:
X_question_batch = GetQuestionsTensor(train_question_batches[i],
word_embedding, word_map)
#X_image_batch = GetImagesMatrix(train_image_batches[i], img_map, VGG_features)
X_caption_batch = GetCaptionsTensor2(train_image_batches[i],
word_embedding, word_map,
caption_map)
Y_answer_batch = GetAnswersMatrix(train_answer_batches[i],
word_embedding, word_map)
loss = model.train_on_batch({
'question': X_question_batch,
'image': X_caption_batch,
'output': Y_answer_batch
})
loss = loss[0].tolist()
progbar.add(args.batch_size, values=[('train loss', loss)])
print('Time: %f s' % (time.time() - start_time))
# evaluate on dev set
pbar = generic_utils.Progbar(
len(dev_question_batches) * args.batch_size)
dev_correct = 0
# feed forward
for i in range(len(dev_question_batches)):
X_question_batch = GetQuestionsTensor(dev_question_batches[i],
word_embedding, word_map)
#X_image_batch = GetImagesMatrix(dev_image_batches[i], img_map, VGG_features)
X_caption_batch = GetCaptionsTensor2(dev_image_batches[i],
word_embedding, word_map,
caption_map)
prob = model.predict_on_batch({
'question': X_question_batch,
'image': X_caption_batch
})
prob = prob[0]
# get word vecs of choices
choice_feats = GetChoicesTensor(dev_choice_batches[i],
word_embedding, word_map)
similarity = np.zeros((5, args.batch_size), float)
# calculate cosine distances
for j in range(5):
similarity[j] = np.diag(
cosine_similarity(prob, choice_feats[j]))
# take argmax of cosine distances
pred = np.argmax(similarity, axis=0) + 1
if i != (len(dev_question_batches) - 1):
dev_correct += np.count_nonzero(dev_answer_batches[i] == pred)
else:
num_padding = args.batch_size * len(
dev_question_batches) - len(dev_questions)
last_idx = args.batch_size - num_padding
dev_correct += np.count_nonzero(
dev_answer_batches[:last_idx] == pred[:last_idx])
pbar.add(args.batch_size)
dev_acc = float(dev_correct) / len(dev_questions)
dev_accs.append(dev_acc)
with open(args.dev_accuracy_path, 'a') as acc_file:
acc_file.write('%f\n' % dev_acc)
print('Validation Accuracy: %f' % dev_acc)
print('Time: %f s' % (time.time() - start_time))
if dev_acc > max_acc:
max_acc = dev_acc
max_acc_epoch = k
model.save_weights(model_filename + '_best.hdf5', overwrite=True)
#model.save_weights(model_filename + '_epoch_{:03d}.hdf5'.format(k+1))
acc_file = open(args.dev_accuracy_path, 'a')
print(dev_accs)
print('Best validation accuracy: %f; epoch#%i' % (max_acc,
(max_acc_epoch + 1)))
acc_file.write('Best validation accuracy: %f; epoch#%i\n' %
(max_acc, (max_acc_epoch + 1)))
print('Training finished.')
acc_file.write('Training finished.\n')
print('Time: %f s' % (time.time() - start_time))
acc_file.write('Time: %f s\n' % (time.time() - start_time))
acc_file.close()
def main():
start_time = time.time()
# argument parser
parser = argparse.ArgumentParser(prog='test_sent.py',
description='Test LSTM-NN model for ABSA sentiment classification')
parser.add_argument('--aspects', type=int, required=True, metavar='<number of aspects>')
parser.add_argument('--domain', type=str, required=True, choices=['rest','lapt'], metavar='<domain>')
parser.add_argument('--cross-val-index', type=int, required=True, choices=range(0,10), metavar='<cross-validation-index>')
parser.add_argument('--model', type=str, required=True, metavar='<model-path>')
parser.add_argument('--weights', type=str, required=True, metavar='<weights-path>')
parser.add_argument('--output', type=str, required=True, metavar='<prediction-path>')
args = parser.parse_args()
sent_vec_dim = 300
batch_size = 128
aspect_dim = args.aspects
polarity_num = 3
#######################
# Load Model #
#######################
print('Loading model and weights...')
model = model_from_json(open(args.model,'r').read())
model.compile(loss='categorical_crossentropy', optimizer='adagrad')
model.load_weights(args.weights)
print('Model and weights loaded.')
print('Time: %f s' % (time.time()-start_time))
######################
# Load Data #
######################
print('Loading data...')
# aspect mapping
asp_map = LoadAspectMap(args.domain)
# sentences
te_sents = LoadSentences(args.domain, 'te', args.cross_val_index)
# aspects
te_asps = LoadAspects(args.domain, 'te', args.cross_val_index, asp_map)
print('Finished loading data.')
print('Time: %f s' % (time.time()-start_time))
#####################
# GloVe #
#####################
print('Loading GloVe vectors...')
word_embedding, word_map = LoadGloVe()
print('GloVe vectors loaded')
print('Time: %f s' % (time.time()-start_time))
#####################
# Encoders #
#####################
asp_encoder = GetAspectEncoder(asp_map)
lab_encoder = joblib.load('models/'+args.domain+'_labelencoder_'+str(args.cross_val_index)+'.pkl')
######################
# Make Batches #
######################
print('Making batches...')
# te batches
te_sent_batches = [ b for b in MakeBatches(te_sents, batch_size, fillvalue=te_sents[-1]) ]
te_asp_batches = [ b for b in MakeBatches(te_asps, batch_size, fillvalue=te_asps[-1]) ]
print('Finished making batches.')
print('Time: %f s' % (time.time()-start_time))
######################
# Testing #
######################
# start testing
print('Testing started...')
pbar = generic_utils.Progbar(len(te_sent_batches)*batch_size)
predictions = []
# testing feedforward
for i in range(len(te_sent_batches)):
X_sent_batch = GetSentenceTensor(te_sent_batches[i], word_embedding, word_map)
X_asp_batch = GetAspectFeatures(te_asp_batches[i], asp_encoder)
pred = model.predict_classes([X_sent_batch, X_asp_batch], batch_size, verbose=0)
pol = lab_encoder.inverse_transform(pred).tolist()
predictions.extend(pol)
pbar.add(batch_size)
SavePredictions(args.output, predictions, len(te_sents))
print('Testing finished.')
print('Time: %f s' % (time.time()-start_time))