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()
# argument parser
parser = argparse.ArgumentParser(
prog='train_sent.py',
description=
'Train 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('--num-epochs',
type=int,
default=100,
metavar='<num-epochs>')
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>')
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)
# save model configuration
json_string = model.to_json()
model_filename = 'models/%s.memNN_wordvec.hops_%i.emb_%i.mlp_units_%i_layers_%i_%s.lr%.1e.dropout%.1f.%i' % (
args.domain, hops, emb_dim, 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
adagrad = Adagrad(lr=args.learning_rate)
model.compile(loss={'output': 'categorical_crossentropy'},
optimizer=adagrad)
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({
'sentence': X_sent_batch,
'aspect': X_asp_batch,
'output': Y_lab_batch
})
loss = loss[0].tolist()
progbar.add(args.batch_size, values=[('train loss', loss)])
print('Time: %f s' % (time.time() - start_time))
pbar = generic_utils.Progbar(len(dev_sent_batches) * args.batch_size)
# evaluate on dev set
# 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_on_batch({
'sentence': X_sent_batch,
'aspect': X_asp_batch
})
pred = pred[0]
pred = np.argmax(pred, axis=1)
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))
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()
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()