def save_pickle():
answers = open('../data/ans_txt.txt', 'r').read().decode('utf8').splitlines()
print(len(answers))
questions = open("../data/ques_txt.txt", 'r').read().decode('utf8').splitlines()
images = open("../data/img_id_txt.txt", 'r').read().decode('utf8').splitlines()
maxAnswers = 1000
questions, answers, images = selectFrequentAnswers(questions,answers,images, maxAnswers)
encoder = preprocessing.LabelEncoder()
encoder.fit(answers)
print ("Number of classes: " + str(len(list(encoder.classes_))))
joblib.dump(encoder, '../data/encoder.pkl')
return "DONE"
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units', type=int, default=512)
parser.add_argument('-num_lstm_layers', type=int, default=2)
parser.add_argument('-dropout', type=float, default=0.2)
parser.add_argument('-activation', type=str, default='tanh')
parser.add_argument('-num_epochs', type=int, default=100)
parser.add_argument('-model_save_interval', type=int, default=5)
parser.add_argument('-batch_size', type=int, default=128)
parser.add_argument('-word_vector', type=str, default='')
args = parser.parse_args()
questions_train = open('../data/preprocessed/questions_train2014.txt', 'r').read().decode('utf8').splitlines()
questions_lengths_train = open('../data/preprocessed/questions_lengths_train2014.txt', 'r').read().decode('utf8').splitlines()
answers_train = open('../data/preprocessed/answers_train2014.txt', 'r').read().decode('utf8').splitlines()
images_train = open('../data/preprocessed/images_train2014.txt', 'r').read().decode('utf8').splitlines()
max_answers = 1000
questions_train, answers_train, images_train = selectFrequentAnswers(questions_train,answers_train,images_train, max_answers)
print 'Loaded questions, sorting by length...'
questions_lengths_train, questions_train, answers_train = (list(t) for t in zip(*sorted(zip(questions_lengths_train, questions_train, answers_train))))
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder,'../models/labelencoder.pkl')
max_len = 30 #25 is max for training, 27 is max for validation
word_vec_dim = 300
model = Sequential()
model.add(LSTM(output_dim = args.num_hidden_units, activation='tanh',
return_sequences=True, input_shape=(max_len, word_vec_dim)))
model.add(Dropout(args.dropout))
model.add(LSTM(args.num_hidden_units, return_sequences=False))
model.add(Dense(nb_classes, init='uniform'))
model.add(Activation('softmax'))
json_string = model.to_json()
model_file_name = '../models/lstm_language_only_num_hidden_units_' + str(args.num_hidden_units) + '_num_lstm_layers_' + str(args.num_lstm_layers) + '_dropout_' + str(args.dropout)
open(model_file_name + '.json', 'w').write(json_string)
print 'Compiling model...'
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print 'Compilation done...'
#set up word vectors
# Code to choose the word vectors, default is Goldberg but GLOVE is preferred
if args.word_vector == 'glove':
nlp = spacy.load('en', vectors='en_glove_cc_300_1m_vectors')
else:
nlp = English()
print 'loaded ' + args.word_vector + ' word2vec features...'
## training
# Moved few variables to args.parser (num_epochs, batch_size, model_save_interval)
print 'Training started...'
for k in xrange(args.num_epochs):
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch,an_batch,im_batch in zip(grouper(questions_train, args.batch_size, fillvalue=questions_train[0]),
grouper(answers_train, args.batch_size, fillvalue=answers_train[0]),
grouper(images_train, args.batch_size, fillvalue=images_train[0])):
timesteps = len(nlp(qu_batch[-1])) #questions sorted in descending order of length
X_q_batch = get_questions_tensor_timeseries(qu_batch, nlp, timesteps)
Y_batch = get_answers_matrix(an_batch, labelencoder)
loss = model.train_on_batch(X_q_batch, Y_batch)
# fix for the Keras v0.3 issue #9
progbar.add(args.batch_size, values=[("train loss", loss[0])])
if k%args.model_save_interval == 0:
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k))
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k+1))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units_mlp', type=int, default=1024)
parser.add_argument('-num_hidden_units_lstm', type=int, default=512)
parser.add_argument('-num_hidden_layers_mlp', type=int, default=3)
parser.add_argument('-num_hidden_layers_lstm', type=int, default=1)
parser.add_argument('-dropout', type=float, default=0.5)
parser.add_argument('-activation_mlp', type=str, default='tanh')
parser.add_argument('-num_epochs', type=int, default=100)
parser.add_argument('-model_save_interval', type=int, default=5)
parser.add_argument('-batch_size', type=int, default=128)
#TODO Feature parser.add_argument('-resume_training', type=str)
#TODO Feature parser.add_argument('-language_only', type=bool, default= False)
args = parser.parse_args()
word_vec_dim = 300
img_dim = 4096
max_len = 30
nb_classes = 1000
#get the data
questions_train = open('../data/preprocessed/questions_train2014.txt',
'r').read().decode('utf8').splitlines()
questions_lengths_train = open(
'../data/preprocessed/questions_lengths_train2014.txt',
'r').read().decode('utf8').splitlines()
answers_train = open('../data/preprocessed/answers_train2014.txt',
'r').read().decode('utf8').splitlines()
images_train = open('../data/preprocessed/images_train2014.txt',
'r').read().decode('utf8').splitlines()
vgg_model_path = '../features/coco/vgg_feats.mat'
max_answers = nb_classes
questions_train, answers_train, images_train = selectFrequentAnswers(
questions_train, answers_train, images_train, max_answers)
questions_lengths_train, questions_train, answers_train, images_train = (
list(t) for t in zip(*sorted(
zip(questions_lengths_train, questions_train, answers_train,
images_train))))
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder, '../models/labelencoder.pkl')
image_model = Sequential()
image_model.add(Reshape(input_shape=(img_dim, ), dims=(img_dim, )))
language_model = Sequential()
if args.num_hidden_layers_lstm == 1:
language_model.add(
LSTM(output_dim=args.num_hidden_units_lstm,
return_sequences=False,
input_shape=(max_len, word_vec_dim)))
else:
language_model.add(
LSTM(output_dim=args.num_hidden_units_lstm,
return_sequences=True,
input_shape=(max_len, word_vec_dim)))
for i in xrange(args.num_hidden_layers_lstm - 2):
language_model.add(
LSTM(output_dim=args.num_hidden_units_lstm,
return_sequences=True))
language_model.add(
LSTM(output_dim=args.num_hidden_units_lstm,
return_sequences=False))
model = Sequential()
model.add(
Merge([language_model, image_model], mode='concat', concat_axis=1))
for i in xrange(args.num_hidden_layers_mlp):
model.add(Dense(args.num_hidden_units_mlp, init='uniform'))
model.add(Activation(args.activation_mlp))
model.add(Dropout(args.dropout))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
json_string = model.to_json()
model_file_name = '../models/lstm_1_num_hidden_units_lstm_' + str(args.num_hidden_units_lstm) + \
'_num_hidden_units_mlp_' + str(args.num_hidden_units_mlp) + '_num_hidden_layers_mlp_' + \
str(args.num_hidden_layers_mlp) + '_num_hidden_layers_lstm_' + str(args.num_hidden_layers_lstm)
open(model_file_name + '.json', 'w').write(json_string)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print 'Compilation done'
features_struct = scipy.io.loadmat(vgg_model_path)
VGGfeatures = features_struct['feats']
print 'loaded vgg features'
image_ids = open('../features/coco_vgg_IDMap.txt').read().splitlines()
img_map = {}
for ids in image_ids:
id_split = ids.split()
img_map[id_split[0]] = int(id_split[1])
nlp = English()
print 'loaded word2vec features...'
## training
print 'Training started...'
for k in xrange(args.num_epochs):
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch, an_batch, im_batch in zip(
grouper(questions_train,
args.batch_size,
fillvalue=questions_train[-1]),
grouper(answers_train,
args.batch_size,
fillvalue=answers_train[-1]),
grouper(images_train,
args.batch_size,
fillvalue=images_train[-1])):
timesteps = len(nlp(
qu_batch[-1])) #questions sorted in descending order of length
X_q_batch = get_questions_tensor_timeseries(
qu_batch, nlp, timesteps)
X_i_batch = get_images_matrix(im_batch, img_map, VGGfeatures)
Y_batch = get_answers_matrix(an_batch, labelencoder)
loss = model.train_on_batch([X_q_batch, X_i_batch], Y_batch)
progbar.add(args.batch_size, values=[("train loss", loss)])
if k % args.model_save_interval == 0:
model.save_weights(model_file_name +
'_epoch_{:03d}.hdf5'.format(k))
model.save_weights(model_file_name + '_epoch_{:03d}.hdf5'.format(k))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units', type=int, default=512)
parser.add_argument('-num_lstm_layers', type=int, default=2)
parser.add_argument('-dropout', type=float, default=0.2)
parser.add_argument('-activation', type=str, default='tanh')
args = parser.parse_args()
questions_train = open('../data/preprocessed/questions_train2014.txt',
'r').read().decode('utf8').splitlines()
questions_lengths_train = open(
'../data/preprocessed/questions_lengths_train2014.txt',
'r').read().decode('utf8').splitlines()
answers_train = open('../data/preprocessed/answers_train2014.txt',
'r').read().decode('utf8').splitlines()
images_train = open('../data/preprocessed/images_train2014.txt',
'r').read().decode('utf8').splitlines()
max_answers = 1000
questions_train, answers_train, images_train = selectFrequentAnswers(
questions_train, answers_train, images_train, max_answers)
print 'Loaded questions, sorting by length...'
questions_lengths_train, questions_train, answers_train = (
list(t) for t in zip(*sorted(
zip(questions_lengths_train, questions_train, answers_train))))
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder, '../models/labelencoder.pkl')
max_len = 30 #25 is max for training, 27 is max for validation
word_vec_dim = 300
model = Sequential()
model.add(
LSTM(output_dim=args.num_hidden_units,
activation='tanh',
return_sequences=True,
input_shape=(max_len, word_vec_dim)))
model.add(Dropout(args.dropout))
model.add(LSTM(args.num_hidden_units, return_sequences=False))
model.add(Dense(nb_classes, init='uniform'))
model.add(Activation('softmax'))
json_string = model.to_json()
model_file_name = '../models/lstm_language_only_num_hidden_units_' + str(
args.num_hidden_units) + '_num_lstm_layers_' + str(
args.num_lstm_layers) + '_dropout_' + str(args.dropout)
open(model_file_name + '.json', 'w').write(json_string)
print 'Compiling model...'
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print 'Compilation done...'
#set up word vectors
nlp = English()
print 'loaded word2vec features...'
## training
print 'Training started...'
numEpochs = 100
model_save_interval = 5
batchSize = 128
for k in xrange(numEpochs):
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch, an_batch, im_batch in zip(
grouper(questions_train,
batchSize,
fillvalue=questions_train[0]),
grouper(answers_train, batchSize, fillvalue=answers_train[0]),
grouper(images_train, batchSize, fillvalue=images_train[0])):
timesteps = len(nlp(
qu_batch[-1])) #questions sorted in descending order of length
X_q_batch = get_questions_tensor_timeseries(
qu_batch, nlp, timesteps)
Y_batch = get_answers_matrix(an_batch, labelencoder)
loss = model.train_on_batch(X_q_batch, Y_batch)
progbar.add(batchSize, values=[("train loss", loss)])
if k % model_save_interval == 0:
model.save_weights(model_file_name +
'_epoch_{:02d}.hdf5'.format(k))
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k + 1))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units', type=int, default=1024)
parser.add_argument('-num_hidden_layers', type=int, default=3)
parser.add_argument('-dropout', type=float, default=0.5)
parser.add_argument('-activation', type=str, default='tanh')
parser.add_argument('-language_only', type=bool, default=False)
parser.add_argument('-num_epochs', type=int, default=10)
parser.add_argument('-model_save_interval', type=int, default=10)
parser.add_argument('-batch_size', type=int, default=128)
args = parser.parse_args()
questions_train = open('../data/preprocessed/questions_train2014.txt',
'r').read().decode('utf8').splitlines()
answers_train = open('../data/preprocessed/answers_train2014_modal.txt',
'r').read().decode('utf8').splitlines()
images_train = open('../data/preprocessed/images_train2014.txt',
'r').read().decode('utf8').splitlines()
vgg_model_path = '../features/coco/vgg_feats.mat'
maxAnswers = 1000
questions_train, answers_train, images_train = selectFrequentAnswers(
questions_train, answers_train, images_train, maxAnswers)
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder, '../models/labelencoder.pkl')
features_struct = scipy.io.loadmat(vgg_model_path)
VGGfeatures = features_struct['feats']
print 'loaded vgg features'
image_ids = open('../features/coco_vgg_IDMap.txt').read().splitlines()
id_map = {}
for ids in image_ids:
id_split = ids.split()
id_map[id_split[0]] = int(id_split[1])
nlp = English()
print 'loaded word2vec features...'
img_dim = 4096
word_vec_dim = 300
model = Sequential()
if args.language_only:
model.add(
Dense(args.num_hidden_units,
input_dim=word_vec_dim,
init='uniform'))
else:
model.add(
Dense(args.num_hidden_units,
input_dim=img_dim + word_vec_dim,
init='uniform'))
model.add(Activation(args.activation))
if args.dropout > 0:
model.add(Dropout(args.dropout))
for i in xrange(args.num_hidden_layers - 1):
model.add(Dense(args.num_hidden_units, init='uniform'))
model.add(Activation(args.activation))
if args.dropout > 0:
model.add(Dropout(args.dropout))
model.add(Dense(nb_classes, init='uniform'))
model.add(Activation('softmax'))
json_string = model.to_json()
if args.language_only:
model_file_name = '../models/mlp_language_only_num_hidden_units_' + str(
args.num_hidden_units) + '_num_hidden_layers_' + str(
args.num_hidden_layers)
else:
model_file_name = '../models/mlp_num_hidden_units_' + str(
args.num_hidden_units) + '_num_hidden_layers_' + str(
args.num_hidden_layers)
open(model_file_name + '.json', 'w').write(json_string)
print 'Compiling model...'
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print 'Compilation done...'
print 'Training started...'
for k in xrange(args.num_epochs):
#shuffle the data points before going through them
index_shuf = range(len(questions_train))
shuffle(index_shuf)
questions_train = [questions_train[i] for i in index_shuf]
answers_train = [answers_train[i] for i in index_shuf]
images_train = [images_train[i] for i in index_shuf]
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch, an_batch, im_batch in zip(
grouper(questions_train,
args.batch_size,
fillvalue=questions_train[-1]),
grouper(answers_train,
args.batch_size,
fillvalue=answers_train[-1]),
grouper(images_train,
args.batch_size,
fillvalue=images_train[-1])):
X_q_batch = get_questions_matrix_sum(qu_batch, nlp)
if args.language_only:
X_batch = X_q_batch
else:
X_i_batch = get_images_matrix(im_batch, id_map, VGGfeatures)
X_batch = np.hstack((X_q_batch, X_i_batch))
Y_batch = get_answers_matrix(an_batch, labelencoder)
loss = model.train_on_batch(X_batch, Y_batch)
progbar.add(args.batch_size, values=[("train loss", loss)])
#print type(loss)
if k % args.model_save_interval == 0:
model.save_weights(model_file_name +
'_epoch_{:02d}.hdf5'.format(k))
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units', type=int, default=1024)
parser.add_argument('-num_hidden_layers', type=int, default=3)
parser.add_argument('-dropout', type=float, default=0.5)
parser.add_argument('-activation', type=str, default='tanh')
parser.add_argument('-language_only', type=bool, default= False)
parser.add_argument('-num_epochs', type=int, default=100)
parser.add_argument('-model_save_interval', type=int, default=10)
parser.add_argument('-batch_size', type=int, default=128)
parser.add_argument('-word_vector', type=str, default='')
args = parser.parse_args()
questions_train = open('../data/preprocessed/questions_train2014.txt', 'r').read().decode('utf8').splitlines()
answers_train = open('../data/preprocessed/answers_train2014_modal.txt', 'r').read().decode('utf8').splitlines()
images_train = open('../data/preprocessed/images_train2014.txt', 'r').read().decode('utf8').splitlines()
vgg_model_path = '../features/coco/vgg_feats.mat'
maxAnswers = 1000
questions_train, answers_train, images_train = selectFrequentAnswers(questions_train,answers_train,images_train, maxAnswers)
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder,'../models/labelencoder.pkl')
features_struct = scipy.io.loadmat(vgg_model_path)
VGGfeatures = features_struct['feats']
print 'loaded vgg features'
image_ids = open('../features/coco_vgg_IDMap.txt').read().splitlines()
id_map = {}
for ids in image_ids:
id_split = ids.split()
id_map[id_split[0]] = int(id_split[1])
# Code to choose the word vectors, default is Goldberg but GLOVE is preferred
if args.word_vector == 'glove':
nlp = spacy.load('en', vectors='en_glove_cc_300_1m_vectors')
else:
nlp = English()
print 'loaded ' + args.word_vector + ' word2vec features...'
img_dim = 4096
word_vec_dim = 300
model = Sequential()
if args.language_only:
model.add(Dense(args.num_hidden_units, input_dim=word_vec_dim, init='uniform'))
else:
model.add(Dense(args.num_hidden_units, input_dim=img_dim+word_vec_dim, init='uniform'))
model.add(Activation(args.activation))
if args.dropout>0:
model.add(Dropout(args.dropout))
for i in xrange(args.num_hidden_layers-1):
model.add(Dense(args.num_hidden_units, init='uniform'))
model.add(Activation(args.activation))
if args.dropout>0:
model.add(Dropout(args.dropout))
model.add(Dense(nb_classes, init='uniform'))
model.add(Activation('softmax'))
json_string = model.to_json()
if args.language_only:
model_file_name = '../models/mlp_language_only_num_hidden_units_' + str(args.num_hidden_units) + '_num_hidden_layers_' + str(args.num_hidden_layers)
else:
model_file_name = '../models/mlp_num_hidden_units_' + str(args.num_hidden_units) + '_num_hidden_layers_' + str(args.num_hidden_layers)
open(model_file_name + '.json', 'w').write(json_string)
print 'Compiling model...'
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print 'Compilation done...'
print 'Training started...'
for k in xrange(args.num_epochs):
#shuffle the data points before going through them
index_shuf = range(len(questions_train))
shuffle(index_shuf)
questions_train = [questions_train[i] for i in index_shuf]
answers_train = [answers_train[i] for i in index_shuf]
images_train = [images_train[i] for i in index_shuf]
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch,an_batch,im_batch in zip(grouper(questions_train, args.batch_size, fillvalue=questions_train[-1]),
grouper(answers_train, args.batch_size, fillvalue=answers_train[-1]),
grouper(images_train, args.batch_size, fillvalue=images_train[-1])):
X_q_batch = get_questions_matrix_sum(qu_batch, nlp)
if args.language_only:
X_batch = X_q_batch
else:
X_i_batch = get_images_matrix(im_batch, id_map, VGGfeatures)
X_batch = np.hstack((X_q_batch, X_i_batch))
Y_batch = get_answers_matrix(an_batch, labelencoder)
loss = model.train_on_batch(X_batch, Y_batch)
# fix for the Keras v0.3 issue #9
progbar.add(args.batch_size, values=[("train loss", loss[0])])
#print type(loss)
if k%args.model_save_interval == 0:
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k))
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k))
def main():
cwd = os.getcwd()
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units', type=int, default=1024)
parser.add_argument('-num_hidden_layers', type=int, default=3)
parser.add_argument('-dropout', type=float, default=0.5)
parser.add_argument('-activation', type=str, default='tanh')
parser.add_argument('-language_only', type=bool, default= False)
parser.add_argument('-num_epochs', type=int, default=2)
parser.add_argument('-model_save_interval', type=int, default=10)
parser.add_argument('-model_weights_path', type=str, default=cwd+'/vgg/vgg16_weights.h5')
parser.add_argument('-batch_size', type=int, default=128)
parser.add_argument('-questions_train',type=str, default = cwd+'/data/preprocessed/questions_train2015.txt')
parser.add_argument('-answers_train',type=str, default = cwd+'/data/preprocessed/answers_train2015_modal.txt')
parser.add_argument('-im_dir',type=str, default =cwd+'/data/preprocessed/scene_img_abstract_v002_train2015/')
#parser.add_argument('-questions_train',type=str, default = cwd+'/data/preprocessed/questions_train2014.txt')
args = parser.parse_args()
questions_train = open(args.questions_train, 'r').read().decode('utf8').splitlines()
answers_train = open(args.answers_train, 'r').read().decode('utf8').splitlines()
images_train = open(cwd+'/data/preprocessed/images_train2015.txt', 'r').read().decode('utf8').splitlines()
#vgg_model_path = cwd+'/features/coco/vgg_feats.mat' #this needs to change
maxAnswers = 100
questions_train, answers_train, images_train = selectFrequentAnswers(questions_train,answers_train,images_train, maxAnswers)
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder,cwd+'/models/labelencoder.pkl')
#features_struct = scipy.io.loadmat(vgg_model_path)
#VGGfeatures = features_struct['feats']
# print 'loaded vgg features'
# image_ids = open(cwd+'/features/coco_vgg_IDMap.txt').read().splitlines()
# id_map = {}
# for ids in image_ids:
# id_split = ids.split()
# id_map[id_split[0]] = int(id_split[1])
vgg_model = vgg16.VGG_16(args.model_weights_path)
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
vgg_model.compile(optimizer=sgd, loss='categorical_crossentropy')
print 'loaded vgg model...'
nlp = English()
print 'loaded word2vec features...'
img_dim = 4096
word_vec_dim = 300
model = Sequential()
if args.language_only:
model.add(Dense(args.num_hidden_units, input_dim=word_vec_dim, init='uniform'))
else:
model.add(Dense(args.num_hidden_units, input_dim=img_dim+word_vec_dim, init='uniform'))
model.add(Activation(args.activation))
if args.dropout>0:
model.add(Dropout(args.dropout))
for i in xrange(args.num_hidden_layers-1):
model.add(Dense(args.num_hidden_units, init='uniform'))
model.add(Activation(args.activation))
if args.dropout>0:
model.add(Dropout(args.dropout))
model.add(Dense(nb_classes, init='uniform'))
model.add(Activation('softmax'))
json_string = model.to_json()
model_file_name = cwd+'/models/mlp_num_hidden_units_' + str(args.num_hidden_units) + '_num_hidden_layers_' + str(args.num_hidden_layers)
open(model_file_name + '.json', 'w').write(json_string)
print 'Training started...'
id_map = {}
f1 = open('abstract_image_precompute')
f2 = open('abstract_image_precompute_reverse')
VGGfeatures = np.loadtxt(f1)
VGGfeatures_reverse = np.loadtxt(f2)
f1.close()
f2.close()
for k in xrange(args.num_epochs):
#shuffle the data points before going through them
index_shuf = range(len(questions_train))
shuffle(index_shuf)
questions_train = [questions_train[i] for i in index_shuf]
answers_train = [answers_train[i] for i in index_shuf]
images_train = [images_train[i] for i in index_shuf]
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch,an_batch,im_batch in zip(grouper(questions_train, args.batch_size, fillvalue=questions_train[-1]),
grouper(answers_train, args.batch_size, fillvalue=answers_train[-1]),
grouper(images_train, args.batch_size, fillvalue=images_train[-1])):
X_q_batch = get_questions_matrix_sum(qu_batch, nlp)
im_path = args.im_dir +"abstract_v002_train2015_"
print 'getting image features...'
X_i_batch = get_images_matrix(im_batch, VGGfeatures, VGGfeatures_reverse)
# X_i_batch = get_images_matrix_from_model(vgg_model, im_batch, im_path, id_map)
X_batch = np.hstack((X_q_batch, X_i_batch))
Y_batch = get_answers_matrix(an_batch, labelencoder)
print 'running training on batch...'
loss = model.train_on_batch(X_batch, Y_batch)
progbar.add(args.batch_size, values=[("train loss", loss)])
if k%args.model_save_interval == 0:
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k))
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units_mlp', type=int, default=1024)
parser.add_argument('-num_hidden_units_lstm', type=int, default=512)
parser.add_argument('-num_hidden_layers_mlp', type=int, default=3)
parser.add_argument('-num_hidden_layers_lstm', type=int, default=1)
parser.add_argument('-dropout', type=float, default=0.5)
parser.add_argument('-activation_mlp', type=str, default='tanh')
parser.add_argument('-num_epochs', type=int, default=100)
parser.add_argument('-model_save_interval', type=int, default=5)
parser.add_argument('-batch_size', type=int, default=128)
parser.add_argument('-word_vector', type=str, default='')
#TODO Feature parser.add_argument('-resume_training', type=str)
#TODO Feature parser.add_argument('-language_only', type=bool, default= False)
args = parser.parse_args()
word_vec_dim= 300
img_dim = 4096
max_len = 30
nb_classes = 1000
#get the data
questions_train = open('../data/preprocessed/questions_train2014.txt', 'r').read().decode('utf8').splitlines()
questions_lengths_train = open('../data/preprocessed/questions_lengths_train2014.txt', 'r').read().decode('utf8').splitlines()
answers_train = open('../data/preprocessed/answers_train2014_modal.txt', 'r').read().decode('utf8').splitlines()
images_train = open('../data/preprocessed/images_train2014.txt', 'r').read().decode('utf8').splitlines()
vgg_model_path = '../features/coco/vgg_feats.mat'
max_answers = nb_classes
questions_train, answers_train, images_train = selectFrequentAnswers(questions_train,answers_train,images_train, max_answers)
questions_lengths_train, questions_train, answers_train, images_train = (list(t) for t in zip(*sorted(zip(questions_lengths_train, questions_train, answers_train, images_train))))
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder,'../models/labelencoder.pkl')
image_model = Sequential()
image_model.add(Reshape(input_shape = (img_dim,), dims=(img_dim,)))
language_model = Sequential()
if args.num_hidden_layers_lstm == 1:
language_model.add(LSTM(output_dim = args.num_hidden_units_lstm, return_sequences=False, input_shape=(max_len, word_vec_dim)))
else:
language_model.add(LSTM(output_dim = args.num_hidden_units_lstm, return_sequences=True, input_shape=(max_len, word_vec_dim)))
for i in xrange(args.num_hidden_layers_lstm-2):
language_model.add(LSTM(output_dim = args.num_hidden_units_lstm, return_sequences=True))
language_model.add(LSTM(output_dim = args.num_hidden_units_lstm, return_sequences=False))
model = Sequential()
model.add(Merge([language_model, image_model], mode='concat', concat_axis=1))
for i in xrange(args.num_hidden_layers_mlp):
model.add(Dense(args.num_hidden_units_mlp, init='uniform'))
model.add(Activation(args.activation_mlp))
model.add(Dropout(args.dropout))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
json_string = model.to_json()
model_file_name = '../models/lstm_1_num_hidden_units_lstm_' + str(args.num_hidden_units_lstm) + \
'_num_hidden_units_mlp_' + str(args.num_hidden_units_mlp) + '_num_hidden_layers_mlp_' + \
str(args.num_hidden_layers_mlp) + '_num_hidden_layers_lstm_' + str(args.num_hidden_layers_lstm)
open(model_file_name + '.json', 'w').write(json_string)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print 'Compilation done'
features_struct = scipy.io.loadmat(vgg_model_path)
VGGfeatures = features_struct['feats']
print 'loaded vgg features'
image_ids = open('../features/coco_vgg_IDMap.txt').read().splitlines()
img_map = {}
for ids in image_ids:
id_split = ids.split()
img_map[id_split[0]] = int(id_split[1])
# Code to choose the word vectors, default is Goldberg but GLOVE is preferred
if args.word_vector == 'glove':
nlp = spacy.load('en', vectors='en_glove_cc_300_1m_vectors')
else:
nlp = English()
print 'loaded ' + args.word_vector + ' word2vec features...'
## training
print 'Training started...'
for k in xrange(args.num_epochs):
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch,an_batch,im_batch in zip(grouper(questions_train, args.batch_size, fillvalue=questions_train[-1]),
grouper(answers_train, args.batch_size, fillvalue=answers_train[-1]),
grouper(images_train, args.batch_size, fillvalue=images_train[-1])):
timesteps = len(nlp(qu_batch[-1])) #questions sorted in descending order of length
X_q_batch = get_questions_tensor_timeseries(qu_batch, nlp, timesteps)
X_i_batch = get_images_matrix(im_batch, img_map, VGGfeatures)
Y_batch = get_answers_matrix(an_batch, labelencoder)
loss = model.train_on_batch([X_q_batch, X_i_batch], Y_batch)
# fix for the Keras v0.3 issue #9
progbar.add(args.batch_size, values=[("train loss", loss[0])])
if k%args.model_save_interval == 0:
model.save_weights(model_file_name + '_epoch_{:03d}.hdf5'.format(k))
model.save_weights(model_file_name + '_epoch_{:03d}.hdf5'.format(k))
parser.add_argument('-batch_size', type=int, default=128)
args = parser.parse_args()
questions_train = open('./data/questions_train2014.txt',
'r').read().decode('utf8').splitlines()
answers_train = open('./data/answers_train2014_modal.txt',
'r').read().decode('utf8').splitlines()
images_train = open('./data/images_train2014.txt',
'r').read().decode('utf8').splitlines()
logging.debug("Length of questions_train %d", len(questions_train))
logging.debug("Length of answers_train %d", len(answers_train))
logging.debug("Length of images_train %d", len(images_train))
maxAnswers = 1000
questions_train, answers_train, images_train = selectFrequentAnswers(
questions_train, answers_train, images_train, maxAnswers)
logging.debug("Length of the lists after select Frequent Answers")
logging.debug("Length of questions_train %d", len(questions_train))
logging.debug("Length of answers_train %d", len(answers_train))
logging.debug("Length of images_train %d", len(images_train))
# generates numerical labels for all the answers in answers_train.
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder, 'labelencoder.pkl')
# TODO Get vectors for each image from Sherlock and load them into an array here
image_ids = open("./id_map.txt").read().splitlines()
id_map = {}
def main():
print 'Train LSTM encoder + MLP decoder'
parser = argparse.ArgumentParser()
parser.add_argument('-num_hidden_units_mlp', type=int, default=1024)
parser.add_argument('-num_hidden_units_lstm', type=int, default=512)
parser.add_argument('-num_hidden_layers_mlp', type=int, default=3)
parser.add_argument('-num_hidden_layers_lstm', type=int, default=1)
parser.add_argument('-dropout', type=float, default=0.5)
parser.add_argument('-activation_mlp', type=str, default='tanh')
parser.add_argument('-num_epochs', type=int, default=100)
parser.add_argument('-model_save_interval', type=int, default=5)
parser.add_argument('-batch_size', type=int, default=4096)
parser.add_argument('-gap_layer_units', type = int, default = 1024)
#TODO Feature parser.add_argument('-resume_training', type=str)
#TODO Feature parser.add_argument('-language_only', type=bool, default= False)
args = parser.parse_args()
word_vec_dim= 300
img_dim = 4096
max_len = 30
nb_classes = 1000
#get the data
questions_train = open('../data/preprocessed/questions_train2014.txt', 'r').read().decode('utf8').splitlines()
questions_lengths_train = open('../data/preprocessed/questions_lengths_train2014.txt', 'r').read().decode('utf8').splitlines()
answers_train = open('../data/preprocessed/answers_train2014_modal.txt', 'r').read().decode('utf8').splitlines()
images_train = open('../data/preprocessed/images_train2014.txt', 'r').read().decode('utf8').splitlines()
answers_train_all = open('../data/preprocessed/answers_train2014_all.txt', 'r').read().decode('utf8').splitlines()
vgg_model_path = '../features/coco/vgg_feats.mat'
max_answers = nb_classes
questions_train, answers_train, answers_train_all, images_train = selectFrequentAnswers(questions_train,answers_train, answers_train_all, images_train, max_answers)
questions_lengths_train, questions_train, answers_train, answers_train_all, images_train = (list(t) for t in zip(*sorted(zip(questions_lengths_train, questions_train, answers_train, answers_train_all, images_train))))
questions_val = open('../data/preprocessed/questions_val2014.txt', 'r').read().decode('utf8').splitlines()
questions_lengths_val = open('../data/preprocessed/questions_lengths_val2014.txt', 'r').read().decode('utf8').splitlines()
answers_val = open('../data/preprocessed/answers_val2014_all.txt', 'r').read().decode('utf8').splitlines()
images_val = open('../data/preprocessed/images_val2014_all.txt', 'r').read().decode('utf8').splitlines()
vgg_model_path = '../features/coco/vgg_feats.mat'
questions_lengths_val, questions_val, answers_val, images_val = (list(t) for t in zip(*sorted(zip(questions_lengths_val, questions_val, answers_val, images_val))))
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder,'../models/labelencoder.pkl')
image_input = Input(shape = (img_dim, ), name = 'image_input')
# image_gap = Dense(args.gap_layer_units, activation = args.activation_mlp)(image_input)
language_input = Input(shape = (None, word_vec_dim), name = 'language_input')
lstm_out = LSTM(args.num_hidden_units_lstm)(language_input)
# lstm_gap = Dense(args.gap_layer_units, activation = args.activation_mlp)(lstm_out)
# x = keras.layers.concatenate([lstm_gap, image_gap])
x = keras.layers.concatenate([lstm_out, image_input])
x = Dense(1024, activation = args.activation_mlp)(x)
x = Dropout(args.dropout)(x)
x = Dense(512, activation = args.activation_mlp)(x)
x = Dropout(args.dropout)(x)
x = Dense(256, activation = args.activation_mlp)(x)
x = Dropout(args.dropout)(x)
main_output = Dense(nb_classes, activation = 'softmax', name = 'main_output')(x)
model = Model(inputs = [language_input, image_input], outputs = [main_output])
# args.model = '../models/lstm_1_num_hidden_units_lstm_512_num_hidden_units_mlp_1024_num_hidden_layers_mlp_3_num_hidden_layers_lstm_1.json'
# args.weights = '../models/lstm_1_num_hidden_units_lstm_512_num_hidden_units_mlp_1024_num_hidden_layers_mlp_3_num_hidden_layers_lstm_1_epoch_100.hdf5'
# model = model_from_json(open(args.model).read())
# model.load_weights(args.weights)
json_string = model.to_json()
model_file_name = '../models/lstm_1_num_hidden_units_lstm_' + str(args.num_hidden_units_lstm) + \
'_num_hidden_units_mlp_' + str(args.num_hidden_units_mlp) + '_num_hidden_layers_mlp_' + \
str(args.num_hidden_layers_mlp) + '_num_hidden_layers_lstm_' + str(args.num_hidden_layers_lstm)
results_path = '../results/lstm_decoder_1_num_hidden_units_lstm_' + str(args.num_hidden_units_lstm) + \
'_num_hidden_units_mlp_' + str(args.num_hidden_units_mlp) + '_num_hidden_layers_mlp_' + \
str(args.num_hidden_layers_mlp) + '_num_hidden_layers_lstm_' + str(args.num_hidden_layers_lstm)
open(model_file_name + '.json', 'w').write(json_string)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print 'Compilation done'
features_struct = scipy.io.loadmat(vgg_model_path)
VGGfeatures = features_struct['feats']
print 'loaded vgg features'
image_ids = open('../features/coco_vgg_IDMap.txt').read().splitlines()
img_map = {}
for ids in image_ids:
id_split = ids.split()
img_map[id_split[0]] = int(id_split[1])
nlp = English()
print 'loaded word2vec features...'
## training
print 'Training started...'
Acc_train = [0] * args.num_epochs
Acc_val = [0] * args.num_epochs
loss_list = [0] * args.num_epochs
f1 = open('../results/loss_accuracy_lstm_encoder.txt', 'a')
f1.write(model_file_name + '\n')
for k in xrange(args.num_epochs):
print str(400 + k + 1) + 'th Iteration ...'
progbar = generic_utils.Progbar(len(questions_train))
loss_sum = 0
it = 0
for qu_batch,an_batch,im_batch in zip(grouper(questions_train, args.batch_size, fillvalue=questions_train[-1]),
grouper(answers_train, args.batch_size, fillvalue=answers_train[-1]),
grouper(images_train, args.batch_size, fillvalue=images_train[-1])):
timesteps = len(nlp(qu_batch[-1])) #questions sorted in descending order of length
X_q_batch = get_questions_tensor_timeseries(qu_batch, nlp, timesteps)
X_i_batch = get_images_matrix(im_batch, img_map, VGGfeatures)
X_i_batch_normalized = preprocessing.normalize(X_i_batch, norm='l2')
#print X_i_batch.shape, X_q_batch.shape
Y_batch = get_answers_matrix(an_batch, labelencoder)
loss = model.train_on_batch([X_q_batch, X_i_batch_normalized], Y_batch)
progbar.add(args.batch_size, values=[("train loss", loss)])
it += 1
loss_sum += loss
print " " + str(loss_sum / float(it))
if (k + 1)%args.model_save_interval == 0:
model.save_weights(model_file_name + '_epoch_{:03d}.hdf5'.format(k + 1))
loss_list[k] = loss_sum /float(it)
f1.write(str(loss_list[k]) + ' ')
# print ' Results on Training set: '
# Acc_train[k] = Validation_LSTM_encoder(model, questions_train, answers_train_all, images_train, img_map ,VGGfeatures, labelencoder, \
# args.batch_size, nlp, nb_classes, results_path+'_train', model_file_name+'_train')
print ' Results on Validation set: '
Acc_val[k] = Validation_LSTM_encoder(model, questions_val, answers_val, images_val, img_map ,VGGfeatures, labelencoder, \
args.batch_size, nlp, nb_classes, results_path, model_file_name)
f1.write(str(Acc_val[k]) + '\n')
f1.close()
plt.figure(1)
plt.xlabel('Iterations')
plt.ylabel('Accuracy')
plt.title('Accuracy on Training and Validation set')
# plt.plot(range(args.num_epochs), Acc_train, 'b-', label = 'Accuracy on Training set')
# plt.hold(True)
plt.plot(range(args.num_epochs), Acc_val, 'r--', label = 'Accuracy on Validation set')
plt.legend(loc = 'lower right')
plt.savefig('accuracy_train_val.png')
plt.figure(2)
plt.xlabel('Iterations')
plt.ylabel('Loss')
plt.title('Convergence curve')
plt.plot(range(args.num_epochs), loss_list, 'r--')
plt.savefig('Convergence_curve.png')
model.save_weights(model_file_name + '_epoch_{:03d}.hdf5'.format(k+1))
def main():
print 'Train MLP'
parser = argparse.ArgumentParser()
parser.add_argument('-featureType', type=str,
default='BoW') #BoW, WordsGlove, SentGlove
parser.add_argument('-num_hidden_units', type=int, default=1024)
parser.add_argument('-num_hidden_layers', type=int, default=3)
parser.add_argument('-dropout', type=float, default=0.5)
parser.add_argument('-activation', type=str, default='tanh')
parser.add_argument('-language_only', type=bool, default=False)
parser.add_argument('-num_epochs', type=int, default=2000)
parser.add_argument('-model_save_interval', type=int, default=10)
parser.add_argument('-batch_size', type=int, default=2048)
parser.add_argument('-num_top_all_words', type=int, default=1000)
parser.add_argument('-num_top_start_words', type=int, default=10)
parser.add_argument('-num_start_words', type=int, default=3)
args = parser.parse_args()
questions_train = open('../data/preprocessed/questions_train2014.txt',
'r').read().decode('utf8').splitlines()
answers_train = open('../data/preprocessed/answers_train2014_modal.txt',
'r').read().decode('utf8').splitlines()
answers_train_all = open('../data/preprocessed/answers_train2014_all.txt',
'r').read().decode('utf8').splitlines()
images_train = open('../data/preprocessed/images_train2014.txt',
'r').read().decode('utf8').splitlines()
vgg_model_path = '../features/coco/vgg_feats.mat'
maxAnswers = 1000
questions_train, answers_train, answers_train_all, images_train = selectFrequentAnswers(
questions_train, answers_train, answers_train_all, images_train,
maxAnswers)
# print [answers_train.count(answers_train[i]) for i in range(1000)]
print max([answers_train.count(answers_train[i]) for i in range(1000)])
print min([answers_train.count(answers_train[i]) for i in range(1000)])
print np.mean([answers_train.count(answers_train[i]) for i in range(1000)])
questions_val = open('../data/preprocessed/questions_val2014.txt',
'r').read().decode('utf8').splitlines()
questions_lengths_val = open(
'../data/preprocessed/questions_lengths_val2014.txt',
'r').read().decode('utf8').splitlines()
answers_val = open('../data/preprocessed/answers_val2014_all.txt',
'r').read().decode('utf8').splitlines()
images_val = open('../data/preprocessed/images_val2014_all.txt',
'r').read().decode('utf8').splitlines()
vgg_model_path = '../features/coco/vgg_feats.mat'
#encode the remaining answers
labelencoder = preprocessing.LabelEncoder()
labelencoder.fit(answers_train)
nb_classes = len(list(labelencoder.classes_))
joblib.dump(labelencoder, '../models/labelencoder.pkl')
features_struct = scipy.io.loadmat(vgg_model_path)
VGGfeatures = features_struct['feats']
print 'loaded vgg features'
image_ids = open('../features/coco_vgg_IDMap.txt').read().splitlines()
img_map = {}
for ids in image_ids:
id_split = ids.split()
img_map[id_split[0]] = int(id_split[1])
if args.featureType == 'WordsGlove' or args.featureType == 'SentGlove':
nlp = English()
print 'loaded word2vec features'
elif args.featureType == 'BoW':
num_top_all_words = args.num_top_all_words
num_top_start_words = args.num_top_start_words
num_start_words = args.num_start_words
train_question_file = '../data/preprocessed/questions_train2014.txt'
vectorizers_list = computeBoWfeatures(num_top_all_words,
num_top_start_words,
num_start_words,
train_question_file)
# print len(vectorizers_list)
print 'computed BoW features'
img_dim = 4096
if args.featureType == 'WordsGlove' or args.featureType == 'SentGlove':
word_vec_dim = 300
elif args.featureType == 'BoW':
word_vec_dim = num_top_all_words + num_top_start_words * num_start_words
model = Sequential()
if args.language_only:
model.add(Dense(args.num_hidden_units, input_dim=word_vec_dim))
else:
model.add(
Dense(1024,
activation=args.activation,
input_dim=img_dim + word_vec_dim))
model.add(Dropout(args.dropout))
model.add(Dense(1024, activation=args.activation))
model.add(Dropout(args.dropout))
model.add(Dense(1024, activation=args.activation))
model.add(Dropout(args.dropout))
# for i in xrange(args.num_hidden_layers - 1):
# model.add(Dense(args.num_hidden_units, activation = args.activation))
# if args.dropout > 0:
# model.add(Dropout(args.dropout))
model.add(Dense(nb_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
print 'Compilation done...'
json_string = model.to_json()
if args.language_only:
model_file_name = '../models/' + args.featureType + '_mlp_language_only_num_hidden_units_' \
+ str(args.num_hidden_units) + '_num_hidden_layers_' + str(args.num_hidden_layers)
else:
model_file_name = '../models/' + args.featureType + '_mlp_num_hidden_units_' \
+ str(args.num_hidden_units) + '_num_hidden_layers_' + str(args.num_hidden_layers)
if args.language_only:
results_path = '../results/' + args.featureType + '_mlp_language_only_num_hidden_units_' \
+ str(args.num_hidden_units) + '_num_hidden_layers_' + str(args.num_hidden_layers)
else:
results_path = '../results/' + args.featureType + '_mlp_num_hidden_units_' \
+ str(args.num_hidden_units) + '_num_hidden_layers_' + str(args.num_hidden_layers)
open(model_file_name + '.json', 'w').write(json_string)
Acc_train = [0] * args.num_epochs
Acc_val = [0] * args.num_epochs
loss_list = [0] * args.num_epochs
index_shuf = range(len(questions_train))
shuffle(index_shuf)
questions_train = [questions_train[i] for i in index_shuf]
answers_train_all = [answers_train_all[i] for i in index_shuf]
answers_train = [answers_train[i] for i in index_shuf]
images_train = [images_train[i] for i in index_shuf]
print 'Training started...'
f1 = open('../results/loss_accuracy_mlp' + args.featureType + '.txt', 'a')
f1.write(model_file_name + '\n')
for k in xrange(args.num_epochs):
print str(k + 1) + 'th Iteration'
#shuffle the data points before going through them
progbar = generic_utils.Progbar(len(questions_train))
for qu_batch, an_batch, im_batch in zip(
grouper(questions_train,
args.batch_size,
fillvalue=questions_train[-1]),
grouper(answers_train,
args.batch_size,
fillvalue=answers_train[-1]),
grouper(images_train,
args.batch_size,
fillvalue=images_train[-1])):
if args.featureType == 'WordsGlove':
X_q_batch = get_questions_matrix_sum(qu_batch, nlp)
elif args.featureType == 'SentGlove':
X_q_batch = get_questions_matrix_sentGlove(qu_batch, nlp)
elif args.featureType == 'BoW':
X_q_batch = get_questions_BoW(qu_batch, vectorizers_list)
# print np.shape(X_q_batch)
if args.language_only:
X_batch = X_q_batch
else:
X_i_batch = get_images_matrix(im_batch, img_map, VGGfeatures)
X_i_batch_normalized = preprocessing.normalize(X_i_batch,
norm='l2')
X_batch = np.hstack((X_q_batch, X_i_batch_normalized))
Y_batch = get_answers_matrix(an_batch, labelencoder)
loss = model.train_on_batch(X_batch, Y_batch)
progbar.add(args.batch_size, values=[("train loss", loss)])
if (k + 1) % args.model_save_interval == 0:
model.save_weights(model_file_name +
'_epoch_{:02d}.hdf5'.format(k + 1))
loss_list[k] = loss
f1.write(str(loss_list[k]) + ' ')
# print ' Results on Training set: '
# Acc_train[k] = ValidationMLP(k, model, questions_train, answers_train_all, images_train, img_map, VGGfeatures, labelencoder, \
# args.batch_size, nb_classes, results_path+'_train', model_file_name+'_train', args.featureType, args.language_only)
print ' Results on Validation set: '
Acc_val[k] = ValidationMLP(k, model, questions_val, answers_val, images_val, img_map, VGGfeatures, labelencoder, \
args.batch_size, nb_classes, results_path, model_file_name, args.featureType, args.language_only)
f1.write(str(Acc_val[k]) + '\n')
f1.close()
plt.figure(1)
plt.xlabel('Iterations')
plt.ylabel('Accuracy')
plt.title('Accuracy on Training and Validation set')
# plt.plot(range(args.num_epochs), Acc_train, 'b-', label = 'Accuracy on Training set')
# plt.hold(True)
plt.plot(range(args.num_epochs),
Acc_val,
'r--',
label='Accuracy on Validation set')
plt.legend(loc='lower right')
plt.savefig('../pic/accuracy_train_val' + args.featureType + '.png')
plt.figure(2)
plt.xlabel('Iterations')
plt.ylabel('Loss')
plt.title('Convergence curve')
plt.plot(range(args.num_epochs), loss_list, 'r--')
plt.savefig('../pic/Convergence_curve' + args.featureType + '.png')
model.save_weights(model_file_name + '_epoch_{:02d}.hdf5'.format(k + 1))
