def train(label_set='full', pool_mode='sum', layer_sizes=[512, 256],
activation='prelu', drop_unk=False, word_vecs=None,
return_net=False, cv=10, val_split=0.00, label_unk=False):
print "Loading data..."
df = sentences_df(SENTENCES_CSV, labels=label_set, drop_unk=drop_unk)
X, y, word2idx, l_enc = load_dataset(df, ngram_order=1, pad=True)
print "X shape:", X.shape
y_orig = y
y_binary = to_categorical(y)
labels = np.unique(y_orig)
nb_labels = labels.shape[0]
if drop_unk:
label_set_str = label_set + ' (-unk)'
else:
label_set_str = label_set
print "Number of labels: %i [%s]" % (nb_labels, label_set_str)
if nb_labels > 2:
y = y_binary
maxlen = X.shape[1]
vocab_size = len(word2idx) + 1 # 0 masking
word_vectors = load_bin_vec(word_vecs, word2idx)
add_unknown_words(word_vectors, word2idx)
embedding_weights = np.zeros((vocab_size+1, emb_dim))
for word, index in word2idx.items():
embedding_weights[index,:] = word_vectors[word]
def build_model(maxlen,
vocab_size,
word2idx,
embeddings_path,
nb_labels,
sent_model='lstm',
ffweights=None):
# load word embeddings
word_vectors = load_bin_vec(embeddings_path, word2idx)
add_unknown_words(word_vectors, word2idx)
embedding_weights = np.zeros((vocab_size + 1, 300))
for word, index in word2idx.items():
embedding_weights[index, :] = word_vectors[word]
prem = Input(shape=(maxlen, ), dtype='int32')
hyp = Input(shape=(maxlen, ), dtype='int32')
if sent_model == 'lstm':
emb = Embedding(input_dim=vocab_size + 1,
output_dim=300,
input_length=maxlen,
weights=[embedding_weights])
prem_emb = emb(prem)
hyp_emb = emb(hyp)
encoder = LSTM(100, activation='relu', return_sequences=False)
prem_enc = encoder(prem_emb)
hyp_enc = encoder(hyp_emb)
else:
sentence_input = Input(shape=(maxlen, ), dtype='int32')
x = Embedding(input_dim=vocab_size + 1,
output_dim=300,
input_length=maxlen,
weights=[embedding_weights])
x = x(sentence_input)
if ffweights:
model.load_weights(ffweights)
prem_emb = x(prem)
hyp_emb = x(hyp)
prem_enc = ff(prem_emb)
hyp_enc = ff(hyp_emb)
merged = merge([prem_enc, hyp_enc], mode='concat', concat_axis=-1)
dropout = Dropout(0.5)(merged)
fc1 = Dense(200, activation='relu')(merged_sents)
fc2 = Dense(200, activation='relu')(fc1)
fc3 = Dense(200, activation='relu')(fc2)
probas = Dense(nb_labels, activation='softmax')(fc3)
model = Model(input=[prem, hyp], output=probas)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
def train(label_set='full', drop_unk=False,
word_vecs=None, setup_only=False, layer_sizes=[512,256],
pool_mode='sum'):
print "Loading data..."
df = sentences_df(SENTENCES_CSV, labels=label_set, drop_unk=drop_unk)
X, y, word2idx, l_enc = load_dataset(df, pad=True)
print "X shape:", X.shape
y_orig = y
y_binary = to_categorical(y)
labels = np.unique(y_orig)
nb_labels = labels.shape[0]
if drop_unk:
label_set_str = label_set + ' (-unk)'
else:
label_set_str = label_set
print "Number of labels: %i [%s]" % (nb_labels, label_set_str)
if nb_labels > 2:
y = y_binary
maxlen = X.shape[1]
vocab_size = len(word2idx) + 1 # 0 masking
if pretrained_embeddings is True:
word_vectors = load_bin_vec(word_vecs, word2idx)
add_unknown_words(word_vectors, word2idx)
embedding_weights = np.zeros((vocab_size+1, emb_dim))
for word, index in word2idx.items():
embedding_weights[index,:] = word_vectors[word]
else:
embedding_weights = None
print "Data loaded."
skf = StratifiedKFold(y_orig, n_folds=10, shuffle=True, random_state=0)
cv_scores = []
for i, (train, test) in enumerate(skf):
start_time = time.time()
nn = None
nn = EnsembleNN(vocab_size, nb_labels, emb_dim, maxlen,
embedding_weights, filter_hs, nb_filters,
dropout_p, trainable_embeddings, pretrained_embeddings,
layer_sizes, pool_mode)
if i == 0:
print_summary(nn.model.layers)
acc = train_and_test_model(nn, X[train], y[train], X[test], y[test],
batch_size, nb_epoch,
lr, beta_1, beta_2, epsilon)
cv_scores.append(acc)
train_time = time.time() - start_time
print('\nLabel frequencies in y[test]')
print_label_frequencies((y_orig[test], l_enc))
y_pred = nn.model.predict(X[test])
y_pred = probas_to_classes(y_pred)
c = Counter(y_pred)
total = float(len(y_pred))
print('\nLabel frequencies in predict(y[test])')
for label, count in c.most_common():
print l_enc.inverse_transform(label), count, count / total
print "fold %i/10 - time: %.2f s - acc: %.4f on %i samples" % \
(i+1, train_time, acc, len(test))
print "Avg cv accuracy: %.4f" % np.mean(cv_scores)
def setup_generation(word_vecs, model_weights=None, scene_model=False):
with open('../../models/l_enc.pkl') as p:
l_enc = pickle.load(p)
with open('../../models/word2idx.json') as j:
word2idx = json.load(j)
vocab_size = len(word2idx) + 1
max_caption_len = 40
word_vectors = load_bin_vec(word_vecs, word2idx)
add_unknown_words(word_vectors, word2idx)
embedding_weights = np.zeros((vocab_size,300))
for word, index in word2idx.items():
embedding_weights[index:,] = word_vectors[word]
model = build_model(vocab_size, max_caption_len, '../../vgg16_weights.h5',
embedding_weights, scene_model=scene_model)
if model_weights:
model.load_weights(model_weights)
return model, word2idx, l_enc
def setup_generation(word_vecs, model_weights=None, scene_model=False):
with open('../../models/l_enc.pkl') as p:
l_enc = pickle.load(p)
with open('../../models/word2idx.json') as j:
word2idx = json.load(j)
vocab_size = len(word2idx) + 1
max_caption_len = 40
word_vectors = load_bin_vec(word_vecs, word2idx)
add_unknown_words(word_vectors, word2idx)
embedding_weights = np.zeros((vocab_size, 300))
for word, index in word2idx.items():
embedding_weights[index:, ] = word_vectors[word]
model = build_model(vocab_size,
max_caption_len,
'../../vgg16_weights.h5',
embedding_weights,
scene_model=scene_model)
if model_weights:
model.load_weights(model_weights)
return model, word2idx, l_enc
next_words.append(indices[j + 1])
partial_captions = [
pc if len(pc) <= 40 else pc[:40] for pc in partial_captions
]
partial_captions = pad_sequences(partial_captions, padding='post')
print "pc shape", partial_captions.shape
print "imfiles shape", len(imfiles)
print "scenes shape", len(scenes)
print "next_words shape", len(next_words)
vocab_size = len(word2idx) + 1
max_caption_len = 40
word_vectors = load_bin_vec(word_vecs, word2idx)
add_unknown_words(word_vectors, word2idx)
embedding_weights = np.zeros((vocab_size, emb_dim))
for word, index in word2idx.items():
embedding_weights[index, :] = word_vectors[word]
model = build_model(vocab_size,
max_caption_len,
vggweights_path,
embedding_weights,
scene_model=scene_model)
if modelweights_path:
model.load_weights(modelweights_path)
for m in model.layers[0].layers:
print_summary(m.layers)
print_summary(model.layers)
def train(model_type='parallel',
label_set='full',
drop_unk=False,
word_vecs=None,
setup_only=False):
print "Loading data..."
df = sentences_df(SENTENCES_CSV, labels=label_set, drop_unk=drop_unk)
X, y, word2idx, l_enc = load_dataset(df, pad=True)
print "X shape:", X.shape
y_orig = y
y_binary = to_categorical(y)
labels = np.unique(y_orig)
nb_labels = labels.shape[0]
if drop_unk:
label_set_str = label_set + ' (-unk)'
else:
label_set_str = label_set
print "Number of labels: %i [%s]" % (nb_labels, label_set_str)
if nb_labels > 2:
y = y_binary
maxlen = X.shape[1]
vocab_size = len(word2idx) + 1 # 0 masking
if pretrained_embeddings is True:
word_vectors = load_bin_vec(word_vecs, word2idx)
add_unknown_words(word_vectors, word2idx)
embedding_weights = np.zeros((vocab_size + 1, emb_dim))
for word, index in word2idx.items():
embedding_weights[index, :] = word_vectors[word]
else:
embedding_weights = None
print "Data loaded."
if setup_only:
cnn = create_model(vocab_size,
nb_labels,
emb_dim,
maxlen,
embedding_weights,
filter_hs,
nb_filters,
dropout_p,
trainable_embeddings,
pretrained_embeddings,
model_type=model_type)
return {
'X': X,
'y': y,
'word2idx': word2idx,
'l_enc': l_enc,
'y_binary': y_binary,
'labels': labels,
'nb_labels': nb_labels,
'maxlen': maxlen,
'emb_dim': emb_dim,
'vocab_size': vocab_size,
'embedding_weights': embedding_weights,
'cnn': cnn
}
params = [('filter_hs', filter_hs), ('nb_filters', nb_filters),
('dropout_p', dropout_p),
('trainable_embeddings', trainable_embeddings),
('pretrained_embeddings', pretrained_embeddings),
('batch_size', batch_size), ('nb_epoch', nb_epoch), ('lr', lr),
('beta_1', beta_1), ('beta_2', beta_2), ('epsilon', epsilon)]
print "\nModel type: %s" % model_type
for (name, value) in params:
print name + ':', value
skf = StratifiedKFold(y_orig, n_folds=10, shuffle=True, random_state=0)
cv_scores = []
for i, (train, test) in enumerate(skf):
start_time = time.time()
cnn = None
cnn = create_model(vocab_size,
nb_labels,
emb_dim,
maxlen,
embedding_weights,
filter_hs,
nb_filters,
dropout_p,
trainable_embeddings,
pretrained_embeddings,
model_type=model_type)
if i == 0:
print_summary(cnn.model.layers)
acc = train_and_test_model(cnn, X[train], y[train], X[test], y[test],
batch_size, nb_epoch, lr, beta_1, beta_2,
epsilon)
cv_scores.append(acc)
train_time = time.time() - start_time
print('\nLabel frequencies in y[test]')
print_label_frequencies((y_orig[test], l_enc))
y_pred = cnn.model.predict(X[test])
y_pred = probas_to_classes(y_pred)
c = Counter(y_pred)
total = float(len(y_pred))
print('\nLabel frequencies in predict(y[test])')
for label, count in c.most_common():
print l_enc.inverse_transform(label), count, count / total
print "fold %i/10 - time: %.2f s - acc: %.4f on %i samples" % \
(i+1, train_time, acc, len(test))
print "Avg cv accuracy: %.4f" % np.mean(cv_scores)
Xdf['sentence'] = Xdf['sentence'].apply(lambda x: ' '.join(x.split()[:-1]))
ydf['sentence'] = ydf['sentence'].apply(lambda x: ' '.join(x.split()[1:]))
X_sents, _, _, _ = load_dataset(Xdf, pad=True, word2idx=word2idx)
y_sents, _, _, _ = load_dataset(ydf, pad=True, word2idx=word2idx)
scene_labels = Xdf.label
vocab_size = len(word2idx) + 1
max_caption_len = X_sents.shape[1]
ims_train, ims_test = ic[:4000], ic[4000:]
X_sents_train, y_sents_train = X_sents[:20000], y_sents[:20000]
X_sents_test, y_sents_test = X_sents[20000:], y_sents[20000:]
word_vectors = load_bin_vec(word_vecs, word2idx)
add_unknown_words(word_vectors, word2idx)
embedding_weights = np.zeros((vocab_size, emb_dim))
for word, index in word2idx.items():
embedding_weights[index,:] = word_vectors[word]
model = load_vgg_weights(build_model(vocab_size,
max_caption_len,
emb_dim,
embedding_weights),
weights_path)
print "VGG 16 weights loaded."
print_summary(model.layers)
samples_per_epoch = X_sents_train.shape[0]
checkpoint = ModelCheckpoint('../../weights.{epoch:02d}.h5')
def train(model_type='parallel', label_set='full', drop_unk=False,
word_vecs=None, setup_only=False):
print "Loading data..."
df = sentences_df(SENTENCES_CSV, labels=label_set, drop_unk=drop_unk)
X, y, word2idx, l_enc = load_dataset(df, pad=True)
print "X shape:", X.shape
y_orig = y
y_binary = to_categorical(y)
labels = np.unique(y_orig)
nb_labels = labels.shape[0]
if drop_unk:
label_set_str = label_set + ' (-unk)'
else:
label_set_str = label_set
print "Number of labels: %i [%s]" % (nb_labels, label_set_str)
if nb_labels > 2:
y = y_binary
maxlen = X.shape[1]
vocab_size = len(word2idx) + 1 # 0 masking
if pretrained_embeddings is True:
word_vectors = load_bin_vec(word_vecs, word2idx)
add_unknown_words(word_vectors, word2idx)
embedding_weights = np.zeros((vocab_size+1, emb_dim))
for word, index in word2idx.items():
embedding_weights[index,:] = word_vectors[word]
else:
embedding_weights = None
print "Data loaded."
if setup_only:
cnn = create_model(vocab_size, nb_labels, emb_dim, maxlen,
embedding_weights, filter_hs, nb_filters,
dropout_p, trainable_embeddings,
pretrained_embeddings, model_type=model_type)
return {'X': X,
'y': y,
'word2idx': word2idx,
'l_enc': l_enc,
'y_binary': y_binary,
'labels': labels,
'nb_labels': nb_labels,
'maxlen': maxlen,
'emb_dim': emb_dim,
'vocab_size': vocab_size,
'embedding_weights': embedding_weights,
'cnn': cnn}
params = [('filter_hs',filter_hs), ('nb_filters',nb_filters),
('dropout_p',dropout_p),
('trainable_embeddings',trainable_embeddings),
('pretrained_embeddings',pretrained_embeddings),
('batch_size',batch_size), ('nb_epoch',nb_epoch),
('lr',lr), ('beta_1',beta_1), ('beta_2',beta_2),
('epsilon',epsilon)]
print "\nModel type: %s" % model_type
for (name, value) in params:
print name + ':', value
skf = StratifiedKFold(y_orig, n_folds=10, shuffle=True, random_state=0)
cv_scores = []
for i, (train, test) in enumerate(skf):
start_time = time.time()
cnn = None
cnn = create_model(vocab_size,
nb_labels,
emb_dim,
maxlen,
embedding_weights,
filter_hs,
nb_filters,
dropout_p,
trainable_embeddings,
pretrained_embeddings,
model_type=model_type)
if i == 0:
print_summary(cnn.model.layers)
acc = train_and_test_model(cnn, X[train], y[train], X[test], y[test],
batch_size, nb_epoch,
lr, beta_1, beta_2, epsilon)
cv_scores.append(acc)
train_time = time.time() - start_time
print('\nLabel frequencies in y[test]')
print_label_frequencies((y_orig[test], l_enc))
y_pred = cnn.model.predict(X[test])
y_pred = probas_to_classes(y_pred)
c = Counter(y_pred)
total = float(len(y_pred))
print('\nLabel frequencies in predict(y[test])')
for label, count in c.most_common():
print l_enc.inverse_transform(label), count, count / total
print "fold %i/10 - time: %.2f s - acc: %.4f on %i samples" % \
(i+1, train_time, acc, len(test))
print "Avg cv accuracy: %.4f" % np.mean(cv_scores)