def get_captions(vgg,sess,images):
batch = 1
print os.getcwd()
gen_cap = load_model("../"+model_name)
features = get_features(vgg,sess,images)
comp_feats = comp_features(images,features)
x_ = reshapeInput(comp_feats, max_time_steps)
pred = gen_cap.predict(x_,batch_size=batch,verbose=1)
out_predictions = print_pred(pred, batch)
print out_predictions
print "Okay"
init_vars = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init_vars)
test_images = load_data(sys.argv[1])
images = pre_process(test_images)
vgg = vgg16(imgs, 'vgg16_weights.npz', sess)
print images.shape
it = 0
features = []
while (it + 40) <= images.shape[0]:
img1 = images[it:it + 40]
#print img1.shape
features.extend(get_features(vgg, sess, img1))
it += 40
features = np.asarray(features)
print features.shape
#features = get_features(vgg,sess,images)
comp_feats = comp_features(images, features)
x_ = comp_feats.astype('float64')
#x_ = reshapeInput(comp_feats, max_time_steps)
print(x_.shape)
vis_data = bh_sne(x_)
vis_x = vis_data[:]
vis_y = vis_data[:]
imgs = tf.placeholder(tf.float32, [None, img_size, img_size, img_color])
#___________________________________Load Data________________________________________#
print('Loading data...')
init_vars = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init_vars)
test_images = load_data(sys.argv[1])
images = pre_process(test_images)
vgg = vgg16(imgs, 'vgg16_weights.npz', sess)
features = get_features(vgg, sess, images)
comp_feats = comp_features(images, features)
x_train = reshapeInput(comp_feats, max_time_steps)
x_test = x_train
train_images = load_data(sys.argv[1])
y_train = load_labels(test_images, vocab_size, max_time_steps)
#y_train = sequence.pad_sequences(y_train, max_time_steps)
y_test = y_train
print(len(x_train), 'train sequences')
print(x_train.shape, 'train shape')