i am working on project and occuring this traceback can anybody suggest me
what changes should i make in below section of code
for index in indices:
key = dataset_keys[index]
data_x = dataset[key]['features'][...].astype(_DTYPE)
L_distance_mat = cdist(data_x, data_x, 'euclidean')
L_dissim_mat = 1 - np.dot(data_x, data_x.T)
Traceback (most recent call last):
File "vsum_train.py", line 155, in <module>
train_dataset_path=args.dataset)
File "vsum_train.py", line 83, in train
key = dataset_keys[index]
TypeError: 'KeysView' object does not support indexing
--
---
You received this message because you are subscribed to the Google Groups
"theano-users" group.
To unsubscribe from this group and stop receiving emails from it, send an email
to [email protected].
For more options, visit https://groups.google.com/d/optout.
import theano
from theano import tensor as T
import theano_nets
from model_reinforceRNN import reinforceRNN
import numpy as np
from datetime import datetime
import time, math, os, sys, h5py, logging, vsum_tools, argparse
from scipy.spatial.distance import cdist
_DTYPE = theano.config.floatX
def train(n_episodes=5,
input_dim=1024,
hidden_dim=256,
W_init='normal',
U_init='normal',
weight_decay=1e-5,
regularizer='L2',
optimizer='adam',
base_lr=1e-5,
decay_rate=0.1,
max_epochs=60,
decay_stepsize=30,
ignore_distant_sim=True,
distant_sim_thre=20,
alpha=0.01,
model_file=None,
disp_freq=1,
train_dataset_path='datasets/eccv16_dataset_tvsum_google_pool5.h5',
):
model_options = locals().copy()
log_dir = 'log-train'
if not os.path.exists(log_dir):
os.mkdir(log_dir)
logging.basicConfig(
filename=log_dir+'/log.txt',
filemode='w',
format='%(asctime)s %(message)s',
datefmt='[%d/%m/%Y %I:%M:%S]',
level=logging.INFO
)
logger = logging.getLogger()
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter(fmt='%(asctime)s %(message)s',datefmt='[%d/%m/%Y %I:%M:%S]')
ch.setFormatter(formatter)
logger.addHandler(ch)
logger.info('model options: ' + str(model_options))
logger.info('initializing net model')
net = reinforceRNN(model_options)
if model_file is not None: logger.info('loaded model from %s' % model_file)
n_params = net.get_n_params()
logger.info('net params size is %d' % (n_params))
logger.info('loading training data from %s' % (train_dataset_path))
dataset = h5py.File(train_dataset_path, 'r')
dataset_keys = dataset.keys()
n_videos = len(dataset_keys)
logger.info('total number of videos for training is %d' % n_videos)
logger.info('=> training')
start_time = time.time()
blrwds = {name:np.array(0).astype(_DTYPE) for name in dataset_keys} # baseline rewards
for i_epoch in range(max_epochs):
indices = np.arange(n_videos)
np.random.shuffle(indices)
epoch_reward = 0.
if decay_stepsize != -1 and i_epoch >= decay_stepsize:
power_n = int(i_epoch/decay_stepsize)
learn_rate = base_lr * (decay_rate**power_n)
else:
learn_rate = base_lr
for index in indices:
key = dataset_keys[index]
data_x = dataset[key]['features'][...].astype(_DTYPE)
L_distance_mat = cdist(data_x, data_x, 'euclidean')
L_dissim_mat = 1 - np.dot(data_x, data_x.T)
if ignore_distant_sim:
inds = np.arange(data_x.shape[0])[:,None]
inds_dist = cdist(inds, inds, 'minkowski', 1)
L_dissim_mat[inds_dist > distant_sim_thre] = 1
rewards = net.model_train(data_x, learn_rate, L_dissim_mat, L_distance_mat, blrwds[key])
blrwds[key] = 0.9 * blrwds[key] + 0.1 * rewards.mean()
epoch_reward += rewards.mean()
epoch_reward /= n_videos
if (i_epoch+1) % disp_freq == 0 or (i_epoch+1) == max_epochs:
logger.info('epoch %3d/%d. reward %f.' % \
(i_epoch+1, max_epochs, epoch_reward))
elapsed_time = time.time() - start_time
logger.info('elapsed time %.2f s' % (elapsed_time))
net.save_net(save_dir=log_dir, model_name='model_reinforceRNN')
dataset.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--n-epi', type=int, default=5,
help="number of episodes for REINFORCE")
parser.add_argument('--input-dim', type=int, default=1024,
help="input dimension, i.e. dimension of CNN features")
parser.add_argument('--hidden-dim', type=int, default=256,
help="hidden dimension of RNN")
parser.add_argument('--W-init', type=str, default='normal', choices=theano_nets.init_choices(),
help="initialization method for non-recurrent weights")
parser.add_argument('--U-init', type=str, default='normal', choices=theano_nets.init_choices(),
help="initialization method for recurrent weights")
parser.add_argument('--weight-decay', type=float, default=1e-5,
help="coefficient for regularization on weight parameters")
parser.add_argument('--reg', type=str, default='L2', choices=theano_nets.reg_choices(),
help="regularizer for weight parameters")
parser.add_argument('--optim', type=str, default='adam', choices=theano_nets.optim_choices())
parser.add_argument('--base-lr', type=float, default=1e-5, help="base learning rate")
parser.add_argument('--decay-rate', type=float, default=0.1, help="learning rate decay")
parser.add_argument('--max-epochs', type=int, default=60, help="maximum training epochs")
parser.add_argument('--decay-stepsize', type=int, default=-1,
help="stepsize to decay learning rate, if -1, then learning rate decay is disabled")
parser.add_argument('--ignore-distant-sim', action='store_true',
help="whether to ignore the similarity between distant frames")
parser.add_argument('--distant-sim-thre', type=int, default=20,
help="threshold to ignore similarity between distant frames")
parser.add_argument('--alpha', type=float, default=0.01, help="coefficient for summary length penalty")
parser.add_argument('--disp-freq', type=int, default=1, help="display frequency")
parser.add_argument('--dataset', type=str, default='datasets/eccv16_dataset_summe_google_pool5.h5')
args = parser.parse_args()
train(n_episodes=args.n_epi,
input_dim=args.input_dim,
hidden_dim=args.hidden_dim,
W_init=args.W_init,
U_init=args.U_init,
weight_decay=args.weight_decay,
regularizer=args.reg,
optimizer=args.optim,
base_lr=args.base_lr,
decay_rate=args.decay_rate,
max_epochs=args.max_epochs,
decay_stepsize=args.decay_stepsize,
ignore_distant_sim=args.ignore_distant_sim,
distant_sim_thre=args.distant_sim_thre,
alpha=args.alpha,
disp_freq=args.disp_freq,
train_dataset_path=args.dataset)
