def __init__(self, dim_image, n_words, dim_hidden, batch_size, n_caption_steps,
n_video_steps, drop_out_rate, bias_init_vector=None):
self.dim_image = dim_image
self.n_words = n_words
self.dim_hidden = dim_hidden
self.batch_size = batch_size
self.n_caption_steps = n_caption_steps
self.drop_out_rate = drop_out_rate
self.n_video_steps = n_video_steps
with tf.device(cpu_device):
self.Wemb = tf.Variable(tf.random_uniform([n_words, dim_hidden], -0.1, 0.1), name='Wemb')
# decoding LSTM for sentence
self.lstm3 = tf.contrib.rnn.LSTMCell(self.dim_hidden, use_peepholes=True, state_is_tuple=True)
# decoding LSTM for video
self.lstm4 = tf.contrib.rnn.LSTMCell(self.dim_hidden, use_peepholes=True, state_is_tuple=True)
self.lstm3_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm3,output_keep_prob=1 - self.drop_out_rate)
self.lstm4_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm4,output_keep_prob=1 - self.drop_out_rate)
self.vae = VAE(self.dim_hidden * 2, self.dim_hidden)
self.encode_image_W = tf.Variable(tf.random_uniform([dim_video_feat, dim_hidden], -0.026, 0.026),name='encode_image_W')
self.encode_image_b = tf.Variable(tf.zeros([dim_hidden]), name='encode_image_b')
self.decode_image_W = tf.Variable(tf.random_uniform([dim_hidden, dim_image], -0.028, 0.028), name='decode_image_W')
self.decode_image_b = tf.Variable(tf.zeros([dim_image]), name='decode_image_b')
self.embed_word_W = tf.Variable(tf.random_uniform([dim_hidden, n_words], -0.1,0.1), name='embed_word_W')
self.sent_emb = tf.Variable(tf.random_uniform([n_words, dim_hidden], -0.1, 0.1), name='sent_emb')
if bias_init_vector is not None:
self.embed_word_b = tf.Variable(bias_init_vector.astype(np.float32), name='embed_word_b')
else:
self.embed_word_b = tf.Variable(tf.zeros([n_words]), name='embed_word_b')
self.loc_matrix = tf.Variable(np.identity(n_words), dtype=tf.float32, name='loc_matrix')
def __init__(self,
dim_image,
n_words,
dim_hidden,
batch_size,
n_caption_steps,
n_video_steps,
drop_out_rate,
bias_init_vector=None):
self.dim_image = dim_image
self.n_words = n_words
self.dim_hidden = dim_hidden
self.batch_size = batch_size
self.n_caption_steps = n_caption_steps
self.drop_out_rate = drop_out_rate
self.n_video_steps = n_video_steps
with tf.device("/cpu:0"):
self.Wemb = tf.Variable(tf.random_uniform([n_words, dim_hidden],
-0.1, 0.1),
name='Wemb')
# encoding LSTM for sentence
self.lstm2 = tf.contrib.rnn.LSTMCell(self.dim_hidden,
use_peepholes=True,
state_is_tuple=True)
# decoding LSTM for sentence
self.lstm3 = tf.contrib.rnn.LSTMCell(self.dim_hidden,
use_peepholes=True,
state_is_tuple=True)
# decoding LSTM for video
self.lstm4 = tf.contrib.rnn.LSTMCell(self.dim_hidden,
use_peepholes=True,
state_is_tuple=True)
self.lstm2_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm2,
output_keep_prob=1 -
self.drop_out_rate)
self.lstm3_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm3,
output_keep_prob=1 -
self.drop_out_rate)
self.lstm4_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm4,
output_keep_prob=1 -
self.drop_out_rate)
self.vae = VAE(self.dim_hidden * 2, self.dim_hidden)
self.encode_image_W = tf.Variable(tf.random_uniform(
[dim_image, dim_hidden], -0.1, 0.1),
name='encode_image_W')
self.encode_image_b = tf.Variable(tf.zeros([dim_hidden]),
name='encode_image_b')
self.decode_image_W = tf.Variable(
tf.random_uniform([dim_hidden, dim_image],
-0.1,
0.1,
name='decode_image_W'))
self.decode_image_b = tf.Variable(tf.random_uniform([dim_image]),
name='decode_image_b')
self.embed_att_w = tf.Variable(tf.random_uniform([dim_hidden, 1], -0.1,
0.1),
name='embed_att_w')
self.embed_att_Wa = tf.Variable(tf.random_uniform(
[dim_hidden, dim_hidden], -0.1, 0.1),
name='embed_att_Wa')
self.embed_att_Ua = tf.Variable(tf.random_uniform(
[dim_hidden, dim_hidden], -0.1, 0.1),
name='embed_att_Ua')
self.embed_att_ba = tf.Variable(tf.zeros([dim_hidden]),
name='embed_att_ba')
self.embed_word_W = tf.Variable(tf.random_uniform(
[dim_hidden, n_words], -0.1, 0.1),
name='embed_word_W')
if bias_init_vector is not None:
self.embed_word_b = tf.Variable(bias_init_vector.astype(
np.float32),
name='embed_word_b')
else:
self.embed_word_b = tf.Variable(tf.zeros([n_words]),
name='embed_word_b')
self.embed_nn_Wp = tf.Variable(tf.random_uniform(
[3 * dim_hidden, dim_hidden], -0.1, 0.1),
name='embed_nn_Wp')
self.embed_nn_bp = tf.Variable(tf.zeros([dim_hidden]),
name='embed_nn_bp')
def __init__(self,
dim_image,
n_words,
dim_hidden,
batch_size,
n_caption_steps,
n_video_steps,
drop_out_rate,
bias_init_vector=None):
self.dim_image = dim_image
self.n_words = n_words
self.dim_hidden = dim_hidden
self.batch_size = batch_size
self.n_caption_steps = n_caption_steps
self.drop_out_rate = drop_out_rate
self.n_video_steps = n_video_steps
with tf.device("/cpu:0"):
self.Wemb = tf.Variable(tf.random_uniform([n_words, dim_hidden],
-0.1, 0.1),
name='Wemb')
# encoding LSTM for video
self.lstm1 = tf.contrib.rnn.LSTMCell(self.dim_hidden,
use_peepholes=True,
state_is_tuple=True)
# encoding LSTM for sentence
self.lstm2 = tf.contrib.rnn.LSTMCell(self.dim_hidden,
use_peepholes=True,
state_is_tuple=True)
# decoding LSTM for sentence
self.lstm3 = tf.contrib.rnn.LSTMCell(self.dim_hidden,
use_peepholes=True,
state_is_tuple=True)
# decoding LSTM for video
self.lstm4 = tf.contrib.rnn.LSTMCell(self.dim_hidden,
use_peepholes=True,
state_is_tuple=True)
self.lstm1_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm1,
output_keep_prob=1 -
self.drop_out_rate)
self.lstm2_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm2,
output_keep_prob=1 -
self.drop_out_rate)
self.lstm3_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm3,
output_keep_prob=1 -
self.drop_out_rate)
self.lstm4_dropout = tf.contrib.rnn.DropoutWrapper(self.lstm4,
output_keep_prob=1 -
self.drop_out_rate)
self.vae = VAE(self.dim_hidden * 2, self.dim_hidden)
self.encode_image_W = tf.Variable(tf.random_uniform(
[dim_video_feat, dim_hidden], -0.1, 0.1),
name='encode_image_W')
self.encode_image_b = tf.Variable(tf.zeros([dim_hidden]),
name='encode_image_b')
self.decode_image_W = tf.Variable(
tf.random_uniform([dim_hidden, dim_image],
-0.1,
0.1,
name='decode_image_W'))
self.decode_image_b = tf.Variable(tf.random_uniform([dim_image]),
name='decode_image_b')
self.embed_word_W = tf.Variable(tf.random_uniform(
[dim_hidden, n_words], -0.1, 0.1),
name='embed_word_W')
if bias_init_vector is not None:
self.embed_word_b = tf.Variable(bias_init_vector.astype(
np.float32),
name='embed_word_b')
else:
self.embed_word_b = tf.Variable(tf.zeros([n_words]),
name='embed_word_b')
# attribute embedding
self.embed_att_w = tf.Variable(tf.random_uniform([dim_att, dim_hidden],
-0.1, 0.1),
name='embed_att_w')
self.embed_att_b = tf.Variable(tf.zeros([dim_hidden]),
name='embed_att_b')
# learnable coefficient for normalized video and sentence feature
self.video_coeff = tf.Variable(tf.ones([1]), name='video_coeff')
self.sent_coeff = tf.Variable(tf.ones([1]), name='sent_coeff')
self.h2h_w = tf.Variable(tf.random_uniform([dim_hidden, dim_hidden],
-0.1, 0.1),
name='h2h_w')
self.h2h_b = tf.Variable(tf.zeros([dim_hidden]), name='h2h_b')
self.h2c_w = tf.Variable(tf.random_uniform([dim_hidden, dim_hidden],
-0.1, 0.1),
name='h2c_w')
self.h2c_b = tf.Variable(tf.zeros([dim_hidden]), name='h2c_b')
