def _build_greedy_inference_decoder(in_embed, vocab_size, num_cells,
start_token_id):
vocab_size += 1
# init state / image embedding
init_h = slim.fully_connected(in_embed,
num_cells,
activation_fn=tf.nn.tanh,
scope='init_h')
init_c = tf.zeros_like(init_h)
init_state = LSTMStateTuple(init_c, init_h)
# build LSTM cell and RNN
lstm_cell = BasicLSTMCell(num_cells)
# word embedding
with tf.variable_scope('word_embedding'):
word_map = tf.get_variable(name="word_map",
shape=[vocab_size, num_cells],
initializer=tf.random_uniform_initializer(
-0.08, 0.08, dtype=tf.float32))
# apply weights for outputs
with tf.variable_scope('logits'):
weights = tf.get_variable('weights',
shape=[num_cells, vocab_size],
dtype=tf.float32)
biases = tf.get_variable('biases', shape=[vocab_size])
softmax_params = [weights, biases]
return create_greedy_decoder(init_state, lstm_cell, word_map,
softmax_params, start_token_id)
def _build_greedy_inference_decoder(glb_ctx, im, ans_embed, vocab_size,
num_cells, start_token_id):
vocab_size += 1
# init state / image embedding
init_h = slim.fully_connected(glb_ctx, num_cells, activation_fn=tf.nn.tanh,
scope='init_h')
init_c = tf.zeros_like(init_h)
init_lstm_state = LSTMStateTuple(init_c, init_h)
batch_size = tf.shape(init_h)[0]
att_zero_state = tf.zeros([batch_size, num_cells], dtype=tf.float32)
init_state = (init_lstm_state, att_zero_state)
# build LSTM cell and RNN
lstm_cell = BasicLSTMCell(num_cells)
attention_cell = MultiModalAttentionCell(512, im, ans_embed, keep_prob=1.0)
multi_cell = MultiRNNCell([lstm_cell, attention_cell], state_is_tuple=True)
# word embedding
with tf.variable_scope('word_embedding'):
word_map = tf.get_variable(
name="word_map",
shape=[vocab_size, num_cells],
initializer=tf.random_uniform_initializer(-0.08, 0.08,
dtype=tf.float32))
# apply weights for outputs
with tf.variable_scope('logits'):
weights = tf.get_variable('weights', shape=[num_cells, vocab_size], dtype=tf.float32)
biases = tf.get_variable('biases', shape=[vocab_size])
softmax_params = [weights, biases]
return create_greedy_decoder(init_state, multi_cell, word_map,
softmax_params, start_token_id)