인코더의 각 단계에서 사용자 지정 계산을 수행해야하는 인코더 - 디코더 lstm을 구현하고 있습니다. 그래서 raw_rnn
을 사용하고 있습니다. 그러나 시간 단계 에서 Batch x Time steps x Embedding dimensionality
이라는 모양의 요소를 액세스 할 때 문제가 발생합니다. 여기 Tensorflow raw_rnn 텐서 형상 검색 BATCH x DIM from embedding matrix
import tensorflow as tf
import numpy as np
batch_size, max_time, input_embedding_size = 5, 10, 16
vocab_size, num_units = 50, 64
encoder_inputs = tf.placeholder(shape=(None, None), dtype=tf.int32, name='encoder_inputs')
encoder_inputs_length = tf.placeholder(shape=(None,), dtype=tf.int32, name='encoder_inputs_length')
embeddings = tf.Variable(tf.random_uniform([vocab_size + 2, input_embedding_size], -1.0, 1.0),
dtype=tf.float32, name='embeddings')
encoder_inputs_embedded = tf.nn.embedding_lookup(embeddings, encoder_inputs)
cell = tf.contrib.rnn.LSTMCell(num_units)
주요 부분 :
with tf.variable_scope('ReaderNetwork'):
def loop_fn_initial():
init_elements_finished = (0 >= encoder_inputs_length)
init_input = cell.zero_state(batch_size, tf.float32)
init_cell_state = None
init_cell_output = None
init_loop_state = None
return (init_elements_finished, init_input,
init_cell_state, init_cell_output, init_loop_state)
def loop_fn_transition(time, previous_output, previous_state, previous_loop_state):
def get_next_input():
# **TODO** read tensor of shape BATCH X EMBEDDING_DIM from encoder_inputs_embedded
# which has shape BATCH x TIME_STEPS x EMBEDDING_DIM
elements_finished = (time >= encoder_inputs_length)
finished = tf.reduce_all(elements_finished) # boolean scalar
input_val = tf.cond(finished,
true_fn=lambda: tf.zeros([batch_size, input_embedding_size]), false_fn=get_next_input)
state = previous_state
output = previous_output
loop_state = None
return elements_finished, input_val, state, output, loop_state
def loop_fn(time, previous_output, previous_state, previous_loop_state):
if previous_state is None: # time = 0
assert previous_output is None and previous_state is None
return loop_fn_initial()
return loop_fn_transition(time, previous_output, previous_state, previous_loop_state)
실행중인 부분 :
reader_loop = loop_fn
encoder_outputs_ta, encoder_final_state, _ = tf.nn.raw_rnn(cell, loop_fn=reader_loop)
outputs = encoder_outputs_ta.stack()
def next_batch():
return {
encoder_inputs: np.random.random((batch_size, max_time)),
encoder_inputs_length: [max_time] * batch_size
}
init = tf.global_variables_initializer()
with tf.Session() as s:
s.run(init)
outs = s.run([outputs], feed_dict=next_batch())
print len(outs), outs[0].shape
질문 : 방법에 묻어의 일부에 액세스 할 수 시간 단계와 모양의 텐서를 반환 batch x embedding dim
? loop_fn_transition
내의 get_next_input
기능을 참조하십시오.
감사합니다.