在TensorFlow中进行的计划采样

Question

关于seq2seq模型的最新张tensorflow API已包括计划采样：

https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/scheduledembeddingtraininghelperhttps://www.tensorflow.org/api_docs/python/tf/contrib/contrib/seq2seq/scheduledoutputtraininghelper

可以在此处找到原始的采样论文：https://arxiv.org/abs/1506.03099

我阅读了论文，但我无法理解ScheduledEmbeddingTrainingHelper和ScheduledOutputTrainingHelper之间的区别。该文档仅表示ScheduledEmbeddingTrainingHelper是一个培训助手，可以添加计划的抽样，而ScheduledOutputTrainingHelper是一个训练助手，可直接添加计划采样到输出。

我想知道这两个帮助者有什么区别？

Answer 1

我与此背后的工程师联系，他回答：

输出采样器要么在该时间步骤中排放原始RNN输出或原始地面真相。嵌入采样器将RNN输出视为分布的徽标，并在该时间步骤中排放从该分类分布中的采样ID的嵌入查找或原始地面真相。

Answer 2

这是使用ScheduledEmbeddingTrainingHelper，使用TensorFlow 1.3和一些更高级别的TF.Contrib API的基本示例。这是一个序列2序列模型，其中解码器的初始隐藏状态是编码器的最终隐藏状态。它仅显示如何在一批上训练(显然任务是"反向此序列"(。对于实际的培训任务，我建议查看tf.contrib.learn apis，例如Learn_runner，实验和tf.estimator.stimator。

import tensorflow as tf
import numpy as np
from tensorflow.python.layers.core import Dense
vocab_size = 7
embedding_size = 5
lstm_units = 10
src_batch = np.array([[1, 2, 3], [4, 5, 6]])
trg_batch = np.array([[3, 2, 1], [6, 5, 4]])
# *_seq will have shape (2, 3), *_seq_len will have shape (2)
source_seq = tf.placeholder(shape=(None, None), dtype=tf.int32)
target_seq = tf.placeholder(shape=(None, None), dtype=tf.int32)
source_seq_len = tf.placeholder(shape=(None,), dtype=tf.int32)
target_seq_len = tf.placeholder(shape=(None,), dtype=tf.int32)
# add Start of Sequence (SOS) tokens to each sequence
batch_size, sequence_size = tf.unstack(tf.shape(target_seq))
sos_slice = tf.zeros([batch_size, 1], dtype=tf.int32) # 0 = start of sentence token
decoder_input = tf.concat([sos_slice, target_seq], axis=1)
embedding_matrix = tf.get_variable(
    name="embedding_matrix",
    shape=[vocab_size, embedding_size],
    dtype=tf.float32)
source_seq_embedded = tf.nn.embedding_lookup(embedding_matrix, source_seq) # shape=(2, 3, 5)
decoder_input_embedded = tf.nn.embedding_lookup(embedding_matrix, decoder_input) # shape=(2, 4, 5)
unused_encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
    tf.contrib.rnn.LSTMCell(lstm_units),
    source_seq_embedded,
    sequence_length=source_seq_len,
    dtype=tf.float32)
# Decoder:
# At each time step t and for each sequence in the batch, we get x_t by either
#   (1) sampling from the distribution output_layer(t-1), or
#   (2) reading from decoder_input_embedded.
# We do (1) with probability sampling_probability and (2) with 1 - sampling_probability.
# Using sampling_probability=0.0 is equivalent to using TrainingHelper (no sampling).
# Using sampling_probability=1.0 is equivalent to doing inference,
# where we don't supervise the decoder at all: output at t-1 is the input at t.
sampling_prob = tf.Variable(0.0, dtype=tf.float32)
helper = tf.contrib.seq2seq.ScheduledEmbeddingTrainingHelper(
    decoder_input_embedded,
    target_seq_len,
    embedding_matrix,
    sampling_probability=sampling_prob)
output_layer = Dense(vocab_size)
decoder = tf.contrib.seq2seq.BasicDecoder(
    tf.contrib.rnn.LSTMCell(lstm_units),
    helper,
    encoder_state,
    output_layer=output_layer)
outputs, state, seq_len = tf.contrib.seq2seq.dynamic_decode(decoder)
loss = tf.contrib.seq2seq.sequence_loss(
    logits=outputs.rnn_output,
    targets=target_seq,
    weights=tf.ones(trg_batch.shape))
train_op = tf.contrib.layers.optimize_loss(
    loss=loss,
    global_step=tf.contrib.framework.get_global_step(),
    optimizer=tf.train.AdamOptimizer,
    learning_rate=0.001)
with tf.Session() as session:
    session.run(tf.global_variables_initializer())
    _, _loss = session.run([train_op, loss], {
        source_seq: src_batch,
        target_seq: trg_batch,
        source_seq_len: [3, 3],
        target_seq_len: [3, 3],
        sampling_prob: 0.5
    })
    print("Loss: " + str(_loss))

对于ScheduledOutputTrainingHelper，我希望只需交换助手并使用：

helper = tf.contrib.seq2seq.ScheduledOutputTrainingHelper(
    target_seq,
    target_seq_len,
    sampling_probability=sampling_prob)

然而，这给出了一个错误，因为LSTM单元期望每个时间段(shape(batch_size，input_dims((的多维输入(。我将在Github提出一个问题，以找出这是否是错误，或者还有其他一些使用ScheduledOutputTrainingHelper的方法。

Answer 3

这也可能对您有所帮助。这是您要分别在每个解码步骤进行计划进行采样的情况。

import tensorflow as tf
import numpy as np
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.distributions import categorical
from tensorflow.python.ops.distributions import bernoulli
batch_size = 64
vocab_size = 50000
emb_dim = 128
output = tf.get_variable('output', 
initializer=tf.constant(np.random.rand(batch_size,vocab_size)))
base_next_inputs = tf.get_variable('input', 
initializer=tf.constant(np.random.rand(batch_size,emb_dim)))
embedding = tf.get_variable('embedding', 
initializer=tf.constant(np.random.rand(vocab_size,emb_dim)))
select_sampler = bernoulli.Bernoulli(probs=0.99, dtype=tf.bool)
select_sample = select_sampler.sample(sample_shape=batch_size, 
seed=123)
sample_id_sampler = categorical.Categorical(logits=output)
sample_ids = array_ops.where(
    select_sample,
    sample_id_sampler.sample(seed=123),
    gen_array_ops.fill([batch_size], -1))
where_sampling = math_ops.cast(
   array_ops.where(sample_ids > -1), tf.int32)
where_not_sampling = math_ops.cast(
   array_ops.where(sample_ids <= -1), tf.int32)
sample_ids_sampling = array_ops.gather_nd(sample_ids, where_sampling)
inputs_not_sampling = array_ops.gather_nd(base_next_inputs, 
     where_not_sampling)
sampled_next_inputs = tf.nn.embedding_lookup(embedding, 
    sample_ids_sampling)
base_shape = array_ops.shape(base_next_inputs)
result1 = array_ops.scatter_nd(indices=where_sampling, 
   updates=sampled_next_inputs, shape=base_shape)
result2 = array_ops.scatter_nd(indices=where_not_sampling, 
   updates=inputs_not_sampling, shape=base_shape)
result = result1 + result2

我使用TensorFlow文档代码进行此示例。https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/contrib/seq2seq/python/ppython/ops/helper.py.py.py