按十位数计划抽样

Question

关于seq2seq模型的最新Tensorflow api包含计划采样：

https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/ScheduledEmbeddingTrainingHelperhttps://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/ScheduledOutputTrainingHelper

计划抽样的原始文件可以在这里找到： https://arxiv.org/abs/1506.03099

我读了这篇论文，但我不明白ScheduledEmbeddingTrainingHelper和{}之间的区别。文档只说ScheduledEmbeddingTrainingHelper是一个培训助手，它添加了计划的采样，而{}是一个直接向输出添加定时采样的训练助手。在

我想知道这两个帮手有什么区别？在

Answer 1

我联系了幕后的工程师，他回答说：

The output sampler either emits the raw rnn output or the raw ground truth at that time step. The embedding sampler treats the rnn output as logits of a distribution and either emits the embedding lookup of a sampled id from that categorical distribution or the raw ground truth at that time step.

Answer 2

这也可能对你有帮助。这种情况下，您希望在每个解码步骤分别进行定时采样。在

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文档代码来创建这个示例。 https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/contrib/seq2seq/python/ops/helper.py

Answer 3

下面是一个使用ScheduledEmbeddingTrainingHelper，使用tensorflow1.3和更高级别的基本示例tf.contrib公司原料药。这是一个sequence2sequence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。它只展示了如何在单个批次上进行训练（显然，任务是“颠倒这个顺序”）。对于实际的训练任务，我建议tf.contrib.学习API，如learn\u runner、Experience和估算器. 在

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，我希望只交换助手并使用：

^{pr2}$

但是这会产生一个错误，因为LSTM单元要求每个时间步都有一个多维输入（形状为（批处理大小，输入尺寸））。我将在GitHub中提出一个问题，以确定这是否是一个bug，或者有其他方法可以使用ScheduledOutputTrainingHelper。在