实现softmax_loss_function部分
基于tensorflow1.4 Seq2seq的实现
import helpers
import tensorflow as tf
from tensorflow.contrib import seq2seq,rnn
tf.__version__
'1.4.0'
tf.reset_default_graph()
sess = tf.InteractiveSession()
PAD = 0
EOS = 1
vocab_size = 10
input_embedding_size = 20
encoder_hidden_units = 25
decoder_hidden_units = encoder_hidden_units
import helpers as data_helpers
batch_size = 50
# 一个generator，每次产生一个minibatch的随机样本
batches = data_helpers.random_sequences(length_from=3, length_to=8,
vocab_lower=2, vocab_upper=10,
batch_size=batch_size)
print('产生%d个长度不一（最短3，最长8）的sequences, 其中前十个是:' % batch_size)
for seq in next(batches)[:min(batch_size, 10)]:
print(seq)
产生50个长度不一（最短3，最长8）的sequences, 其中前十个是:
[5, 3, 9, 9, 5]
[3, 6, 7, 6]
[4, 3, 5, 7, 6, 3]
[6, 3, 4, 3, 6]
[2, 3, 9, 3, 3]
[4, 8, 2, 4, 9, 7, 8, 7]
[6, 9, 2, 7, 3, 3]
[9, 2, 7]
[2, 2, 5, 2, 5, 2]
[5, 4, 2, 7, 8, 5]
1.使用seq2seq库实现seq2seq模型（encoder 部分不变）
tf.reset_default_graph()
sess = tf.InteractiveSession()
mode = tf.contrib.learn.ModeKeys.TRAIN
with tf.name_scope('minibatch'):
encoder_inputs = tf.placeholder(tf.int32, [None, None], name='encoder_inputs')
encoder_inputs_length = tf.placeholder(tf.int32, [None], name='encoder_inputs_length')
decoder_targets = tf.placeholder(tf.int32, [None, None], name='decoder_targets')
decoder_inputs = tf.placeholder(shape=(None, None),dtype=tf.int32,name='decoder_inputs')
#decoder_inputs_length和decoder_targets_length是一样的
decoder_inputs_length = tf.placeholder(shape=(None,),
dtype=tf.int32,
name='decoder_inputs_length')
def _create_rnn_cell():
def single_rnn_cell(encoder_hidden_units):
# 创建单个cell，这里需要注意的是一定要使用一个single_rnn_cell的函数，不然直接把cell放在MultiRNNCell
# 的列表中最终模型会发生错误
single_cell = rnn.LSTMCell(encoder_hidden_units)
#添加dropout
single_cell = rnn.DropoutWrapper(single_cell, output_keep_prob=0.5)
return single_cell
#列表中每个元素都是调用single_rnn_cell函数
#cell = rnn.MultiRNNCell([single_rnn_cell() for _ in range(self.num_layers)])
cell = rnn.MultiRNNCell([single_rnn_cell(encoder_hidden_units) for _ in range(1)])
return cell
2.Candidate Sampling实现
实际过程中vocab_size过大，在计算loss生产one_hot的时候传统的softmax由于要计算每一个类的logits就会有问题
具体参照这篇论文https://arxiv.org/pdf/1409.0473v7.pdf
num_samples = 5
w_sample = tf.get_variable('proj_w', [vocab_size,encoder_hidden_units])
#w_t = tf.transpose(w)
b_sample = tf.get_variable('proj_b', [vocab_size])
# 调用sampled_softmax_loss函数计算sample loss，这样可以节省计算时间
def sample_loss(logits, labels):
labels = tf.cast(labels, tf.int64)
labels = tf.reshape(labels, [-1, 1])
logits = tf.cast(logits, tf.float32)
#logits = tf.reshape(labels, [-1, 1])
#decoder_logits_train = tf.unstack(logits,axis=1)
#decoder_targets = tf.unstack(labels,axis=1)
return tf.cast(tf.nn.sampled_softmax_loss(w_sample, b_sample, labels=labels, inputs=logits,
num_sampled=num_samples, num_classes=vocab_size),tf.float32)
softmax_loss_function = sample_loss
tensorflow seq2seq.sequence_loss接口：
seq2seq_loss接口.png
tensorflow tf.nn.sampled_softmax_loss接口：
sampled_softmax_loss接口.png
1.定义encoder部分
with tf.variable_scope('encoder'):
# 创建LSTMCell
encoder_cell = _create_rnn_cell()
# 构建embedding矩阵,encoder和decoder公用该词向量矩阵
embedding = tf.get_variable('embedding', [vocab_size,input_embedding_size])
encoder_inputs_embedded = tf.nn.embedding_lookup(embedding,encoder_inputs)
# 使用dynamic_rnn构建LSTM模型，将输入编码成隐层向量。
# encoder_outputs用于attention，batch_size*encoder_inputs_length*rnn_size,
# encoder_state用于decoder的初始化状态，batch_size*rnn_szie
encoder_outputs, encoder_state = tf.nn.dynamic_rnn(encoder_cell, encoder_inputs_embedded,
sequence_length=encoder_inputs_length,
dtype=tf.float32)
这里我们使用双向 dynamic_rnn:
RNN-bidirectional.png
图片来自于Colah的blog
2.定义decoder 部分(暂时不添加attention部分)
此处不添加output_layer，在sample_loss那里有一层
with tf.variable_scope('decoder'):
decoder_cell = _create_rnn_cell()
#定义decoder的初始状态
decoder_initial_state = encoder_state
#定义output_layer
#output_layer = tf.layers.Dense(encoder_hidden_units,kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1))
decoder_inputs_embedded = tf.nn.embedding_lookup(embedding, decoder_inputs)
# 训练阶段，使用TrainingHelper+BasicDecoder的组合，这一般是固定的，当然也可以自己定义Helper类，实现自己的功能
training_helper = seq2seq.TrainingHelper(inputs=decoder_inputs_embedded,
sequence_length=decoder_inputs_length,
time_major=False, name='training_helper')
training_decoder = seq2seq.BasicDecoder(cell=decoder_cell, helper=training_helper,
initial_state=decoder_initial_state,
output_layer=None)
# 调用dynamic_decode进行解码，decoder_outputs是一个namedtuple，里面包含两项(rnn_outputs, sample_id)
# rnn_output: [batch_size, decoder_targets_length, vocab_size]，保存decode每个时刻每个单词的概率，可以用来计算loss
# sample_id: [batch_size], tf.int32，保存最终的编码结果。可以表示最后的答案
max_target_sequence_length = tf.reduce_max(decoder_inputs_length, name='max_target_len')
decoder_outputs, _, _ = seq2seq.dynamic_decode(decoder=training_decoder,
impute_finished=True,
maximum_iterations=max_target_sequence_length)
#创建一个与decoder_outputs.rnn_output一样的tensor给decoder_logits_train
decoder_logits_train = tf.identity(decoder_outputs.rnn_output)
sample_id = decoder_outputs.sample_id
# 根据目标序列长度，选出其中最大值，然后使用该值构建序列长度的mask标志。用一个sequence_mask的例子来说明起作用
# tf.sequence_mask([1, 3, 2], 5)
# [[True, False, False, False, False],
# [True, True, True, False, False],
# [True, True, False, False, False]]
max_target_sequence_length = tf.reduce_max(decoder_inputs_length, name='max_target_len')
mask = tf.sequence_mask(decoder_inputs_length,max_target_sequence_length, dtype=tf.float32, name='masks')
print('\t%s' % repr(decoder_logits_train))
print('\t%s' % repr(decoder_targets))
print('\t%s' % repr(sample_id))
loss = seq2seq.sequence_loss(logits=decoder_logits_train,targets=decoder_targets,
weights=mask,softmax_loss_function=softmax_loss_function)
<tf.Tensor 'decoder/Identity:0' shape=(?, ?, 25) dtype=float32>
<tf.Tensor 'minibatch/decoder_targets:0' shape=(?, ?) dtype=int32>
<tf.Tensor 'decoder/decoder/transpose_1:0' shape=(?, ?) dtype=int32>
train_op = tf.train.AdamOptimizer(learning_rate = 0.001).minimize(loss)
sess.run(tf.global_variables_initializer())
def next_feed():
batch = next(batches)
encoder_inputs_, encoder_inputs_length_ = data_helpers.batch(batch)
decoder_targets_, decoder_targets_length_ = data_helpers.batch(
[(sequence) + [EOS] for sequence in batch]
)
decoder_inputs_, decoder_inputs_length_ = data_helpers.batch(
[[EOS] + (sequence) for sequence in batch]
)
# 在feedDict里面，key可以是一个Tensor
return {
encoder_inputs: encoder_inputs_.T,
decoder_inputs: decoder_inputs_.T,
decoder_targets: decoder_targets_.T,
encoder_inputs_length: encoder_inputs_length_,
decoder_inputs_length: decoder_inputs_length_
}
x = next_feed()
print('encoder_inputs:')
print(x[encoder_inputs][0,:])
print('encoder_inputs_length:')
print(x[encoder_inputs_length][0])
print('decoder_inputs:')
print(x[decoder_inputs][0,:])
print('decoder_inputs_length:')
print(x[decoder_inputs_length][0])
print('decoder_targets:')
print(x[decoder_targets][0,:])
encoder_inputs:
[6 9 7 7 3 7 0 0]
encoder_inputs_length:
6
decoder_inputs:
[1 6 9 7 7 3 7 0 0]
decoder_inputs_length:
7
decoder_targets:
[6 9 7 7 3 7 1 0 0]
loss_track = []
max_batches = 3001
batches_in_epoch = 100
try:
# 一个epoch的learning
for batch in range(max_batches):
fd = next_feed()
_, l = sess.run([train_op, loss], fd)
loss_track.append(l)
if batch == 0 or batch % batches_in_epoch == 0:
print('batch {}'.format(batch))
print(' minibatch loss: {}'.format(sess.run(loss, fd)))
predict_ = sess.run(decoder_outputs.sample_id, fd)
for i, (inp, pred) in enumerate(zip(fd[encoder_inputs], predict_)):
print(' sample {}:'.format(i + 1))
print(' input > {}'.format(inp))
print(' predicted > {}'.format(pred))
if i >= 2:
break
print()
except KeyboardInterrupt:
print('training interrupted')
batch 0
minibatch loss: 1.7552857398986816
sample 1:
input > [4 3 2 0 0 0 0 0]
predicted > [ 8 21 3 14 0 0 0 0 0]
sample 2:
input > [2 8 8 2 4 0 0 0]
predicted > [21 21 21 3 4 3 0 0 0]
sample 3:
input > [8 5 4 4 4 0 0 0]
predicted > [24 21 24 15 21 18 0 0 0]
batch 100
minibatch loss: 1.4532331228256226
sample 1:
input > [4 3 2 5 9 4 8 0]
predicted > [ 0 12 13 13 12 13 13 11 0]
sample 2:
input > [5 8 7 5 4 0 0 0]
predicted > [24 24 21 18 13 13 0 0 0]
sample 3:
input > [2 4 2 5 9 4 8 8]
predicted > [24 12 0 13 13 12 13 11 13]
batch 200
minibatch loss: 1.1243680715560913
sample 1:
input > [9 3 9 2 5 4 0 0]
predicted > [ 3 12 3 13 21 13 21 0 0]
sample 2:
input > [9 8 5 2 5 4 0 0]
predicted > [15 13 21 7 13 16 11 0 0]
sample 3:
input > [3 8 5 3 8 2 6 5]
predicted > [12 12 15 3 7 3 7 18 18]
batch 300
minibatch loss: 1.1811888217926025
sample 1:
input > [2 2 2 7 7 0 0 0]
predicted > [ 0 9 12 12 6 18 0 0 0]
sample 2:
input > [9 8 4 4 8 5 2 6]
predicted > [ 0 0 0 0 0 17 11 13 2]
sample 3:
input > [2 7 4 4 9 0 0 0]
predicted > [ 9 13 12 12 17 6 0 0 0]
batch 400
minibatch loss: 1.0878313779830933
sample 1:
input > [9 9 3 8 3 0 0 0]
predicted > [ 9 12 6 18 18 21 0 0 0]
sample 2:
input > [5 6 2 6 8 0 0 0]
predicted > [15 0 13 12 7 18 0 0 0]
sample 3:
input > [2 9 4 7 6 0 0 0]
predicted > [ 8 13 13 0 12 18 0 0 0]
batch 500
minibatch loss: 0.8977712988853455
sample 1:
input > [6 7 2 5 7 9 0 0]
predicted > [ 5 12 12 15 15 21 18 0 0]
sample 2:
input > [7 4 3 3 4 8 5 0]
predicted > [24 9 9 9 0 12 13 13 0]
sample 3:
input > [2 9 8 0 0 0 0 0]
predicted > [15 21 18 21 0 0 0 0 0]
batch 600
minibatch loss: 0.9903306365013123
sample 1:
input > [9 5 3 8 4 5 2 0]
predicted > [15 15 24 12 15 3 3 7 0]
sample 2:
input > [5 2 3 2 5 3 3 8]
predicted > [ 9 3 10 24 9 24 6 11 11]
sample 3:
input > [6 3 4 6 0 0 0 0]
predicted > [ 8 12 12 13 11 0 0 0 0]
batch 700
minibatch loss: 1.0557962656021118
sample 1:
input > [4 5 9 0 0 0 0 0]
predicted > [15 7 10 18 0 0 0 0 0]
sample 2:
input > [2 4 7 7 5 8 5 0]
predicted > [ 9 9 11 12 13 13 13 11 0]
sample 3:
input > [2 4 9 0 0 0 0 0]
predicted > [21 13 18 21 0 0 0 0 0]
batch 800
minibatch loss: 0.7463603019714355
sample 1:
input > [8 3 8 7 0 0 0 0]
predicted > [24 24 12 17 18 0 0 0 0]
sample 2:
input > [7 2 5 6 9 0 0 0]
predicted > [ 9 12 7 7 18 7 0 0 0]
sample 3:
input > [2 8 5 9 2 2 7 0]
predicted > [18 21 3 3 18 0 7 16 0]
batch 900
minibatch loss: 0.57407546043396
sample 1:
input > [2 2 3 0 0 0 0 0]
predicted > [ 3 12 18 18 0 0 0 0 0]
sample 2:
input > [6 9 2 9 0 0 0 0]
predicted > [ 3 21 21 10 18 0 0 0 0]
sample 3:
input > [2 5 4 3 0 0 0 0]
predicted > [ 9 0 12 24 11 0 0 0 0]
batch 1000
minibatch loss: 0.5782870650291443
sample 1:
input > [3 4 8 5 5 4 8 8]
predicted > [ 5 5 9 20 13 17 16 11 11]
sample 2:
input > [4 8 3 5 3 7 0 0]
predicted > [ 5 9 10 1 7 9 11 0 0]
sample 3:
input > [4 6 5 4 4 2 9 6]
predicted > [ 5 5 5 12 12 12 12 7 7]
batch 1100
minibatch loss: 0.5811575055122375
sample 1:
input > [9 4 8 7 6 4 7 3]
predicted > [ 5 17 5 17 12 12 12 17 13]
sample 2:
input > [3 3 9 5 2 5 0 0]
predicted > [10 12 15 15 12 7 13 0 0]
sample 3:
input > [4 5 6 2 3 4 5 0]
predicted > [ 5 5 0 24 9 11 13 13 0]
batch 1200
minibatch loss: 0.6396902203559875
sample 1:
input > [2 3 5 4 4 9 0 0]
predicted > [ 3 9 5 0 12 21 16 0 0]
sample 2:
input > [2 8 9 9 5 6 7 5]
predicted > [18 0 15 12 0 11 0 21 11]
sample 3:
input > [4 6 9 7 4 0 0 0]
predicted > [ 0 0 11 17 11 13 0 0 0]
batch 1300
minibatch loss: 0.6583465337753296
sample 1:
input > [3 6 8 9 8 7 6 0]
predicted > [12 8 9 9 9 12 7 18 0]
sample 2:
input > [9 8 7 8 0 0 0 0]
predicted > [ 5 12 12 13 21 0 0 0 0]
sample 3:
input > [6 9 9 4 9 5 2 7]
predicted > [21 15 15 12 15 7 18 18 6]
batch 1400
minibatch loss: 0.6087316870689392
sample 1:
input > [9 6 8 2 3 9 9 0]
predicted > [15 12 12 3 3 15 7 11 0]
sample 2:
input > [2 8 7 7 8 0 0 0]
predicted > [ 5 9 9 17 18 16 0 0 0]
sample 3:
input > [4 2 7 5 4 3 9 0]
predicted > [ 5 9 10 12 9 10 21 6 0]
batch 1500
minibatch loss: 0.717374324798584
sample 1:
input > [3 5 3 6 3 3 6 0]
predicted > [ 8 6 17 8 12 0 22 22 0]
sample 2:
input > [8 5 6 5 2 6 0 0]
predicted > [ 8 8 8 15 12 15 7 0 0]
sample 3:
input > [7 6 4 6 8 8 7 0]
predicted > [ 0 13 0 0 13 12 22 13 0]
batch 1600
minibatch loss: 0.4989091753959656
sample 1:
input > [6 5 6 4 5 8 0 0]
predicted > [ 1 15 5 0 11 13 7 0 0]
sample 2:
input > [4 9 4 6 6 0 0 0]
predicted > [ 5 12 0 15 0 2 0 0 0]
sample 3:
input > [2 8 3 8 0 0 0 0]
predicted > [21 12 13 13 21 0 0 0 0]
batch 1700
minibatch loss: 0.5497146248817444
sample 1:
input > [9 6 7 6 0 0 0 0]
predicted > [ 8 0 0 0 11 0 0 0 0]
sample 2:
input > [7 5 9 7 0 0 0 0]
predicted > [15 1 0 11 11 0 0 0 0]
sample 3:
input > [6 5 2 2 0 0 0 0]
predicted > [21 3 3 13 13 0 0 0 0]
batch 1800
minibatch loss: 0.606837272644043
sample 1:
input > [8 6 2 6 0 0 0 0]
predicted > [13 12 12 22 13 0 0 0 0]
sample 2:
input > [3 6 4 5 5 9 0 0]
predicted > [15 1 1 20 1 10 21 0 0]
sample 3:
input > [8 7 2 4 0 0 0 0]
predicted > [ 9 12 9 11 7 0 0 0 0]
batch 1900
minibatch loss: 0.5147760510444641
sample 1:
input > [7 4 9 2 0 0 0 0]
predicted > [12 12 21 18 18 0 0 0 0]
sample 2:
input > [3 3 6 3 6 2 5 0]
predicted > [ 6 8 8 0 13 7 7 22 0]
sample 3:
input > [8 4 9 0 0 0 0 0]
predicted > [ 5 12 7 11 0 0 0 0 0]
batch 2000
minibatch loss: 0.33478930592536926
sample 1:
input > [4 8 9 2 9 9 0 0]
predicted > [ 5 12 21 21 10 7 11 0 0]
sample 2:
input > [8 5 2 8 6 2 3 0]
predicted > [20 5 13 13 13 12 7 14 0]
sample 3:
input > [4 9 5 4 5 5 4 2]
predicted > [ 5 5 5 20 12 12 12 18 6]
batch 2100
minibatch loss: 0.47149085998535156
sample 1:
input > [2 3 3 8 9 3 4 0]
predicted > [17 9 17 9 9 10 12 6 0]
sample 2:
input > [7 2 8 6 8 9 2 0]
predicted > [18 12 12 12 12 7 21 18 0]
sample 3:
input > [9 3 6 6 9 3 0 0]
predicted > [15 0 15 15 12 10 10 0 0]
batch 2200
minibatch loss: 0.35661277174949646
sample 1:
input > [3 6 2 2 7 4 5 3]
predicted > [18 0 12 12 0 12 15 22 22]
sample 2:
input > [4 8 7 5 6 0 0 0]
predicted > [12 12 22 15 22 2 0 0 0]
sample 3:
input > [9 3 3 2 4 0 0 0]
predicted > [10 9 12 12 13 14 0 0 0]
batch 2300
minibatch loss: 0.490815132856369
sample 1:
input > [4 2 9 5 2 0 0 0]
predicted > [ 0 3 12 15 7 7 0 0 0]
sample 2:
input > [2 6 6 8 0 0 0 0]
predicted > [15 8 18 12 13 0 0 0 0]
sample 3:
input > [7 2 9 3 3 7 0 0]
predicted > [12 3 24 10 6 22 18 0 0]
batch 2400
minibatch loss: 0.45908093452453613
sample 1:
input > [3 7 7 7 0 0 0 0]
predicted > [24 18 0 0 18 0 0 0 0]
sample 2:
input > [9 9 3 0 0 0 0 0]
predicted > [ 3 18 6 18 0 0 0 0 0]
sample 3:
input > [6 8 3 4 9 8 0 0]
predicted > [13 5 10 5 12 12 20 0 0]
batch 2500
minibatch loss: 0.3008703291416168
sample 1:
input > [7 6 2 0 0 0 0 0]
predicted > [12 8 12 7 0 0 0 0 0]
sample 2:
input > [9 5 9 9 8 7 0 0]
predicted > [15 3 21 12 12 24 7 0 0]
sample 3:
input > [4 8 3 3 0 0 0 0]
predicted > [17 12 6 17 11 0 0 0 0]
batch 2600
minibatch loss: 0.5544220805168152
sample 1:
input > [6 7 4 3 2 5 2 0]
predicted > [13 13 13 3 7 3 12 11 0]
sample 2:
input > [2 4 5 5 7 9 7 0]
predicted > [ 9 1 5 0 10 18 10 18 0]
sample 3:
input > [2 3 2 8 3 6 0 0]
predicted > [ 3 12 21 13 12 7 18 0 0]
batch 2700
minibatch loss: 0.3938275873661041
sample 1:
input > [5 5 8 8 0 0 0 0]
predicted > [ 1 20 13 16 13 0 0 0 0]
sample 2:
input > [8 9 8 2 2 5 7 0]
predicted > [21 12 12 18 24 12 18 18 0]
sample 3:
input > [2 8 2 5 0 0 0 0]
predicted > [12 12 3 7 7 0 0 0 0]
batch 2800
minibatch loss: 0.42015042901039124
sample 1:
input > [9 8 2 7 8 0 0 0]
predicted > [21 12 12 17 7 11 0 0 0]
sample 2:
input > [6 9 4 0 0 0 0 0]
predicted > [15 12 11 4 0 0 0 0 0]
sample 3:
input > [9 6 5 7 5 6 0 0]
predicted > [15 15 1 0 20 0 11 0 0]
batch 2900
minibatch loss: 0.30204904079437256
sample 1:
input > [7 9 5 8 6 5 0 0]
predicted > [10 1 1 13 12 7 7 0 0]
sample 2:
input > [3 5 4 0 0 0 0 0]
predicted > [ 3 20 16 14 0 0 0 0 0]
sample 3:
input > [2 6 8 2 0 0 0 0]
predicted > [21 12 12 12 7 0 0 0 0]
batch 3000
minibatch loss: 0.2870854437351227
sample 1:
input > [5 8 7 4 8 6 5 4]
predicted > [24 13 12 12 12 15 12 12 21]
sample 2:
input > [2 4 5 5 6 6 2 0]
predicted > [20 20 15 0 15 18 21 22 0]
sample 3:
input > [6 5 4 6 7 2 0 0]
predicted > [15 15 0 13 12 18 18 0 0]
