实现多层双向的dynamic_lstm+beam_search
基于tensorflow1.4 Seq2seq的实现
encoder使用的两层双向的LSTM，注意multi_RNN与bi_dynamic_lstm（并不兼容）
import helpers
import tensorflow as tf
from tensorflow.python.util import nest
from tensorflow.contrib import seq2seq,rnn
tf.__version__
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 = 10
# 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)
tf.reset_default_graph()
sess = tf.InteractiveSession()
mode = tf.contrib.learn.ModeKeys.TRAIN
产生10个长度不一（最短3，最长8）的sequences, 其中前十个是:
[6, 6, 3, 9, 7, 7, 9, 4]
[9, 3, 6, 3, 6, 6, 4, 5]
[5, 4, 2, 2, 3, 9, 8, 7]
[3, 2, 7]
[8, 5, 9, 4, 5, 2]
[6, 5, 8, 9, 4]
[3, 9, 6, 5, 2, 2]
[3, 2, 2, 3]
[8, 8, 7, 6, 8]
[5, 3, 3, 6, 8, 7, 4, 9]
1.使用seq2seq库实现seq2seq模型
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')
# 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)
#fw_cell = bw_cell = rnn.LSTMCell(encoder_hidden_units)
定义encoder，两层双向lstm
_inputs=encoder_inputs_embedded
for _ in range(2):
#为什么在这加个variable_scope,被逼的,tf在rnn_cell的__call__中非要搞一个命名空间检查
#恶心的很.如果不在这加的话,会报错的.
with tf.variable_scope(None, default_name="bidirectional-rnn"):
rnn_cell_bw = rnn_cell_fw = rnn.LSTMCell(encoder_hidden_units)
#rnn_cell_bw = rnn.LSTMCell(encoder_hidden_units)
#initial_state_fw = rnn_cell_fw.zero_state(batch_size, dtype=tf.float32)
#initial_state_bw = rnn_cell_bw.zero_state(batch_size, dtype=tf.float32)
((encoder_fw_outputs,encoder_bw_outputs),(encoder_fw_final_state,encoder_bw_final_state))\
= tf.nn.bidirectional_dynamic_rnn(cell_fw=rnn_cell_fw,
cell_bw=rnn_cell_bw,
inputs=_inputs,
sequence_length=encoder_inputs_length,
dtype=tf.float32)
_inputs = tf.concat((encoder_fw_outputs,encoder_bw_outputs), 2)
#取最后一层的 final_state
encoder_final_state_h = tf.concat((encoder_fw_final_state.h, encoder_bw_final_state.h), 1)
encoder_final_state_c = tf.concat((encoder_fw_final_state.c, encoder_bw_final_state.c), 1)
encoder_final_state = rnn.LSTMStateTuple(c=encoder_final_state_c, h=encoder_final_state_h)
encoder_final_output = _inputs
encoder_final_state
LSTMStateTuple(c=<tf.Tensor 'concat_3:0' shape=(?, 50) dtype=float32>, h=<tf.Tensor 'concat_2:0' shape=(?, 50) dtype=float32>)
encoder_final_output
<tf.Tensor 'bidirectional-rnn_4/concat:0' shape=(?, ?, 50) dtype=float32>
5.定义decoder 部分
def _create_rnn_cell2():
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*2)
#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
with tf.variable_scope('decoder'):
#single_cell = rnn.LSTMCell(encoder_hidden_units)
#decoder_cell = rnn.MultiRNNCell([single_cell for _ in range(1)])
decoder_cell = rnn.LSTMCell(encoder_hidden_units*2)
#定义decoder的初始状态
decoder_initial_state = encoder_final_state
#定义output_layer
output_layer = tf.layers.Dense(vocab_size,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=output_layer)
# 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)
decoder_logits_train = tf.identity(decoder_outputs.rnn_output)
sample_id = decoder_outputs.sample_id
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)
<tf.Tensor 'decoder/Identity:0' shape=(?, ?, 10) dtype=float32>
<tf.Tensor 'minibatch/decoder_targets:0' shape=(?, ?) dtype=int32>
<tf.Tensor 'decoder/decoder/transpose_1:0' shape=(?, ?) dtype=int32>
with tf.variable_scope('decoder',reuse=True):
start_tokens = tf.ones([batch_size, ], tf.int32)*1 #[batch_size] 数值为1
encoder_state = nest.map_structure(lambda s: seq2seq.tile_batch(s, 3),
encoder_final_state)
inference_decoder = tf.contrib.seq2seq.BeamSearchDecoder(cell=decoder_cell, embedding=embedding,
start_tokens=start_tokens,
end_token=1,
initial_state=encoder_state,
beam_width=3,
output_layer=output_layer)
beam_decoder_outputs, _, _ = seq2seq.dynamic_decode(decoder=inference_decoder,maximum_iterations=10)
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:
[3 3 7 3 0 0 0 0]
encoder_inputs_length:
4
decoder_inputs:
[1 3 3 7 3 0 0 0 0]
decoder_inputs_length:
5
decoder_targets:
[3 3 7 3 1 0 0 0 0]
loss_track = []
max_batches = 6001
batches_in_epoch = 200
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(beam_decoder_outputs.predicted_ids, fd)
#print(predict_)
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: 2.2935664653778076
sample 1:
input > [9 2 8 0 0 0 0 0]
predicted > [[5 5 5]
[5 5 5]
[5 5 5]
[5 5 8]
[8 5 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 8 8]]
sample 2:
input > [6 8 9 7 2 6 9 3]
predicted > [[5 5 5]
[5 5 5]
[5 5 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 9 8]]
sample 3:
input > [3 6 6 0 0 0 0 0]
predicted > [[5 5 5]
[5 5 5]
[5 5 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 8 8]
[8 9 8]]
batch 200
minibatch loss: 1.4949365854263306
sample 1:
input > [4 7 8 6 7 9 0 0]
predicted > [[ 3 3 4]
[ 4 4 3]
[ 5 5 5]
[ 7 5 5]
[ 4 9 9]
[ 9 4 4]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [9 3 7 4 0 0 0 0]
predicted > [[ 4 4 4]
[ 9 9 9]
[ 5 4 9]
[ 4 4 4]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [2 5 9 6 0 0 0 0]
predicted > [[ 9 6 9]
[ 6 9 6]
[ 6 2 6]
[ 1 1 2]
[-1 -1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 400
minibatch loss: 1.2325794696807861
sample 1:
input > [6 7 2 9 0 0 0 0]
predicted > [[ 6 6 6]
[ 6 2 2]
[ 2 4 4]
[ 4 6 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [8 7 3 3 3 2 9 0]
predicted > [[8 8 8]
[3 3 3]
[3 3 3]
[2 2 2]
[3 5 5]
[5 2 2]
[9 5 9]
[1 1 1]]
sample 3:
input > [6 4 2 4 3 7 2 0]
predicted > [[4 4 4]
[2 2 2]
[7 2 2]
[2 7 7]
[4 4 4]
[6 7 7]
[4 6 2]
[1 1 1]]
batch 600
minibatch loss: 0.9292899370193481
sample 1:
input > [4 9 5 9 9 2 0 0]
predicted > [[ 9 9 9]
[ 4 4 4]
[ 5 5 4]
[ 9 9 9]
[ 2 2 7]
[ 4 5 2]
[ 1 1 1]
[-1 -1 -1]]
sample 2:
input > [8 2 6 4 7 0 0 0]
predicted > [[ 7 7 4]
[ 2 2 2]
[ 4 4 7]
[ 6 6 6]
[ 4 5 8]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [7 9 7 9 6 0 0 0]
predicted > [[ 9 9 9]
[ 7 7 7]
[ 7 7 7]
[ 6 9 9]
[ 9 7 6]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
batch 800
minibatch loss: 0.7363898754119873
sample 1:
input > [9 2 6 0 0 0 0 0]
predicted > [[ 9 2 9]
[ 2 9 6]
[ 6 6 2]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [6 8 7 9 6 3 2 0]
predicted > [[ 6 6 6]
[ 5 8 5]
[ 3 6 6]
[ 6 5 3]
[ 9 6 3]
[ 7 3 9]
[ 3 2 7]
[ 1 1 1]
[-1 -1 -1]]
sample 3:
input > [9 2 8 4 9 6 9 3]
predicted > [[9 9 9]
[3 2 3]
[9 9 9]
[2 8 4]
[4 4 2]
[9 6 9]
[7 9 7]
[8 7 8]
[1 1 1]]
batch 1000
minibatch loss: 0.7347214221954346
sample 1:
input > [3 3 8 0 0 0 0 0]
predicted > [[ 3 3 3]
[ 3 3 8]
[ 8 3 6]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [3 8 4 9 6 3 5 4]
predicted > [[ 3 3 3]
[ 3 3 3]
[ 4 4 4]
[ 5 5 5]
[ 9 6 6]
[ 5 9 9]
[ 6 4 4]
[ 1 1 8]
[-1 -1 1]]
sample 3:
input > [3 4 7 0 0 0 0 0]
predicted > [[ 3 4 7]
[ 4 3 4]
[ 7 7 3]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 1200
minibatch loss: 0.43508097529411316
sample 1:
input > [5 5 5 4 4 4 4 0]
predicted > [[ 5 5 5]
[ 5 5 4]
[ 4 4 5]
[ 4 5 5]
[ 5 4 4]
[ 5 4 5]
[ 1 5 1]
[-1 1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [2 7 3 0 0 0 0 0]
predicted > [[ 2 7 2]
[ 7 2 7]
[ 3 3 6]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [2 2 8 0 0 0 0 0]
predicted > [[ 2 2 2]
[ 2 2 8]
[ 8 5 2]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 1400
minibatch loss: 0.41912826895713806
sample 1:
input > [7 8 5 3 2 0 0 0]
predicted > [[ 5 7 8]
[ 7 8 7]
[ 8 5 5]
[ 3 3 3]
[ 2 2 2]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [8 7 6 9 2 0 0 0]
predicted > [[ 8 7 8]
[ 7 8 6]
[ 6 9 7]
[ 9 6 2]
[ 2 2 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [7 8 3 8 5 8 7 6]
predicted > [[8 8 8]
[7 8 7]
[8 7 8]
[3 3 3]
[7 7 7]
[8 8 5]
[6 6 3]
[5 5 5]
[1 1 1]]
batch 1600
minibatch loss: 0.3989475965499878
sample 1:
input > [3 7 4 4 7 2 0 0]
predicted > [[ 3 7 7]
[ 7 3 4]
[ 4 4 3]
[ 4 4 3]
[ 7 3 4]
[ 2 2 2]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [5 7 6 4 2 4 9 5]
predicted > [[5 5 5]
[7 7 7]
[6 6 6]
[4 4 4]
[2 2 2]
[5 4 9]
[9 9 5]
[4 5 4]
[1 1 1]]
sample 3:
input > [7 6 4 0 0 0 0 0]
predicted > [[ 7 6 7]
[ 6 7 4]
[ 4 4 6]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 1800
minibatch loss: 0.31475651264190674
sample 1:
input > [8 5 9 9 9 4 0 0]
predicted > [[ 8 8 8]
[ 5 9 9]
[ 9 5 5]
[ 9 4 9]
[ 4 9 4]
[ 9 9 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [9 6 6 0 0 0 0 0]
predicted > [[ 9 6 6]
[ 6 9 9]
[ 6 6 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [7 3 6 3 3 7 0 0]
predicted > [[ 3 7 7]
[ 7 3 3]
[ 6 6 3]
[ 3 3 6]
[ 7 3 7]
[ 3 7 3]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
batch 2000
minibatch loss: 0.41449815034866333
sample 1:
input > [6 6 8 0 0 0 0 0]
predicted > [[ 6 6 6]
[ 6 9 3]
[ 8 7 9]
[ 1 1 7]
[-1 -1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [8 8 5 6 2 9 2 3]
predicted > [[8 8 8]
[8 8 8]
[5 5 6]
[6 6 5]
[2 2 2]
[9 9 9]
[2 3 2]
[3 2 3]
[1 1 1]]
sample 3:
input > [5 2 3 2 4 7 7 6]
predicted > [[2 5 2]
[5 2 5]
[4 2 4]
[3 3 3]
[2 5 2]
[7 7 7]
[7 4 6]
[6 6 5]
[1 1 1]]
batch 2200
minibatch loss: 0.2028750777244568
sample 1:
input > [2 5 6 8 6 7 7]
predicted > [[2 2 2]
[5 9 6]
[6 7 5]
[7 5 8]
[9 7 7]
[7 6 6]
[8 5 7]
[1 1 1]]
sample 2:
input > [9 3 3 0 0 0 0]
predicted > [[ 9 9 3]
[ 3 3 9]
[ 3 6 9]
[ 1 1 3]
[-1 -1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [7 9 9 0 0 0 0]
predicted > [[ 7 8 6]
[ 9 6 8]
[ 9 9 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 2400
minibatch loss: 0.17885658144950867
sample 1:
input > [3 6 8 3 2 0 0 0]
predicted > [[ 3 6 3]
[ 6 3 6]
[ 8 8 8]
[ 3 3 3]
[ 2 3 3]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [4 5 4 6 5 5 0 0]
predicted > [[ 4 4 4]
[ 5 5 5]
[ 4 4 4]
[ 5 6 5]
[ 6 5 6]
[ 5 5 4]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [9 7 2 3 3 0 0 0]
predicted > [[ 9 9 9]
[ 7 7 7]
[ 3 2 3]
[ 2 3 2]
[ 3 3 6]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 2600
minibatch loss: 0.20247018337249756
sample 1:
input > [3 7 9 0 0 0 0 0]
predicted > [[ 3 7 3]
[ 7 3 5]
[ 9 9 2]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [2 5 2 5 8 0 0 0]
predicted > [[ 2 2 2]
[ 5 5 5]
[ 2 2 2]
[ 5 5 5]
[ 8 5 3]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [5 7 3 7 0 0 0 0]
predicted > [[ 5 5 7]
[ 7 7 5]
[ 3 7 5]
[ 7 3 3]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 2800
minibatch loss: 0.24160973727703094
sample 1:
input > [8 2 2 8 4 0 0 0]
predicted > [[ 8 2 8]
[ 2 8 2]
[ 2 8 2]
[ 8 2 4]
[ 4 4 8]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [3 3 8 7 0 0 0 0]
predicted > [[ 3 3 3]
[ 3 3 8]
[ 8 7 3]
[ 7 8 7]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [5 5 8 7 3 7 8 5]
predicted > [[5 5 5]
[5 5 5]
[8 8 8]
[7 7 7]
[3 3 7]
[7 7 3]
[8 5 8]
[5 8 5]
[1 1 1]]
batch 3000
minibatch loss: 0.23292377591133118
sample 1:
input > [4 4 2 7 0 0 0 0]
predicted > [[ 4 4 4]
[ 4 4 2]
[ 2 7 4]
[ 7 2 7]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [9 5 3 4 8 7 6 9]
predicted > [[9 9 9]
[5 5 5]
[3 3 8]
[4 4 3]
[8 8 4]
[7 7 6]
[9 6 7]
[6 9 9]
[1 1 1]]
sample 3:
input > [5 5 2 4 2 0 0 0]
predicted > [[ 5 5 5]
[ 5 5 5]
[ 2 4 2]
[ 4 2 2]
[ 2 2 4]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 3200
minibatch loss: 0.13823337852954865
sample 1:
input > [3 3 7 8 2 3 7 2]
predicted > [[3 3 3]
[3 3 3]
[7 8 8]
[8 7 7]
[2 2 2]
[3 3 3]
[7 7 2]
[2 2 7]
[1 1 1]]
sample 2:
input > [6 2 7 3 5 4 7 2]
predicted > [[6 6 6]
[2 2 2]
[7 7 7]
[3 3 5]
[4 5 3]
[5 4 2]
[7 2 4]
[2 7 7]
[1 1 1]]
sample 3:
input > [2 2 7 7 2 0 0 0]
predicted > [[ 2 2 2]
[ 2 7 2]
[ 7 2 7]
[ 7 2 2]
[ 2 7 7]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 3400
minibatch loss: 0.118137888610363
sample 1:
input > [5 5 7 7 6 0 0 0]
predicted > [[ 5 5 5]
[ 5 7 7]
[ 7 5 5]
[ 7 5 5]
[ 6 7 6]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [8 2 4 5 0 0 0 0]
predicted > [[ 8 4 8]
[ 2 8 2]
[ 4 2 4]
[ 5 5 4]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [3 5 2 4 0 0 0 0]
predicted > [[ 3 3 3]
[ 5 5 5]
[ 2 2 2]
[ 4 5 8]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 3600
minibatch loss: 0.18091285228729248
sample 1:
input > [9 2 3 2 7 6 6 3]
predicted > [[9 9 9]
[2 2 2]
[3 2 3]
[2 3 2]
[6 7 7]
[7 6 6]
[6 6 6]
[3 3 3]
[1 1 1]]
sample 2:
input > [9 7 6 8 5 3 0 0]
predicted > [[ 9 9 9]
[ 7 7 7]
[ 6 6 9]
[ 8 8 7]
[ 5 5 3]
[ 3 7 5]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [3 4 4 9 8 8 8 5]
predicted > [[3 3 3]
[4 4 4]
[4 4 9]
[9 8 8]
[8 9 4]
[8 5 8]
[5 8 5]
[8 8 5]
[1 1 1]]
batch 3800
minibatch loss: 0.1578817516565323
sample 1:
input > [4 9 4 5 4 3 9 0]
predicted > [[ 4 4 4]
[ 4 9 4]
[ 9 4 9]
[ 5 5 5]
[ 9 4 9]
[ 8 3 3]
[ 3 9 4]
[ 1 1 1]
[-1 -1 -1]]
sample 2:
input > [8 3 3 4 0 0 0 0]
predicted > [[ 8 3 3]
[ 3 8 8]
[ 3 8 8]
[ 4 3 4]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [5 4 7 9 5 0 0 0]
predicted > [[ 5 5 5]
[ 4 4 7]
[ 7 5 4]
[ 9 7 9]
[ 5 9 4]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 4000
minibatch loss: 0.21402882039546967
sample 1:
input > [2 4 9 4 4 3 2 0]
predicted > [[ 4 2 4]
[ 2 4 2]
[ 9 9 9]
[ 4 4 4]
[ 2 4 2]
[ 8 3 8]
[ 4 2 1]
[ 1 1 -1]
[-1 -1 -1]]
sample 2:
input > [7 8 5 0 0 0 0 0]
predicted > [[ 7 7 7]
[ 8 8 8]
[ 5 8 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [6 6 2 0 0 0 0 0]
predicted > [[ 6 6 6]
[ 6 2 6]
[ 2 6 4]
[ 1 1 2]
[-1 -1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 4200
minibatch loss: 0.07165724784135818
sample 1:
input > [5 8 2 6 0 0 0 0]
predicted > [[ 5 8 8]
[ 8 5 5]
[ 2 6 2]
[ 6 2 6]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [4 3 9 8 7 3 9 2]
predicted > [[4 4 4]
[3 3 3]
[9 9 9]
[8 7 8]
[7 8 7]
[3 3 3]
[9 9 2]
[2 3 9]
[1 1 1]]
sample 3:
input > [4 2 3 8 2 0 0 0]
predicted > [[ 4 2 4]
[ 2 4 3]
[ 3 8 2]
[ 8 3 2]
[ 2 2 8]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 4400
minibatch loss: 0.08584733307361603
sample 1:
input > [5 6 4 5 2 5 5]
predicted > [[5 5 5]
[6 6 6]
[4 4 4]
[5 5 5]
[2 6 2]
[5 2 5]
[5 5 6]
[1 1 1]]
sample 2:
input > [5 5 8 7 9 3 0]
predicted > [[ 5 5 5]
[ 5 5 5]
[ 8 8 8]
[ 9 7 6]
[ 7 9 8]
[ 3 3 7]
[ 1 1 1]
[-1 -1 -1]]
sample 3:
input > [9 5 9 2 5 7 3]
predicted > [[9 9 9]
[5 5 5]
[9 9 9]
[2 2 2]
[5 7 5]
[7 5 7]
[3 3 7]
[1 1 1]]
batch 4600
minibatch loss: 0.08049434423446655
sample 1:
input > [3 9 4 0 0 0 0 0]
predicted > [[ 3 3 9]
[ 9 4 3]
[ 4 9 4]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [7 8 9 4 3 5 2 3]
predicted > [[7 7 8]
[8 8 7]
[9 9 9]
[4 4 4]
[3 3 3]
[5 7 5]
[2 9 2]
[3 3 3]
[1 1 1]]
sample 3:
input > [9 3 6 4 4 6 5 9]
predicted > [[9 9 9]
[3 3 3]
[6 4 6]
[4 6 4]
[4 6 4]
[6 5 5]
[5 4 6]
[9 9 6]
[1 1 1]]
batch 4800
minibatch loss: 0.037724826484918594
sample 1:
input > [5 6 8 2 5 0 0]
predicted > [[ 5 6 6]
[ 6 5 5]
[ 8 8 8]
[ 2 2 5]
[ 5 5 2]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [7 9 5 0 0 0 0]
predicted > [[ 7 5 5]
[ 9 7 2]
[ 5 9 7]
[ 1 1 9]
[-1 -1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [5 3 3 6 0 0 0]
predicted > [[ 5 3 3]
[ 3 5 5]
[ 3 5 3]
[ 6 3 5]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 5000
minibatch loss: 0.12354864180088043
sample 1:
input > [4 6 9 6 5 7 8 9]
predicted > [[4 4 4]
[6 6 6]
[9 9 6]
[6 6 9]
[5 5 5]
[7 7 8]
[9 8 6]
[8 9 7]
[1 1 1]]
sample 2:
input > [6 5 9 9 8 0 0 0]
predicted > [[ 6 6 6]
[ 5 5 9]
[ 9 9 5]
[ 9 8 8]
[ 8 9 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [6 7 2 8 9 7 0 0]
predicted > [[ 6 6 6]
[ 7 7 7]
[ 2 2 2]
[ 8 8 8]
[ 9 6 7]
[ 7 8 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]]
batch 5200
minibatch loss: 0.05009409785270691
sample 1:
input > [6 3 8 7 0 0 0]
predicted > [[ 6 6 6]
[ 3 8 8]
[ 8 3 3]
[ 7 7 6]
[ 1 1 7]
[-1 -1 1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [9 2 5 9 0 0 0]
predicted > [[ 9 2 9]
[ 2 9 5]
[ 5 5 2]
[ 9 9 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 3:
input > [3 5 2 7 7 6 0]
predicted > [[ 3 5 3]
[ 5 3 5]
[ 2 2 7]
[ 7 7 2]
[ 7 6 2]
[ 6 7 5]
[ 1 1 1]
[-1 -1 -1]]
batch 5400
minibatch loss: 0.09247519075870514
sample 1:
input > [8 6 5 3 8 7 4 2]
predicted > [[8 8 8]
[6 6 6]
[5 5 5]
[3 8 8]
[8 3 3]
[7 7 2]
[4 2 7]
[2 4 4]
[1 1 1]]
sample 2:
input > [8 6 3 9 4 7 5 0]
predicted > [[ 8 3 3]
[ 6 8 8]
[ 3 9 9]
[ 9 6 6]
[ 4 4 7]
[ 7 7 4]
[ 5 7 4]
[ 1 1 1]
[-1 -1 -1]]
sample 3:
input > [5 3 8 8 6 6 3 0]
predicted > [[ 5 5 5]
[ 8 3 8]
[ 3 8 3]
[ 6 8 6]
[ 8 6 3]
[ 3 6 8]
[ 6 3 9]
[ 1 1 1]
[-1 -1 -1]]
batch 5600
minibatch loss: 0.05249354988336563
sample 1:
input > [5 9 5 0 0 0 0 0]
predicted > [[ 5 5 5]
[ 9 9 5]
[ 5 8 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [9 6 6 7 3 6 5 6]
predicted > [[ 9 9 9]
[ 6 6 6]
[ 6 6 6]
[ 7 3 7]
[ 3 7 3]
[ 6 6 6]
[ 5 5 5]
[ 6 6 1]
[ 1 1 -1]]
sample 3:
input > [6 3 9 5 9 9 3 0]
predicted > [[ 6 3 3]
[ 3 6 6]
[ 9 9 9]
[ 5 5 5]
[ 9 9 9]
[ 9 9 9]
[ 3 3 8]
[ 1 1 1]
[-1 -1 -1]]
batch 5800
minibatch loss: 0.08289551734924316
sample 1:
input > [8 9 3 5 2 0 0 0]
predicted > [[ 8 8 9]
[ 9 9 8]
[ 3 5 3]
[ 5 3 5]
[ 2 2 2]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [7 4 8 3 2 3 4 9]
predicted > [[ 7 7 7]
[ 4 4 4]
[ 8 8 3]
[ 3 3 8]
[ 2 2 2]
[ 3 3 4]
[ 4 9 3]
[ 9 4 9]
[ 1 1 6]
[-1 -1 1]]
sample 3:
input > [2 3 6 6 0 0 0 0]
predicted > [[ 2 2 3]
[ 3 3 2]
[ 6 6 6]
[ 6 9 6]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
batch 6000
minibatch loss: 0.03706203028559685
sample 1:
input > [3 5 3 9 0 0 0 0]
predicted > [[ 3 3 5]
[ 5 3 3]
[ 3 5 3]
[ 9 9 9]
[ 1 1 1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]
[-1 -1 -1]]
sample 2:
input > [8 2 9 6 2 8 2 3]
predicted > [[8 8 8]
[2 2 9]
[9 9 2]
[6 6 2]
[2 2 6]
[8 8 8]
[3 2 3]
[2 3 9]
[1 1 1]]
sample 3:
input > [6 4 8 4 9 7 4 0]
predicted > [[ 6 6 6]
[ 4 4 4]
[ 8 8 4]
[ 4 9 8]
[ 9 4 8]
[ 7 7 9]
[ 4 4 6]
[ 1 1 1]
[-1 -1 -1]]
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(loss_track)
print('loss {:.4f} after {} examples (batch_size={})'.format(loss_track[-1],
len(loss_track)*batch_size, batch_size))
loss 0.0375 after 60010 examples (batch_size=10)
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