DL之Attention-ED:基于TF NMT利用带有Attention的 ED模型训练、测试(中英文平行语料库)实现将英文翻译为中文的LSTM翻译模型过程全记录

简介: DL之Attention-ED:基于TF NMT利用带有Attention的 ED模型训练、测试(中英文平行语料库)实现将英文翻译为中文的LSTM翻译模型过程全记录

测试输出结果


轻轻的我走了, 正如我轻轻的来; 我轻轻的招手, 作别...




模型监控


1、SCALARS

image.png

image.png

image.png

image.png


clipped_gradient

grad_norm

train_loss

dev_bleu  dev_ppl

lr_1  test_bleu

test_ppl  train_ppl

2、IMAGES

image.png

attention_images_1/image/0   step 6,000




3、GRAPHS

image.png

image.png






训练过程全记录


开始训练

# Job id 0

# Loading hparams from tmp/nmt_model_zh\hparams

 saving hparams to tmp/nmt_model_zh\hparams

 saving hparams to tmp/nmt_model_zh\best_bleu\hparams

 attention=scaled_luong

 attention_architecture=standard

 batch_size=128

 beam_width=0

 best_bleu=0

 best_bleu_dir=tmp/nmt_model_zh\best_bleu

 bpe_delimiter=None

 colocate_gradients_with_ops=True

 decay_factor=0.98

 decay_steps=10000

 dev_prefix=tmp/nmt_zh/dev

 dropout=0.2

 encoder_type=uni

 eos=</s>

 epoch_step=0

 forget_bias=1.0

 infer_batch_size=32

 init_op=uniform

 init_weight=0.1

 learning_rate=1.0

 length_penalty_weight=0.0

 log_device_placement=False

 max_gradient_norm=5.0

 max_train=0

 metrics=['bleu']

 num_buckets=5

 num_embeddings_partitions=0

 num_gpus=1

 num_layers=3

 num_residual_layers=0

 num_train_steps=200000

 num_units=256

 optimizer=sgd

 out_dir=tmp/nmt_model_zh

 pass_hidden_state=True

 random_seed=None

 residual=False

 share_vocab=False

 sos=<s>

 source_reverse=False

 src=en

 src_max_len=50

 src_max_len_infer=None

 src_vocab_file=tmp/nmt_zh/vocab.en

 src_vocab_size=35028

 start_decay_step=0

 steps_per_external_eval=None

 steps_per_stats=100

 test_prefix=tmp/nmt_zh/test

 tgt=zh

 tgt_max_len=50

 tgt_max_len_infer=None

 tgt_vocab_file=tmp/nmt_zh/vocab.zh

 tgt_vocab_size=53712

 time_major=True

 train_prefix=tmp/nmt_zh/train

 unit_type=lstm

 vocab_prefix=tmp/nmt_zh/vocab

# creating train graph ...

 num_layers = 3, num_residual_layers=0

 cell 0  LSTM, forget_bias=1WARNING:tensorflow:From

Instructions for updating:

This class is deprecated, please use tf.nn.rnn_cell.LSTMCell, which supports all the feature this cell currently has. Please replace the existing code with tf.nn.rnn_cell.LSTMCell(name='basic_lstm_cell').

 DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0

 cell 1  LSTM, forget_bias=1  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0

 cell 2  LSTM, forget_bias=1  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0

 cell 0  LSTM, forget_bias=1  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0

 cell 1  LSTM, forget_bias=1  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0

 cell 2  LSTM, forget_bias=1  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0

 start_decay_step=0, learning_rate=1, decay_steps 10000,decay_factor 0.98

# Trainable variables

 embeddings/encoder/embedding_encoder:0, (35028, 256),

 embeddings/decoder/embedding_decoder:0, (53712, 256),

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/memory_layer/kernel:0, (256, 256),

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0, (768, 1024), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0, (), /device:GPU:0

 dynamic_seq2seq/decoder/attention/attention_layer/kernel:0, (512, 256), /device:GPU:0

 dynamic_seq2seq/decoder/output_projection/kernel:0, (256, 53712), /device:GPU:0

# creating eval graph ...

 num_layers = 3, num_residual_layers=0

 cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 1  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 2  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 1  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 2  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 start_decay_step=0, learning_rate=1, decay_steps 10000,decay_factor 0.98

# Trainable variables

 embeddings/encoder/embedding_encoder:0, (35028, 256),

 embeddings/decoder/embedding_decoder:0, (53712, 256),

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/memory_layer/kernel:0, (256, 256),

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0, (768, 1024), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0, (), /device:GPU:0

 dynamic_seq2seq/decoder/attention/attention_layer/kernel:0, (512, 256), /device:GPU:0

 dynamic_seq2seq/decoder/output_projection/kernel:0, (256, 53712), /device:GPU:0

# creating infer graph ...

 num_layers = 3, num_residual_layers=0

 cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 1  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 2  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 1  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 cell 2  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0

 start_decay_step=0, learning_rate=1, decay_steps 10000,decay_factor 0.98

# Trainable variables

 embeddings/encoder/embedding_encoder:0, (35028, 256),

 embeddings/decoder/embedding_decoder:0, (53712, 256),

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/memory_layer/kernel:0, (256, 256),

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0, (768, 1024), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0, (512, 1024), /device:GPU:0

 dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0, (1024,), /device:GPU:0

 dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0, (), /device:GPU:0

 dynamic_seq2seq/decoder/attention/attention_layer/kernel:0, (512, 256), /device:GPU:0

 dynamic_seq2seq/decoder/output_projection/kernel:0, (256, 53712),

# log_file=tmp/nmt_model_zh\log_1539923931

2018-10-19 12:38:51.109178: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

 created train model with fresh parameters, time 0.94s

 created infer model with fresh parameters, time 0.50s

 # 215

   src: second , the human capital , knowledge reserves , and other high - grade economic elements have formed the comparative advantages of the province in terms of technical innovation and the development of new high - tech industries .

   ref: 其二 , 人力 资本 , 知识 储备 等 高级 经济 要素 , 形成 了 吉林省 技术 创新 和 发展 高新技术 产业 的 比较 优势 .

   nmt: b'\xe9\xaa\x87 \xe6\x9d\x83\xe8\xa1\xa1 \xe6\x9d\x83\xe8\xa1\xa1 \xe6\x9d\x83\xe8\xa1\xa1 \xe6\x9d\x83\xe8\xa1\xa1 \xe6\x9d\x83\xe8\xa1\xa1 \xe5\x8d\x8a\xe8\xbe\xb9\xe5\xa4\xa9 \xe5\x8d\x8a\xe8\xbe\xb9\xe5\xa4\xa9 \xe5\x8d\x8a\xe8\xbe\xb9\xe5\xa4\xa9 \xe8\xb4\xa7\xe6\xac\xbe \xe8\xb4\xa7\xe6\xac\xbe \xe8\xb4\xa7\xe6\xac\xbe \xe8\xb4\xa7\xe6\xac\xbe \xe8\xb4\xa7\xe6\xac\xbe \xe6\xb1\x9f\xe5\x8d\xab\xe5\x9b\xbd \xe6\xb1\x9f\xe5\x8d\xab\xe5\x9b\xbd \xe6\xb1\x9f\xe5\x8d\xab\xe5\x9b\xbd \xe6\xb1\x9f\xe5\x8d\xab\xe5\x9b\xbd \xe6\xa1\x91\xe4\xb9\x85\xe7\xbe\x8e \xe6\xa1\x91\xe4\xb9\x85\xe7\xbe\x8e \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe9\x93\xb8\xe6\x88\x90\xe5\xa4\xa7\xe9\x94\x99 \xe4\xbe\x9b\xe9\x94\x80 \xe6\x9d\x8e\xe4\xb8\xbd\xe8\xbe\x89 \xe6\x9d\x8e\xe4\xb8\xbd\xe8\xbe\x89 \xe6\x9d\x8e\xe4\xb8\xbd\xe8\xbe\x89 \xe6\x97\xa5\xe6\x9c\xac\xe6\xb5\xb7 \xe6\x97\xa5\xe6\x9c\xac\xe6\xb5\xb7 \xe6\x97\xa5\xe6\x9c\xac\xe6\xb5\xb7 \xe6\x97\xa5\xe6\x9c\xac\xe6\xb5\xb7 \xe6\x97\xa5\xe6\x9c\xac\xe6\xb5\xb7 \xe6\x97\xa5\xe6\x9c\xac\xe6\xb5\xb7 \xe6\x97\xa5\xe6\x9c\xac\xe6\xb5\xb7 \xe6\x9d\x8e\xe6\xb0\xb8\xe5\x88\x9d \xe6\x9d\x8e\xe6\xb0\xb8\xe5\x88\x9d \xe6\x9d\x8e\xe6\xb0\xb8\xe5\x88\x9d \xe6\x9d\x8e\xe6\xb0\xb8\xe5\x88\x9d \xe6\x9d\x8e\xe6\xb0\xb8\xe5\x88\x9d \xe5\x9b\xb4\xe6\xad\xbc \xe5\x9b\xb4\xe6\xad\xbc \xe5\x9b\xb4\xe6\xad\xbc \xe5\x9b\xb4\xe6\xad\xbc \xe5\x9b\xb4\xe6\xad\xbc \xe6\x9d\x8e\xe5\xae\x89\xe5\xb9\xb3 \xe6\x9d\x8e\xe5\xae\x89\xe5\xb9\xb3 \xe5\xae\x8b\xe8\xb1\xab \xe5\xae\x8b\xe8\xb1\xab \xe5\xae\x8b\xe8\xb1\xab \xe5\xae\x8b\xe8\xb1\xab \xe5\xae\x8b\xe8\xb1\xab \xe5\xae\x8b\xe8\xb1\xab \xe5\xae\x8b\xe8\xb1\xab \xe6\xaf\x8f\xe5\x86\xb5\xe6\x84\x88\xe4\xb8\x8b \xe5\xbd\xb1\xe5\x93\x8d\xe5\x88\xb0 \xe5\xbd\xb1\xe5\x93\x8d\xe5\x88\xb0 \xe5\xbd\xb1\xe5\x93\x8d\xe5\x88\xb0 \xe5\xbd\xb1\xe5\x93\x8d\xe5\x88\xb0 \xe5\xbd\xb1\xe5\x93\x8d\xe5\x88\xb0 \xe7\xbb\xa7\xe4\xbd\x8d \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbe\x9d\xe9\x82\xa3\xe5\x90\x90\xe6\x8b\x89 \xe4\xbb\xa5\xe9\x82\xbb\xe4\xb8\xba\xe5\xa3\x91'

 created eval model with fresh parameters, time 0.61s

2018-10-19 12:38:57.783584: W tensorflow/core/framework/allocator.cc:113] Allocation of 641750976 exceeds 10% of system memory.

2018-10-19 12:39:01.312543: W tensorflow/core/framework/allocator.cc:113] Allocation of 812769984 exceeds 10% of system memory.

2018-10-19 12:39:05.244676: W tensorflow/core/framework/allocator.cc:113] Allocation of 660013056 exceeds 10% of system memory.

2018-10-19 12:39:08.661067: W tensorflow/core/framework/allocator.cc:113] Allocation of 800523648 exceeds 10% of system memory.

 eval dev: perplexity 53779.34, time 16s, Fri Oct 19 12:39:11 2018.

2018-10-19 12:39:14.135415: W tensorflow/core/framework/allocator.cc:113] Allocation of 641750976 exceeds 10% of system memory.

 eval test: perplexity 53779.35, time 17s, Fri Oct 19 12:39:29 2018.

2018-10-19 12:39:29.544562: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 12:39:29.544589: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-19 12:39:29.544778: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 created infer model with fresh parameters, time 0.50s

# Start step 0, lr 1, Fri Oct 19 12:39:29 2018

# Init train iterator, skipping 0 elements

 global step 100 lr 1 step-time 12.18s wps 0.61K ppl 370383.80 bleu 0.00

 global step 200 lr 1 step-time 11.44s wps 0.64K ppl 69384.04 bleu 0.00

 global step 300 lr 1 step-time 11.49s wps 0.65K ppl 22598.76 bleu 0.00

 global step 400 lr 1 step-time 11.55s wps 0.64K ppl 14178.53 bleu 0.00

 global step 500 lr 1 step-time 11.50s wps 0.65K ppl 10184.07 bleu 0.00

 global step 600 lr 1 step-time 12.13s wps 0.61K ppl 6656.94 bleu 0.00

 global step 700 lr 1 step-time 13.51s wps 0.55K ppl 2673.24 bleu 0.00

# Finished an epoch, step 785. Perform external evaluation

2018-10-19 15:16:44.846427: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 15:16:44.846427: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 15:16:44.867191: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

 created infer model with fresh parameters, time 1.07s

 # 64

   src: under the leadership of the state civil air defense office , the center will assume overall work related to china 's civil air defense engineering as well as nuclear and chemical protection .

   ref: 这个 中心 将 在 人民 办公室  下 , 担负 我国 人防 工程 化 防护 的 全面 工作 .

   nmt: b'\xe6\x96\xb9\xe5\x85\xb3\xe6\x96\xbc 2020\xe5\xb9\xb4 \xe9\x9f\xa6\xe5\xa4\x9a \xe9\x9f\xa6\xe5\xa4\x9a 19-02-2000 19-02-2000 19-02-2000 19-02-2000 \xe7\xb3\xbb\xe7\xbb\x9f\xe5\x9c\xb0 \xe7\xb3\xbb\xe7\xbb\x9f\xe5\x9c\xb0 \xe5\x97\x85\xe5\x87\xba \xe5\x97\x85\xe5\x87\xba \xe5\x97\x85\xe5\x87\xba \xe6\xac\xa1\xe4\xbe\x8d \xe6\xac\xa1\xe4\xbe\x8d \xe6\xac\xa1\xe4\xbe\x8d \xe6\xac\xa1\xe4\xbe\x8d \xe6\xac\xa1\xe4\xbe\x8d \xe5\x8f\x8d\xe8\xb4\xaa\xe5\xb1\x80 \xe5\x8f\x8d\xe8\xb4\xaa\xe5\xb1\x80 \xe5\x8f\x8d\xe8\xb4\xaa\xe5\xb1\x80 \xe8\x96\x84\xe2\x85\xb0\xe5\x82\xb2 \xe8\x96\x84\xe2\x85\xb0\xe5\x82\xb2 \xe8\x96\x84\xe2\x85\xb0\xe5\x82\xb2 \xe8\x96\x84\xe2\x85\xb0\xe5\x82\xb2 \xe5\x8f\x8c\xe5\xba\xa7 \xe5\x8f\x8c\xe5\xba\xa7 \xe6\x96\xbd\xe5\x90\x9b\xe7\x8e\x89 \xe6\x96\xbd\xe5\x90\x9b\xe7\x8e\x89 \xe6\x96\xbd\xe5\x90\x9b\xe7\x8e\x89 \xe6\x96\xbd\xe5\x90\x9b\xe7\x8e\x89 \xe6\x96\xbd\xe5\x90\x9b\xe7\x8e\x89 \xe6\x96\xbd\xe5\x90\x9b\xe7\x8e\x89 \xe6\x96\xbd\xe5\x90\x9b\xe7\x8e\x89 \xe6\x96\xbd\xe5\x90\x9b\xe7\x8e\x89 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe9\x99\xa2 \xe5\xa4\xa7\xe4\xbd\xbf\xe7\xba\xa7 \xe5\xa4\xa7\xe4\xbd\xbf\xe7\xba\xa7 \xe5\xa4\xa7\xe4\xbd\xbf\xe7\xba\xa7 \xe5\xa4\xa7\xe4\xbd\xbf\xe7\xba\xa7 \xe5\xa4\xa7\xe4\xbd\xbf\xe7\xba\xa7 \xe5\xa4\xa7\xe4\xbd\xbf\xe7\xba\xa7 \xe5\xa4\xa7\xe4\xbd\xbf\xe7\xba\xa7 \xe5\xa4\xa7\xe4\xbd\xbf\xe7\xba\xa7 \xe5\x98\x89 \xe5\x98\x89 \xe5\x98\x89 \xe5\x98\x89 \xe5\x98\x89 \xe5\x98\x89 \xe5\x98\x89 \xe6\xb1\x82\xe6\x95\x99 \xe6\xb1\x82\xe6\x95\x99 \xe6\xb1\x82\xe6\x95\x99'

2018-10-19 15:16:46.110426: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-19 15:16:46.110445: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 15:16:46.110444: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 created infer model with fresh parameters, time 0.88s

 global step 800 lr 1 step-time 12.50s wps 0.57K ppl 2048.36 bleu 0.00

 global step 900 lr 1 step-time 13.74s wps 0.54K ppl 1883.18 bleu 0.00

 global step 1000 lr 1 step-time 14.76s wps 0.50K ppl 1583.37 bleu 0.00

# Save eval, global step 1000

2018-10-19 16:07:39.926588: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 16:07:39.926612: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-19 16:07:39.926588: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-1000, time 1.62s

 # 125

   src: the united states suffered heavy casualties in the korean war in the 1950 's and the vietnam war of the 1960 's , especially the latter , and this directly caused strong antiwar sentiment within the united states and played a very great driving role in the ultimate and undoubted us defeat in the war .

   ref: 最终 战败 无疑 起到 了 巨大 的 推动 作用 .

   nmt: b'\xe4\xbb\x96 \xe8\xaf\xb4 , \xe4\xbb\x96 \xe5\x9c\xa8 \xe4\xbb\x96 , \xe4\xbb\x96 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 . </s>'

2018-10-19 16:07:41.873322: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-19 16:07:41.873376: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded eval model parameters from tmp/nmt_model_zh\translate.ckpt-1000, time 1.54s

 eval dev: perplexity 1055.18, time 19s, Fri Oct 19 16:08:01 2018.

 eval test: perplexity 1055.18, time 22s, Fri Oct 19 16:08:24 2018.

 global step 1100 lr 1 step-time 14.13s wps 0.53K ppl 1435.07 bleu 0.00

 global step 1200 lr 1 step-time 13.73s wps 0.54K ppl 1276.74 bleu 0.00

 global step 1300 lr 1 step-time 12.87s wps 0.58K ppl 1170.78 bleu 0.00

 global step 1400 lr 1 step-time 14.89s wps 0.50K ppl 1066.19 bleu 0.00

 global step 1500 lr 1 step-time 15.34s wps 0.48K ppl 1046.93 bleu 0.00

# Finished an epoch, step 1570. Perform external evaluation

2018-10-19 18:26:54.761219: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-19 18:26:54.784158: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 18:26:54.784232: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-1000, time 3.16s

 # 21

   src: this saying figuratively shows that this is indeed an absurd cycle .

   ref: 这个 说法 以 拟人化 手法 生动 地 表明 , 这 的确 是 个 怪圈 .

   nmt: b'\xe4\xbb\x96 \xe8\xaf\xb4 , \xe4\xbb\x96 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 \xe7\x9a\x84 . </s>'

2018-10-19 18:26:55.501112: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 18:26:55.501112: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 18:26:55.501131: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-1000, time 0.59s

# External evaluation, global step 1000

 decoding to output tmp/nmt_model_zh\output_dev.

 done, num sentences 400, time 34s, Fri Oct 19 18:27:29 2018.

 bleu dev: 0.0

 saving hparams to tmp/nmt_model_zh\hparams

# External evaluation, global step 1000

 decoding to output tmp/nmt_model_zh\output_test.

 done, num sentences 400, time 33s, Fri Oct 19 18:28:04 2018.

 bleu test: 0.0

 saving hparams to tmp/nmt_model_zh\hparams

 global step 1600 lr 1 step-time 17.33s wps 0.41K ppl 935.22 bleu 0.00

 global step 1700 lr 1 step-time 17.57s wps 0.43K ppl 914.06 bleu 0.00

 global step 1800 lr 1 step-time 15.53s wps 0.48K ppl 833.33 bleu 0.00

 global step 1900 lr 1 step-time 14.46s wps 0.51K ppl 784.47 bleu 0.00

 global step 2000 lr 1 step-time 15.27s wps 0.49K ppl 730.18 bleu 0.00

# Save eval, global step 2000

2018-10-19 20:21:36.887237: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-19 20:21:36.887234: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 20:21:36.887234: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-2000, time 2.08s

 # 171

   src: the sino - dprk traditional friendship created and nurtured personally by chairman mao zedong , premier zhou enlai , comrade deng xiaoping , president kim il - song and other leaders of the older generation have withstood historical tests and have taken deep roots in the hearts of peoples of the two countries .

   ref: 友谊 经受 了 历史 的 考验 , 已 深深 扎根 於 人民 心中 .

   nmt: b'\xe4\xb8\xad\xe5\x9b\xbd \xe6\x98\xaf \xe4\xb8\xad\xe5\x9b\xbd \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe4\xb8\xad\xe5\x9b\xbd \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe4\xb8\xad\xe5\x9b\xbd \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe4\xb8\xad\xe5\x9b\xbd \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe5\x92\x8c \xe4\xb8\xad\xe5\x9b\xbd \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe5\x92\x8c \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe5\x92\x8c \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe5\x92\x8c \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe5\x92\x8c \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 . </s>'

2018-10-19 20:21:37.898203: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-19 20:21:37.898825: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded eval model parameters from tmp/nmt_model_zh\translate.ckpt-2000, time 0.49s

 eval dev: perplexity 694.89, time 26s, Fri Oct 19 20:22:04 2018.

 eval test: perplexity 694.89, time 28s, Fri Oct 19 20:22:32 2018.

 global step 2100 lr 1 step-time 14.39s wps 0.51K ppl 689.72 bleu 0.00

 global step 2200 lr 1 step-time 13.70s wps 0.54K ppl 649.35 bleu 0.00

 global step 2300 lr 1 step-time 13.78s wps 0.54K ppl 609.52 bleu 0.00

# Finished an epoch, step 2355. Perform external evaluation

2018-10-19 21:44:15.494641: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-19 21:44:15.494641: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 21:44:15.494654: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-2000, time 0.75s

 # 277

   src: the complete reunification of the motherland is the trend of the times .

   ref: 统一 是 大势所趋 .

   nmt: b'\xe4\xb8\xad\xe5\x9b\xbd \xe6\x98\xaf \xe4\xb8\xad\xe5\x9b\xbd \xe7\x9a\x84 \xe4\xb8\xad\xe5\x9b\xbd \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 . </s>'

2018-10-19 21:44:16.136067: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-19 21:44:16.136100: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-19 21:44:16.136067: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-2000, time 0.57s

# External evaluation, global step 2000

 decoding to output tmp/nmt_model_zh\output_dev.

 done, num sentences 400, time 44s, Fri Oct 19 21:45:00 2018.

 bleu dev: 0.0

 saving hparams to tmp/nmt_model_zh\hparams

# External evaluation, global step 2000

 decoding to output tmp/nmt_model_zh\output_test.

 done, num sentences 400, time 42s, Fri Oct 19 21:45:43 2018.

 bleu test: 0.0

 saving hparams to tmp/nmt_model_zh\hparams

 global step 2400 lr 1 step-time 13.68s wps 0.52K ppl 578.41 bleu 0.00

 global step 2500 lr 1 step-time 14.21s wps 0.52K ppl 532.20 bleu 0.00

 global step 2600 lr 1 step-time 14.41s wps 0.52K ppl 522.94 bleu 0.00

 global step 2700 lr 1 step-time 13.97s wps 0.53K ppl 477.39 bleu 0.00

 global step 2800 lr 1 step-time 11.82s wps 0.63K ppl 458.58 bleu 0.00

 global step 2900 lr 1 step-time 11.21s wps 0.66K ppl 426.22 bleu 0.00

 global step 3000 lr 1 step-time 11.19s wps 0.66K ppl 408.34 bleu 0.00

# Save eval, global step 3000

2018-10-20 00:04:43.141117: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 00:04:43.141117: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 00:04:43.162239: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-3000, time 1.25s

 # 22

   src: we will seize the historic opportunity of the development of the western region and earnestly study new ideas , mechanism , and measures suited to tibet 's characteristics .

   ref: 我们 要 抓住大 开发 的 历史 机遇 , 认真 研究 适应 特点 的 新 思路 , 新 机制 , 新 措施 .

   nmt: b'\xe5\x9c\xa8 \xe8\xbf\x99 \xe4\xb8\x80 \xe9\x97\xae\xe9\xa2\x98 \xe4\xb8\x8a , \xe5\x9c\xa8 \xe7\xbb\x8f\xe6\xb5\x8e \xe4\xb8\x8a , \xe7\xbb\x8f\xe6\xb5\x8e \xe5\x8f\x91\xe5\xb1\x95 , \xe5\x8f\x91\xe5\xb1\x95 \xe5\x8f\x91\xe5\xb1\x95 , \xe5\x8f\x91\xe5\xb1\x95 \xe5\x8f\x91\xe5\xb1\x95 , \xe5\x8f\x91\xe5\xb1\x95 \xe5\x8f\x91\xe5\xb1\x95 . </s>'

2018-10-20 00:04:43.760353: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 00:04:43.760378: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded eval model parameters from tmp/nmt_model_zh\translate.ckpt-3000, time 0.45s

 eval dev: perplexity 387.54, time 15s, Sat Oct 20 00:04:59 2018.

 eval test: perplexity 387.54, time 17s, Sat Oct 20 00:05:17 2018.

 global step 3100 lr 1 step-time 11.16s wps 0.66K ppl 385.81 bleu 0.00

# Finished an epoch, step 3140. Perform external evaluation

2018-10-20 00:30:34.679555: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 00:30:34.680522: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 00:30:34.680586: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-3000, time 0.56s

 # 232

   src: because taiwan 's new leader deliberately obscured the one - china principle and denied the " 1992 consensus " of the association for relations across the taiwan strait and the strait exchange foundation , the two sides have been unable to ease their tense relations and achieve a breakthrough in their political impasse .

   ref: 过去 一 年 , .

   nmt: b'\xe5\x9c\xa8 \xe8\xbf\x99 \xe4\xb8\x80 \xe9\x97\xae\xe9\xa2\x98 \xe4\xb8\x8a , \xe5\x9c\xa8 \xe7\xbb\x8f\xe6\xb5\x8e \xe4\xb8\x8a , \xe4\xb8\xad\xe5\x9b\xbd \xe4\xba\xba\xe6\xb0\x91 , \xe4\xba\xba\xe6\xb0\x91 , \xe4\xba\xba\xe6\xb0\x91 , \xe4\xba\xba\xe6\xb0\x91 , \xe4\xba\xba\xe6\xb0\x91 , \xe4\xba\xba\xe6\xb0\x91 , \xe4\xba\xba\xe6\xb0\x91 , \xe4\xba\xba\xe6\xb0\x91 , \xe4\xba\xba\xe6\xb0\x91 \xe7\xad\x89 \xe7\xbb\x8f\xe6\xb5\x8e \xe5\x90\x88\xe4\xbd\x9c , \xe4\xbf\x83\xe8\xbf\x9b \xe7\xbb\x8f\xe6\xb5\x8e \xe5\x8f\x91\xe5\xb1\x95 , \xe4\xbf\x83\xe8\xbf\x9b \xe7\xbb\x8f\xe6\xb5\x8e \xe5\x8f\x91\xe5\xb1\x95 . </s>'

2018-10-20 00:30:35.313256: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 00:30:35.313274: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 00:30:35.313289: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-3000, time 0.38s

# External evaluation, global step 3000

 decoding to output tmp/nmt_model_zh\output_dev.

 done, num sentences 400, time 30s, Sat Oct 20 00:31:05 2018.

 bleu dev: 0.2

 saving hparams to tmp/nmt_model_zh\hparams

# External evaluation, global step 3000

 decoding to output tmp/nmt_model_zh\output_test.

 done, num sentences 400, time 28s, Sat Oct 20 00:31:37 2018.

 bleu test: 0.2

 saving hparams to tmp/nmt_model_zh\hparams

 global step 3200 lr 1 step-time 10.83s wps 0.66K ppl 357.57 bleu 0.17

 global step 3300 lr 1 step-time 11.15s wps 0.66K ppl 342.12 bleu 0.17

 global step 3400 lr 1 step-time 11.37s wps 0.66K ppl 325.57 bleu 0.17

 global step 3500 lr 1 step-time 11.14s wps 0.66K ppl 312.20 bleu 0.17

 global step 3600 lr 1 step-time 11.14s wps 0.66K ppl 307.63 bleu 0.17

 global step 3700 lr 1 step-time 11.28s wps 0.66K ppl 296.23 bleu 0.17

 global step 3800 lr 1 step-time 11.14s wps 0.66K ppl 278.77 bleu 0.17

 global step 3900 lr 1 step-time 11.11s wps 0.67K ppl 275.24 bleu 0.17

# Finished an epoch, step 3925. Perform external evaluation

2018-10-20 02:57:30.646778: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 02:57:30.646780: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 02:57:30.646874: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-3000, time 0.54s

 # 120

   src: in china , socialism has succeeded in greatly emancipating and developing productive forces in society .

   ref: 生产力 得到 极大 解放 和 发展 .

   nmt: b'\xe5\x9c\xa8 \xe8\xbf\x99 \xe4\xb8\x80 \xe9\x97\xae\xe9\xa2\x98 \xe4\xb8\x8a , \xe6\x88\x91\xe4\xbb\xac \xe8\xa6\x81 \xe5\x8a\xa0\xe5\xbc\xba \xe5\x8f\x91\xe5\xb1\x95 \xe5\x92\x8c \xe5\x8f\x91\xe5\xb1\x95 . </s>'

2018-10-20 02:57:31.074731: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 02:57:31.074738: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 02:57:31.074738: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-3000, time 0.35s

# External evaluation, global step 3000

 decoding to output tmp/nmt_model_zh\output_dev.

 done, num sentences 400, time 29s, Sat Oct 20 02:58:01 2018.

 bleu dev: 0.2

 saving hparams to tmp/nmt_model_zh\hparams

# External evaluation, global step 3000

 decoding to output tmp/nmt_model_zh\output_test.

 done, num sentences 400, time 30s, Sat Oct 20 02:58:31 2018.

 bleu test: 0.2

 saving hparams to tmp/nmt_model_zh\hparams

 global step 4000 lr 1 step-time 10.69s wps 0.66K ppl 252.99 bleu 0.17

# Save eval, global step 4000

2018-10-20 03:12:28.024198: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 03:12:28.024218: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 03:12:28.024223: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-4000, time 0.36s

 # 224

   src: the ninth guangdong provincial people 's congress concluded its meeting this morning . pursuant to the proposal of guangdong provincial higher people 's court president lu botao , ling qiman and li yifeng were appointed vice presidents of the provincial higher people 's court .

   ref: 今天 上午 闭幕 的  .

   nmt: b'\xe5\x9c\xa8 \xe8\xb0\x88\xe5\x88\xb0 \xe4\xb8\xad\xe5\x9b\xbd \xe5\x85\xb1\xe4\xba\xa7\xe5\x85\x9a , \xe5\x9b\xbd\xe5\x8a\xa1\xe9\x99\xa2 \xe6\x80\xbb\xe7\x90\x86 \xe6\x9c\xb1\xe9\x8e\x94\xe5\x9f\xba \xe4\xbb\x8a\xe5\xa4\xa9 \xe4\xb8\x8a\xe5\x8d\x88 , \xe5\x9b\xbd\xe5\x8a\xa1\xe9\x99\xa2 \xe5\x89\xaf\xe6\x80\xbb\xe7\x90\x86 \xe9\x92\xb1\xe5\x85\xb6\xe7\x90\x9b , \xe5\x9b\xbd\xe5\x8a\xa1\xe9\x99\xa2 , \xe4\xb9\xa6\xe8\xae\xb0\xe5\xa4\x84 , \xe4\xb9\xa6\xe8\xae\xb0\xe5\xa4\x84 , \xe4\xb9\xa6\xe8\xae\xb0\xe5\xa4\x84 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 , \xe6\x9b\xbe\xe5\xba\x86\xe7\xba\xa2 . </s>'

2018-10-20 03:12:28.632761: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 03:12:28.632820: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

 loaded eval model parameters from tmp/nmt_model_zh\translate.ckpt-4000, time 0.36s

 eval dev: perplexity 252.28, time 15s, Sat Oct 20 03:12:44 2018.

 eval test: perplexity 252.28, time 16s, Sat Oct 20 03:13:00 2018.

 global step 4100 lr 1 step-time 11.56s wps 0.64K ppl 246.02 bleu 0.17

 global step 4200 lr 1 step-time 11.29s wps 0.66K ppl 237.87 bleu 0.17

 global step 4300 lr 1 step-time 11.32s wps 0.66K ppl 233.55 bleu 0.17

 global step 4400 lr 1 step-time 11.36s wps 0.66K ppl 221.90 bleu 0.17

 global step 4500 lr 1 step-time 11.06s wps 0.66K ppl 224.83 bleu 0.17

 global step 4600 lr 1 step-time 11.32s wps 0.66K ppl 211.41 bleu 0.17

 global step 4700 lr 1 step-time 11.21s wps 0.66K ppl 213.31 bleu 0.17

# Finished an epoch, step 4710. Perform external evaluation

2018-10-20 05:26:06.693124: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 05:26:06.693124: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 05:26:06.693124: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-4000, time 0.84s

 # 33

   src: peng peiyun and he luli respectively attended the plenary meetings of the zhejiang and beijing delegations .

   ref: 云 参加 了 浙江 代表团 的 全体 会议 . 何鲁丽 参加 了 北京 代表团 的 全体 会议 .

   nmt: b'\xe4\xb8\xad\xe5\x9b\xbd \xe6\x94\xbf\xe5\xba\x9c \xe5\x92\x8c \xe4\xba\xba\xe6\xb0\x91 , \xe4\xba\xba\xe6\xb0\x91 , \xe7\x9b\xb4\xe8\xbe\x96\xe5\xb8\x82 , \xe7\x9b\xb4\xe8\xbe\x96\xe5\xb8\x82 , \xe7\x9b\xb4\xe8\xbe\x96\xe5\xb8\x82 . </s>'

2018-10-20 05:26:07.132553: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 05:26:07.132553: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 05:26:07.132618: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-4000, time 0.37s

# External evaluation, global step 4000

 decoding to output tmp/nmt_model_zh\output_dev.

 done, num sentences 400, time 45s, Sat Oct 20 05:26:52 2018.

 bleu dev: 0.2

 saving hparams to tmp/nmt_model_zh\hparams

# External evaluation, global step 4000

 decoding to output tmp/nmt_model_zh\output_test.

 done, num sentences 400, time 43s, Sat Oct 20 05:27:39 2018.

 bleu test: 0.2

 saving hparams to tmp/nmt_model_zh\hparams

 global step 4800 lr 1 step-time 10.88s wps 0.66K ppl 199.44 bleu 0.18

 global step 4900 lr 1 step-time 11.33s wps 0.66K ppl 192.33 bleu 0.18

 global step 5000 lr 1 step-time 11.41s wps 0.65K ppl 186.30 bleu 0.18

# Save eval, global step 5000

2018-10-20 06:22:32.135143: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 06:22:32.135150: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 06:22:32.135223: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-5000, time 0.76s

 # 119

   src: as a result they could only avoid the differences of opinion and delay the solutions .

   ref: 结果 只能 是 回避 分歧 , 推迟 解决 .

   nmt: b'\xe4\xbb\x8e \xe6\xa0\xb9\xe6\x9c\xac\xe4\xb8\x8a \xe7\x9c\x8b , \xe6\x88\x91\xe4\xbb\xac \xe5\xbf\x85\xe9\xa1\xbb \xe5\x9d\x9a\xe6\x8c\x81 \xe4\xb8\x80\xe4\xb8\xaa \xe4\xb8\xad\xe5\x9b\xbd \xe7\x9a\x84 \xe5\x8e\x9f\xe5\x88\x99 . </s>'

2018-10-20 06:22:32.937075: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 06:22:32.937075: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

 loaded eval model parameters from tmp/nmt_model_zh\translate.ckpt-5000, time 0.94s

 eval dev: perplexity 188.49, time 15s, Sat Oct 20 06:22:49 2018.

 eval test: perplexity 188.49, time 17s, Sat Oct 20 06:23:06 2018.

2018-10-20 06:23:09.323110: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 06:23:09.323133: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 06:23:09.323159: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-5000, time 0.36s

 # 208

   src: by the middle of this century , we should build the western region into a new region enjoying economic prosperity , social progress , a good living environment , and nationality unity , and having green mountains and clean rivers .

   ref: 到 21世纪中叶 , 将 西部 地区 建成 一个 经济 繁荣 , 社会 进步 , 生活 安定 , 民族团结 , 山川 秀美 的 新 西部 .

   nmt: b'\xe4\xbb\x8e \xe4\xbb\x8a\xe5\xb9\xb4 \xe4\xb8\x8b\xe5\x8d\x8a\xe5\xb9\xb4 \xe5\xbc\x80\xe5\xa7\x8b , \xe6\x88\x91\xe5\x9b\xbd \xe5\xb0\x86 \xe7\xbb\xa7\xe7\xbb\xad \xe5\x8a\xa0\xe5\xbc\xba \xe5\xaf\xb9 \xe8\xa5\xbf\xe9\x83\xa8 \xe5\x9c\xb0\xe5\x8c\xba \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 , \xe5\xb9\xb6 \xe8\xbf\x9b\xe4\xb8\x80\xe6\xad\xa5 \xe6\x8e\xa8\xe5\x8a\xa8 \xe4\xb8\xad\xe5\x9b\xbd \xe6\x94\xbf\xe5\xba\x9c \xe5\xaf\xb9 \xe4\xb8\xad\xe5\x9b\xbd \xe4\xba\xba\xe6\xb0\x91 \xe7\x9a\x84 \xe5\x88\xa9\xe7\x9b\x8a . </s>'

2018-10-20 06:23:09.887178: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 06:23:09.887162: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 06:23:09.887366: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-5000, time 0.77s

# External evaluation, global step 5000

 decoding to output tmp/nmt_model_zh\output_dev.

 done, num sentences 400, time 29s, Sat Oct 20 06:23:39 2018.

 bleu dev: 0.4

 saving hparams to tmp/nmt_model_zh\hparams

# External evaluation, global step 5000

 decoding to output tmp/nmt_model_zh\output_test.

 done, num sentences 400, time 29s, Sat Oct 20 06:24:12 2018.

 bleu test: 0.4

 saving hparams to tmp/nmt_model_zh\hparams

 global step 5100 lr 1 step-time 11.38s wps 0.65K ppl 185.57 bleu 0.39

 global step 5200 lr 1 step-time 11.30s wps 0.65K ppl 178.95 bleu 0.39

 global step 5300 lr 1 step-time 11.18s wps 0.66K ppl 174.15 bleu 0.39

 global step 5400 lr 1 step-time 11.39s wps 0.66K ppl 173.02 bleu 0.39

# Finished an epoch, step 5495. Perform external evaluation

2018-10-20 07:57:03.000782: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 07:57:03.000804: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 07:57:03.000921: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-5000, time 0.82s

 # 6

   src: we should truly realize " six unifications , " especially the unification of fees to improve efficiency . we should never allow the practice of " ostensibly combining the three types of inspections while failing to do so in private , " and should truly combine the " three types of inspections . "

   ref: 真正 实现率 , 决不能 出现 那种 " 明合暗 不合 , 面 合心 不合 " 的 现 像 , 把真正 落实 下去 .

   nmt: b'\xe6\x88\x91\xe4\xbb\xac \xe8\xa6\x81 \xe6\x8a\x8a " \xe4\xb8\x89 \xe4\xbb\xa3\xe8\xa1\xa8 " \xe4\xb8\xba \xe6\xa0\xb8\xe5\xbf\x83 \xe7\x9a\x84 \xe6\x80\x9d\xe6\x83\xb3 \xe6\x94\xbf\xe6\xb2\xbb \xe5\xbb\xba\xe8\xae\xbe \xe4\xbd\x9c\xe4\xb8\xba \xe6\x96\xb0 \xe7\x9a\x84 \xe4\xbc\x9f\xe5\xa4\xa7 \xe7\x9a\x84 \xe4\xbc\x9f\xe5\xa4\xa7 \xe4\xbb\xbb\xe5\x8a\xa1 , \xe6\x88\x91\xe4\xbb\xac \xe5\x85\x9a \xe7\x9a\x84 \xe5\xbb\xba\xe8\xae\xbe \xe6\x98\xaf \xe4\xb8\x80\xe4\xb8\xaa \xe6\x96\xb0 \xe7\x9a\x84 \xe5\x8f\x91\xe5\xb1\x95 \xe6\x97\xb6\xe6\x9c\x9f , \xe6\x98\xaf \xe6\x88\x91\xe4\xbb\xac \xe5\x85\x9a \xe7\x9a\x84 \xe5\xbb\xba\xe8\xae\xbe \xe7\x9a\x84 \xe4\xbc\x9f\xe5\xa4\xa7 \xe4\xbb\xbb\xe5\x8a\xa1 . </s>'

2018-10-20 07:57:04.047105: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.en is already initialized.

2018-10-20 07:57:04.047147: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

2018-10-20 07:57:04.047113: I tensorflow/core/kernels/lookup_util.cc:376] Table trying to initialize from file tmp/nmt_zh/vocab.zh is already initialized.

 loaded infer model parameters from tmp/nmt_model_zh\translate.ckpt-5000, time 0.79s

# External evaluation, global step 5000

 decoding to output tmp/nmt_model_zh\output_dev.

 done, num sentences 400, time 30s, Sat Oct 20 07:57:35 2018.

 bleu dev: 0.4

 saving hparams to tmp/nmt_model_zh\hparams

# External evaluation, global step 5000

 decoding to output tmp/nmt_model_zh\output_test.

 done, num sentences 400, time 30s, Sat Oct 20 07:58:06 2018.

 bleu test: 0.4

 saving hparams to tmp/nmt_model_zh\hparams

 global step 5500 lr 1 step-time 10.96s wps 0.64K ppl 170.63 bleu 0.39

 global step 5600 lr 1 step-time 11.52s wps 0.65K ppl 154.09 bleu 0.39

……

 global step 6100


相关文章
|
2天前
|
机器学习/深度学习 监控 算法
构建高效机器学习模型的五大技巧
【5月更文挑战第13天】 在数据科学领域,机器学习模型的性能往往决定了项目成功与否。本文将深入探讨提升机器学习模型效率和准确度的五个关键技巧。这些技巧包括数据处理优化、特征工程精炼、算法选择与调整、模型集成以及持续监控与调优。文章将结合实例分析每个技巧的实施过程及其对模型性能的影响。通过这些策略,读者可以构建出更加健壮、高效的机器学习模型,并为未来的项目提供实用的技术参考。
|
3天前
|
机器学习/深度学习 监控 算法
LabVIEW使用机器学习分类模型探索基于技能课程的学习
LabVIEW使用机器学习分类模型探索基于技能课程的学习
|
4天前
|
机器学习/深度学习 数据采集
构建高效机器学习模型的最佳实践
【5月更文挑战第11天】 在数据驱动的时代背景下,机器学习已经成为企业与研究者解决复杂问题的重要工具。本文将探讨构建高效机器学习模型的关键步骤,包括数据预处理、特征工程、模型选择与调参、以及性能评估。我们将深入分析这些步骤的重要性,并提供实用的技巧和最佳实践,以助读者提高模型的预测能力与泛化性能。通过案例分析和经验总结,本文旨在为从业者提供一套系统的方法论,帮助他们在面对各种机器学习项目时能够更有效地设计和实现解决方案。
6 0
|
4天前
|
机器学习/深度学习 存储 算法
【机器学习】使用贝叶斯模型做分类时,可能会碰到什么问题?怎么解决?
【5月更文挑战第11天】【机器学习】使用贝叶斯模型做分类时,可能会碰到什么问题?怎么解决?
|
4天前
|
机器学习/深度学习
【机器学习】噪声数据对贝叶斯模型有什么样的影响?
【5月更文挑战第10天】【机器学习】噪声数据对贝叶斯模型有什么样的影响?
|
4天前
|
机器学习/深度学习 数据处理
【机器学习】生成式模型与判别式模型有什么区别?
【5月更文挑战第10天】【机器学习】生成式模型与判别式模型有什么区别?
|
4天前
|
机器学习/深度学习 人工智能 算法
高性价比发文典范——101种机器学习算法组合革新骨肉瘤预后模型
随着高通量测序技术的飞速发展和多组学分析的广泛应用,科研人员在探索生物学奥秘时经常遇到一个令人又爱又恼的问题:如何从浩如烟海的数据中挖掘出潜在的疾病关联靶点?又如何构建一个全面而有效的诊断或预后模型?只有通过优雅的数据挖掘、精致的结果展示、深入的讨论分析,并且辅以充分的湿实验验证,我们才能锻造出一篇兼具深度与广度的“干湿结合”佳作。
16 0
高性价比发文典范——101种机器学习算法组合革新骨肉瘤预后模型
|
5天前
|
机器学习/深度学习 数据采集 监控
构建高效机器学习模型的最佳实践
【5月更文挑战第10天】 在面对海量数据和复杂问题时,构建一个既高效又准确的机器学习模型显得至关重要。本文将探讨一系列实用的技术和策略,旨在帮助数据科学家和工程师优化他们的机器学习工作流程。从数据预处理到模型训练,再到最终的评估与部署,我们将深入讨论如何通过最佳实践提升模型性能,同时确保过程的可复现性和可扩展性。
|
5天前
|
机器学习/深度学习 数据采集 监控
构建高效机器学习模型的五大技巧
【5月更文挑战第10天】 在数据驱动的时代,机器学习模型的性能往往决定了一个项目的成败。本文将深入探讨如何通过五个关键步骤——数据预处理、特征工程、模型选择、超参数调优以及模型评估与部署,来构建一个高效的机器学习模型。我们将提供实用的技术细节和策略,帮助读者避免常见的陷阱,并提升模型的准确性和泛化能力。无论你是机器学习新手还是有经验的开发者,本文的技巧都将对你构建更健壮、高效的模型大有裨益。
|
6天前
|
机器学习/深度学习 算法 异构计算
构建高效机器学习模型的策略与实践
【5月更文挑战第8天】 随着数据科学领域的不断进步,机器学习(ML)已成为解决复杂问题的重要工具。然而,构建一个既高效又准确的ML模型并非易事。本文将详细探讨在设计和训练机器学习模型时可以采用的一系列策略,以优化其性能和效率。我们将讨论特征工程的重要性、选择合适的算法、调整参数以及评估模型的有效性。通过这些策略,读者将能够更好地理解如何提升模型的预测能力并避免常见的陷阱。