In [1]:
from google.colab import drive
drive.mount('/content/gdrive')
import os
os.chdir('/content/gdrive/My Drive/finch/tensorflow1/free_chat/chinese_lccc/main')
Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
In [2]:
%tensorflow_version 1.x
TensorFlow 1.x selected.
In [3]:
import tensorflow as tf

import numpy as np
import pprint
import logging

from pathlib import Path

print("TensorFlow Version", tf.__version__)
print('GPU Enabled:', tf.test.is_gpu_available())
TensorFlow Version 1.15.2
GPU Enabled: True
In [4]:
# stream data from text files
def data_generator(f_path, params):
  char2idx = params['char2idx']
  with open(f_path) as f:
    print('Reading', f_path)
    for line in f:
      line = line.rstrip()
      source, target = line.split('<SEP>')
      source = [char2idx.get(c, len(char2idx)) for c in list(source)]
      target = [char2idx.get(c, len(char2idx)) for c in list(target)]
      if len(source) > params['max_len']:
        source = source[:params['max_len']]
      if len(target) > params['max_len']:
        target = target[:params['max_len']]
      target_in = [1] + target
      target_out = target + [2]
      yield (source, (target_in, target_out))
In [5]:
def dataset(is_training, params):
  _shapes = ([None], ([None], [None]))
  _types = (tf.int32, (tf.int32, tf.int32))
  _pads = (0, (0, 0))
  
  if is_training:
    ds = tf.data.Dataset.from_generator(
      lambda: data_generator(params['train_path'], params),
      output_shapes = _shapes,
      output_types = _types,)
    ds = ds.shuffle(params['buffer_size'])
    ds = ds.padded_batch(params['batch_size'], _shapes, _pads)
    ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
  else:
    ds = tf.data.Dataset.from_generator(
      lambda: data_generator(params['test_path'], params),
      output_shapes = _shapes,
      output_types = _types,)
    ds = ds.padded_batch(params['batch_size'], _shapes, _pads)
    ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
  
  return ds
In [6]:
def clip_grads(loss):
    variables = tf.trainable_variables()
    pprint.pprint(variables)
    grads = tf.gradients(loss, variables)
    clipped_grads, _ = tf.clip_by_global_norm(grads, params['clip_norm'])
    return zip(clipped_grads, variables)


def rnn_cell():
    def cell_fn():
        cell = tf.nn.rnn_cell.LSTMCell(params['rnn_units'],
                                       initializer=tf.orthogonal_initializer())
        return cell
    if params['dec_layers'] > 1:
      cells = []
      for i in range(params['dec_layers']):
        if i == params['dec_layers'] - 1:
          cells.append(cell_fn())
        else:
          cells.append(tf.nn.rnn_cell.ResidualWrapper(cell_fn(), residual_fn=lambda i,o: tf.concat((i,o), -1)))
      return tf.nn.rnn_cell.MultiRNNCell(cells)
    else:
      return cell_fn()

  
def dec_cell(enc_out, enc_seq_len):
    attn = tf.contrib.seq2seq.BahdanauAttention(
        num_units = params['rnn_units'],
        memory = enc_out,
        memory_sequence_length = enc_seq_len)
    
    return tf.contrib.seq2seq.AttentionWrapper(
        cell = rnn_cell(),
        attention_mechanism = attn,
        attention_layer_size = params['rnn_units'])
In [7]:
class TiedDense(tf.layers.Layer):
  def __init__(self, tied_embed, out_dim):
    super().__init__()
    self.tied_embed = tied_embed
    self.out_dim = out_dim
  
  def build(self, input_shape):
    self.bias = self.add_weight(name='bias',
                                shape=[self.out_dim],
                                trainable=True)
    if params['rnn_units'] != 300:
      self.proj_W = self.add_weight(name='proj_W',
                                    shape=[params['rnn_units'], params['embed_dim']],
                                    trainable=True)
      self.proj_b = self.add_weight(name='proj_b',
                                    shape=[params['embed_dim']],
                                    trainable=True)
    super().build(input_shape)
  
  def call(self, inputs):
    if params['rnn_units'] != 300:
      inputs = params['activation'](tf.nn.bias_add(tf.matmul(inputs, self.proj_W), self.proj_b))
    x = tf.matmul(inputs, self.tied_embed, transpose_b=True)
    x = tf.nn.bias_add(x, self.bias)
    return x
  
  def compute_output_shape(self, input_shape):
    return input_shape[:-1].concatenate(self.out_dim)
In [8]:
def forward(words, labels, mode):
    words_len = tf.count_nonzero(words, 1, dtype=tf.int32)
    
    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
    batch_sz = tf.shape(words)[0]
    mask = tf.sign(words)
    
    
    with tf.variable_scope('Embedding'):
        embedding = tf.Variable(np.load('../vocab/char.npy'),
                                dtype=tf.float32,
                                name='fasttext_vectors')
        x = tf.nn.embedding_lookup(embedding, words)
        x = tf.layers.dropout(x, params['dropout_rate'], training=is_training)
    
    
    with tf.variable_scope('Encoder'):
        encoder = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
          params['rnn_units'], return_state=True, return_sequences=True, zero_output_for_mask=True))
        enc_out, state_fw_h, state_fw_c, state_bw_h, state_bw_c = encoder(x, mask=mask)

        enc_state = tf.concat((tf.reduce_max(enc_out, 1), state_fw_h, state_bw_h), axis=-1)
        enc_state = tf.layers.dense(enc_state, params['rnn_units'], params['activation'], name='state_fc')
        enc_state = tf.nn.rnn_cell.LSTMStateTuple(c=enc_state, h=enc_state)
        if params['dec_layers'] > 1:
          enc_state = tuple(params['dec_layers'] * [enc_state])
    
    
    with tf.variable_scope('Decoder'):
        output_proj = TiedDense(embedding, len(params['char2idx'])+1)
        
        if is_training or (mode == tf.estimator.ModeKeys.EVAL):
            dec_inputs, dec_outputs = labels
            dec_seq_len = tf.count_nonzero(dec_inputs, 1, dtype=tf.int32)
            dec_inputs = tf.nn.embedding_lookup(embedding, dec_inputs)
            dec_inputs = tf.layers.dropout(dec_inputs, params['dropout_rate'], training=is_training)
            cell = dec_cell(enc_out, words_len)
            
            init_state = cell.zero_state(batch_sz, tf.float32).clone(
                cell_state=enc_state)
            
            helper = tf.contrib.seq2seq.TrainingHelper(
                inputs = dec_inputs,
                sequence_length = dec_seq_len,)
            decoder = tf.contrib.seq2seq.BasicDecoder(
                cell = cell,
                helper = helper,
                initial_state = init_state,
                output_layer = output_proj)
            decoder_output, _, _ = tf.contrib.seq2seq.dynamic_decode(
                decoder = decoder,
                maximum_iterations = tf.reduce_max(dec_seq_len))
            
            return decoder_output.rnn_output
        else:
            enc_out_t = tf.contrib.seq2seq.tile_batch(enc_out, params['beam_width'])
            enc_state_t = tf.contrib.seq2seq.tile_batch(enc_state, params['beam_width'])
            enc_seq_len_t = tf.contrib.seq2seq.tile_batch(words_len, params['beam_width'])
            
            cell = dec_cell(enc_out_t, enc_seq_len_t)
            
            init_state = cell.zero_state(batch_sz*params['beam_width'], tf.float32).clone(
                cell_state=enc_state_t)
            
            decoder = tf.contrib.seq2seq.BeamSearchDecoder(
                cell = cell,
                embedding = embedding,
                start_tokens = tf.tile(tf.constant([1], tf.int32), [batch_sz]),
                end_token = 2,
                initial_state = init_state,
                beam_width = params['beam_width'],
                output_layer = output_proj,
                length_penalty_weight = params['length_penalty'],
                coverage_penalty_weight = params['coverage_penalty'],)
            decoder_output, _, _ = tf.contrib.seq2seq.dynamic_decode(
                decoder = decoder,
                maximum_iterations = params['max_len'],)
            
            return decoder_output.predicted_ids[:, :, :params['top_k']]
In [9]:
def clr(step,
        initial_learning_rate,
        maximal_learning_rate,
        step_size,
        scale_fn,
        scale_mode,):
  step = tf.cast(step, tf.float32)
  
  initial_learning_rate = tf.convert_to_tensor(
    initial_learning_rate, name='initial_learning_rate')
  dtype = initial_learning_rate.dtype
  maximal_learning_rate = tf.cast(maximal_learning_rate, dtype)
  step_size = tf.cast(step_size, dtype)
  cycle = tf.floor(1 + step / (2 * step_size))
  x = tf.abs(step / step_size - 2 * cycle + 1)

  mode_step = cycle if scale_mode == 'cycle' else step

  return initial_learning_rate + (
    maximal_learning_rate - initial_learning_rate) * tf.maximum(
      tf.cast(0, dtype), (1 - x)) * scale_fn(mode_step)
In [10]:
def model_fn(features, labels, mode, params):
    logits_or_ids = forward(features, labels, mode)
    
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode, predictions=logits_or_ids)
    
    dec_inputs, dec_outputs = labels
    if (params['label_smoothing'] <= .0) or (mode == tf.estimator.ModeKeys.EVAL):
      loss_op = tf.contrib.seq2seq.sequence_loss(logits = logits_or_ids,
                                                 targets = dec_outputs,
                                                 weights = tf.to_float(tf.sign(dec_outputs)))
    else:
      loss_op = tf.losses.softmax_cross_entropy(onehot_labels = tf.one_hot(dec_outputs, len(params['char2idx'])+1),
                                                logits = logits_or_ids,
                                                weights = tf.to_float(tf.sign(dec_outputs)),
                                                label_smoothing = params['label_smoothing'],)
      
    if mode == tf.estimator.ModeKeys.TRAIN:
        global_step=tf.train.get_or_create_global_step()

        decay_lr = clr(
          step = global_step,
          initial_learning_rate = 1e-4,
          maximal_learning_rate = 8e-4,
          step_size = params['num_samples'] / params['batch_size'] // 2,
          scale_fn=lambda x: 1 / (2.0 ** (x - 1)),
          scale_mode = 'cycle',)
        
        train_op = tf.train.AdamOptimizer(decay_lr).apply_gradients(
            clip_grads(loss_op), global_step=global_step)
        
        hook = tf.train.LoggingTensorHook({'lr': decay_lr}, every_n_iter=100)
        
        return tf.estimator.EstimatorSpec(
            mode=mode, loss=loss_op, train_op=train_op, training_hooks=[hook],)
      
    if mode == tf.estimator.ModeKeys.EVAL:
      return tf.estimator.EstimatorSpec(mode=mode, loss=loss_op)
In [11]:
def get_vocab(f_path):
  word2idx = {}
  with open(f_path) as f:
    for i, line in enumerate(f):
      line = line.rstrip('\n')
      word2idx[line] = i
  return word2idx
In [12]:
def pad(test_strs):
  max_len = max([len(test_str) for test_str in test_strs])
  for test_str in test_strs:
    if len(test_str) < max_len:
      test_str += ['<pad>'] * (max_len - len(test_str))


def unit_test(estimator):
  test_strs = [
    '你好',
    '早上好',
    '晚上好',
    '再见',
    '好久不见',
    '想死你了',
    '谢谢你',
    '爱你',
    '你好厉害啊',
    '你叫什么',
    '你几岁了',
    '现在几点',
    '今天天气怎么样',
    '你今天心情好吗',
    '我们现在在哪里',
    '讲个笑话',
    '你会几种语言呀',
    '你觉得我帅吗',
    '讨厌的周一',
    '好烦啊',
    '天气真好',
    '今天好冷',
    '今天好热',
    '下雨了',
    '风好大',
    '终于周五了',
    '我想去唱歌',
  ]
  test_strs = [list(test_str) for test_str in test_strs]
  pad(test_strs)
  test_arrs = [[params['char2idx'].get(c, len(params['char2idx'])) for c in test_str] for test_str in test_strs]
  predicted = list(estimator.predict(tf.estimator.inputs.numpy_input_fn(
    x = np.asarray(test_arrs), shuffle = False)))
  predicted = np.asarray(predicted)
  print('-'*12)
  print('unit test')
  for i, test_str in enumerate(test_strs):
    print('Q:', ' '.join([c for c in test_str if c != '<pad>']))
    for j in range(params['top_k']):
      sent = ' '.join([params['idx2char'].get(idx, '<unk>') for idx in predicted[i, :, j] if (idx!=0 and idx!=2)])
      print('A{}:'.format(j+1), sent)
    print()
  print('-'*12)
In [13]:
params = {
    'model_dir': '../model/lstm_seq2seq',
    'train_path': '../data/train.txt',
    'test_path': '../data/test.txt',
    'vocab_path': '../vocab/char.txt',
    'max_len': 30,
    'dropout_rate': .2,
    'rnn_units': 300,
    'activation': tf.nn.swish,
    'dec_layers': 1,
    'beam_width': 10,
    'top_k': 3,
    'length_penalty': .6,
    'coverage_penalty': .0,
    'label_smoothing': .2,
    'clip_norm': .1,
    'num_samples': 5000000,
    'buffer_size': 500000,
    'batch_size': 64,
    'num_patience': 5,
}
In [14]:
params['char2idx'] = get_vocab(params['vocab_path'])
params['idx2char'] = {idx: char for char, idx in params['char2idx'].items()}
print(len(params['char2idx']), 'Chars')

# Create directory if not exist
Path(params['model_dir']).mkdir(exist_ok=True, parents=True)

# Create an estimator
estimator = tf.estimator.Estimator(
  model_fn=model_fn,
  model_dir=params['model_dir'],
  config=tf.estimator.RunConfig(
    save_checkpoints_steps = params['num_samples'] // params['batch_size'] + 1,
    keep_checkpoint_max = 2),
  params=params)

best_ppl = 10000.
count = 0
tf.enable_eager_execution()

while True:
  estimator.train(input_fn=lambda: dataset(is_training=True, params=params))

  unit_test(estimator)
  
  loss = estimator.evaluate(input_fn=lambda: dataset(is_training=False, params=params))['loss']
  ppl = np.exp(loss)
  print("Perplexity: {:.3f}".format(ppl))

  if ppl < best_ppl:
    best_ppl = ppl
    count = 0
  else:
    count += 1
  print("Best Perplexity: {:.3f}".format(best_ppl))

  if count == params['num_patience']:
    print(params['num_patience'], "times not improve the best result, therefore stop training")
    break
3042 Chars
INFO:tensorflow:Using config: {'_model_dir': '../model/lstm_seq2seq', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 78126, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 2, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f3e80050630>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/util/deprecation.py:507: calling count_nonzero (from tensorflow.python.ops.math_ops) with axis is deprecated and will be removed in a future version.
Instructions for updating:
reduction_indices is deprecated, use axis instead
WARNING:tensorflow:From <ipython-input-8-decc5ee5bcc2>:14: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.dropout instead.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/layers/core.py:271: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.__call__` method instead.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/init_ops.py:97: calling Orthogonal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/keras/backend.py:3994: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From <ipython-input-8-decc5ee5bcc2>:23: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.Dense instead.
WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From <ipython-input-6-33c7bca471bf>:12: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/rnn_cell_impl.py:958: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.
WARNING:tensorflow:Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7f3e6e2d5cf8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
WARNING: Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7f3e6e2d5cf8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
WARNING:tensorflow:From <ipython-input-10-34f5836bdfd0>:15: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
[<tf.Variable 'Embedding/fasttext_vectors:0' shape=(3043, 300) dtype=float32_ref>,
 <tf.Variable 'Encoder/bidirectional/forward_lstm/kernel:0' shape=(300, 1200) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/forward_lstm/recurrent_kernel:0' shape=(300, 1200) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/forward_lstm/bias:0' shape=(1200,) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/backward_lstm/kernel:0' shape=(300, 1200) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/backward_lstm/recurrent_kernel:0' shape=(300, 1200) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/backward_lstm/bias:0' shape=(1200,) dtype=float32>,
 <tf.Variable 'Encoder/state_fc/kernel:0' shape=(1200, 300) dtype=float32_ref>,
 <tf.Variable 'Encoder/state_fc/bias:0' shape=(300,) dtype=float32_ref>,
 <tf.Variable 'Decoder/memory_layer/kernel:0' shape=(600, 300) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/lstm_cell/kernel:0' shape=(900, 1200) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/lstm_cell/bias:0' shape=(1200,) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/bahdanau_attention/query_layer/kernel:0' shape=(300, 300) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/bahdanau_attention/attention_v:0' shape=(300,) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/attention_layer/kernel:0' shape=(900, 300) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/tied_dense/bias:0' shape=(3043,) dtype=float32_ref>]
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into ../model/lstm_seq2seq/model.ckpt.
Reading ../data/train.txt
INFO:tensorflow:loss = 8.0280285, step = 0
INFO:tensorflow:lr = 1e-04
INFO:tensorflow:global_step/sec: 5.52268
INFO:tensorflow:loss = 6.5790267, step = 100 (18.112 sec)
INFO:tensorflow:lr = 0.00010179201 (18.114 sec)
INFO:tensorflow:global_step/sec: 5.83107
INFO:tensorflow:loss = 6.325684, step = 200 (17.148 sec)
INFO:tensorflow:lr = 0.000103584025 (17.149 sec)
INFO:tensorflow:global_step/sec: 5.78168
INFO:tensorflow:loss = 6.34679, step = 300 (17.294 sec)
INFO:tensorflow:lr = 0.00010537608 (17.293 sec)
INFO:tensorflow:global_step/sec: 5.79017
INFO:tensorflow:loss = 6.2656126, step = 400 (17.270 sec)
INFO:tensorflow:lr = 0.000107168096 (17.271 sec)
INFO:tensorflow:global_step/sec: 5.76918
INFO:tensorflow:loss = 6.1021075, step = 500 (17.338 sec)
INFO:tensorflow:lr = 0.00010896011 (17.336 sec)
INFO:tensorflow:global_step/sec: 5.78043
INFO:tensorflow:loss = 6.267239, step = 600 (17.298 sec)
INFO:tensorflow:lr = 0.000110752124 (17.299 sec)
INFO:tensorflow:global_step/sec: 5.68325
INFO:tensorflow:loss = 6.1172314, step = 700 (17.593 sec)
INFO:tensorflow:lr = 0.00011254418 (17.592 sec)
INFO:tensorflow:global_step/sec: 5.74522
INFO:tensorflow:loss = 6.1652985, step = 800 (17.407 sec)
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INFO:tensorflow:lr = 0.00010401411 (17.689 sec)
INFO:tensorflow:global_step/sec: 5.65179
INFO:tensorflow:loss = 4.8487144, step = 78000 (17.691 sec)
INFO:tensorflow:lr = 0.0001022221 (17.691 sec)
INFO:tensorflow:global_step/sec: 5.66983
INFO:tensorflow:loss = 4.8597016, step = 78100 (17.636 sec)
INFO:tensorflow:lr = 0.00010043008 (17.636 sec)
INFO:tensorflow:Saving checkpoints for 78125 into ../model/lstm_seq2seq/model.ckpt.
INFO:tensorflow:Loss for final step: 5.024053.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7f3df7de64a8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
WARNING: Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7f3df7de64a8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/contrib/seq2seq/python/ops/beam_search_decoder.py:971: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/contrib/seq2seq/python/ops/beam_search_decoder.py:1252: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from ../model/lstm_seq2seq/model.ckpt-78125
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/monitored_session.py:882: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
------------
unit test
Q: 你 好
A1: 你 好
A2: 你 好 !
A3: 我 好

Q: 早 上 好
A1: 早 上 好
A2: 早 上 好 !
A3: 晚 上 好

Q: 晚 上 好
A1: 晚 上 好
A2: 晚 上 好 !
A3: 晚 安

Q: 再 见
A1: 再 见
A2: 哈 哈
A3: 再 见 再 见

Q: 好 久 不 见
A1: 好 久 不 见
A2: 是 啊
A3: 好 久 不 见 啊

Q: 想 死 你 了
A1: 我 也 想 你
A2: 我 也 想
A3: 我 也 想 死

Q: 谢 谢 你
A1: 不 客 气
A2: 不 用 谢
A3: 客 气

Q: 爱 你
A1: 我 也 爱 你
A2: 爱 你
A3: 我 爱 你

Q: 你 好 厉 害 啊
A1: 厉 害 了
A2: 哈 哈
A3: 不 厉 害

Q: 你 叫 什 么
A1: 你 猜
A2: 不 知 道
A3: 我 叫 什 么

Q: 你 几 岁 了
A1: 问 女 孩 子 年 龄 可 不 是 绅 士 所 为 哦 ~
A2: 问 女 孩 子 年 龄 可 不 是 绅 士 所 为 哦 ~ ~
A3: 问 女 孩 子 年 龄 可 不 是 绅 士 所 为 ~

Q: 现 在 几 点
A1: 1 0 点
A2: 1 1 点
A3: 1 2 点

Q: 今 天 天 气 怎 么 样
A1: 挺 好 的
A2: 今 天 天 气 好
A3: 今 天 天 气 不 错

Q: 你 今 天 心 情 好 吗
A1: 不 好
A2: 心 情 好
A3: 心 情 不 好

Q: 我 们 现 在 在 哪 里
A1: 你 们 在 哪 里
A2: 你 在 哪 里
A3: 你 在 哪 里 ?

Q: 讲 个 笑 话
A1: 笑 话
A2: 哈 哈
A3: 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈

Q: 你 会 几 种 语 言 呀
A1: 不 会
A2: 一 种 语 言
A3: 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈 哈

Q: 你 觉 得 我 帅 吗
A1: 不 帅
A2: 我 觉 得 你 帅
A3: 我 觉 得 很 帅

Q: 讨 厌 的 周 一
A1: 哈 哈
A2: 我 也 是
A3: 我 也 讨 厌

Q: 好 烦 啊
A1: 哈 哈
A2: 怎 么 了 ?
A3: 怎 么 了

Q: 天 气 真 好
A1: 是 啊
A2: 是 的
A3: 天 气 真 好

Q: 今 天 好 冷
A1: 是 啊
A2: 我 也 是
A3: 今 天 好 冷

Q: 今 天 好 热
A1: 是 啊
A2: 我 也 是
A3: 好 热 啊

Q: 下 雨 了
A1: 是 啊
A2: 是 的
A3: 下 雨 了

Q: 风 好 大
A1: 是 的
A2: 是 啊
A3: 哈 哈

Q: 终 于 周 五 了
A1: 是 的
A2: 我 也 是
A3: 我 也 是 周 五

Q: 我 想 去 唱 歌
A1: 来 吧
A2: 来 吧 来 吧
A3: 来 啊 来 啊

------------
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7f3df80b3f98>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
WARNING: Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7f3df80b3f98>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2020-08-28T04:42:32Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from ../model/lstm_seq2seq/model.ckpt-78125
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
Reading ../data/test.txt
INFO:tensorflow:Finished evaluation at 2020-08-28-04:42:55
INFO:tensorflow:Saving dict for global step 78125: global_step = 78125, loss = 3.7196558
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 78125: ../model/lstm_seq2seq/model.ckpt-78125
Perplexity: 41.250
Best Perplexity: 41.250
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7f3e10187fd0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
WARNING: Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7f3e10187fd0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
[<tf.Variable 'Embedding/fasttext_vectors:0' shape=(3043, 300) dtype=float32_ref>,
 <tf.Variable 'Encoder/bidirectional/forward_lstm/kernel:0' shape=(300, 1200) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/forward_lstm/recurrent_kernel:0' shape=(300, 1200) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/forward_lstm/bias:0' shape=(1200,) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/backward_lstm/kernel:0' shape=(300, 1200) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/backward_lstm/recurrent_kernel:0' shape=(300, 1200) dtype=float32>,
 <tf.Variable 'Encoder/bidirectional/backward_lstm/bias:0' shape=(1200,) dtype=float32>,
 <tf.Variable 'Encoder/state_fc/kernel:0' shape=(1200, 300) dtype=float32_ref>,
 <tf.Variable 'Encoder/state_fc/bias:0' shape=(300,) dtype=float32_ref>,
 <tf.Variable 'Decoder/memory_layer/kernel:0' shape=(600, 300) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/lstm_cell/kernel:0' shape=(900, 1200) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/lstm_cell/bias:0' shape=(1200,) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/bahdanau_attention/query_layer/kernel:0' shape=(300, 300) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/bahdanau_attention/attention_v:0' shape=(300,) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/attention_wrapper/attention_layer/kernel:0' shape=(900, 300) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/tied_dense/bias:0' shape=(3043,) dtype=float32_ref>]
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from ../model/lstm_seq2seq/model.ckpt-78125
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/saver.py:1069: get_checkpoint_mtimes (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file utilities to get mtimes.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 78125 into ../model/lstm_seq2seq/model.ckpt.
Reading ../data/train.txt
INFO:tensorflow:loss = 4.861157, step = 78125
INFO:tensorflow:lr = 0.000100008925
INFO:tensorflow:global_step/sec: 5.24386
INFO:tensorflow:loss = 4.863012, step = 78225 (19.076 sec)
INFO:tensorflow:lr = 0.000100904974 (19.077 sec)
INFO:tensorflow:global_step/sec: 5.60323
INFO:tensorflow:loss = 4.898035, step = 78325 (17.842 sec)
INFO:tensorflow:lr = 0.00010180094 (17.842 sec)
INFO:tensorflow:global_step/sec: 5.52429
INFO:tensorflow:loss = 5.027878, step = 78425 (18.102 sec)
INFO:tensorflow:lr = 0.00010269699 (18.103 sec)
INFO:tensorflow:global_step/sec: 5.50753
INFO:tensorflow:loss = 4.8898134, step = 78525 (18.163 sec)
INFO:tensorflow:lr = 0.00010359304 (18.160 sec)
INFO:tensorflow:global_step/sec: 5.51503
INFO:tensorflow:loss = 4.967149, step = 78625 (18.132 sec)
INFO:tensorflow:lr = 0.000104489 (18.132 sec)
INFO:tensorflow:global_step/sec: 5.53581
INFO:tensorflow:loss = 4.862857, step = 78725 (18.062 sec)
INFO:tensorflow:lr = 0.00010538505 (18.064 sec)
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-14-8df9e51aaaed> in <module>()
     20 
     21 while True:
---> 22   estimator.train(input_fn=lambda: dataset(is_training=True, params=params))
     23 
     24   unit_test(estimator)

/tensorflow-1.15.2/python3.6/tensorflow_estimator/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
    368 
    369       saving_listeners = _check_listeners_type(saving_listeners)
--> 370       loss = self._train_model(input_fn, hooks, saving_listeners)
    371       logging.info('Loss for final step: %s.', loss)
    372       return self

/tensorflow-1.15.2/python3.6/tensorflow_estimator/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
   1159       return self._train_model_distributed(input_fn, hooks, saving_listeners)
   1160     else:
-> 1161       return self._train_model_default(input_fn, hooks, saving_listeners)
   1162 
   1163   def _train_model_default(self, input_fn, hooks, saving_listeners):

/tensorflow-1.15.2/python3.6/tensorflow_estimator/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
   1193       return self._train_with_estimator_spec(estimator_spec, worker_hooks,
   1194                                              hooks, global_step_tensor,
-> 1195                                              saving_listeners)
   1196 
   1197   def _train_model_distributed(self, input_fn, hooks, saving_listeners):

/tensorflow-1.15.2/python3.6/tensorflow_estimator/python/estimator/estimator.py in _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners)
   1492       any_step_done = False
   1493       while not mon_sess.should_stop():
-> 1494         _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
   1495         any_step_done = True
   1496     if not any_step_done:

/tensorflow-1.15.2/python3.6/tensorflow_core/python/training/monitored_session.py in run(self, fetches, feed_dict, options, run_metadata)
    752         feed_dict=feed_dict,
    753         options=options,
--> 754         run_metadata=run_metadata)
    755 
    756   def run_step_fn(self, step_fn):

/tensorflow-1.15.2/python3.6/tensorflow_core/python/training/monitored_session.py in run(self, fetches, feed_dict, options, run_metadata)
   1257             feed_dict=feed_dict,
   1258             options=options,
-> 1259             run_metadata=run_metadata)
   1260       except _PREEMPTION_ERRORS as e:
   1261         logging.info(

/tensorflow-1.15.2/python3.6/tensorflow_core/python/training/monitored_session.py in run(self, *args, **kwargs)
   1343   def run(self, *args, **kwargs):
   1344     try:
-> 1345       return self._sess.run(*args, **kwargs)
   1346     except _PREEMPTION_ERRORS:
   1347       raise

/tensorflow-1.15.2/python3.6/tensorflow_core/python/training/monitored_session.py in run(self, fetches, feed_dict, options, run_metadata)
   1416         feed_dict=feed_dict,
   1417         options=options,
-> 1418         run_metadata=run_metadata)
   1419 
   1420     for hook in self._hooks:

/tensorflow-1.15.2/python3.6/tensorflow_core/python/training/monitored_session.py in run(self, *args, **kwargs)
   1174 
   1175   def run(self, *args, **kwargs):
-> 1176     return self._sess.run(*args, **kwargs)
   1177 
   1178   def run_step_fn(self, step_fn, raw_session, run_with_hooks):

/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    954     try:
    955       result = self._run(None, fetches, feed_dict, options_ptr,
--> 956                          run_metadata_ptr)
    957       if run_metadata:
    958         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1178     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1179       results = self._do_run(handle, final_targets, final_fetches,
-> 1180                              feed_dict_tensor, options, run_metadata)
   1181     else:
   1182       results = []

/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1357     if handle is None:
   1358       return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1359                            run_metadata)
   1360     else:
   1361       return self._do_call(_prun_fn, handle, feeds, fetches)

/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py in _do_call(self, fn, *args)
   1363   def _do_call(self, fn, *args):
   1364     try:
-> 1365       return fn(*args)
   1366     except errors.OpError as e:
   1367       message = compat.as_text(e.message)

/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
   1348       self._extend_graph()
   1349       return self._call_tf_sessionrun(options, feed_dict, fetch_list,
-> 1350                                       target_list, run_metadata)
   1351 
   1352     def _prun_fn(handle, feed_dict, fetch_list):

/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
   1441     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
   1442                                             fetch_list, target_list,
-> 1443                                             run_metadata)
   1444 
   1445   def _call_tf_sessionprun(self, handle, feed_dict, fetch_list):

KeyboardInterrupt: