In [ ]:
from google.colab import drive
drive.mount('/content/gdrive')
import os
os.chdir('/content/gdrive/My Drive/finch/tensorflow1/free_chat/chinese_lccc/main')
In [ ]:
%tensorflow_version 1.x
TensorFlow 1.x selected.
In [ ]:
import tensorflow as tf

import numpy as np
import pprint
import logging

from pathlib import Path
from modified_beam_search_decoder import BeamSearchDecoder

print("TensorFlow Version", tf.__version__)
print('GPU Enabled:', tf.test.is_gpu_available())
TensorFlow Version 1.15.2
GPU Enabled: True
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# 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 [ ]:
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
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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'])
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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)
    super().build(input_shape)
  
  def call(self, inputs):
    x = tf.matmul(inputs, self.tied_embed, transpose_b=True)
    x = tf.nn.bias_add(x, self.bias)
    return tf.nn.softmax(x)
  
  def compute_output_shape(self, input_shape):
    return input_shape[:-1].concatenate(self.out_dim)
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class Pointer(tf.layers.Layer):
  def __init__(self, vocab_size, encoder_ids, encoder_out, is_beam_search):
    super().__init__()
    self.encoder_ids = encoder_ids
    self.encoder_out = encoder_out
    self.vocab_size = vocab_size
    self.is_beam_search = is_beam_search

  def call(self, inputs):
    _max_len = tf.shape(self.encoder_ids)[1]
    _batch_size_ori = tf.shape(inputs)[0]
    if self.is_beam_search:
      _batch_size= _batch_size_ori * params['beam_width']
    else:
      _batch_size = _batch_size_ori
    inputs = tf.reshape(inputs, (_batch_size, params['rnn_units']))

    attn_weights = tf.matmul(self.encoder_out, tf.expand_dims(inputs, -1))
    attn_weights = tf.squeeze(attn_weights, -1)
    updates = tf.nn.softmax(attn_weights)
    
    batch_nums = tf.range(0, _batch_size)
    batch_nums = tf.expand_dims(batch_nums, axis=1)
    batch_nums = tf.tile(batch_nums, [1, _max_len])

    indices = tf.stack([batch_nums, self.encoder_ids], axis=2)
    if self.is_beam_search:
      x = tf.scatter_nd(indices, updates, (_batch_size, self.vocab_size))
      return tf.reshape(x, (_batch_size_ori, params['beam_width'], self.vocab_size))
    else:
      x = tf.scatter_nd(indices, updates, (_batch_size, self.vocab_size))
      return x
  
  def compute_output_shape(self, input_shape):
    return input_shape[:-1].concatenate(self.vocab_size)
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class OutputProj(tf.layers.Layer):
  def __init__(self, tied_embed, vocab_size, encoder_ids, encoder_out, is_beam_search):
    super().__init__()
    self.generator = TiedDense(tied_embed, vocab_size)
    self.pointer = Pointer(vocab_size, encoder_ids, encoder_out, is_beam_search)
    self.vocab_size = vocab_size

  def build(self, input_shape):
    self.gate_fc = tf.keras.layers.Dense(1, tf.sigmoid, use_bias=False)
    super().build(input_shape)
  
  def call(self, inputs):
    gen_dist = self.generator(inputs)
    copy_dist = self.pointer(inputs)
    gate = self.gate_fc(inputs)
    return gate * gen_dist + (1 - gate) * copy_dist
  
  def compute_output_shape(self, input_shape):
    return input_shape[:-1].concatenate(self.vocab_size)
In [ ]:
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_out = tf.layers.dense(enc_out, params['rnn_units'], params['activation'], name='out_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'):
        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)
            output_proj = OutputProj(embedding, len(params['char2idx'])+1, words, enc_out, is_beam_search=False)
            
            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_id_t = tf.contrib.seq2seq.tile_batch(words, params['beam_width'])
            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)
            output_proj = OutputProj(embedding, len(params['char2idx'])+1, enc_id_t, enc_out_t, is_beam_search=True)
            
            init_state = cell.zero_state(batch_sz*params['beam_width'], tf.float32).clone(
                cell_state=enc_state_t)
            
            decoder = 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 [ ]:
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)
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def cross_entropy_loss(logits, labels, vocab_size, smoothing):
  soft_targets = tf.one_hot(tf.cast(labels, tf.int32), depth=vocab_size)
  soft_targets = ((1-smoothing) * soft_targets) + (smoothing / vocab_size)

  logits = tf.math.minimum(1., logits + 1e-6)
  log_probs = tf.math.log(logits)
  xentropy = - tf.reduce_sum(soft_targets * log_probs, axis=-1)

  weights = tf.cast(tf.math.not_equal(labels, 0), tf.float32)
  xentropy *= weights
  return tf.reduce_sum(xentropy) / tf.reduce_sum(weights)
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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 (mode == tf.estimator.ModeKeys.EVAL):
      loss_op = cross_entropy_loss(logits_or_ids, dec_outputs, len(params['char2idx'])+1, .0)
    else:
      loss_op = cross_entropy_loss(logits_or_ids, dec_outputs, len(params['char2idx'])+1, 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 [ ]:
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 [ ]:
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, dtype=np.int32), 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 [ ]:
params = {
    'model_dir': '../model/lstm_pointer',
    '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 [ ]:
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_pointer', '_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 0x7fa7c53417f0>, '_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-10-e160818cd8e1>: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-10-e160818cd8e1>: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: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 OutputProj.call of <__main__.OutputProj object at 0x7fa7b703c828>> 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: Bad argument number for Name: 3, expecting 4
WARNING: Entity <bound method OutputProj.call of <__main__.OutputProj object at 0x7fa7b703c828>> 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: Bad argument number for Name: 3, expecting 4
WARNING:tensorflow:Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7fa7b703c898>> 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: Bad argument number for Name: 3, expecting 4
WARNING: Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7fa7b703c898>> 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: Bad argument number for Name: 3, expecting 4
WARNING:tensorflow:Entity <bound method Pointer.call of <__main__.Pointer object at 0x7fa7b703c9e8>> 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 Pointer.call of <__main__.Pointer object at 0x7fa7b703c9e8>> 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 'Encoder/out_fc/kernel:0' shape=(600, 300) dtype=float32_ref>,
 <tf.Variable 'Encoder/out_fc/bias:0' shape=(300,) dtype=float32_ref>,
 <tf.Variable 'Decoder/memory_layer/kernel:0' shape=(300, 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=(600, 300) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/output_proj/tied_dense/bias:0' shape=(3043,) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/while/BasicDecoderStep/output_proj/dense/kernel:0' shape=(300, 1) dtype=float32>]
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from ../model/lstm_pointer/model.ckpt-468750
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 468750 into ../model/lstm_pointer/model.ckpt.
Reading ../data/train.txt
INFO:tensorflow:loss = 4.8338313, step = 468750
INFO:tensorflow:lr = 0.00010000168
INFO:tensorflow:global_step/sec: 4.29535
INFO:tensorflow:loss = 4.9151487, step = 468850 (23.289 sec)
INFO:tensorflow:lr = 0.000100029676 (23.289 sec)
INFO:tensorflow:global_step/sec: 4.56632
INFO:tensorflow:loss = 4.7346883, step = 468950 (21.896 sec)
INFO:tensorflow:lr = 0.00010005768 (21.898 sec)
INFO:tensorflow:global_step/sec: 4.62822
INFO:tensorflow:loss = 4.9791636, step = 469050 (21.607 sec)
INFO:tensorflow:lr = 0.00010008568 (21.606 sec)
INFO:tensorflow:global_step/sec: 4.65782
INFO:tensorflow:loss = 4.8442717, step = 469150 (21.468 sec)
INFO:tensorflow:lr = 0.000100113684 (21.470 sec)
INFO:tensorflow:global_step/sec: 4.53397
INFO:tensorflow:loss = 4.819277, step = 469250 (22.054 sec)
INFO:tensorflow:lr = 0.00010014168 (22.053 sec)
INFO:tensorflow:global_step/sec: 4.50894
INFO:tensorflow:loss = 4.6225505, step = 469350 (22.180 sec)
INFO:tensorflow:lr = 0.00010016967 (22.179 sec)
INFO:tensorflow:global_step/sec: 4.60375
INFO:tensorflow:loss = 4.8636303, step = 469450 (21.722 sec)
INFO:tensorflow:lr = 0.000100197685 (21.721 sec)
INFO:tensorflow:global_step/sec: 4.67648
INFO:tensorflow:loss = 4.9095054, step = 469550 (21.382 sec)
INFO:tensorflow:lr = 0.000100225676 (21.384 sec)
INFO:tensorflow:global_step/sec: 4.71276
INFO:tensorflow:loss = 4.8736057, step = 469650 (21.220 sec)
INFO:tensorflow:lr = 0.00010025369 (21.219 sec)
INFO:tensorflow:global_step/sec: 4.76173
INFO:tensorflow:loss = 4.8860507, step = 469750 (21.001 sec)
INFO:tensorflow:lr = 0.00010028168 (21.001 sec)
INFO:tensorflow:global_step/sec: 4.71093
INFO:tensorflow:loss = 4.709522, step = 469850 (21.225 sec)
INFO:tensorflow:lr = 0.00010030968 (21.226 sec)
INFO:tensorflow:global_step/sec: 4.75515
INFO:tensorflow:loss = 4.981556, step = 469950 (21.030 sec)
INFO:tensorflow:lr = 0.00010033768 (21.032 sec)
INFO:tensorflow:global_step/sec: 4.78187
INFO:tensorflow:loss = 4.752223, step = 470050 (20.912 sec)
INFO:tensorflow:lr = 0.00010036568 (20.909 sec)
INFO:tensorflow:global_step/sec: 4.86801
INFO:tensorflow:loss = 4.7807965, step = 470150 (20.545 sec)
INFO:tensorflow:lr = 0.000100393685 (20.545 sec)
INFO:tensorflow:global_step/sec: 4.89069
INFO:tensorflow:loss = 4.6636114, step = 470250 (20.447 sec)
INFO:tensorflow:lr = 0.00010042168 (20.447 sec)
INFO:tensorflow:global_step/sec: 4.90614
INFO:tensorflow:loss = 4.781418, step = 470350 (20.384 sec)
INFO:tensorflow:lr = 0.00010044968 (20.383 sec)
INFO:tensorflow:global_step/sec: 4.94224
INFO:tensorflow:loss = 4.761419, step = 470450 (20.233 sec)
INFO:tensorflow:lr = 0.000100477686 (20.233 sec)
INFO:tensorflow:global_step/sec: 4.94661
INFO:tensorflow:loss = 4.8034267, step = 470550 (20.213 sec)
INFO:tensorflow:lr = 0.000100505684 (20.214 sec)
INFO:tensorflow:global_step/sec: 4.96831
INFO:tensorflow:loss = 4.758636, step = 470650 (20.130 sec)
INFO:tensorflow:lr = 0.00010053368 (20.130 sec)
INFO:tensorflow:global_step/sec: 4.92685
INFO:tensorflow:loss = 4.8762784, step = 470750 (20.296 sec)
INFO:tensorflow:lr = 0.00010056169 (20.295 sec)
INFO:tensorflow:global_step/sec: 4.99704
INFO:tensorflow:loss = 4.7422996, step = 470850 (20.013 sec)
INFO:tensorflow:lr = 0.000100589685 (20.014 sec)
INFO:tensorflow:global_step/sec: 5.00876
INFO:tensorflow:loss = 4.9509087, step = 470950 (19.962 sec)
INFO:tensorflow:lr = 0.00010061769 (19.962 sec)
INFO:tensorflow:global_step/sec: 4.98257
INFO:tensorflow:loss = 4.9344764, step = 471050 (20.073 sec)
INFO:tensorflow:lr = 0.00010064569 (20.073 sec)
INFO:tensorflow:global_step/sec: 4.99773
INFO:tensorflow:loss = 4.8978896, step = 471150 (20.006 sec)
INFO:tensorflow:lr = 0.00010067368 (20.011 sec)
INFO:tensorflow:global_step/sec: 5.04218
INFO:tensorflow:loss = 4.7367687, step = 471250 (19.836 sec)
INFO:tensorflow:lr = 0.00010070169 (19.831 sec)
INFO:tensorflow:global_step/sec: 4.96171
INFO:tensorflow:loss = 4.7009826, step = 471350 (20.155 sec)
INFO:tensorflow:lr = 0.00010072968 (20.154 sec)
INFO:tensorflow:global_step/sec: 5.0118
INFO:tensorflow:loss = 4.7168036, step = 471450 (19.951 sec)
INFO:tensorflow:lr = 0.000100757694 (19.953 sec)
INFO:tensorflow:global_step/sec: 4.96536
INFO:tensorflow:loss = 4.890582, step = 471550 (20.140 sec)
INFO:tensorflow:lr = 0.000100785684 (20.140 sec)
INFO:tensorflow:global_step/sec: 5.05843
INFO:tensorflow:loss = 4.8490863, step = 471650 (19.767 sec)
INFO:tensorflow:lr = 0.00010081368 (19.767 sec)
INFO:tensorflow:global_step/sec: 5.02783
INFO:tensorflow:loss = 4.8679633, step = 471750 (19.889 sec)
INFO:tensorflow:lr = 0.00010084169 (19.887 sec)
INFO:tensorflow:global_step/sec: 5.07397
INFO:tensorflow:loss = 4.731988, step = 471850 (19.710 sec)
INFO:tensorflow:lr = 0.000100869685 (19.712 sec)
INFO:tensorflow:global_step/sec: 4.99964
INFO:tensorflow:loss = 4.778894, step = 471950 (20.002 sec)
INFO:tensorflow:lr = 0.00010089769 (20.001 sec)
INFO:tensorflow:global_step/sec: 5.03352
INFO:tensorflow:loss = 4.649537, step = 472050 (19.869 sec)
INFO:tensorflow:lr = 0.00010092569 (19.868 sec)
INFO:tensorflow:global_step/sec: 5.05541
INFO:tensorflow:loss = 4.907662, step = 472150 (19.779 sec)
INFO:tensorflow:lr = 0.000100953686 (19.780 sec)
INFO:tensorflow:global_step/sec: 5.08498
INFO:tensorflow:loss = 4.896853, step = 472250 (19.666 sec)
INFO:tensorflow:lr = 0.00010098169 (19.665 sec)
INFO:tensorflow:global_step/sec: 5.02663
INFO:tensorflow:loss = 4.774584, step = 472350 (19.893 sec)
INFO:tensorflow:lr = 0.00010100969 (19.894 sec)
INFO:tensorflow:global_step/sec: 5.07148
INFO:tensorflow:loss = 4.8778615, step = 472450 (19.719 sec)
INFO:tensorflow:lr = 0.00010103769 (19.718 sec)
INFO:tensorflow:global_step/sec: 5.05558
INFO:tensorflow:loss = 4.679188, step = 472550 (19.780 sec)
INFO:tensorflow:lr = 0.00010106569 (19.780 sec)
INFO:tensorflow:global_step/sec: 5.05931
INFO:tensorflow:loss = 4.8882947, step = 472650 (19.766 sec)
INFO:tensorflow:lr = 0.00010109369 (19.766 sec)
INFO:tensorflow:global_step/sec: 5.00396
INFO:tensorflow:loss = 4.6220007, step = 472750 (19.984 sec)
INFO:tensorflow:lr = 0.000101121695 (19.985 sec)
INFO:tensorflow:global_step/sec: 4.96525
INFO:tensorflow:loss = 4.843534, step = 472850 (20.139 sec)
INFO:tensorflow:lr = 0.00010114969 (20.139 sec)
INFO:tensorflow:global_step/sec: 5.01905
INFO:tensorflow:loss = 4.6142883, step = 472950 (19.922 sec)
INFO:tensorflow:lr = 0.00010117769 (19.924 sec)
INFO:tensorflow:global_step/sec: 5.08959
INFO:tensorflow:loss = 4.810038, step = 473050 (19.650 sec)
INFO:tensorflow:lr = 0.0001012057 (19.649 sec)
INFO:tensorflow:global_step/sec: 5.08418
INFO:tensorflow:loss = 4.8165956, step = 473150 (19.669 sec)
INFO:tensorflow:lr = 0.000101233694 (19.669 sec)
INFO:tensorflow:global_step/sec: 5.09861
INFO:tensorflow:loss = 4.9624023, step = 473250 (19.614 sec)
INFO:tensorflow:lr = 0.0001012617 (19.613 sec)
INFO:tensorflow:global_step/sec: 5.04424
INFO:tensorflow:loss = 4.842603, step = 473350 (19.824 sec)
INFO:tensorflow:lr = 0.0001012897 (19.825 sec)
INFO:tensorflow:global_step/sec: 5.02914
INFO:tensorflow:loss = 4.8569, step = 473450 (19.886 sec)
INFO:tensorflow:lr = 0.00010131769 (19.884 sec)
INFO:tensorflow:global_step/sec: 4.98359
INFO:tensorflow:loss = 4.7590847, step = 473550 (20.065 sec)
INFO:tensorflow:lr = 0.0001013457 (20.065 sec)
INFO:tensorflow:global_step/sec: 5.05578
INFO:tensorflow:loss = 4.724683, step = 473650 (19.779 sec)
INFO:tensorflow:lr = 0.00010137369 (19.778 sec)
INFO:tensorflow:global_step/sec: 5.04878
INFO:tensorflow:loss = 4.877709, step = 473750 (19.808 sec)
INFO:tensorflow:lr = 0.0001014017 (19.808 sec)
INFO:tensorflow:global_step/sec: 5.04727
INFO:tensorflow:loss = 4.84971, step = 473850 (19.813 sec)
INFO:tensorflow:lr = 0.000101429694 (19.813 sec)
INFO:tensorflow:global_step/sec: 5.02967
INFO:tensorflow:loss = 4.7077565, step = 473950 (19.887 sec)
INFO:tensorflow:lr = 0.00010145769 (19.887 sec)
INFO:tensorflow:global_step/sec: 5.01122
INFO:tensorflow:loss = 4.866624, step = 474050 (19.951 sec)
INFO:tensorflow:lr = 0.0001014857 (19.950 sec)
INFO:tensorflow:global_step/sec: 5.06281
INFO:tensorflow:loss = 4.764505, step = 474150 (19.750 sec)
INFO:tensorflow:lr = 0.000101513695 (19.750 sec)
INFO:tensorflow:global_step/sec: 4.98196
INFO:tensorflow:loss = 4.9581017, step = 474250 (20.073 sec)
INFO:tensorflow:lr = 0.00010154169 (20.075 sec)
INFO:tensorflow:global_step/sec: 4.9198
INFO:tensorflow:loss = 4.808321, step = 474350 (20.327 sec)
INFO:tensorflow:lr = 0.0001015697 (20.327 sec)
INFO:tensorflow:global_step/sec: 4.77148
INFO:tensorflow:loss = 4.7849083, step = 474450 (20.958 sec)
INFO:tensorflow:lr = 0.000101597696 (20.957 sec)
INFO:tensorflow:global_step/sec: 4.83897
INFO:tensorflow:loss = 4.8641458, step = 474550 (20.665 sec)
INFO:tensorflow:lr = 0.0001016257 (20.665 sec)
INFO:tensorflow:global_step/sec: 4.73018
INFO:tensorflow:loss = 4.858291, step = 474650 (21.139 sec)
INFO:tensorflow:lr = 0.0001016537 (21.140 sec)
INFO:tensorflow:global_step/sec: 4.63758
INFO:tensorflow:loss = 4.983636, step = 474750 (21.566 sec)
INFO:tensorflow:lr = 0.0001016817 (21.566 sec)
INFO:tensorflow:global_step/sec: 4.75141
INFO:tensorflow:loss = 4.7238374, step = 474850 (21.043 sec)
INFO:tensorflow:lr = 0.0001017097 (21.043 sec)
INFO:tensorflow:global_step/sec: 4.73694
INFO:tensorflow:loss = 4.857829, step = 474950 (21.113 sec)
INFO:tensorflow:lr = 0.0001017377 (21.113 sec)
INFO:tensorflow:global_step/sec: 4.69476
INFO:tensorflow:loss = 4.7574263, step = 475050 (21.301 sec)
INFO:tensorflow:lr = 0.000101765705 (21.300 sec)
INFO:tensorflow:global_step/sec: 4.67902
INFO:tensorflow:loss = 4.843785, step = 475150 (21.372 sec)
INFO:tensorflow:lr = 0.0001017937 (21.371 sec)
INFO:tensorflow:global_step/sec: 4.76754
INFO:tensorflow:loss = 4.7001953, step = 475250 (20.975 sec)
INFO:tensorflow:lr = 0.0001018217 (20.975 sec)
INFO:tensorflow:global_step/sec: 4.80513
INFO:tensorflow:loss = 4.829166, step = 475350 (20.811 sec)
INFO:tensorflow:lr = 0.000101849706 (20.812 sec)
INFO:tensorflow:global_step/sec: 4.71152
INFO:tensorflow:loss = 4.9196525, step = 475450 (21.222 sec)
INFO:tensorflow:lr = 0.0001018777 (21.222 sec)
INFO:tensorflow:global_step/sec: 4.63675
INFO:tensorflow:loss = 4.803467, step = 475550 (21.567 sec)
INFO:tensorflow:lr = 0.00010190571 (21.567 sec)
INFO:tensorflow:global_step/sec: 4.58963
INFO:tensorflow:loss = 4.785481, step = 475650 (21.792 sec)
INFO:tensorflow:lr = 0.00010193371 (21.791 sec)
INFO:tensorflow:global_step/sec: 4.67632
INFO:tensorflow:loss = 4.80142, step = 475750 (21.383 sec)
INFO:tensorflow:lr = 0.0001019617 (21.383 sec)
INFO:tensorflow:global_step/sec: 4.7599
INFO:tensorflow:loss = 4.8614364, step = 475850 (21.010 sec)
INFO:tensorflow:lr = 0.0001019897 (21.011 sec)
INFO:tensorflow:global_step/sec: 4.77288
INFO:tensorflow:loss = 4.7303815, step = 475950 (20.950 sec)
INFO:tensorflow:lr = 0.0001020177 (20.954 sec)
INFO:tensorflow:global_step/sec: 4.86666
INFO:tensorflow:loss = 4.8367834, step = 476050 (20.550 sec)
INFO:tensorflow:lr = 0.0001020457 (20.545 sec)
INFO:tensorflow:global_step/sec: 4.86395
INFO:tensorflow:loss = 4.755257, step = 476150 (20.558 sec)
INFO:tensorflow:lr = 0.0001020737 (20.559 sec)
INFO:tensorflow:global_step/sec: 4.81198
INFO:tensorflow:loss = 4.833772, step = 476250 (20.779 sec)
INFO:tensorflow:lr = 0.0001021017 (20.779 sec)
INFO:tensorflow:global_step/sec: 4.82617
INFO:tensorflow:loss = 4.69455, step = 476350 (20.724 sec)
INFO:tensorflow:lr = 0.00010212971 (20.723 sec)
INFO:tensorflow:global_step/sec: 4.96068
INFO:tensorflow:loss = 4.7399907, step = 476450 (20.155 sec)
INFO:tensorflow:lr = 0.000102157705 (20.154 sec)
INFO:tensorflow:global_step/sec: 4.99505
INFO:tensorflow:loss = 4.7536826, step = 476550 (20.020 sec)
INFO:tensorflow:lr = 0.0001021857 (20.027 sec)
INFO:tensorflow:global_step/sec: 4.986
INFO:tensorflow:loss = 4.8427896, step = 476650 (20.058 sec)
INFO:tensorflow:lr = 0.00010221371 (20.052 sec)
INFO:tensorflow:global_step/sec: 4.99131
INFO:tensorflow:loss = 4.9754953, step = 476750 (20.033 sec)
INFO:tensorflow:lr = 0.000102241705 (20.038 sec)
INFO:tensorflow:global_step/sec: 4.98665
INFO:tensorflow:loss = 4.7605524, step = 476850 (20.055 sec)
INFO:tensorflow:lr = 0.00010226971 (20.050 sec)
INFO:tensorflow:global_step/sec: 5.03214
INFO:tensorflow:loss = 4.893359, step = 476950 (19.873 sec)
INFO:tensorflow:lr = 0.00010229771 (19.872 sec)
INFO:tensorflow:global_step/sec: 5.03105
INFO:tensorflow:loss = 4.670141, step = 477050 (19.876 sec)
INFO:tensorflow:lr = 0.00010232571 (19.876 sec)
INFO:tensorflow:global_step/sec: 5.03689
INFO:tensorflow:loss = 4.668399, step = 477150 (19.855 sec)
INFO:tensorflow:lr = 0.00010235371 (19.854 sec)
INFO:tensorflow:global_step/sec: 5.05859
INFO:tensorflow:loss = 4.9618406, step = 477250 (19.768 sec)
INFO:tensorflow:lr = 0.00010238171 (19.769 sec)
INFO:tensorflow:global_step/sec: 5.09384
INFO:tensorflow:loss = 4.7575173, step = 477350 (19.631 sec)
INFO:tensorflow:lr = 0.000102409715 (19.631 sec)
INFO:tensorflow:global_step/sec: 4.98363
INFO:tensorflow:loss = 4.838988, step = 477450 (20.066 sec)
INFO:tensorflow:lr = 0.00010243771 (20.068 sec)
INFO:tensorflow:global_step/sec: 5.07886
INFO:tensorflow:loss = 4.7795763, step = 477550 (19.687 sec)
INFO:tensorflow:lr = 0.0001024657 (19.686 sec)
INFO:tensorflow:global_step/sec: 5.06825
INFO:tensorflow:loss = 4.862156, step = 477650 (19.733 sec)
INFO:tensorflow:lr = 0.000102493716 (19.732 sec)
INFO:tensorflow:global_step/sec: 5.05576
INFO:tensorflow:loss = 4.8420067, step = 477750 (19.780 sec)
INFO:tensorflow:lr = 0.000102521706 (19.781 sec)
INFO:tensorflow:global_step/sec: 5.07929
INFO:tensorflow:loss = 4.8398004, step = 477850 (19.685 sec)
INFO:tensorflow:lr = 0.000102549704 (19.684 sec)
INFO:tensorflow:global_step/sec: 5.08561
INFO:tensorflow:loss = 4.7570105, step = 477950 (19.664 sec)
INFO:tensorflow:lr = 0.00010257771 (19.666 sec)
INFO:tensorflow:global_step/sec: 5.09369
INFO:tensorflow:loss = 4.8740582, step = 478050 (19.633 sec)
INFO:tensorflow:lr = 0.00010260571 (19.631 sec)
INFO:tensorflow:global_step/sec: 5.12173
INFO:tensorflow:loss = 4.7648005, step = 478150 (19.525 sec)
INFO:tensorflow:lr = 0.00010263371 (19.525 sec)
INFO:tensorflow:global_step/sec: 5.06853
INFO:tensorflow:loss = 4.793591, step = 478250 (19.727 sec)
INFO:tensorflow:lr = 0.00010266171 (19.728 sec)
INFO:tensorflow:global_step/sec: 5.08994
INFO:tensorflow:loss = 4.8006096, step = 478350 (19.651 sec)
INFO:tensorflow:lr = 0.00010268971 (19.650 sec)
INFO:tensorflow:global_step/sec: 5.12959
INFO:tensorflow:loss = 4.7846327, step = 478450 (19.493 sec)
INFO:tensorflow:lr = 0.00010271771 (19.494 sec)
INFO:tensorflow:global_step/sec: 5.15034
INFO:tensorflow:loss = 4.7795515, step = 478550 (19.416 sec)
INFO:tensorflow:lr = 0.00010274571 (19.417 sec)
INFO:tensorflow:global_step/sec: 5.12708
INFO:tensorflow:loss = 4.821649, step = 478650 (19.503 sec)
INFO:tensorflow:lr = 0.000102773716 (19.504 sec)
INFO:tensorflow:global_step/sec: 5.07316
INFO:tensorflow:loss = 4.6419873, step = 478750 (19.712 sec)
INFO:tensorflow:lr = 0.000102801714 (19.711 sec)
INFO:tensorflow:global_step/sec: 5.09497
INFO:tensorflow:loss = 4.91121, step = 478850 (19.625 sec)
INFO:tensorflow:lr = 0.00010282971 (19.625 sec)
INFO:tensorflow:global_step/sec: 5.1187
INFO:tensorflow:loss = 4.8033166, step = 478950 (19.539 sec)
INFO:tensorflow:lr = 0.00010285772 (19.539 sec)
INFO:tensorflow:global_step/sec: 5.014
INFO:tensorflow:loss = 4.749983, step = 479050 (19.942 sec)
INFO:tensorflow:lr = 0.000102885715 (19.944 sec)
INFO:tensorflow:global_step/sec: 5.08681
INFO:tensorflow:loss = 4.7962947, step = 479150 (19.659 sec)
INFO:tensorflow:lr = 0.00010291372 (19.659 sec)
INFO:tensorflow:global_step/sec: 5.11539
INFO:tensorflow:loss = 4.7498813, step = 479250 (19.551 sec)
INFO:tensorflow:lr = 0.00010294172 (19.549 sec)
INFO:tensorflow:global_step/sec: 5.07071
INFO:tensorflow:loss = 4.705895, step = 479350 (19.721 sec)
INFO:tensorflow:lr = 0.000102969716 (19.720 sec)
INFO:tensorflow:global_step/sec: 5.1123
INFO:tensorflow:loss = 4.858578, step = 479450 (19.562 sec)
INFO:tensorflow:lr = 0.00010299772 (19.562 sec)
INFO:tensorflow:global_step/sec: 5.088
INFO:tensorflow:loss = 4.747156, step = 479550 (19.654 sec)
INFO:tensorflow:lr = 0.00010302571 (19.655 sec)
INFO:tensorflow:global_step/sec: 5.15245
INFO:tensorflow:loss = 4.776051, step = 479650 (19.409 sec)
INFO:tensorflow:lr = 0.00010305371 (19.407 sec)
INFO:tensorflow:global_step/sec: 5.13807
INFO:tensorflow:loss = 4.8339887, step = 479750 (19.463 sec)
INFO:tensorflow:lr = 0.00010308172 (19.463 sec)
INFO:tensorflow:global_step/sec: 5.1456
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INFO:tensorflow:global_step/sec: 5.26254
INFO:tensorflow:loss = 4.9255447, step = 543550 (19.007 sec)
INFO:tensorflow:lr = 0.00010092905 (19.006 sec)
INFO:tensorflow:global_step/sec: 5.2596
INFO:tensorflow:loss = 4.8087015, step = 543650 (19.011 sec)
INFO:tensorflow:lr = 0.00010090105 (19.016 sec)
INFO:tensorflow:global_step/sec: 5.24569
INFO:tensorflow:loss = 4.8079185, step = 543750 (19.062 sec)
INFO:tensorflow:lr = 0.00010087305 (19.057 sec)
INFO:tensorflow:global_step/sec: 5.30821
INFO:tensorflow:loss = 4.853241, step = 543850 (18.841 sec)
INFO:tensorflow:lr = 0.00010084505 (18.841 sec)
INFO:tensorflow:global_step/sec: 5.22905
INFO:tensorflow:loss = 4.833821, step = 543950 (19.124 sec)
INFO:tensorflow:lr = 0.000100817044 (19.124 sec)
INFO:tensorflow:global_step/sec: 5.25373
INFO:tensorflow:loss = 4.903915, step = 544050 (19.034 sec)
INFO:tensorflow:lr = 0.000100789046 (19.034 sec)
INFO:tensorflow:global_step/sec: 5.19816
INFO:tensorflow:loss = 4.70137, step = 544150 (19.236 sec)
INFO:tensorflow:lr = 0.00010076105 (19.238 sec)
INFO:tensorflow:global_step/sec: 5.28144
INFO:tensorflow:loss = 4.9349947, step = 544250 (18.935 sec)
INFO:tensorflow:lr = 0.00010073304 (18.934 sec)
INFO:tensorflow:global_step/sec: 5.24553
INFO:tensorflow:loss = 4.944304, step = 544350 (19.061 sec)
INFO:tensorflow:lr = 0.000100705045 (19.061 sec)
INFO:tensorflow:global_step/sec: 5.27628
INFO:tensorflow:loss = 4.8752446, step = 544450 (18.953 sec)
INFO:tensorflow:lr = 0.000100677054 (18.952 sec)
INFO:tensorflow:global_step/sec: 5.2569
INFO:tensorflow:loss = 4.8745656, step = 544550 (19.023 sec)
INFO:tensorflow:lr = 0.00010064904 (19.023 sec)
INFO:tensorflow:global_step/sec: 5.24429
INFO:tensorflow:loss = 4.988264, step = 544650 (19.071 sec)
INFO:tensorflow:lr = 0.00010062105 (19.072 sec)
INFO:tensorflow:global_step/sec: 5.25576
INFO:tensorflow:loss = 4.747557, step = 544750 (19.025 sec)
INFO:tensorflow:lr = 0.00010059304 (19.028 sec)
INFO:tensorflow:global_step/sec: 5.25015
INFO:tensorflow:loss = 4.671842, step = 544850 (19.049 sec)
INFO:tensorflow:lr = 0.00010056505 (19.045 sec)
INFO:tensorflow:global_step/sec: 5.23328
INFO:tensorflow:loss = 4.8195634, step = 544950 (19.107 sec)
INFO:tensorflow:lr = 0.00010053705 (19.108 sec)
INFO:tensorflow:global_step/sec: 5.1746
INFO:tensorflow:loss = 4.740954, step = 545050 (19.325 sec)
INFO:tensorflow:lr = 0.000100509045 (19.325 sec)
INFO:tensorflow:global_step/sec: 5.27681
INFO:tensorflow:loss = 4.8269086, step = 545150 (18.955 sec)
INFO:tensorflow:lr = 0.00010048105 (18.953 sec)
INFO:tensorflow:global_step/sec: 5.29187
INFO:tensorflow:loss = 4.7582192, step = 545250 (18.896 sec)
INFO:tensorflow:lr = 0.00010045304 (18.897 sec)
INFO:tensorflow:global_step/sec: 5.21665
INFO:tensorflow:loss = 4.729404, step = 545350 (19.168 sec)
INFO:tensorflow:lr = 0.000100425044 (19.168 sec)
INFO:tensorflow:global_step/sec: 5.2592
INFO:tensorflow:loss = 4.8175745, step = 545450 (19.016 sec)
INFO:tensorflow:lr = 0.00010039705 (19.015 sec)
INFO:tensorflow:global_step/sec: 5.29877
INFO:tensorflow:loss = 4.850289, step = 545550 (18.869 sec)
INFO:tensorflow:lr = 0.00010036904 (18.869 sec)
INFO:tensorflow:global_step/sec: 5.27672
INFO:tensorflow:loss = 4.624925, step = 545650 (18.954 sec)
INFO:tensorflow:lr = 0.00010034104 (18.955 sec)
INFO:tensorflow:global_step/sec: 5.22631
INFO:tensorflow:loss = 4.755604, step = 545750 (19.135 sec)
INFO:tensorflow:lr = 0.00010031304 (19.134 sec)
INFO:tensorflow:global_step/sec: 5.25184
INFO:tensorflow:loss = 4.7803545, step = 545850 (19.037 sec)
INFO:tensorflow:lr = 0.00010028504 (19.040 sec)
INFO:tensorflow:global_step/sec: 5.2387
INFO:tensorflow:loss = 4.795782, step = 545950 (19.092 sec)
INFO:tensorflow:lr = 0.00010025704 (19.091 sec)
INFO:tensorflow:global_step/sec: 5.30868
INFO:tensorflow:loss = 4.850092, step = 546050 (18.838 sec)
INFO:tensorflow:lr = 0.00010022904 (18.837 sec)
INFO:tensorflow:global_step/sec: 5.28036
INFO:tensorflow:loss = 4.8004365, step = 546150 (18.938 sec)
INFO:tensorflow:lr = 0.00010020104 (18.938 sec)
INFO:tensorflow:global_step/sec: 5.22224
INFO:tensorflow:loss = 4.818487, step = 546250 (19.147 sec)
INFO:tensorflow:lr = 0.00010017304 (19.148 sec)
INFO:tensorflow:global_step/sec: 5.28516
INFO:tensorflow:loss = 4.766133, step = 546350 (18.923 sec)
INFO:tensorflow:lr = 0.00010014504 (18.921 sec)
INFO:tensorflow:global_step/sec: 5.26212
INFO:tensorflow:loss = 4.764241, step = 546450 (19.003 sec)
INFO:tensorflow:lr = 0.00010011704 (19.003 sec)
INFO:tensorflow:global_step/sec: 5.24984
INFO:tensorflow:loss = 4.8767257, step = 546550 (19.047 sec)
INFO:tensorflow:lr = 0.00010008903 (19.047 sec)
INFO:tensorflow:global_step/sec: 5.14729
INFO:tensorflow:loss = 4.909703, step = 546650 (19.429 sec)
INFO:tensorflow:lr = 0.000100061035 (19.429 sec)
INFO:tensorflow:global_step/sec: 5.25357
INFO:tensorflow:loss = 4.865176, step = 546750 (19.034 sec)
INFO:tensorflow:lr = 0.000100033045 (19.034 sec)
INFO:tensorflow:global_step/sec: 5.24244
INFO:tensorflow:loss = 4.832292, step = 546850 (19.073 sec)
INFO:tensorflow:lr = 0.00010000503 (19.075 sec)
INFO:tensorflow:Saving checkpoints for 546875 into ../model/lstm_pointer/model.ckpt.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Loss for final step: 4.727198.
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 OutputProj.call of <__main__.OutputProj object at 0x7fa75e1b8d68>> 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: Bad argument number for Name: 3, expecting 4
WARNING: Entity <bound method OutputProj.call of <__main__.OutputProj object at 0x7fa75e1b8d68>> 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: Bad argument number for Name: 3, expecting 4
WARNING:tensorflow:Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7fa75e1a52e8>> 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: Bad argument number for Name: 3, expecting 4
WARNING: Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7fa75e1a52e8>> 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: Bad argument number for Name: 3, expecting 4
WARNING:tensorflow:Entity <bound method Pointer.call of <__main__.Pointer object at 0x7fa75e1a5080>> 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 Pointer.call of <__main__.Pointer object at 0x7fa75e1a5080>> 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 /content/gdrive/My Drive/finch/tensorflow1/free_chat/chinese_lccc/main/modified_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 /content/gdrive/My Drive/finch/tensorflow1/free_chat/chinese_lccc/main/modified_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_pointer/model.ckpt-546875
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: 你 好 你 好

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

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

Q: 再 见
A1: 再 见
A2: 再 见 !
A3: 不 见 不 散

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

Q: 想 死 你 了
A1: 我 也 是
A2: 来 吧 来 吧
A3: 我 也 想 死

Q: 谢 谢 你
A1: 不 客 气
A2: 不 用 谢
A3: 客 气

Q: 爱 你
A1: 爱 你
A2: 我 也 是
A3: 我 也 爱 你

Q: 你 好 厉 害 啊
A1: 哈 哈
A2: 不 厉 害
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 叫 什 么
A1: 你 猜
A2: 你 叫 什 么
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: 小 女 子 年 方 二 八 你 猜 我 多 大
A3: 怎 么 能 问 一 个 淑 女 这 么 私 密 的 问 题 除 非 你 先 说

Q: 现 在 几 点
A1: 八 点 半
A2: 不 知 道
A3: 十 点 半

Q: 今 天 天 气 怎 么 样
A1: 下 雨 了
A2: 今 天 下 雨 了
A3: 今 天 天 气 很 好

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

Q: 我 们 现 在 在 哪 里
A1: 在 这 里
A2: 在 家
A3: 在 哪 里

Q: 讲 个 笑 话
A1: 笑 话
A2: 讲 个 笑 话
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: 你 猜
A2: 不 会
A3: 我 不 会

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

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

Q: 好 烦 啊
A1: 我 也 是
A2: 我 也 好 烦
A3: 怎 么 了 ?

Q: 天 气 真 好
A1: 是 的
A2: 是 啊
A3: 嗯 嗯

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

Q: 今 天 好 热
A1: 是 啊
A2: 热 死 了
A3: 是 的

Q: 下 雨 了
A1: 是 的
A2: 是 啊
A3: 下 雨 了

Q: 风 好 大
A1: 是 啊
A2: 是 的
A3: 是 的 呢

Q: 终 于 周 五 了
A1: 是 啊
A2: 是 的
A3: 是 的 呢

Q: 我 想 去 唱 歌
A1: 去 吧
A2: 我 也 想 去
A3: 我 也 想

------------
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:Entity <bound method OutputProj.call of <__main__.OutputProj object at 0x7fa7b63160f0>> 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: Bad argument number for Name: 3, expecting 4
WARNING: Entity <bound method OutputProj.call of <__main__.OutputProj object at 0x7fa7b63160f0>> 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: Bad argument number for Name: 3, expecting 4
WARNING:tensorflow:Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7fa7b6316160>> 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: Bad argument number for Name: 3, expecting 4
WARNING: Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7fa7b6316160>> 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: Bad argument number for Name: 3, expecting 4
WARNING:tensorflow:Entity <bound method Pointer.call of <__main__.Pointer object at 0x7fa7b6316278>> 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 Pointer.call of <__main__.Pointer object at 0x7fa7b6316278>> 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 2020-09-04T04:35:29Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from ../model/lstm_pointer/model.ckpt-546875
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
Reading ../data/test.txt
INFO:tensorflow:Finished evaluation at 2020-09-04-04:35:55
INFO:tensorflow:Saving dict for global step 546875: global_step = 546875, loss = 3.5979872
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 546875: ../model/lstm_pointer/model.ckpt-546875
Perplexity: 36.525
Best Perplexity: 36.525
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:Entity <bound method OutputProj.call of <__main__.OutputProj object at 0x7fa7b6226518>> 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: Bad argument number for Name: 3, expecting 4
WARNING: Entity <bound method OutputProj.call of <__main__.OutputProj object at 0x7fa7b6226518>> 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: Bad argument number for Name: 3, expecting 4
WARNING:tensorflow:Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7fa7b62265c0>> 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: Bad argument number for Name: 3, expecting 4
WARNING: Entity <bound method TiedDense.call of <__main__.TiedDense object at 0x7fa7b62265c0>> 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: Bad argument number for Name: 3, expecting 4
WARNING:tensorflow:Entity <bound method Pointer.call of <__main__.Pointer object at 0x7fa7b62266d8>> 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 Pointer.call of <__main__.Pointer object at 0x7fa7b62266d8>> 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 'Encoder/out_fc/kernel:0' shape=(600, 300) dtype=float32_ref>,
 <tf.Variable 'Encoder/out_fc/bias:0' shape=(300,) dtype=float32_ref>,
 <tf.Variable 'Decoder/memory_layer/kernel:0' shape=(300, 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=(600, 300) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/output_proj/tied_dense/bias:0' shape=(3043,) dtype=float32_ref>,
 <tf.Variable 'Decoder/decoder/while/BasicDecoderStep/output_proj/dense/kernel:0' shape=(300, 1) dtype=float32>]
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from ../model/lstm_pointer/model.ckpt-546875
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 546875 into ../model/lstm_pointer/model.ckpt.
Reading ../data/train.txt
INFO:tensorflow:loss = 4.859482, step = 546875
INFO:tensorflow:lr = 0.00010000098
INFO:tensorflow:global_step/sec: 4.86019
INFO:tensorflow:loss = 4.769424, step = 546975 (20.580 sec)
INFO:tensorflow:lr = 0.00010001498 (20.580 sec)
INFO:tensorflow:global_step/sec: 5.14327
INFO:tensorflow:loss = 4.7927804, step = 547075 (19.443 sec)
INFO:tensorflow:lr = 0.00010002898 (19.442 sec)
INFO:tensorflow:global_step/sec: 5.16572
INFO:tensorflow:loss = 4.9296594, step = 547175 (19.358 sec)
INFO:tensorflow:lr = 0.00010004298 (19.358 sec)
INFO:tensorflow:global_step/sec: 5.12387
INFO:tensorflow:loss = 4.765398, step = 547275 (19.515 sec)
INFO:tensorflow:lr = 0.000100056975 (19.516 sec)
INFO:tensorflow:global_step/sec: 5.10601
INFO:tensorflow:loss = 4.859722, step = 547375 (19.586 sec)
INFO:tensorflow:lr = 0.00010007098 (19.585 sec)
INFO:tensorflow:global_step/sec: 5.15777
INFO:tensorflow:loss = 4.8544745, step = 547475 (19.387 sec)
INFO:tensorflow:lr = 0.00010008498 (19.388 sec)
INFO:tensorflow:global_step/sec: 5.13427
INFO:tensorflow:loss = 4.745908, step = 547575 (19.479 sec)
INFO:tensorflow:lr = 0.00010009898 (19.478 sec)
INFO:tensorflow:global_step/sec: 5.07439
INFO:tensorflow:loss = 4.6880245, step = 547675 (19.707 sec)
INFO:tensorflow:lr = 0.00010011298 (19.707 sec)
INFO:tensorflow:global_step/sec: 5.06613
INFO:tensorflow:loss = 4.7233443, step = 547775 (19.735 sec)
INFO:tensorflow:lr = 0.00010012698 (19.738 sec)
INFO:tensorflow:global_step/sec: 5.1302
INFO:tensorflow:loss = 4.8185525, step = 547875 (19.496 sec)
INFO:tensorflow:lr = 0.000100140984 (19.493 sec)
INFO:tensorflow:global_step/sec: 5.12415
INFO:tensorflow:loss = 4.8493986, step = 547975 (19.515 sec)
INFO:tensorflow:lr = 0.000100154975 (19.516 sec)
INFO:tensorflow:global_step/sec: 5.05698
INFO:tensorflow:loss = 4.8022285, step = 548075 (19.771 sec)
INFO:tensorflow:lr = 0.00010016898 (19.771 sec)
INFO:tensorflow:global_step/sec: 5.10405
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INFO:tensorflow:lr = 0.00010539244 (19.417 sec)
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INFO:tensorflow:lr = 0.00010537845 (19.533 sec)
INFO:tensorflow:global_step/sec: 5.13503
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INFO:tensorflow:lr = 0.000105364445 (19.472 sec)
INFO:tensorflow:global_step/sec: 5.15364
INFO:tensorflow:loss = 4.7964926, step = 586775 (19.403 sec)
INFO:tensorflow:lr = 0.000105350446 (19.404 sec)
INFO:tensorflow:global_step/sec: 5.15304
INFO:tensorflow:loss = 4.797984, step = 586875 (19.407 sec)
INFO:tensorflow:lr = 0.00010533645 (19.406 sec)
INFO:tensorflow:global_step/sec: 5.09841
INFO:tensorflow:loss = 4.756148, step = 586975 (19.615 sec)
INFO:tensorflow:lr = 0.00010532245 (19.614 sec)
INFO:tensorflow:global_step/sec: 5.11557
INFO:tensorflow:loss = 4.8135295, step = 587075 (19.552 sec)
INFO:tensorflow:lr = 0.00010530845 (19.550 sec)
INFO:tensorflow:global_step/sec: 5.03442
INFO:tensorflow:loss = 4.7865157, step = 587175 (19.860 sec)
INFO:tensorflow:lr = 0.00010529444 (19.860 sec)
INFO:tensorflow:global_step/sec: 5.1692
INFO:tensorflow:loss = 4.722356, step = 587275 (19.345 sec)
INFO:tensorflow:lr = 0.000105280444 (19.348 sec)
INFO:tensorflow:global_step/sec: 5.12963
INFO:tensorflow:loss = 4.656241, step = 587375 (19.497 sec)
INFO:tensorflow:lr = 0.000105266445 (19.494 sec)
INFO:tensorflow:global_step/sec: 5.12777
INFO:tensorflow:loss = 4.7795777, step = 587475 (19.502 sec)
INFO:tensorflow:lr = 0.000105252446 (19.501 sec)
INFO:tensorflow:global_step/sec: 5.11998
INFO:tensorflow:loss = 4.955843, step = 587575 (19.532 sec)
INFO:tensorflow:lr = 0.00010523845 (19.533 sec)
INFO:tensorflow:global_step/sec: 5.14086
INFO:tensorflow:loss = 4.8504777, step = 587675 (19.449 sec)
INFO:tensorflow:lr = 0.00010522444 (19.450 sec)
INFO:tensorflow:global_step/sec: 5.18564
INFO:tensorflow:loss = 4.724047, step = 587775 (19.285 sec)
INFO:tensorflow:lr = 0.00010521044 (19.284 sec)
INFO:tensorflow:global_step/sec: 5.10395
INFO:tensorflow:loss = 4.9300528, step = 587875 (19.593 sec)
INFO:tensorflow:lr = 0.00010519644 (19.593 sec)
INFO:tensorflow:global_step/sec: 5.12749
INFO:tensorflow:loss = 4.7038484, step = 587975 (19.503 sec)
INFO:tensorflow:lr = 0.000105182444 (19.503 sec)
INFO:tensorflow:global_step/sec: 5.13584
INFO:tensorflow:loss = 4.7842503, step = 588075 (19.471 sec)
INFO:tensorflow:lr = 0.000105168445 (19.471 sec)
INFO:tensorflow:global_step/sec: 5.11921
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INFO:tensorflow:lr = 0.00010515444 (19.535 sec)
INFO:tensorflow:global_step/sec: 5.10776
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INFO:tensorflow:lr = 0.00010514044 (19.579 sec)
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INFO:tensorflow:lr = 0.00010512644 (19.453 sec)
INFO:tensorflow:global_step/sec: 5.13833
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INFO:tensorflow:lr = 0.00010511244 (19.461 sec)
INFO:tensorflow:global_step/sec: 5.15095
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INFO:tensorflow:lr = 0.00010509844 (19.414 sec)
INFO:tensorflow:global_step/sec: 5.08264
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INFO:tensorflow:lr = 0.000105084444 (19.675 sec)
INFO:tensorflow:global_step/sec: 5.02569
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INFO:tensorflow:lr = 0.000105070445 (19.898 sec)
INFO:tensorflow:global_step/sec: 5.11881
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INFO:tensorflow:lr = 0.000105056446 (19.533 sec)
INFO:tensorflow:global_step/sec: 5.10652
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INFO:tensorflow:lr = 0.00010504244 (19.583 sec)
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INFO:tensorflow:lr = 0.00010502844 (19.694 sec)
INFO:tensorflow:global_step/sec: 5.11633
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INFO:tensorflow:lr = 0.00010501444 (19.545 sec)
INFO:tensorflow:global_step/sec: 5.07379
INFO:tensorflow:loss = 4.8218412, step = 589275 (19.706 sec)
INFO:tensorflow:lr = 0.00010500044 (19.707 sec)
INFO:tensorflow:global_step/sec: 5.05547
INFO:tensorflow:loss = 4.6019344, step = 589375 (19.781 sec)
INFO:tensorflow:lr = 0.000104986444 (19.780 sec)
INFO:tensorflow:global_step/sec: 4.96454
INFO:tensorflow:loss = 4.859253, step = 589475 (20.148 sec)
INFO:tensorflow:lr = 0.00010497244 (20.148 sec)
INFO:tensorflow:global_step/sec: 5.06493
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INFO:tensorflow:lr = 0.00010495844 (19.745 sec)
INFO:tensorflow:global_step/sec: 5.11853
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INFO:tensorflow:lr = 0.00010494444 (19.534 sec)
INFO:tensorflow:global_step/sec: 5.13406
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INFO:tensorflow:lr = 0.00010493044 (19.475 sec)
INFO:tensorflow:global_step/sec: 5.12033
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INFO:tensorflow:lr = 0.00010491644 (19.528 sec)
INFO:tensorflow:global_step/sec: 5.16052
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INFO:tensorflow:lr = 0.000104902436 (19.380 sec)
INFO:tensorflow:global_step/sec: 5.13451
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INFO:tensorflow:lr = 0.00010488844 (19.475 sec)
INFO:tensorflow:global_step/sec: 5.14865
INFO:tensorflow:loss = 4.711407, step = 590175 (19.421 sec)
INFO:tensorflow:lr = 0.00010487444 (19.422 sec)
INFO:tensorflow:global_step/sec: 5.08054
INFO:tensorflow:loss = 4.7720995, step = 590275 (19.685 sec)
INFO:tensorflow:lr = 0.00010486044 (19.684 sec)
INFO:tensorflow:global_step/sec: 5.03086
INFO:tensorflow:loss = 4.8394756, step = 590375 (19.876 sec)
INFO:tensorflow:lr = 0.00010484644 (19.877 sec)
INFO:tensorflow:global_step/sec: 5.14359
INFO:tensorflow:loss = 4.827785, step = 590475 (19.444 sec)
INFO:tensorflow:lr = 0.00010483244 (19.444 sec)
INFO:tensorflow:global_step/sec: 5.14625
INFO:tensorflow:loss = 4.8487988, step = 590575 (19.432 sec)
INFO:tensorflow:lr = 0.000104818435 (19.431 sec)
INFO:tensorflow:global_step/sec: 5.11411
INFO:tensorflow:loss = 4.794856, step = 590675 (19.553 sec)
INFO:tensorflow:lr = 0.00010480444 (19.554 sec)
INFO:tensorflow:global_step/sec: 5.13709
INFO:tensorflow:loss = 4.7642956, step = 590775 (19.465 sec)
INFO:tensorflow:lr = 0.00010479044 (19.468 sec)
INFO:tensorflow:global_step/sec: 5.13807
INFO:tensorflow:loss = 4.7323895, step = 590875 (19.463 sec)
INFO:tensorflow:lr = 0.00010477644 (19.459 sec)
INFO:tensorflow:global_step/sec: 5.16897
INFO:tensorflow:loss = 4.85469, step = 590975 (19.349 sec)
INFO:tensorflow:lr = 0.00010476244 (19.348 sec)
INFO:tensorflow:global_step/sec: 5.14237
INFO:tensorflow:loss = 4.763725, step = 591075 (19.441 sec)
INFO:tensorflow:lr = 0.00010474844 (19.443 sec)
INFO:tensorflow:global_step/sec: 5.10983
INFO:tensorflow:loss = 4.724722, step = 591175 (19.573 sec)
INFO:tensorflow:lr = 0.00010473444 (19.571 sec)
INFO:tensorflow:global_step/sec: 5.12583
INFO:tensorflow:loss = 4.8285646, step = 591275 (19.511 sec)
INFO:tensorflow:lr = 0.000104720435 (19.511 sec)
INFO:tensorflow:global_step/sec: 5.1568
INFO:tensorflow:loss = 4.745642, step = 591375 (19.388 sec)
INFO:tensorflow:lr = 0.000104706436 (19.390 sec)
INFO:tensorflow:global_step/sec: 5.16419
INFO:tensorflow:loss = 4.751034, step = 591475 (19.366 sec)
INFO:tensorflow:lr = 0.00010469244 (19.364 sec)
INFO:tensorflow:global_step/sec: 5.11846
INFO:tensorflow:loss = 4.6309013, step = 591575 (19.537 sec)
INFO:tensorflow:lr = 0.00010467844 (19.537 sec)
INFO:tensorflow:global_step/sec: 5.16242
INFO:tensorflow:loss = 4.796167, step = 591675 (19.370 sec)
INFO:tensorflow:lr = 0.00010466444 (19.370 sec)
INFO:tensorflow:global_step/sec: 5.11388
INFO:tensorflow:loss = 4.780007, step = 591775 (19.556 sec)
INFO:tensorflow:lr = 0.00010465043 (19.556 sec)
INFO:tensorflow:global_step/sec: 5.12811
INFO:tensorflow:loss = 4.806933, step = 591875 (19.497 sec)
INFO:tensorflow:lr = 0.000104636434 (19.500 sec)
INFO:tensorflow:global_step/sec: 5.01938
INFO:tensorflow:loss = 4.6357675, step = 591975 (19.927 sec)
INFO:tensorflow:lr = 0.000104622435 (19.924 sec)
INFO:tensorflow:global_step/sec: 5.081
INFO:tensorflow:loss = 4.5470433, step = 592075 (19.679 sec)
INFO:tensorflow:lr = 0.000104608436 (19.680 sec)
INFO:tensorflow:global_step/sec: 5.05546
INFO:tensorflow:loss = 4.591142, step = 592175 (19.782 sec)
INFO:tensorflow:lr = 0.00010459444 (19.782 sec)
INFO:tensorflow:global_step/sec: 5.06521
INFO:tensorflow:loss = 4.8358, step = 592275 (19.739 sec)
INFO:tensorflow:lr = 0.00010458044 (19.741 sec)
INFO:tensorflow:global_step/sec: 5.00823
INFO:tensorflow:loss = 4.835816, step = 592375 (19.971 sec)
INFO:tensorflow:lr = 0.00010456643 (19.969 sec)
INFO:tensorflow:global_step/sec: 5.13794
INFO:tensorflow:loss = 4.8464494, step = 592475 (19.465 sec)
INFO:tensorflow:lr = 0.00010455244 (19.465 sec)
INFO:tensorflow:global_step/sec: 5.1055
INFO:tensorflow:loss = 4.9021, step = 592575 (19.584 sec)
INFO:tensorflow:lr = 0.000104538434 (19.585 sec)
INFO:tensorflow:global_step/sec: 5.06493
INFO:tensorflow:loss = 4.868694, step = 592675 (19.745 sec)
INFO:tensorflow:lr = 0.000104524435 (19.744 sec)