def create_estimator( model_dir: str = defaults.MODEL_DIR, batch_size: int = defaults.BATCH_SIZE, save_summary_steps: int = defaults.SAVE_SUMMARY_STEPS, save_checkpoints_steps: int = defaults.SAVE_CHECKPOINTS_STEPS, learning_rate: float = defaults.LEARNING_RATE, num_train_steps: int = 0, warmup_proportion: float = defaults.WARMUP_PROPORTION, ) -> tf.estimator.Estimator: # Specify output directory and number of checkpoint steps to save run_config = tf.estimator.RunConfig( model_dir=model_dir, save_summary_steps=save_summary_steps, save_checkpoints_steps=save_checkpoints_steps, ) num_warmup_steps = int(num_train_steps * warmup_proportion) model_fn = model_fn_builder( num_labels=len(defaults.LABEL_LIST), learning_rate=learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, ) return tf.estimator.Estimator(model_fn=model_fn, config=run_config, params={"batch_size": batch_size})
if args.units is not None: args.units = [int(u) for u in args.units.strip().split(',')] ModelConfig = model_config_dict['fm'] model_config = ModelConfig(dnn_feature_columns, linear_feature_columns, class_num=2, use_bn=args.use_bn, use_deep=args.use_deep, units=args.units, dropout_rate=args.dropout_rate, activation=args.activation) model_fn = model_fn_builder(model_name='fm', model_config=model_config, learning_rate=args.learning_rate, init_checkpoint=None, summary_save_dir=args.summary_save_dir, summary_every_n_step=args.summary_every_n_step, task='binary_classification') # ================================= estimator配置 ================================= session_config = tf.ConfigProto(allow_soft_placement=True) run_config = tf.estimator.RunConfig( log_step_count_steps=args.log_step_count_steps, save_checkpoints_steps=args.save_checkpoints_steps, session_config=session_config, model_dir=args.ckpt_save_dir) estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=args.ckpt_save_dir, params={}, config=run_config)
'restecg':3, 'exang':2, 'oldpeak':40, 'slope':3, 'ca':5, 'thal':4 } sparse_feature_columns = [SparseFeature(feature_name=feat, vocab_size=vocab_dict[feat], embedding_dim=16) for feat in sparse_features] dense_feature_columns = [DenseFeature(feature_name=feat) for feat in dense_features] model_config = FMConfig(sparse_feature_columns, dense_feature_columns, class_num=2) model_fn = model_fn_builder( model_config=model_config, learning_rate=0.001, init_checkpoint=None, summary_save_dir='./log/summary/', summary_every_n_step=100, task='binary_classification' ) # ================================= estimator配置 ================================= session_config = tf.ConfigProto(allow_soft_placement=True) run_config = tf.estimator.RunConfig( log_step_count_steps=1000, save_checkpoints_steps=1000, session_config=session_config, model_dir='./log/model' ) estimator = tf.estimator.Estimator( model_fn=model_fn,
def main(_): tf.logging.set_verbosity(tf.logging.INFO) processors = { # "cola": ColaProcessor, # "mnli": MnliProcessor, # "mrpc": MrpcProcessor, # "xnli": XnliProcessor, "xpro": XProcessor } tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, FLAGS.init_checkpoint) if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict: raise ValueError( "At least one of `do_train`, `do_eval` or `do_predict' must be True." ) bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) if FLAGS.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length %d because the BERT model " "was only trained up to sequence length %d" % (FLAGS.max_seq_length, bert_config.max_position_embeddings)) tf.gfile.MakeDirs(FLAGS.output_dir) task_name = FLAGS.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() label_list = processor.get_labels() tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) tpu_cluster_resolver = None if FLAGS.use_tpu and FLAGS.tpu_name: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 run_config = tf.contrib.tpu.RunConfig( cluster=tpu_cluster_resolver, master=FLAGS.master, model_dir=FLAGS.output_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps, tpu_config=tf.contrib.tpu.TPUConfig( iterations_per_loop=FLAGS.iterations_per_loop, num_shards=FLAGS.num_tpu_cores, per_host_input_for_training=is_per_host)) train_examples = None num_train_steps = None num_warmup_steps = None if FLAGS.do_train: train_examples = processor.get_train_examples(FLAGS.data_dir) num_train_steps = int( len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) model_fn = model_fn_builder(bert_config=bert_config, num_labels=len(label_list), init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu) # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. estimator = tf.contrib.tpu.TPUEstimator( use_tpu=FLAGS.use_tpu, model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, eval_batch_size=FLAGS.eval_batch_size, predict_batch_size=FLAGS.predict_batch_size) if FLAGS.do_train: train_file = os.path.join(FLAGS.output_dir, "train.tf_record") file_based_convert_examples_to_features(train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file) tf.logging.info("***** Running training *****") tf.logging.info(" Num examples = %d", len(train_examples)) tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) tf.logging.info(" Num steps = %d", num_train_steps) train_input_fn = file_based_input_fn_builder( input_file=train_file, seq_length=FLAGS.max_seq_length, is_training=True, drop_remainder=True) estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) if FLAGS.do_eval: eval_examples = processor.get_dev_examples(FLAGS.data_dir) num_actual_eval_examples = len(eval_examples) if FLAGS.use_tpu: # TPU requires a fixed batch size for all batches, therefore the number # of examples must be a multiple of the batch size, or else examples # will get dropped. So we pad with fake examples which are ignored # later on. These do NOT count towards the metric (all tf.metrics # support a per-instance weight, and these get a weight of 0.0). while len(eval_examples) % FLAGS.eval_batch_size != 0: eval_examples.append(PaddingInputExample()) eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record") file_based_convert_examples_to_features(eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file) tf.logging.info("***** Running evaluation *****") tf.logging.info(" Num examples = %d (%d actual, %d padding)", len(eval_examples), num_actual_eval_examples, len(eval_examples) - num_actual_eval_examples) tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) # This tells the estimator to run through the entire set. eval_steps = None # However, if running eval on the TPU, you will need to specify the # number of steps. if FLAGS.use_tpu: assert len(eval_examples) % FLAGS.eval_batch_size == 0 eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size) eval_drop_remainder = True if FLAGS.use_tpu else False eval_input_fn = file_based_input_fn_builder( input_file=eval_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=eval_drop_remainder) result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") with tf.gfile.GFile(output_eval_file, "w") as writer: tf.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if FLAGS.do_predict: predict_examples = processor.get_test_examples(FLAGS.data_dir) num_actual_predict_examples = len(predict_examples) if FLAGS.use_tpu: # TPU requires a fixed batch size for all batches, therefore the number # of examples must be a multiple of the batch size, or else examples # will get dropped. So we pad with fake examples which are ignored # later on. while len(predict_examples) % FLAGS.predict_batch_size != 0: predict_examples.append(PaddingInputExample()) predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record") file_based_convert_examples_to_features(predict_examples, label_list, FLAGS.max_seq_length, tokenizer, predict_file) tf.logging.info("***** Running prediction*****") tf.logging.info(" Num examples = %d (%d actual, %d padding)", len(predict_examples), num_actual_predict_examples, len(predict_examples) - num_actual_predict_examples) tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size) predict_drop_remainder = True if FLAGS.use_tpu else False predict_input_fn = file_based_input_fn_builder( input_file=predict_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=predict_drop_remainder) result = estimator.predict(input_fn=predict_input_fn) output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv") with tf.gfile.GFile(output_predict_file, "w") as writer: num_written_lines = 0 tf.logging.info("***** Predict results *****") for (i, prediction) in enumerate(result): probabilities = prediction["probabilities"] if i >= num_actual_predict_examples: break output_line = "\t".join( str(class_probability) for class_probability in probabilities) + "\n" writer.write(output_line) num_written_lines += 1 assert num_written_lines == num_actual_predict_examples
def main(_): tf.logging.set_verbosity(tf.logging.INFO) bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) validate_flags_or_throw(bert_config) tf.gfile.MakeDirs(FLAGS.output_dir) tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) tpu_cluster_resolver = None if FLAGS.use_tpu and FLAGS.tpu_name: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 run_config = tf.contrib.tpu.RunConfig( cluster=tpu_cluster_resolver, master=FLAGS.master, model_dir=FLAGS.output_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps, tpu_config=tf.contrib.tpu.TPUConfig( iterations_per_loop=FLAGS.iterations_per_loop, num_shards=FLAGS.num_tpu_cores, per_host_input_for_training=is_per_host)) train_examples = None num_train_steps = None num_warmup_steps = None if FLAGS.do_train: train_examples = read_squad_examples(input_file=FLAGS.train_file, is_training=True) num_train_steps = int( len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) # Pre-shuffle the input to avoid having to make a very large shuffle # buffer in in the `input_fn`. rng = random.Random(12345) rng.shuffle(train_examples) model_fn = model_fn_builder(bert_config=bert_config, init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu) # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. estimator = tf.contrib.tpu.TPUEstimator( use_tpu=FLAGS.use_tpu, model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, predict_batch_size=FLAGS.predict_batch_size) if FLAGS.do_train: # We write to a temporary file to avoid storing very large constant tensors # in memory. train_writer = FeatureWriter(filename=os.path.join( FLAGS.output_dir, "train.tf_record"), is_training=True) convert_examples_to_features(examples=train_examples, tokenizer=tokenizer, max_seq_length=FLAGS.max_seq_length, doc_stride=FLAGS.doc_stride, max_query_length=FLAGS.max_query_length, is_training=True, output_fn=train_writer.process_feature) train_writer.close() tf.logging.info("***** Running training *****") tf.logging.info(" Num orig examples = %d", len(train_examples)) tf.logging.info(" Num split examples = %d", train_writer.num_features) tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) tf.logging.info(" Num steps = %d", num_train_steps) del train_examples train_input_fn = input_fn_builder(input_file=train_writer.filename, seq_length=FLAGS.max_seq_length, is_training=True, drop_remainder=True) estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) if FLAGS.do_predict: eval_examples = read_squad_examples(input_file=FLAGS.predict_file, is_training=False) eval_writer = FeatureWriter(filename=os.path.join( FLAGS.output_dir, "eval.tf_record"), is_training=False) eval_features = [] def append_feature(feature): eval_features.append(feature) eval_writer.process_feature(feature) convert_examples_to_features(examples=eval_examples, tokenizer=tokenizer, max_seq_length=FLAGS.max_seq_length, doc_stride=FLAGS.doc_stride, max_query_length=FLAGS.max_query_length, is_training=False, output_fn=append_feature) eval_writer.close() tf.logging.info("***** Running predictions *****") tf.logging.info(" Num orig examples = %d", len(eval_examples)) tf.logging.info(" Num split examples = %d", len(eval_features)) tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size) all_results = [] predict_input_fn = input_fn_builder(input_file=eval_writer.filename, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=False) # If running eval on the TPU, you will need to specify the number of # steps. all_results = [] for result in estimator.predict(predict_input_fn, yield_single_examples=True): if len(all_results) % 1000 == 0: tf.logging.info("Processing example: %d" % (len(all_results))) unique_id = int(result["unique_ids"]) start_logits = [float(x) for x in result["start_logits"].flat] end_logits = [float(x) for x in result["end_logits"].flat] all_results.append( RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json") output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json") output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json") write_predictions(eval_examples, eval_features, all_results, FLAGS.n_best_size, FLAGS.max_answer_length, FLAGS.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file)
def main(_): tf.logging.set_verbosity(tf.logging.INFO) processors = {"ner": NerProcessor} if not FLAGS.do_train and not FLAGS.do_eval: raise ValueError( "At least one of `do_train` or `do_eval` must be True.") bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) if FLAGS.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length %d because the BERT model " "was only trained up to sequence length %d" % (FLAGS.max_seq_length, bert_config.max_position_embeddings)) task_name = FLAGS.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() label_list = processor.get_labels() tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) tpu_cluster_resolver = None if FLAGS.use_tpu and FLAGS.tpu_name: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 run_config = tf.contrib.tpu.RunConfig( cluster=tpu_cluster_resolver, master=FLAGS.master, model_dir=FLAGS.output_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps, tpu_config=tf.contrib.tpu.TPUConfig( iterations_per_loop=FLAGS.iterations_per_loop, num_shards=FLAGS.num_tpu_cores, per_host_input_for_training=is_per_host)) train_examples = None num_train_steps = None num_warmup_steps = None if FLAGS.do_train: train_examples = processor.get_train_examples(FLAGS.data_dir) num_train_steps = int( len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) model_fn = model_fn_builder(bert_config=bert_config, num_labels=len(label_list) + 1, init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps) estimator = tf.contrib.tpu.TPUEstimator( use_tpu=FLAGS.use_tpu, model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, eval_batch_size=FLAGS.eval_batch_size, predict_batch_size=FLAGS.predict_batch_size) if FLAGS.do_train: train_file = os.path.join(FLAGS.output_dir, "train.tf_record") filed_based_convert_examples_to_features(train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file, FLAGS) tf.logging.info("***** Running training *****") tf.logging.info(" Num examples = %d", len(train_examples)) tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) tf.logging.info(" Num steps = %d", num_train_steps) train_input_fn = file_based_input_fn_builder( input_file=train_file, seq_length=FLAGS.max_seq_length, is_training=True, drop_remainder=True) estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) if FLAGS.do_eval: eval_examples = processor.get_dev_examples(FLAGS.data_dir) eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record") filed_based_convert_examples_to_features(eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file, FLAGS) tf.logging.info("***** Running evaluation *****") tf.logging.info(" Num examples = %d", len(eval_examples)) tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) eval_steps = None if FLAGS.use_tpu: eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size) eval_drop_remainder = True if FLAGS.use_tpu else False eval_input_fn = file_based_input_fn_builder( input_file=eval_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=eval_drop_remainder) result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: tf.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if FLAGS.do_predict: token_path = os.path.join(FLAGS.output_dir, "token_test.txt") with open('./output/label2id.pkl', 'rb') as rf: label2id = pickle.load(rf) id2label = {value: key for key, value in label2id.items()} if os.path.exists(token_path): os.remove(token_path) predict_examples = processor.get_test_examples(FLAGS.data_dir) predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record") filed_based_convert_examples_to_features(predict_examples, label_list, FLAGS.max_seq_length, tokenizer, predict_file, FLAGS, mode="test") tf.logging.info("***** Running prediction*****") tf.logging.info(" Num examples = %d", len(predict_examples)) tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size) if FLAGS.use_tpu: # Warning: According to tpu_estimator.py Prediction on TPU is an # experimental feature and hence not supported here raise ValueError("Prediction in TPU not supported") predict_drop_remainder = True if FLAGS.use_tpu else False predict_input_fn = file_based_input_fn_builder( input_file=predict_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=predict_drop_remainder) result = estimator.predict(input_fn=predict_input_fn) output_predict_file = os.path.join(FLAGS.output_dir, "label_test.txt") with open(output_predict_file, 'w') as writer: for prediction in result: output_line = "\n".join( id2label[id_] for id_ in prediction if id_ != 0) + "\n" writer.write(output_line)
def main(): tf.logging.set_verbosity(tf.logging.INFO) tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, FLAGS.init_checkpoint) bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) tf.gfile.MakeDirs(FLAGS.output_dir) processor = MultiLabelTextProcessor() label_list = processor.get_labels() tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) # construct estimator tf.logging.info("load estimator ...") config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True, gpu_options={"allow_growth": True}) config.gpu_options.allow_growth = True config.gpu_options.per_process_gpu_memory_fraction = 0.99 run_config = tf.estimator.RunConfig( session_config=config, model_dir=FLAGS.output_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps) train_examples = None num_train_steps = None num_warmup_steps = None if FLAGS.do_train: train_examples = processor.get_train_examples(FLAGS.data_dir) num_train_steps = int( len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) model_fn = model_fn_builder(bert_config=bert_config, num_labels=len(label_list), init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu) estimator = tf.estimator.Estimator(model_fn=model_fn, config=run_config) print("train") if FLAGS.do_train: train_file = os.path.join(FLAGS.output_dir, "train.tf_record") file_based_convert_examples_to_features(train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file) train_input_fn = file_based_input_fn_builder( input_file=train_file, seq_length=FLAGS.max_seq_length, is_training=True, drop_remainder=True) estimator.train(input_fn=train_input_fn, max_steps=num_train_steps) print("eval") def model_dev(eval_examples, type_data): eval_file = os.path.join(FLAGS.output_dir, "eval_" + type_data + ".tf_record") file_based_convert_examples_to_features(eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file) eval_steps = None eval_drop_remainder = True if FLAGS.use_tpu else False eval_input_fn = file_based_input_fn_builder( input_file=eval_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=eval_drop_remainder) result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) output_eval_file = os.path.join(FLAGS.output_dir, "eval_results_" + type_data + ".txt") with tf.gfile.GFile(output_eval_file, "w") as writer: for key in sorted(result.keys()): writer.write("%s = %s\n" % (key, str(result[key]))) if FLAGS.do_eval: eval_examples = processor.get_dev_twitter_examples(FLAGS.data_dir) model_dev(eval_examples, "twitter") eval_examples = processor.get_dev_reddit_examples(FLAGS.data_dir) model_dev(eval_examples, "reddit") print("predict") def model_test(predict_examples, type_data): # type_data = 'reddit' num_actual_predict_examples = len(predict_examples) predict_file = os.path.join(FLAGS.output_dir, "predict_" + type_data + ".tf_record") file_based_convert_examples_to_features(predict_examples, label_list, FLAGS.max_seq_length, tokenizer, predict_file) predict_drop_remainder = True if FLAGS.use_tpu else False predict_input_fn = file_based_input_fn_builder( input_file=predict_file, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=predict_drop_remainder) result = estimator.predict(input_fn=predict_input_fn) output_predict_file = os.path.join(FLAGS.output_dir, "my_" + type_data + "_answer.tsv") with tf.gfile.GFile(output_predict_file, "w") as writer: num_written_lines = 0 i = 0 for predict_example, prediction in zip(predict_examples, result): my_label = prediction["my_label"] if i >= num_actual_predict_examples: break labels = processor.get_labels() writer.write(type_data + "_" + predict_examples[i].guid + ',' + labels[my_label] + "\n") num_written_lines += 1 i += 1 if FLAGS.do_predict: predict_examples = processor.get_twitter_examples(FLAGS.data_dir) model_test(predict_examples, 'twitter') predict_examples = processor.get_reddit_examples(FLAGS.data_dir) model_test(predict_examples, 'reddit')