("*** Features ***") for name in sorted(features.keys()): tf.logging.info(" name = %s, shape...("*** Input Files ***") for input_file in input_files: tf.logging.info(" %s" % input_file)...("***** Running training *****") tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)...("***** Running evaluation *****") tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)...("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s =
("*** Features ***") for name in sorted(features.keys()): tf.logging.info(" name = %s, shape...("***** 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",...("***** Running evaluation *****") tf.logging.info(" Num examples = %d (%d actual, %d padding)",...("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s =
("*** Example ***") tf.logging.info("guid: %s" % (example.guid)) tf.logging.info("tokens...for x in segment_ids])) tf.logging.info("loc:") tf.logging.info("\n" + str(loc))...tf.logging.info("mas:") tf.logging.info("\n" + str(mas)) tf.logging.info("e1_mas:")...tf.logging.info("\n" + str(e1_mas)) tf.logging.info("e2_mas:") tf.logging.info("\n" +...str(e2_mas)) tf.logging.info("cls_mask:") tf.logging.info("\n" + str(cls_mask))
input_pattern in FLAGS.input_file.split(","): input_files.extend(tf.gfile.Glob(input_pattern)) tf.logging.info...("*** Reading from input files ***") for input_file in input_files: tf.logging.info(" %s", input_file...("*** Writing to output files ***") for output_file in output_files: tf.logging.info(" %s", output_file...("*** Example ***") tf.logging.info("tokens: %s" % " ".join( [tokenization.printable_text...(feature_name, " ".join([str(x) for x in values]))) for writer in writers: writer.close() tf.logging.info
input_pattern in FLAGS.input_file.split(","): input_files.extend(tf.gfile.Glob(input_pattern)) tf.logging.info...("*** Reading from input files ***") for input_file in input_files: tf.logging.info(" %s", input_file...("*** Writing to output files ***") for output_file in output_files: tf.logging.info(" %s", output_file...writer_index + 1) % len(writers) total_written += 1 # 打印前20个样本 if inst_index < 20: tf.logging.info...("*** Example ***") tf.logging.info("tokens: %s" % " ".join( [tokenization.printable_text
Main入口 首先来看构造数据的整体流程， 1def main(_): 2 tf.logging.set_verbosity(tf.logging.INFO) 3 4 tokenizer =...("*** Reading from input files ***") 12 for input_file in input_files: 13 tf.logging.info(" %s",...FLAGS.max_predictions_per_seq, 19 rng) 20 21 output_files = FLAGS.output_file.split(",") 22 tf.logging.info...("*** Writing to output files ***") 23 for output_file in output_files: 24 tf.logging.info(" %s"...("*** Example ***") 61 tf.logging.info("tokens: %s" % " ".join( 62 [tokenization.printable_text
processed_generated, processed_images], 0), spatial_squeeze=False) # 损失网络日志结构 tf.logging.info...them in "content_layers" and "style_layers"):') for key in endpoints_dict: tf.logging.info...返回: 管理者初始化函数. """ tf.logging.info('Use pretrained model %s' % FLAGS.loss_model_file)...time.time() - start_time start_time = time.time() """每训练10次,打印一次运行日志""" if step % 10 == 0: tf.logging.info...step: %f' % (step, loss_t, elapsed_time)) """每训练25次,更新tensorboard的数据一次""" if step % 25 == 0: tf.logging.info
("*** Example ***") tf.logging.info("guid: %s" % (example.guid)) tf.logging.info("tokens...("input_ids: %s" % " ".join([str(x) for x in input_ids])) tf.logging.info("input_mask: %s" %..." ".join([str(x) for x in input_mask])) tf.logging.info("segment_ids: %s" % " ".join([str(x)...for x in segment_ids])) tf.logging.info("label_ids: %s" % " ".join([str(x) for x in label_ids...])) #tf.logging.info("label_mask: %s" % " ".join([str(x) for x in label_mask])) feature
saver = tf.train.Saver(self.inception_variables) def restore_fn(sess): tf.logging.info(...# 创建训练结果存储路径 train_dir = FLAGS.train_dir if not tf.gfile.IsDirectory(train_dir): tf.logging.info...for file_pattern in FLAGS.input_files.split(","): filenames.extend(tf.gfile.Glob(file_pattern)) tf.logging.info...saver = tf.train.Saver(self.inception_variables) def restore_fn(sess): tf.logging.info(...# 创建训练结果存储路径 train_dir = FLAGS.train_dir if not tf.gfile.IsDirectory(train_dir): tf.logging.info
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",
，但是这个时候训练时静默模式的，没有log输出到控制太，作为码农一般都有日志强迫症，所以最后在model_main.py中导出部分之后添加一行代码： tf.logging.set_verbosity(tf.logging.INFO
import print_function # Imports import numpy as np import tensorflow as tf tf.logging.set_verbosity(tf.logging.INFO
input_fn=predict_input_fn) 其他模块 1. tf日志模块 import tensorflow as tf # 日志的显示等级 tf.logging.set_verbosity(tf.logging.INFO...) # 打印提示日志 tf.logging.info("***** Runningtraining *****") # 打印传参日志 tf.logging.info(" Num examples =
print_function import itertools import pandas as pd import tensorflow as tf tf.logging.set_verbosity(tf.logging.INFO
TPU_WORKER = 'grpc://' + os.environ['COLAB_TPU_ADDR'] tf.logging.set_verbosity(tf.logging.INFO) tpu_model
方法是在import后面加上： tf.logging.set_verbosity(tf.logging.INFO) 这时候运行代码的时候就会看到： INFO:tensorflow:loss = 1.18812...import print_function import os import numpy as np import tensorflow as tf tf.logging.set_verbosity(tf.logging.INFO
randomBatchPos] yOut = trainDataY[randomBatchPos] return xOut, yOut tf.logging.set_verbosity(tf.logging.INFO
import tensorflow as tf import pandas as pd tf.logging.set_verbosity(tf.logging.INFO) project_name=
领取专属 10元无门槛券
手把手带您无忧上云
云服务器
ICP备案
对象存储
腾讯会议
实时音视频
