{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"demo_chinese_text_binary_classification_with_bert.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"84bZG6NlL5ig","colab_type":"text"},"source":["查看当前GPU信息"]},{"cell_type":"code","metadata":{"id":"rIEr_CwhcCBh","colab_type":"code","outputId":"c7f8354d-044e-49e9-ed71-ce3c53aa2864","executionInfo":{"status":"ok","timestamp":1574680166673,"user_tz":-480,"elapsed":2368,"user":{"displayName":"李云","photoUrl":"","userId":"11496847789952362169"}},"colab":{"base_uri":"https://localhost:8080/","height":503}},"source":["from tensorflow.python.client import device_lib\n","\n","device_lib.list_local_devices()"],"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[name: \"/device:CPU:0\"\n"," device_type: \"CPU\"\n"," memory_limit: 268435456\n"," locality {\n"," }\n"," incarnation: 10566334529591942713, name: \"/device:XLA_CPU:0\"\n"," device_type: \"XLA_CPU\"\n"," memory_limit: 17179869184\n"," locality {\n"," }\n"," incarnation: 8480217429325042128\n"," physical_device_desc: \"device: XLA_CPU device\", name: \"/device:XLA_GPU:0\"\n"," device_type: \"XLA_GPU\"\n"," memory_limit: 17179869184\n"," locality {\n"," }\n"," incarnation: 4146199070848845009\n"," physical_device_desc: \"device: XLA_GPU device\", name: \"/device:GPU:0\"\n"," device_type: \"GPU\"\n"," memory_limit: 11330115994\n"," locality {\n"," bus_id: 1\n"," links {\n"," }\n"," }\n"," incarnation: 10101602871662389155\n"," physical_device_desc: \"device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7\"]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"jviywGyWyKsA","colab_type":"code","trusted":true,"outputId":"2dfbcb0e-dfd2-4302-a976-d21887250f60","executionInfo":{"status":"ok","timestamp":1574680171337,"user_tz":-480,"elapsed":7020,"user":{"displayName":"李云","photoUrl":"","userId":"11496847789952362169"}},"colab":{"base_uri":"https://localhost:8080/","height":53}},"source":["!pip install bert-tensorflow"],"execution_count":34,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: bert-tensorflow in /usr/local/lib/python3.6/dist-packages (1.0.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from bert-tensorflow) (1.12.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"hsZvic2YxnTz","colab_type":"code","trusted":true,"colab":{}},"source":["import pandas as pd\n","import tensorflow as tf\n","import tensorflow_hub as hub\n","import pickle\n","import bert\n","from bert import run_classifier\n","from bert import optimization\n","from bert import tokenization"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"NZM71PjIOF_I","colab":{}},"source":["def pretty_print(result):\n"," df = pd.DataFrame([result]).T\n"," df.columns = [\"values\"]\n"," return df"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"id":"ZCYOL8HbIZu2","colab_type":"code","colab":{}},"source":["def create_tokenizer_from_hub_module(bert_model_hub):\n"," \"\"\"Get the vocab file and casing info from the Hub module.\"\"\"\n"," with tf.Graph().as_default():\n"," bert_module = hub.Module(bert_model_hub)\n"," tokenization_info = bert_module(signature=\"tokenization_info\", as_dict=True)\n"," with tf.Session() as sess:\n"," vocab_file, do_lower_case = sess.run([tokenization_info[\"vocab_file\"],\n"," tokenization_info[\"do_lower_case\"]])\n"," \n"," return bert.tokenization.FullTokenizer(\n"," vocab_file=vocab_file, do_lower_case=do_lower_case)\n","\n","def make_features(dataset, label_list, MAX_SEQ_LENGTH, tokenizer, DATA_COLUMN, LABEL_COLUMN):\n"," input_example = dataset.apply(lambda x: bert.run_classifier.InputExample(guid=None, \n"," text_a = x[DATA_COLUMN], \n"," text_b = None, \n"," label = x[LABEL_COLUMN]), axis = 1)\n"," features = bert.run_classifier.convert_examples_to_features(input_example, label_list, MAX_SEQ_LENGTH, tokenizer)\n"," return features\n","\n","def create_model(bert_model_hub, is_predicting, input_ids, input_mask, segment_ids, labels,\n"," num_labels):\n"," \"\"\"Creates a classification model.\"\"\"\n","\n"," bert_module = hub.Module(\n"," bert_model_hub,\n"," trainable=True)\n"," bert_inputs = dict(\n"," input_ids=input_ids,\n"," input_mask=input_mask,\n"," segment_ids=segment_ids)\n"," bert_outputs = bert_module(\n"," inputs=bert_inputs,\n"," signature=\"tokens\",\n"," as_dict=True)\n","\n"," # Use \"pooled_output\" for classification tasks on an entire sentence.\n"," # Use \"sequence_outputs\" for token-level output.\n"," output_layer = bert_outputs[\"pooled_output\"]\n","\n"," hidden_size = output_layer.shape[-1].value\n","\n"," # Create our own layer to tune for politeness data.\n"," output_weights = tf.get_variable(\n"," \"output_weights\", [num_labels, hidden_size],\n"," initializer=tf.truncated_normal_initializer(stddev=0.02))\n","\n"," output_bias = tf.get_variable(\n"," \"output_bias\", [num_labels], initializer=tf.zeros_initializer())\n","\n"," with tf.variable_scope(\"loss\"):\n","\n"," # Dropout helps prevent overfitting\n"," output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)\n","\n"," logits = tf.matmul(output_layer, output_weights, transpose_b=True)\n"," logits = tf.nn.bias_add(logits, output_bias)\n"," log_probs = tf.nn.log_softmax(logits, axis=-1)\n","\n"," # Convert labels into one-hot encoding\n"," one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)\n","\n"," predicted_labels = tf.squeeze(tf.argmax(log_probs, axis=-1, output_type=tf.int32))\n"," # If we're predicting, we want predicted labels and the probabiltiies.\n"," if is_predicting:\n"," return (predicted_labels, log_probs)\n","\n"," # If we're train/eval, compute loss between predicted and actual label\n"," per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)\n"," loss = tf.reduce_mean(per_example_loss)\n"," return (loss, predicted_labels, log_probs)\n","\n","# model_fn_builder actually creates our model function\n","# using the passed parameters for num_labels, learning_rate, etc.\n","def model_fn_builder(bert_model_hub, num_labels, learning_rate, num_train_steps,\n"," num_warmup_steps):\n"," \"\"\"Returns `model_fn` closure for TPUEstimator.\"\"\"\n"," def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n"," \"\"\"The `model_fn` for TPUEstimator.\"\"\"\n","\n"," input_ids = features[\"input_ids\"]\n"," input_mask = features[\"input_mask\"]\n"," segment_ids = features[\"segment_ids\"]\n"," label_ids = features[\"label_ids\"]\n","\n"," is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)\n"," \n"," # TRAIN and EVAL\n"," if not is_predicting:\n","\n"," (loss, predicted_labels, log_probs) = create_model(\n"," bert_model_hub, is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)\n","\n"," train_op = bert.optimization.create_optimizer(\n"," loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=False)\n","\n"," # Calculate evaluation metrics. \n"," def metric_fn(label_ids, predicted_labels):\n"," accuracy = tf.metrics.accuracy(label_ids, predicted_labels)\n"," f1_score = tf.contrib.metrics.f1_score(\n"," label_ids,\n"," predicted_labels)\n"," auc = tf.metrics.auc(\n"," label_ids,\n"," predicted_labels)\n"," recall = tf.metrics.recall(\n"," label_ids,\n"," predicted_labels)\n"," precision = tf.metrics.precision(\n"," label_ids,\n"," predicted_labels) \n"," true_pos = tf.metrics.true_positives(\n"," label_ids,\n"," predicted_labels)\n"," true_neg = tf.metrics.true_negatives(\n"," label_ids,\n"," predicted_labels) \n"," false_pos = tf.metrics.false_positives(\n"," label_ids,\n"," predicted_labels) \n"," false_neg = tf.metrics.false_negatives(\n"," label_ids,\n"," predicted_labels)\n"," return {\n"," \"eval_accuracy\": accuracy,\n"," \"f1_score\": f1_score,\n"," \"auc\": auc,\n"," \"precision\": precision,\n"," \"recall\": recall,\n"," \"true_positives\": true_pos,\n"," \"true_negatives\": true_neg,\n"," \"false_positives\": false_pos,\n"," \"false_negatives\": false_neg\n"," }\n","\n"," eval_metrics = metric_fn(label_ids, predicted_labels)\n","\n"," if mode == tf.estimator.ModeKeys.TRAIN:\n"," return tf.estimator.EstimatorSpec(mode=mode,\n"," loss=loss,\n"," train_op=train_op)\n"," else:\n"," return tf.estimator.EstimatorSpec(mode=mode,\n"," loss=loss,\n"," eval_metric_ops=eval_metrics)\n"," else:\n"," (predicted_labels, log_probs) = create_model(\n"," bert_model_hub, is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)\n","\n"," predictions = {\n"," 'probabilities': log_probs,\n"," 'labels': predicted_labels\n"," }\n"," return tf.estimator.EstimatorSpec(mode, predictions=predictions)\n","\n"," # Return the actual model function in the closure\n"," return model_fn\n","\n","def estimator_builder(bert_model_hub, OUTPUT_DIR, SAVE_SUMMARY_STEPS, SAVE_CHECKPOINTS_STEPS, label_list, LEARNING_RATE, num_train_steps, num_warmup_steps, BATCH_SIZE):\n","\n"," # Specify outpit directory and number of checkpoint steps to save\n"," run_config = tf.estimator.RunConfig(\n"," model_dir=OUTPUT_DIR,\n"," save_summary_steps=SAVE_SUMMARY_STEPS,\n"," save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS)\n","\n"," model_fn = model_fn_builder(\n"," bert_model_hub = bert_model_hub,\n"," num_labels=len(label_list),\n"," learning_rate=LEARNING_RATE,\n"," num_train_steps=num_train_steps,\n"," num_warmup_steps=num_warmup_steps)\n","\n"," estimator = tf.estimator.Estimator(\n"," model_fn=model_fn,\n"," config=run_config,\n"," params={\"batch_size\": BATCH_SIZE})\n"," return estimator, model_fn, run_config\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"IuMOGwFui4it","colab_type":"code","trusted":true,"colab":{}},"source":["def run_on_dfs(train, test, DATA_COLUMN, LABEL_COLUMN, \n"," MAX_SEQ_LENGTH = 128,\n"," BATCH_SIZE = 32,\n"," LEARNING_RATE = 2e-5,\n"," NUM_TRAIN_EPOCHS = 3.0,\n"," WARMUP_PROPORTION = 0.1,\n"," SAVE_SUMMARY_STEPS = 100,\n"," SAVE_CHECKPOINTS_STEPS = 10000,\n"," bert_model_hub = \"https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1\"):\n","\n"," label_list = train[LABEL_COLUMN].unique().tolist()\n"," \n"," tokenizer = create_tokenizer_from_hub_module(bert_model_hub)\n","\n"," train_features = make_features(train, label_list, MAX_SEQ_LENGTH, tokenizer, DATA_COLUMN, LABEL_COLUMN)\n"," test_features = make_features(test, label_list, MAX_SEQ_LENGTH, tokenizer, DATA_COLUMN, LABEL_COLUMN)\n","\n"," num_train_steps = int(len(train_features) / BATCH_SIZE * NUM_TRAIN_EPOCHS)\n"," num_warmup_steps = int(num_train_steps * WARMUP_PROPORTION)\n","\n"," estimator, model_fn, run_config = estimator_builder(\n"," bert_model_hub, \n"," OUTPUT_DIR, \n"," SAVE_SUMMARY_STEPS, \n"," SAVE_CHECKPOINTS_STEPS, \n"," label_list, \n"," LEARNING_RATE, \n"," num_train_steps, \n"," num_warmup_steps, \n"," BATCH_SIZE)\n","\n"," train_input_fn = bert.run_classifier.input_fn_builder(\n"," features=train_features,\n"," seq_length=MAX_SEQ_LENGTH,\n"," is_training=True,\n"," drop_remainder=False)\n","\n"," estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)\n","\n"," test_input_fn = run_classifier.input_fn_builder(\n"," features=test_features,\n"," seq_length=MAX_SEQ_LENGTH,\n"," is_training=False,\n"," drop_remainder=False)\n","\n"," result_dict = estimator.evaluate(input_fn=test_input_fn, steps=None)\n"," return result_dict, estimator\n"," "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"id":"wpUHTA8LIZu_","colab_type":"code","colab":{}},"source":["import random\n","random.seed(10)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"id":"qmP8MBvjIZvB","colab_type":"code","colab":{}},"source":["OUTPUT_DIR = 'output'"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UV3ExI2JJMhd","colab_type":"text"},"source":["----- 只需更改下方代码 ------"]},{"cell_type":"markdown","metadata":{"id":"XSUjVoFtJVEO","colab_type":"text"},"source":["导入数据集"]},{"cell_type":"code","metadata":{"id":"20hnmIEeLb4F","colab_type":"code","outputId":"0021e66b-fe51-4de3-8203-a186f165c038","executionInfo":{"status":"ok","timestamp":1574680180358,"user_tz":-480,"elapsed":15989,"user":{"displayName":"李云","photoUrl":"","userId":"11496847789952362169"}},"colab":{"base_uri":"https://localhost:8080/","height":593}},"source":["!wget https://github.com/yaoyue123/SocialComputing/raw/master/spam_message/training.txt\n","!wget https://github.com/yaoyue123/SocialComputing/raw/master/spam_message/validation.txt"],"execution_count":41,"outputs":[{"output_type":"stream","text":["--2019-11-25 11:09:32-- https://github.com/yaoyue123/SocialComputing/raw/master/spam_message/training.txt\n","Resolving github.com (github.com)... 140.82.113.3\n","Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://raw.githubusercontent.com/yaoyue123/SocialComputing/master/spam_message/training.txt [following]\n","--2019-11-25 11:09:33-- https://raw.githubusercontent.com/yaoyue123/SocialComputing/master/spam_message/training.txt\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1149258 (1.1M) [text/plain]\n","Saving to: ‘training.txt.1’\n","\n","\rtraining.txt.1 0%[ ] 0 --.-KB/s \rtraining.txt.1 100%[===================>] 1.10M --.-KB/s in 0.07s \n","\n","2019-11-25 11:09:33 (14.7 MB/s) - ‘training.txt.1’ saved [1149258/1149258]\n","\n","--2019-11-25 11:09:36-- https://github.com/yaoyue123/SocialComputing/raw/master/spam_message/validation.txt\n","Resolving github.com (github.com)... 140.82.113.3\n","Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://raw.githubusercontent.com/yaoyue123/SocialComputing/master/spam_message/validation.txt [following]\n","--2019-11-25 11:09:37-- https://raw.githubusercontent.com/yaoyue123/SocialComputing/master/spam_message/validation.txt\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 144983 (142K) [text/plain]\n","Saving to: ‘validation.txt.1’\n","\n","validation.txt.1 100%[===================>] 141.58K --.-KB/s in 0.04s \n","\n","2019-11-25 11:09:37 (3.78 MB/s) - ‘validation.txt.1’ saved [144983/144983]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BA_TjeIigK3X","colab_type":"code","outputId":"90dd406a-0459-4cc8-f842-a7a702894af7","executionInfo":{"status":"ok","timestamp":1574680180360,"user_tz":-480,"elapsed":15973,"user":{"displayName":"李云","photoUrl":"","userId":"11496847789952362169"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["train = pd.read_table(\"training.txt\",sep='\\t',error_bad_lines=False)\n","#mytrain= mytrain[order]\n","\n","test = pd.read_table(\"validation.txt\",sep='\\t',error_bad_lines=False)\n","#mytest= mytest[order]"],"execution_count":42,"outputs":[{"output_type":"stream","text":["b'Skipping line 11224: expected 2 fields, saw 3\\n'\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"YJXN3NQ4nNd0","colab_type":"code","outputId":"35a66662-f6f4-470b-d361-328601bb2a5f","executionInfo":{"status":"ok","timestamp":1574680180362,"user_tz":-480,"elapsed":15961,"user":{"displayName":"李云","photoUrl":"","userId":"11496847789952362169"}},"colab":{"base_uri":"https://localhost:8080/","height":202}},"source":["train.head()"],"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
labelmassage
00结果发现对方律师和法官像自己一样浑
10本基金的基金管理人为富国基金管理有限公司
20的电了秀完这一波我要继续做安静看书的美少女xxx就是我的家耶斯
30xxxx年x月x日君寿大师与天吕集团达成战略合作
40在30个身高、体重、年龄都差不多的女孩里面
\n","
"],"text/plain":[" label massage\n","0 0 结果发现对方律师和法官像自己一样浑\n","1 0 本基金的基金管理人为富国基金管理有限公司\n","2 0 的电了秀完这一波我要继续做安静看书的美少女xxx就是我的家耶斯\n","3 0 xxxx年x月x日君寿大师与天吕集团达成战略合作\n","4 0 在30个身高、体重、年龄都差不多的女孩里面"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"id":"liSd6ZBFnaTq","colab_type":"code","outputId":"faa8da26-1530-4214-b5e9-e9335eca201e","executionInfo":{"status":"ok","timestamp":1574680180364,"user_tz":-480,"elapsed":15948,"user":{"displayName":"李云","photoUrl":"","userId":"11496847789952362169"}},"colab":{"base_uri":"https://localhost:8080/","height":202}},"source":["test.head()"],"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
labelmassage
00中国好声音怎么赶脚要变成中国怪声音的节奏
10促使苏州的二手楼市愈发兴旺起来
203、看副驾驶出租车姓名并记下车牌号
31喜闹元宵 ，史无前例， 夜更精彩 ！ x月x日凡来好润佳财富购物，全场满xxx立减xx ...
40曝光一个骗子不做链接不给验票必须直接支付宝还说现在让快递拿走真心搞笑智商呢
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"],"text/plain":[" label massage\n","0 0 中国好声音怎么赶脚要变成中国怪声音的节奏\n","1 0 促使苏州的二手楼市愈发兴旺起来\n","2 0 3、看副驾驶出租车姓名并记下车牌号\n","3 1 喜闹元宵 ，史无前例， 夜更精彩 ！ x月x日凡来好润佳财富购物，全场满xxx立减xx ...\n","4 0 曝光一个骗子不做链接不给验票必须直接支付宝还说现在让快递拿走真心搞笑智商呢"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"qgcK6-xkOy4Z","colab_type":"text"},"source":["在此更改你的参数，如标签，bert模型地址，epochs"]},{"cell_type":"code","metadata":{"id":"hM8M7k0gKk5H","colab_type":"code","colab":{}},"source":["myparam = {\n"," \"DATA_COLUMN\": \"massage\",\n"," \"LABEL_COLUMN\": \"label\",\n"," \"LEARNING_RATE\": 2e-5,\n"," \"NUM_TRAIN_EPOCHS\":1,\n"," \"bert_model_hub\":\"https://tfhub.dev/google/bert_chinese_L-12_H-768_A-12/1\"\n","\n"," }"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"x_Q8kZHiTma2","colab_type":"text"},"source":["训练模型，通常情况下，一个epochs用k80训练大概在10min左右"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"Dg2apeXpMqG-","outputId":"1350c684-dc85-4f3e-f52a-1ee96b9a6128","executionInfo":{"status":"ok","timestamp":1574681036220,"user_tz":-480,"elapsed":871782,"user":{"displayName":"李云","photoUrl":"","userId":"11496847789952362169"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["result, estimator = run_on_dfs(train, test, **myparam)"],"execution_count":46,"outputs":[{"output_type":"stream","text":["INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Writing example 0 of 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0\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:label: 0 (id = 0)\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:label: 0 (id = 0)\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Using config: {'_model_dir': 'output', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 10000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true\n","graph_options {\n"," rewrite_options {\n"," meta_optimizer_iterations: ONE\n"," }\n","}\n",", '_keep_checkpoint_max': 5, '_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': , '_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}\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Using config: {'_model_dir': 'output', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 10000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true\n","graph_options {\n"," rewrite_options {\n"," meta_optimizer_iterations: ONE\n"," }\n","}\n",", '_keep_checkpoint_max': 5, '_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': , '_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}\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Calling model_fn.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Calling model_fn.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n","/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n"," \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Done calling model_fn.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Done calling model_fn.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Create CheckpointSaverHook.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Create CheckpointSaverHook.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Graph was finalized.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Graph was finalized.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Restoring parameters from output/model.ckpt-0\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Restoring parameters from output/model.ckpt-0\n"],"name":"stderr"},{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/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.\n","Instructions for updating:\n","Use standard file utilities to get mtimes.\n"],"name":"stdout"},{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/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.\n","Instructions for updating:\n","Use standard file utilities to get mtimes.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Running local_init_op.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Running local_init_op.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Done running local_init_op.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Done running local_init_op.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Saving checkpoints for 0 into output/model.ckpt.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Saving checkpoints for 0 into output/model.ckpt.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.40486598, step = 0\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.40486598, step = 0\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:global_step/sec: 0.574887\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:global_step/sec: 0.574887\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.0010435606, step = 100 (173.953 sec)\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.0010435606, step = 100 (173.953 sec)\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:global_step/sec: 0.651929\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:global_step/sec: 0.651929\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.0037862405, step = 200 (153.388 sec)\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.0037862405, step = 200 (153.388 sec)\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:global_step/sec: 0.643517\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:global_step/sec: 0.643517\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.0011707861, step = 300 (155.401 sec)\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.0011707861, step = 300 (155.401 sec)\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:global_step/sec: 0.648718\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:global_step/sec: 0.648718\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.001997455, step = 400 (154.147 sec)\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:loss = 0.001997455, step = 400 (154.147 sec)\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Saving checkpoints for 463 into output/model.ckpt.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Saving checkpoints for 463 into output/model.ckpt.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Loss for final step: 0.0015291406.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Loss for final step: 0.0015291406.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Calling model_fn.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Calling model_fn.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n","/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n"," \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Done calling model_fn.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Done calling model_fn.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Starting evaluation at 2019-11-25T11:23:11Z\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Starting evaluation at 2019-11-25T11:23:11Z\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Graph was finalized.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Graph was finalized.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Restoring parameters from output/model.ckpt-463\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Restoring parameters from output/model.ckpt-463\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Running local_init_op.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Running local_init_op.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Done running local_init_op.\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Done running local_init_op.\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Finished evaluation at 2019-11-25-11:23:51\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Finished evaluation at 2019-11-25-11:23:51\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Saving dict for global step 463: auc = 0.9902439, eval_accuracy = 0.9979737, f1_score = 0.9901477, false_negatives = 4.0, false_positives = 0.0, global_step = 463, loss = 0.010224212, precision = 1.0, recall = 0.9804878, true_negatives = 1769.0, true_positives = 201.0\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Saving dict for global step 463: auc = 0.9902439, eval_accuracy = 0.9979737, f1_score = 0.9901477, false_negatives = 4.0, false_positives = 0.0, global_step = 463, loss = 0.010224212, precision = 1.0, recall = 0.9804878, true_negatives = 1769.0, true_positives = 201.0\n"],"name":"stderr"},{"output_type":"stream","text":["INFO:tensorflow:Saving 'checkpoint_path' summary for global step 463: output/model.ckpt-463\n"],"name":"stdout"},{"output_type":"stream","text":["INFO:tensorflow:Saving 'checkpoint_path' summary for global step 463: output/model.ckpt-463\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"iJfHK6IRT49J","colab_type":"text"},"source":["bert模型还是比较强的，一个epochs就能达到准确率为99%"]},{"cell_type":"code","metadata":{"id":"YLOvtveTMqwp","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":386},"outputId":"1d129704-da63-40d1-b6b6-6381d7b2c37c","executionInfo":{"status":"ok","timestamp":1574681063338,"user_tz":-480,"elapsed":1743,"user":{"displayName":"李云","photoUrl":"","userId":"11496847789952362169"}}},"source":["pretty_print(result)"],"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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values
auc0.990244
eval_accuracy0.997974
f1_score0.990148
false_negatives4.000000
false_positives0.000000
loss0.010224
precision1.000000
recall0.980488
true_negatives1769.000000
true_positives201.000000
global_step463.000000
\n","
"],"text/plain":[" values\n","auc 0.990244\n","eval_accuracy 0.997974\n","f1_score 0.990148\n","false_negatives 4.000000\n","false_positives 0.000000\n","loss 0.010224\n","precision 1.000000\n","recall 0.980488\n","true_negatives 1769.000000\n","true_positives 201.000000\n","global_step 463.000000"]},"metadata":{"tags":[]},"execution_count":48}]}]}