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Commit cd3c6c57 authored by Chen Chen's avatar Chen Chen Committed by A. Unique TensorFlower
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official/colab/fine_tuning_bert.ipynb
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}, },
"source": [ "source": [
"## How-to Guide: Using a PIP package for fine-tuning a BERT model\n", "## How-to Guide: Using a PIP package for fine-tuning a BERT model\n",
...@@ -1996,8 +17,8 @@ ...@@ -1996,8 +17,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "T7BBEc1-RNCQ", "colab_type": "text",
"colab_type": "text" "id": "T7BBEc1-RNCQ"
}, },
"source": [ "source": [
"## License\n", "## License\n",
...@@ -2020,8 +41,8 @@ ...@@ -2020,8 +41,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "Pf6xzoKjywY_", "colab_type": "text",
"colab_type": "text" "id": "Pf6xzoKjywY_"
}, },
"source": [ "source": [
"## Learning objectives\n", "## Learning objectives\n",
...@@ -2032,8 +53,8 @@ ...@@ -2032,8 +53,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "YHkmV89jRWkS", "colab_type": "text",
"colab_type": "text" "id": "YHkmV89jRWkS"
}, },
"source": [ "source": [
"## Enable the GPU acceleration\n", "## Enable the GPU acceleration\n",
...@@ -2046,8 +67,8 @@ ...@@ -2046,8 +67,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "s2d9S2CSSO1z", "colab_type": "text",
"colab_type": "text" "id": "s2d9S2CSSO1z"
}, },
"source": [ "source": [
"##Install and import" "##Install and import"
...@@ -2056,149 +77,34 @@ ...@@ -2056,149 +77,34 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "fsACVQpVSifi", "colab_type": "text",
"colab_type": "text" "id": "fsACVQpVSifi"
}, },
"source": [ "source": [
"### Install the TensorFlow Model Garden pip package\n", "### Install the TensorFlow Model Garden pip package\n",
"\n", "\n",
"* tf-models-nightly is the nightly Model Garden package created daily automatically. \n", "* tf-models-nightly is the nightly Model Garden package created daily automatically.\n",
"* pip will install all models and dependencies automatically." "* pip will install all models and dependencies automatically."
] ]
}, },
{ {
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"execution_count": 0,
"metadata": { "metadata": {
"id": "NvNr2svBM-p3", "colab": {},
"colab_type": "code", "colab_type": "code",
"outputId": "f0be17be-2474-4f18-c87d-5b3f4237fab4", "id": "NvNr2svBM-p3"
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
}, },
"outputs": [],
"source": [ "source": [
"pip install tf-models-nightly" "!pip install tf-models-nightly"
],
"execution_count": 0,
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/98/2c/8df20f3ac6c22ac224fff307ebc102818206c53fc454ecd37d8ac2060df5/sentencepiece-0.1.86-cp36-cp36m-manylinux1_x86_64.whl (1.0MB)\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/09/7e/e94aa029999ec30951e8129fa992fecbbaffda66eba97c65d5a83f8ea96d/tensorflow_model_optimization-0.3.0-py2.py3-none-any.whl (165kB)\n",
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],
"name": "stdout"
}
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "U-7qPCjWUAyy", "colab_type": "text",
"colab_type": "text" "id": "U-7qPCjWUAyy"
}, },
"source": [ "source": [
"### Import Tensorflow and other libraries" "### Import Tensorflow and other libraries"
...@@ -2206,48 +112,49 @@ ...@@ -2206,48 +112,49 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0,
"metadata": { "metadata": {
"id": "lXsXev5MNr20", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "lXsXev5MNr20"
}, },
"outputs": [],
"source": [ "source": [
"import json\n", "import os\n",
"import math\n",
"\n", "\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"\n",
"from official.modeling import tf_utils\n",
"from official.nlp import optimization\n", "from official.nlp import optimization\n",
"from official.nlp.bert import bert_models\n",
"from official.nlp.bert import configs as bert_configs\n", "from official.nlp.bert import configs as bert_configs\n",
"from official.nlp.bert import run_classifier\n",
"from official.nlp.bert import tokenization\n", "from official.nlp.bert import tokenization\n",
"from official.nlp.data import classifier_data_lib\n", "from official.nlp.data import classifier_data_lib\n",
"from official.utils.misc import distribution_utils\n", "from official.nlp.modeling import losses\n",
"\n", "from official.nlp.modeling import models\n",
"import tensorflow as tf" "from official.nlp.modeling import networks"
], ]
"execution_count": 0,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "C2drjD7OVCmh", "colab_type": "text",
"colab_type": "text" "id": "C2drjD7OVCmh"
}, },
"source": [ "source": [
"## Get dataset" "## Preprocess the raw data and output tf.record files"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "qfjcKj5FYQOp", "colab_type": "text",
"colab_type": "text" "id": "qfjcKj5FYQOp"
}, },
"source": [ "source": [
"### Introduction of dataset\n", "### Introduction of dataset\n",
"\n", "\n",
"The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.\n", "The Microsoft Research Paraphrase Corpus (Dolan \u0026 Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.\n",
"\n", "\n",
"* Number of labels: 2.\n", "* Number of labels: 2.\n",
"* Size of training dataset: 3668.\n", "* Size of training dataset: 3668.\n",
...@@ -2259,8 +166,8 @@ ...@@ -2259,8 +166,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "28DvUhC1YUiB", "colab_type": "text",
"colab_type": "text" "id": "28DvUhC1YUiB"
}, },
"source": [ "source": [
"### Get dataset from TensorFlow Datasets (TFDS)\n", "### Get dataset from TensorFlow Datasets (TFDS)\n",
...@@ -2271,8 +178,8 @@ ...@@ -2271,8 +178,8 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "4PhRLWh9jaXp", "colab_type": "text",
"colab_type": "text" "id": "4PhRLWh9jaXp"
}, },
"source": [ "source": [
"### Preprocess the data and write to TensorFlow record file\n", "### Preprocess the data and write to TensorFlow record file\n",
...@@ -2281,429 +188,278 @@ ...@@ -2281,429 +188,278 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0,
"metadata": { "metadata": {
"id": "FhcMdzsrjWzG", "colab": {},
"colab_type": "code", "colab_type": "code",
"outputId": "f75ff71f-f05f-4cf3-e748-ca1949fcf5ec", "id": "FhcMdzsrjWzG"
"colab": {
"base_uri": "https://localhost:8080/",
"height": 449,
"referenced_widgets": [
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]
}
}, },
"outputs": [],
"source": [ "source": [
"gs_folder_bert = \"gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-12_H-768_A-12\"\n", "gs_folder_bert = \"gs://cloud-tpu-checkpoints/bert/keras_bert/uncased_L-12_H-768_A-12\"\n",
"\n", "\n",
"# Get vocabulary file\n",
"vocab_file = gs_folder_bert + \"/vocab.txt\"\n",
"\n",
"# Set up output of training and evaluation Tensorflow dataset\n",
"train_data_output_path=\"./mrpc_train.tf_record\"\n",
"eval_data_output_path=\"./mrpc_eval.tf_record\"\n",
"\n",
"# Set up tokenizer to generate Tensorflow dataset\n", "# Set up tokenizer to generate Tensorflow dataset\n",
"tokenizer = tokenization.FullTokenizer(\n", "tokenizer = tokenization.FullTokenizer(\n",
" vocab_file=vocab_file, do_lower_case=True)\n", " vocab_file=os.path.join(gs_folder_bert, \"vocab.txt\"), do_lower_case=True)\n",
"\n", "\n",
"# Set up processor to generate Tensorflow dataset\n", "# Set up processor to generate Tensorflow dataset\n",
"processor_text_fn = tokenization.convert_to_unicode\n",
"processor = classifier_data_lib.TfdsProcessor(\n", "processor = classifier_data_lib.TfdsProcessor(\n",
" tfds_params=\"dataset=glue/mrpc,text_key=sentence1,text_b_key=sentence2\",\n", " tfds_params=\"dataset=glue/mrpc,text_key=sentence1,text_b_key=sentence2\",\n",
" process_text_fn=processor_text_fn)\n", " process_text_fn=tokenization.convert_to_unicode)\n",
"\n",
"# Set up output of training and evaluation Tensorflow dataset\n",
"train_data_output_path=\"./mrpc_train.tf_record\"\n",
"eval_data_output_path=\"./mrpc_eval.tf_record\"\n",
"\n", "\n",
"# Generate and save training data into a tf record file\n", "# Generate and save training data into a tf record file\n",
"input_meta_data = classifier_data_lib.generate_tf_record_from_data_file(\n", "input_meta_data = classifier_data_lib.generate_tf_record_from_data_file(\n",
" processor,\n", " processor=processor,\n",
" None,\n", " data_dir=None, # It is `None` because data is from tfds, not local dir.\n",
" tokenizer,\n", " tokenizer=tokenizer,\n",
" train_data_output_path=\"./mrpc_train.tf_record\",\n", " train_data_output_path=train_data_output_path,\n",
" eval_data_output_path=\"./mrpc_eval.tf_record\",\n", " eval_data_output_path=eval_data_output_path,\n",
" max_seq_length=128)" " max_seq_length=128)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[1mDownloading and preparing dataset glue/mrpc/1.0.0 (download: 1.43 MiB, generated: Unknown size, total: 1.43 MiB) to /root/tensorflow_datasets/glue/mrpc/1.0.0...\u001b[0m\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2a9e31a9fb264e86b4ee0a1c81cceaa5",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
] ]
}, },
"metadata": {
"tags": []
}
},
{ {
"output_type": "display_data", "cell_type": "markdown",
"data": { "metadata": {
"application/vnd.jupyter.widget-view+json": { "colab_type": "text",
"model_id": "6f3e0af4934d4b6885336c79a46055af", "id": "dbJ76vSJj77j"
"version_minor": 0,
"version_major": 2
}, },
"text/plain": [ "source": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…" "### Create tf.dataset for training and evaluation\n"
] ]
}, },
"metadata": {
"tags": []
}
},
{ {
"output_type": "stream", "cell_type": "code",
"text": [ "execution_count": 0,
"/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n", "metadata": {
" InsecureRequestWarning)\n", "colab": {},
"/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n", "colab_type": "code",
" InsecureRequestWarning)\n", "id": "gCvaLLAxPuMc"
"/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
" InsecureRequestWarning)\n"
],
"name": "stderr"
}, },
{ "outputs": [],
"output_type": "stream", "source": [
"text": [ "def create_classifier_dataset(file_path, seq_length, batch_size, is_training):\n",
" \"\"\"Creates input dataset from (tf)records files for train/eval.\"\"\"\n",
" dataset = tf.data.TFRecordDataset(file_path)\n",
" if is_training:\n",
" dataset = dataset.shuffle(100)\n",
" dataset = dataset.repeat()\n",
"\n", "\n",
" def decode_record(record):\n",
" name_to_features = {\n",
" 'input_ids': tf.io.FixedLenFeature([seq_length], tf.int64),\n",
" 'input_mask': tf.io.FixedLenFeature([seq_length], tf.int64),\n",
" 'segment_ids': tf.io.FixedLenFeature([seq_length], tf.int64),\n",
" 'label_ids': tf.io.FixedLenFeature([], tf.int64),\n",
" }\n",
" return tf.io.parse_single_example(record, name_to_features)\n",
"\n", "\n",
" def _select_data_from_record(record):\n",
" x = {\n",
" 'input_word_ids': record['input_ids'],\n",
" 'input_mask': record['input_mask'],\n",
" 'input_type_ids': record['segment_ids']\n",
" }\n",
" y = record['label_ids']\n",
" return (x, y)\n",
"\n", "\n",
"\n" " dataset = dataset.map(decode_record,\n",
], " num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
"name": "stdout" " dataset = dataset.map(\n",
}, " _select_data_from_record,\n",
{ " num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
"output_type": "display_data", " dataset = dataset.batch(batch_size, drop_remainder=is_training)\n",
"data": { " dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n",
"application/vnd.jupyter.widget-view+json": { " return dataset\n",
"model_id": "707d210e5505474b9994ddb2aa3e4b65", "\n",
"version_minor": 0, "# Set up batch sizes\n",
"version_major": 2 "batch_size = 32\n",
}, "eval_batch_size = 32\n",
"text/plain": [ "\n",
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" "# Return Tensorflow dataset\n",
"training_dataset = create_classifier_dataset(\n",
" train_data_output_path,\n",
" input_meta_data['max_seq_length'],\n",
" batch_size,\n",
" is_training=True)\n",
"\n",
"evaluation_dataset = create_classifier_dataset(\n",
" eval_data_output_path,\n",
" input_meta_data['max_seq_length'],\n",
" eval_batch_size,\n",
" is_training=False)\n"
] ]
}, },
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/glue/mrpc/1.0.0.incompleteC1ZQ3K/glue-train.tfrecord\n"
],
"name": "stdout"
},
{ {
"output_type": "display_data", "cell_type": "markdown",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d210dc6092a42fabff575536a383941",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=3668.0), HTML(value='')))"
]
},
"metadata": { "metadata": {
"tags": [] "colab_type": "text",
} "id": "Efrj3Cn1kLAp"
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
}, },
{ "source": [
"output_type": "display_data", "## Create, compile and train the model"
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dcdb5006adc5492eab470930b6335d47",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
] ]
}, },
"metadata": {
"tags": []
}
},
{ {
"output_type": "stream", "cell_type": "markdown",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/glue/mrpc/1.0.0.incompleteC1ZQ3K/glue-validation.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "99b14b614f204e85a3d681bcec8c45e0",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=408.0), HTML(value='')))"
]
},
"metadata": { "metadata": {
"tags": [] "colab_type": "text",
} "id": "96ldxDSwkVkj"
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "43d3c73f5c9c4614a81603ec7323bd85",
"version_minor": 0,
"version_major": 2
}, },
"text/plain": [ "source": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" "### Construct a Bert Model\n",
"\n",
"Here, a Bert Model is constructed from the json file with parameters. The bert_config defines the core Bert Model, which is a Keras model to predict the outputs of *num_classes* from the inputs with maximum sequence length *max_seq_length*. "
] ]
}, },
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/glue/mrpc/1.0.0.incompleteC1ZQ3K/glue-test.tfrecord\n"
],
"name": "stdout"
},
{ {
"output_type": "display_data", "cell_type": "code",
"data": { "execution_count": 0,
"application/vnd.jupyter.widget-view+json": {
"model_id": "7758bd45630e4eae88bf47c34055de38",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=1725.0), HTML(value='')))"
]
},
"metadata": { "metadata": {
"tags": [] "colab": {},
} "colab_type": "code",
"id": "Qgajw8WPYzJZ"
}, },
{ "outputs": [],
"output_type": "stream", "source": [
"text": [ "bert_config_file = os.path.join(gs_folder_bert, \"bert_config.json\")\n",
"\u001b[1mDataset glue downloaded and prepared to /root/tensorflow_datasets/glue/mrpc/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n" "bert_config = bert_configs.BertConfig.from_json_file(bert_config_file)\n",
], "\n",
"name": "stdout" "bert_encoder = networks.TransformerEncoder(vocab_size=bert_config.vocab_size,\n",
} " hidden_size=bert_config.hidden_size,\n",
" num_layers=bert_config.num_hidden_layers,\n",
" num_attention_heads=bert_config.num_attention_heads,\n",
" intermediate_size=bert_config.intermediate_size,\n",
" activation=tf_utils.get_activation(bert_config.hidden_act),\n",
" dropout_rate=bert_config.hidden_dropout_prob,\n",
" attention_dropout_rate=bert_config.attention_probs_dropout_prob,\n",
" sequence_length=input_meta_data['max_seq_length'],\n",
" max_sequence_length=bert_config.max_position_embeddings,\n",
" type_vocab_size=bert_config.type_vocab_size,\n",
" embedding_width=bert_config.embedding_size,\n",
" initializer=tf.keras.initializers.TruncatedNormal(\n",
" stddev=bert_config.initializer_range))\n",
"\n",
"classifier_model = models.BertClassifier(\n",
" bert_encoder,\n",
" num_classes=input_meta_data['num_labels'],\n",
" dropout_rate=bert_config.hidden_dropout_prob,\n",
" initializer=tf.keras.initializers.TruncatedNormal(\n",
" stddev=bert_config.initializer_range))"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "dbJ76vSJj77j", "colab_type": "text",
"colab_type": "text" "id": "pkSq1wbNXBaa"
}, },
"source": [ "source": [
"### Get Tensorflow dataset\n", "### Initialize the encoder from a pretrained model"
"\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"metadata": {
"id": "gCvaLLAxPuMc",
"colab_type": "code",
"colab": {}
},
"source": [
"# Get dataset information from meta data\n",
"max_seq_length = input_meta_data['max_seq_length']\n",
"num_classes = input_meta_data['num_labels']\n",
"\n",
"# Set up batch sizes\n",
"batch_size = 32\n",
"eval_batch_size = 32\n",
"\n",
"# Return Tensorflow dataset\n",
"train_input_fn = run_classifier.get_dataset_fn(train_data_output_path, max_seq_length, batch_size, is_training=True)\n",
"eval_input_fn = run_classifier.get_dataset_fn(eval_data_output_path, max_seq_length, eval_batch_size, is_training=False)\n",
"training_dataset = train_input_fn()\n",
"evaluation_dataset = eval_input_fn()"
],
"execution_count": 0, "execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": { "metadata": {
"id": "Efrj3Cn1kLAp", "colab": {},
"colab_type": "text" "colab_type": "code",
"id": "X6N9NEqfXJCx"
}, },
"outputs": [],
"source": [ "source": [
"## Create, compile and train the model" "checkpoint = tf.train.Checkpoint(model=bert_encoder)\n",
"checkpoint.restore(\n",
" os.path.join(gs_folder_bert, 'bert_model.ckpt')).assert_consumed()"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "96ldxDSwkVkj", "colab_type": "text",
"colab_type": "text" "id": "115caFLMk-_l"
}, },
"source": [ "source": [
"### Construct a Bert Model\n", "### Set up an optimizer for the model\n",
"\n", "\n",
"Here, a Bert Model is constructed from the json file with parameters. The bert_config defines the core Bert Model, which is a Keras model to predict the outputs of *num_classes* from the inputs with maximum sequence length *max_seq_length*. " "BERT model adopts the Adam optimizer with weight decay.\n",
"It also employs a learning rate schedule that firstly warms up from 0 and then decays to 0."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0,
"metadata": { "metadata": {
"id": "Qgajw8WPYzJZ", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "2Hf2rpRXk89N"
}, },
"outputs": [],
"source": [ "source": [
"bert_config_file = gs_folder_bert + \"/bert_config.json\"\n", "# Set up epochs and steps\n",
"bert_config = bert_configs.BertConfig.from_json_file(bert_config_file)\n", "epochs = 3\n",
"classifier_model, encoder = bert_models.classifier_model(\n", "train_data_size = input_meta_data['train_data_size']\n",
" bert_config, num_classes, max_seq_length)" "steps_per_epoch = int(train_data_size / batch_size)\n",
], "num_train_steps = steps_per_epoch * epochs\n",
"execution_count": 0, "warmup_steps = int(epochs * train_data_size * 0.1 / batch_size)\n",
"outputs": [] "\n",
"# Create learning rate schedule that firstly warms up from 0 and they decy to 0.\n",
"lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay(\n",
" initial_learning_rate=2e-5,\n",
" decay_steps=num_train_steps,\n",
" end_learning_rate=0)\n",
"lr_schedule = optimization.WarmUp(\n",
" initial_learning_rate=2e-5,\n",
" decay_schedule_fn=lr_schedule,\n",
" warmup_steps=warmup_steps)\n",
"optimizer = optimization.AdamWeightDecay(\n",
" learning_rate=lr_schedule,\n",
" weight_decay_rate=0.01,\n",
" beta_1=0.9,\n",
" beta_2=0.999,\n",
" epsilon=1e-6,\n",
" exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'])"
]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "115caFLMk-_l", "colab_type": "text",
"colab_type": "text" "id": "OTNcA0O0nSq9"
}, },
"source": [ "source": [
"### Set up an optimizer for the model" "### Define metric_fn and loss_fn\n",
"\n",
"The metric is accuracy and we use sparse categorical cross-entropy as loss."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0,
"metadata": { "metadata": {
"id": "2Hf2rpRXk89N", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "ELHjRp87nVNH"
}, },
"outputs": [],
"source": [ "source": [
"# Set up epochs and steps\n", "def metric_fn():\n",
"epochs = 3\n", " return tf.keras.metrics.SparseCategoricalAccuracy(\n",
"train_data_size = input_meta_data['train_data_size']\n", " 'accuracy', dtype=tf.float32)\n",
"steps_per_epoch = int(train_data_size / batch_size)\n",
"num_train_steps = steps_per_epoch * epochs\n",
"warmup_steps = int(epochs * train_data_size * 0.1 / batch_size)\n",
"\n",
"# Set up evaluation batch size and steps\n",
"eval_batch_size = 32\n",
"eval_data_size = input_meta_data['eval_data_size']\n",
"eval_steps = int(eval_data_size / eval_batch_size)\n",
"\n", "\n",
"# creates an optimizer with learning rate schedule\n", "def classification_loss_fn(labels, logits):\n",
"optimizer = optimization.create_optimizer(\n", " return losses.weighted_sparse_categorical_crossentropy_loss(\n",
" 2e-5, num_train_steps=num_train_steps, num_warmup_steps=warmup_steps)" " labels=labels, predictions=tf.nn.log_softmax(logits, axis=-1))\n"
], ]
"execution_count": 0,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "78FEUOOEkoP0", "colab_type": "text",
"colab_type": "text" "id": "78FEUOOEkoP0"
}, },
"source": [ "source": [
"### Compile and train the model" "### Compile and train the model"
...@@ -2711,80 +467,30 @@ ...@@ -2711,80 +467,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0,
"metadata": { "metadata": {
"id": "nzi8hjeTQTRs", "colab": {},
"colab_type": "code", "colab_type": "code",
"outputId": "0738882c-1522-4cc4-d9ba-a6234cc82a0e", "id": "nzi8hjeTQTRs"
"colab": {
"base_uri": "https://localhost:8080/",
"height": 188
}
}, },
"outputs": [],
"source": [ "source": [
"# Function: calculates how often predictions matches integer labels.\n",
"def metric_fn():\n",
" return tf.keras.metrics.SparseCategoricalAccuracy(\n",
" 'test_accuracy', dtype=tf.float32)\n",
"\n",
"# Compile and train the model\n",
"classifier_model.compile(optimizer=optimizer,\n", "classifier_model.compile(optimizer=optimizer,\n",
" loss=run_classifier.get_loss_fn(num_classes=2),\n", " loss=classification_loss_fn,\n",
" metrics=[metric_fn()])\n", " metrics=[metric_fn()])\n",
"\n",
"classifier_model.fit(\n", "classifier_model.fit(\n",
" x=training_dataset,\n", " x=training_dataset,\n",
" validation_data=evaluation_dataset,\n", " validation_data=evaluation_dataset,\n",
" steps_per_epoch=steps_per_epoch,\n", " steps_per_epoch=steps_per_epoch,\n",
" epochs=epochs,\n", " epochs=epochs,\n",
" validation_steps=int(eval_data_size / eval_batch_size))" " validation_steps=int(input_meta_data['eval_data_size'] / eval_batch_size))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/3\n",
" 2/114 [..............................] - ETA: 57s - loss: 0.7512 - test_accuracy: 0.2500WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time. Check your callbacks.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time. Check your callbacks.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"114/114 [==============================] - 90s 785ms/step - loss: 0.6498 - test_accuracy: 0.6595 - val_loss: 0.6397 - val_test_accuracy: 0.6797\n",
"Epoch 2/3\n",
"114/114 [==============================] - 97s 848ms/step - loss: 0.6334 - test_accuracy: 0.6743 - val_loss: 0.6215 - val_test_accuracy: 0.6797\n",
"Epoch 3/3\n",
"114/114 [==============================] - 96s 842ms/step - loss: 0.6179 - test_accuracy: 0.6763 - val_loss: 0.6106 - val_test_accuracy: 0.6797\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7febe2eed128>"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "fVo_AnT0l26j", "colab_type": "text",
"colab_type": "text" "id": "fVo_AnT0l26j"
}, },
"source": [ "source": [
"### Save the model" "### Save the model"
...@@ -2792,97 +498,55 @@ ...@@ -2792,97 +498,55 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0,
"metadata": { "metadata": {
"id": "Nl5x6nElZqkP", "colab": {},
"colab_type": "code", "colab_type": "code",
"outputId": "197a1ebe-02cc-46ec-eeb7-f83158795d91", "id": "Nl5x6nElZqkP"
"colab": {
"base_uri": "https://localhost:8080/",
"height": 171
}
}, },
"outputs": [],
"source": [ "source": [
"classifier_model.save('/tmp/saved_model', include_optimizer=False, save_format='tf')" "classifier_model.save('./saved_model', include_optimizer=False, save_format='tf')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:105: Network.state_updates (from tensorflow.python.keras.engine.network) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:105: Network.state_updates (from tensorflow.python.keras.engine.network) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /tmp/saved_model/assets\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /tmp/saved_model/assets\n"
],
"name": "stderr"
}
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "nWsE6yeyfW00", "colab_type": "text",
"colab_type": "text" "id": "nWsE6yeyfW00"
}, },
"source": [ "source": [
"## Use the trained model\n" "## Use the trained model to predict\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0,
"metadata": { "metadata": {
"id": "vz7YJY2QYAjP", "colab": {},
"colab_type": "code", "colab_type": "code",
"outputId": "49d82b71-1473-45d3-e2e8-f517b83b4d21", "id": "vz7YJY2QYAjP"
"colab": {
"base_uri": "https://localhost:8080/",
"height": 70
}
}, },
"outputs": [],
"source": [ "source": [
"# Set up distribution strategy\n", "eval_predictions = classifier_model.predict(evaluation_dataset)\n",
"strategy = distribution_utils.get_distribution_strategy(\n", "for prediction in eval_predictions:\n",
" distribution_strategy='one_device', num_gpus=1)\n", " print(\"Predicted label id: %s\" % np.argmax(prediction))"
"\n",
"# Get predictiona and labels for evaluation dataset\n",
"eval_predictions, eval_labels = run_classifier.get_predictions_and_labels(strategy, classifier_model, eval_input_fn,\n",
" eval_steps)\n",
"print(eval_predictions)\n",
"print(eval_labels)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
}
] ]
} }
] ],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "How-to Guide: Using a PIP package for fine-tuning a BERT model",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
} }
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