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Commit c4f0d46a authored by Chen Chen's avatar Chen Chen Committed by A. Unique TensorFlower
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Replace modeling.losses.weighted_sparse_categorical_crossentropy_loss with... 

Replace modeling.losses.weighted_sparse_categorical_crossentropy_loss with tf.keras.losses.sparse_categorical_crossentropy

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Showing with 84 additions and 84 deletions
official/colab/nlp/nlp_modeling_library_intro.ipynb
View file @ c4f0d46a
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Introduction to the TensorFlow Models NLP library",
"private_outputs": true,
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
......@@ -26,10 +11,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "8nvTnfs6Q692"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
......@@ -42,9 +29,7 @@
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -61,20 +46,20 @@
"id": "cH-oJ8R6AHMK"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/official_models/nlp/nlp_modeling_library_intro\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://storage.googleapis.com/tensorflow_docs/models/official/colab/nlp/nlp_modeling_library_intro.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
" </td>\n",
"</table>"
"\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/official_models/nlp/nlp_modeling_library_intro\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/models/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
" \u003c/td\u003e\n",
"\u003c/table\u003e"
]
},
{
......@@ -112,14 +97,14 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Y-qGkdh6_sZc"
},
"outputs": [],
"source": [
"!pip install -q tf-models-official==2.4.0"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -132,18 +117,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jqYXqtjBAJd9"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"\n",
"from official.nlp import modeling\n",
"from official.nlp.modeling import layers, losses, models, networks"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -173,18 +158,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EXkcXz-9BwB3"
},
"outputs": [],
"source": [
"# Build a small transformer network.\n",
"vocab_size = 100\n",
"sequence_length = 16\n",
"network = modeling.networks.BertEncoder(\n",
" vocab_size=vocab_size, num_layers=2, sequence_length=16)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -199,28 +184,28 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lZNoZkBrIoff"
},
"outputs": [],
"source": [
"tf.keras.utils.plot_model(network, show_shapes=True, dpi=48)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "o7eFOZXiIl-b"
},
"outputs": [],
"source": [
"# Create a BERT pretrainer with the created network.\n",
"num_token_predictions = 8\n",
"bert_pretrainer = modeling.models.BertPretrainer(\n",
" network, num_classes=2, num_token_predictions=num_token_predictions, output='predictions')"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -233,20 +218,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2tcNfm03IBF7"
},
"outputs": [],
"source": [
"tf.keras.utils.plot_model(bert_pretrainer, show_shapes=True, dpi=48)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "F2oHrXGUIS0M"
},
"outputs": [],
"source": [
"# We can feed some dummy data to get masked language model and sentence output.\n",
"batch_size = 2\n",
......@@ -261,9 +248,7 @@
"sentence_output = outputs[\"classification\"]\n",
"print(lm_output)\n",
"print(sentence_output)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -277,9 +262,11 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "k30H4Q86f52x"
},
"outputs": [],
"source": [
"masked_lm_ids_data = np.random.randint(vocab_size, size=(batch_size, num_token_predictions))\n",
"masked_lm_weights_data = np.random.randint(2, size=(batch_size, num_token_predictions))\n",
......@@ -294,9 +281,7 @@
" predictions=sentence_output)\n",
"loss = mlm_loss + sentence_loss\n",
"print(loss)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -337,18 +322,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "B941M4iUCejO"
},
"outputs": [],
"source": [
"network = modeling.networks.BertEncoder(\n",
" vocab_size=vocab_size, num_layers=2, sequence_length=sequence_length)\n",
"\n",
"# Create a BERT trainer with the created network.\n",
"bert_span_labeler = modeling.models.BertSpanLabeler(network)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -361,20 +346,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RbqRNJCLJu4H"
},
"outputs": [],
"source": [
"tf.keras.utils.plot_model(bert_span_labeler, show_shapes=True, dpi=48)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fUf1vRxZJwio"
},
"outputs": [],
"source": [
"# Create a set of 2-dimensional data tensors to feed into the model.\n",
"word_id_data = np.random.randint(vocab_size, size=(batch_size, sequence_length))\n",
......@@ -385,9 +372,7 @@
"start_logits, end_logits = bert_span_labeler([word_id_data, mask_data, type_id_data])\n",
"print(start_logits)\n",
"print(end_logits)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -401,9 +386,11 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "waqs6azNl3Nn"
},
"outputs": [],
"source": [
"start_positions = np.random.randint(sequence_length, size=(batch_size))\n",
"end_positions = np.random.randint(sequence_length, size=(batch_size))\n",
......@@ -415,9 +402,7 @@
"\n",
"total_loss = (tf.reduce_mean(start_loss) + tf.reduce_mean(end_loss)) / 2\n",
"print(total_loss)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -452,9 +437,11 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cXXCsffkCphk"
},
"outputs": [],
"source": [
"network = modeling.networks.BertEncoder(\n",
" vocab_size=vocab_size, num_layers=2, sequence_length=sequence_length)\n",
......@@ -463,9 +450,7 @@
"num_classes = 2\n",
"bert_classifier = modeling.models.BertClassifier(\n",
" network, num_classes=num_classes)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -478,20 +463,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "snlutm9ZJgEZ"
},
"outputs": [],
"source": [
"tf.keras.utils.plot_model(bert_classifier, show_shapes=True, dpi=48)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yyHPHsqBJkCz"
},
"outputs": [],
"source": [
"# Create a set of 2-dimensional data tensors to feed into the model.\n",
"word_id_data = np.random.randint(vocab_size, size=(batch_size, sequence_length))\n",
......@@ -501,9 +488,7 @@
"# Feed the data to the model.\n",
"logits = bert_classifier([word_id_data, mask_data, type_id_data])\n",
"print(logits)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -518,18 +503,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9X0S1DoFn_5Q"
},
"outputs": [],
"source": [
"labels = np.random.randint(num_classes, size=(batch_size))\n",
"\n",
"loss = modeling.losses.weighted_sparse_categorical_crossentropy_loss(\n",
" labels=labels, predictions=tf.nn.log_softmax(logits, axis=-1))\n",
"loss = tf.keras.losses.sparse_categorical_crossentropy(\n",
" labels, logits, from_logits=True)\n",
"print(loss)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
......@@ -540,5 +525,20 @@
"With the `loss`, you can optimize the model. Please see [run_classifier.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_classifier.py) or the colab [fine_tuning_bert.ipynb](https://github.com/tensorflow/models/blob/master/official/colab/fine_tuning_bert.ipynb) for the full example."
]
}
]
}
\ No newline at end of file
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "Introduction to the TensorFlow Models NLP library",
"private_outputs": true,
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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