Add new TensorFlow Serving + Flutter text classification codelabs (fl…

…utter#289)

flutter_ci_script_beta.sh

@@ -19,6 +19,7 @@ declare -a CODELABS=(
   "star_counter"
   "startup_namer"
   "testing_codelab"
+  "tfserving-flutter"
   "webview_flutter"
   )
 

flutter_ci_script_stable.sh

@@ -19,6 +19,7 @@ declare -a CODELABS=(
   "star_counter"
   "startup_namer"
   "testing_codelab"
+  "tfserving-flutter"
   "webview_flutter"
   )
 

tfserving-flutter/README.md

@@ -0,0 +1,8 @@
+# tfserving_flutter
+
+This folder contains the codelabs for the TensorFlow Serving Text Classification Learning Pathway (TODO: update link).
+
+The folder is organized as:
+ - `codelab1` contains the first codelab code to train a spam detection model
+ - `codelab2` contains the second codelab code to connect Flutter frontend to TensorFlow Serving backend
+ - `codelab3` contains the third codelab code to enhance the spam detection model to detect specific spam phrases

tfserving-flutter/codelab1/SpamCommentsModelMaker.ipynb

@@ -0,0 +1,194 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "SpamCommentsModelMaker.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Awq8sMaIFHJ1"
+      },
+      "source": [
+        "# Install Model maker\n",
+        "!pip install -q tflite-model-maker &> /dev/null"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2fEAqoLLF6O9"
+      },
+      "source": [
+        "# Imports and check that we are using TF2.x\n",
+        "import numpy as np\n",
+        "import os\n",
+        "\n",
+        "from tflite_model_maker import configs\n",
+        "from tflite_model_maker import ExportFormat\n",
+        "from tflite_model_maker import model_spec\n",
+        "from tflite_model_maker import text_classifier\n",
+        "from tflite_model_maker.text_classifier import DataLoader\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "assert tf.__version__.startswith('2')\n",
+        "tf.get_logger().setLevel('ERROR')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lsuDZvjgREsS"
+      },
+      "source": [
+        "# Download the dataset as a CSV and store as data_file\n",
+        "data_file = tf.keras.utils.get_file(fname='comment-spam.csv', origin='https://storage.googleapis.com/laurencemoroney-blog.appspot.com/lmblog_comments.csv', extract=False)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pbew43TbG9HQ"
+      },
+      "source": [
+        "# Use a model spec from model maker. Options are 'mobilebert_classifier', 'bert_classifier' and 'average_word_vec'\n",
+        "# The first 2 use the BERT model, which is accurate, but larger and slower to train\n",
+        "# Average Word Vec is kinda like transfer learning where there are pre-trained word weights\n",
+        "# and dictionaries\n",
+        "spec = model_spec.get('average_word_vec')\n",
+        "spec.num_words = 2000\n",
+        "spec.seq_len = 20\n",
+        "spec.wordvec_dim = 7"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-WdQmzTKHFVn"
+      },
+      "source": [
+        "# Load the CSV using DataLoader.from_csv to make the training_data\n",
+        "data = DataLoader.from_csv(\n",
+        "      filename=data_file,\n",
+        "      text_column='commenttext', \n",
+        "      label_column='spam', \n",
+        "      model_spec=spec,\n",
+        "      delimiter=',',\n",
+        "      shuffle=True,\n",
+        "      is_training=True)\n",
+        "\n",
+        "train_data, test_data = data.split(0.9)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qThBoIIyG_Du"
+      },
+      "source": [
+        "# Build the model\n",
+        "model = text_classifier.create(train_data, model_spec=spec, epochs=50, validation_data=test_data)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5QXEwKYxdPzs"
+      },
+      "source": [
+        "loss, accuracy = model.evaluate(train_data)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "In-1-rzW-_9b"
+      },
+      "source": [
+        "# This will export to SavedModel format with the model, vocabulary and labels. \n",
+        "model.export(export_dir='/mm_spam_savedmodel/', export_format=[ExportFormat.LABEL, ExportFormat.VOCAB, ExportFormat.SAVED_MODEL])\n",
+        "\n",
+        "# You can find your files in colab by clicking the 'folder' tab to the left of\n",
+        "# this code window, and then navigating 'up' a directory to find the root\n",
+        "# directory listing -- and from there you should see /mm_spam_savedmodel/"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Rename the SavedModel subfolder to a version number\n",
+        "!mv /mm_spam_savedmodel/saved_model /mm_spam_savedmodel/123\n",
+        "!zip -r mm_spam_savedmodel.zip /mm_spam_savedmodel/ "
+      ],
+      "metadata": {
+        "id": "mIhBu3pI_2Xz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3UNNrCWbKbxh"
+      },
+      "source": [
+        "# Optional extra\n",
+        "# You can use this cell to export details for projector.tensorflow.org\n",
+        "# Where you can explore the embeddings that were learned for this dataset\n",
+        "embeddings = model.model.layers[0]\n",
+        "weights = embeddings.get_weights()[0]\n",
+        "tokenizer = model.model_spec.vocab\n",
+        "\n",
+        "import io\n",
+        "\n",
+        "out_v = io.open('vecs.tsv', 'w', encoding='utf-8')\n",
+        "out_m = io.open('meta.tsv', 'w', encoding='utf-8')\n",
+        "for word in tokenizer:\n",
+        "  #word = tokenizer.decode([word_num])\n",
+        "  value = tokenizer[word]\n",
+        "  embeddings = weights[value]\n",
+        "  out_m.write(word + \"\\n\")\n",
+        "  out_v.write('\\t'.join([str(x) for x in embeddings]) + \"\\n\")\n",
+        "out_v.close()\n",
+        "out_m.close()\n",
+        "\n",
+        "\n",
+        "try:\n",
+        "  from google.colab import files\n",
+        "except ImportError:\n",
+        "  pass\n",
+        "else:\n",
+        "  files.download('vecs.tsv')\n",
+        "  files.download('meta.tsv')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

tfserving-flutter/codelab1/mm_spam_savedmodel/123/keras_metadata.pb

@@ -0,0 +1,9 @@
+
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+�
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+�
Iroot.keras_api.metrics.1"_tf_keras_metric*�
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tfserving-flutter/codelab1/mm_spam_savedmodel/labels.txt

@@ -0,0 +1,2 @@
+false
+true