Learning Objectives
In this notebook, we will implement text models to recognize the probable source (Github, Tech-Crunch, or The New-York Times) of the titles we have in the title dataset.
First, we will load and pre-process the texts and labels so that they are suitable to be fed to sequential Keras models with first layer being TF-hub pre-trained modules. Thanks to this first layer, we won't need to tokenize and integerize the text before passing it to our models. The pre-trained layer will take care of that for us, and consume directly raw text. However, we will still have to one-hot-encode each of the 3 classes into a 3 dimensional basis vector.
Then we will build, train and compare simple DNN models starting with different pre-trained TF-Hub layers.
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import os
from google.cloud import bigquery
import pandas as pd
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%load_ext google.cloud.bigquery
Replace the variable values in the cell below:
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PROJECT = "cloud-training-demos" # Replace with your PROJECT
BUCKET = PROJECT # defaults to PROJECT
REGION = "us-central1" # Replace with your REGION
SEED = 0
Hacker news headlines are available as a BigQuery public dataset. The dataset contains all headlines from the sites inception in October 2006 until October 2015.
Here is a sample of the dataset:
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%%bigquery --project $PROJECT
SELECT
url, title, score
FROM
`bigquery-public-data.hacker_news.stories`
WHERE
LENGTH(title) > 10
AND score > 10
AND LENGTH(url) > 0
LIMIT 10
Let's do some regular expression parsing in BigQuery to get the source of the newspaper article from the URL. For example, if the url is http://mobile.nytimes.com/...., I want to be left with nytimes
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%%bigquery --project $PROJECT
SELECT
ARRAY_REVERSE(SPLIT(REGEXP_EXTRACT(url, '.*://(.[^/]+)/'), '.'))[OFFSET(1)] AS source,
COUNT(title) AS num_articles
FROM
`bigquery-public-data.hacker_news.stories`
WHERE
REGEXP_CONTAINS(REGEXP_EXTRACT(url, '.*://(.[^/]+)/'), '.com$')
AND LENGTH(title) > 10
GROUP BY
source
ORDER BY num_articles DESC
LIMIT 100
Now that we have good parsing of the URL to get the source, let's put together a dataset of source and titles. This will be our labeled dataset for machine learning.
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regex = '.*://(.[^/]+)/'
sub_query = """
SELECT
title,
ARRAY_REVERSE(SPLIT(REGEXP_EXTRACT(url, '{0}'), '.'))[OFFSET(1)] AS source
FROM
`bigquery-public-data.hacker_news.stories`
WHERE
REGEXP_CONTAINS(REGEXP_EXTRACT(url, '{0}'), '.com$')
AND LENGTH(title) > 10
""".format(regex)
query = """
SELECT
LOWER(REGEXP_REPLACE(title, '[^a-zA-Z0-9 $.-]', ' ')) AS title,
source
FROM
({sub_query})
WHERE (source = 'github' OR source = 'nytimes' OR source = 'techcrunch')
""".format(sub_query=sub_query)
print(query)
For ML training, we usually need to split our dataset into training and evaluation datasets (and perhaps an independent test dataset if we are going to do model or feature selection based on the evaluation dataset). AutoML however figures out on its own how to create these splits, so we won't need to do that here.
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bq = bigquery.Client(project=PROJECT)
title_dataset = bq.query(query).to_dataframe()
title_dataset.head()
AutoML for text classification requires that
The dataset we pulled from BiqQuery satisfies these requirements.
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print("The full dataset contains {n} titles".format(n=len(title_dataset)))
Let's make sure we have roughly the same number of labels for each of our three labels:
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title_dataset.source.value_counts()
Finally we will save our data, which is currently in-memory, to disk.
We will create a csv file containing the full dataset and another containing only 1000 articles for development.
Note: It may take a long time to train AutoML on the full dataset, so we recommend to use the sample dataset for the purpose of learning the tool.
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DATADIR = './data/'
if not os.path.exists(DATADIR):
os.makedirs(DATADIR)
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FULL_DATASET_NAME = 'titles_full.csv'
FULL_DATASET_PATH = os.path.join(DATADIR, FULL_DATASET_NAME)
# Let's shuffle the data before writing it to disk.
title_dataset = title_dataset.sample(n=len(title_dataset))
title_dataset.to_csv(
FULL_DATASET_PATH, header=False, index=False, encoding='utf-8')
Now let's sample 1000 articles from the full dataset and make sure we have enough examples for each label in our sample dataset (see here for further details on how to prepare data for AutoML).
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sample_title_dataset = title_dataset.sample(n=1000)
sample_title_dataset.source.value_counts()
Let's write the sample datatset to disk.
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SAMPLE_DATASET_NAME = 'titles_sample.csv'
SAMPLE_DATASET_PATH = os.path.join(DATADIR, SAMPLE_DATASET_NAME)
sample_title_dataset.to_csv(
SAMPLE_DATASET_PATH, header=False, index=False, encoding='utf-8')
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sample_title_dataset.head()
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import datetime
import os
import shutil
import pandas as pd
import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard, EarlyStopping
from tensorflow_hub import KerasLayer
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical
print(tf.__version__)
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%matplotlib inline
Let's start by specifying where the information about the trained models will be saved as well as where our dataset is located:
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MODEL_DIR = "./text_models"
DATA_DIR = "./data"
As in the previous labs, our dataset consists of titles of articles along with the label indicating from which source these articles have been taken from (GitHub, Tech-Crunch, or the New-York Times):
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ls ./data/
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DATASET_NAME = "titles_full.csv"
TITLE_SAMPLE_PATH = os.path.join(DATA_DIR, DATASET_NAME)
COLUMNS = ['title', 'source']
titles_df = pd.read_csv(TITLE_SAMPLE_PATH, header=None, names=COLUMNS)
titles_df.head()
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Let's look again at the number of examples per label to make sure we have a well-balanced dataset:
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titles_df.source.value_counts()
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In this lab, we will use pre-trained TF-Hub embeddings modules for english for the first layer of our models. One immediate advantage of doing so is that the TF-Hub embedding module will take care for us of processing the raw text. This also means that our model will be able to consume text directly instead of sequences of integers representing the words.
However, as before, we still need to preprocess the labels into one-hot-encoded vectors:
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CLASSES = {
'github': 0,
'nytimes': 1,
'techcrunch': 2
}
N_CLASSES = len(CLASSES)
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def encode_labels(sources):
classes = [CLASSES[source] for source in sources]
one_hots = to_categorical(classes, num_classes=N_CLASSES)
return one_hots
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encode_labels(titles_df.source[:4])
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Let's split our data into train and test splits:
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N_TRAIN = int(len(titles_df) * 0.95)
titles_train, sources_train = (
titles_df.title[:N_TRAIN], titles_df.source[:N_TRAIN])
titles_valid, sources_valid = (
titles_df.title[N_TRAIN:], titles_df.source[N_TRAIN:])
To be on the safe side, we verify that the train and test splits have roughly the same number of examples per class.
Since it is the case, accuracy will be a good metric to use to measure the performance of our models.
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sources_train.value_counts()
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sources_valid.value_counts()
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Now let's create the features and labels we will feed our models with:
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X_train, Y_train = titles_train.values, encode_labels(sources_train)
X_valid, Y_valid = titles_valid.values, encode_labels(sources_valid)
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X_train[:3]
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Y_train[:3]
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We will first try a word embedding pre-trained using a Neural Probabilistic Language Model. TF-Hub has a 50-dimensional one called nnlm-en-dim50-with-normalization, which also normalizes the vectors produced.
Once loaded from its url, the TF-hub module can be used as a normal Keras layer in a sequential or functional model. Since we have enough data to fine-tune the parameters of the pre-trained embedding itself, we will set trainable=True in the KerasLayer that loads the pre-trained embedding:
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# TODO 1
NNLM = "https://tfhub.dev/google/nnlm-en-dim50/2"
nnlm_module = KerasLayer(
NNLM, output_shape=[50], input_shape=[], dtype=tf.string, trainable=True)
Note that this TF-Hub embedding produces a single 50-dimensional vector when passed a sentence:
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# TODO 1
nnlm_module(tf.constant(["The dog is happy to see people in the street."]))
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Then we will try a word embedding obtained using Swivel, an algorithm that essentially factorizes word co-occurrence matrices to create the words embeddings. TF-Hub hosts the pretrained gnews-swivel-20dim-with-oov 20-dimensional Swivel module.
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# TODO 1
SWIVEL = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim-with-oov/1"
swivel_module = KerasLayer(
SWIVEL, output_shape=[20], input_shape=[], dtype=tf.string, trainable=True)
Similarly as the previous pre-trained embedding, it outputs a single vector when passed a sentence:
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# TODO 1
swivel_module(tf.constant(["The dog is happy to see people in the street."]))
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Let's write a function that
KerasLayer (i.e. the swivel_module or the nnlm_module we constructed above) as well as the name of the model (say swivel or nnlm)
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def build_model(hub_module, name):
model = Sequential([
hub_module, # TODO 2
Dense(16, activation='relu'),
Dense(N_CLASSES, activation='softmax')
], name=name)
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy']
)
return model
Let's also wrap the training code into a train_and_evaluate function that
batch_sizehistory object, which will help us to plot the learning curves
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def train_and_evaluate(train_data, val_data, model, batch_size=5000):
X_train, Y_train = train_data
tf.random.set_seed(33)
model_dir = os.path.join(MODEL_DIR, model.name)
if tf.io.gfile.exists(model_dir):
tf.io.gfile.rmtree(model_dir)
history = model.fit(
X_train, Y_train,
epochs=100,
batch_size=batch_size,
validation_data=val_data,
callbacks=[EarlyStopping(), TensorBoard(model_dir)],
)
return history
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data = (X_train, Y_train)
val_data = (X_valid, Y_valid)
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nnlm_model = build_model(nnlm_module, 'nnlm')
nnlm_history = train_and_evaluate(data, val_data, nnlm_model)
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history = nnlm_history
pd.DataFrame(history.history)[['loss', 'val_loss']].plot()
pd.DataFrame(history.history)[['accuracy', 'val_accuracy']].plot()
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swivel_model = build_model(swivel_module, name='swivel')
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swivel_history = train_and_evaluate(data, val_data, swivel_model)
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history = swivel_history
pd.DataFrame(history.history)[['loss', 'val_loss']].plot()
pd.DataFrame(history.history)[['accuracy', 'val_accuracy']].plot()
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Swivel trains faster but achieves a lower validation accuracy, and requires more epochs to train on.
At last, let's compare all the models we have trained at once using TensorBoard in order to choose the one that overfits the less for the same performance level.
Running the following command will launch TensorBoard on port 6006. This will block the notebook execution, so you'll have to interrupt that cell first before you can run other cells.
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!tensorboard --logdir $MODEL_DIR --port 6006
The first step is to serialize one of our trained Keras model as a SavedModel:
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OUTPUT_DIR = "./savedmodels"
shutil.rmtree(OUTPUT_DIR, ignore_errors=True)
EXPORT_PATH = os.path.join(OUTPUT_DIR, 'swivel')
os.environ['EXPORT_PATH'] = EXPORT_PATH
shutil.rmtree(EXPORT_PATH, ignore_errors=True)
tf.saved_model.save(swivel_model, EXPORT_PATH)
Then we can deploy the model using the gcloud CLI as before:
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%%bash
# TODO 5
PROJECT=# TODO: Change this to your PROJECT
BUCKET=${PROJECT}
REGION=us-east1
MODEL_NAME=title_model
VERSION_NAME=swivel
EXPORT_PATH=$EXPORT_PATH
if [[ $(gcloud ai-platform models list --format='value(name)' | grep $MODEL_NAME) ]]; then
echo "$MODEL_NAME already exists"
else
echo "Creating $MODEL_NAME"
gcloud ai-platform models create --regions=$REGION $MODEL_NAME
fi
if [[ $(gcloud ai-platform versions list --model $MODEL_NAME --format='value(name)' | grep $VERSION_NAME) ]]; then
echo "Deleting already existing $MODEL_NAME:$VERSION_NAME ... "
echo yes | gcloud ai-platform versions delete --model=$MODEL_NAME $VERSION_NAME
echo "Please run this cell again if you don't see a Creating message ... "
sleep 2
fi
echo "Creating $MODEL_NAME:$VERSION_NAME"
gcloud beta ai-platform versions create \
--model=$MODEL_NAME $VERSION_NAME \
--framework=tensorflow \
--python-version=3.7 \
--runtime-version=1.15 \
--origin=$EXPORT_PATH \
--staging-bucket=gs://$BUCKET \
--machine-type n1-standard-4
Before we try our deployed model, let's inspect its signature to know what to send to the deployed API:
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!saved_model_cli show \
--tag_set serve \
--signature_def serving_default \
--dir {EXPORT_PATH}
!find {EXPORT_PATH}
Let's go ahead and hit our model:
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%%writefile input.json
{"keras_layer_1_input": "hello"}
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!gcloud ai-platform predict \
--model title_model \
--json-instances input.json \
--version swivel
Try to beat the best model by modifying the model architecture, changing the TF-Hub embedding, and tweaking the training parameters.
Copyright 2019 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License