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דוגמה מקצה לקצה עבור קורא BigQuery TensorFlow

צפה ב TensorFlow.org הפעל ב- Google Colab צפה במקור ב- GitHub הורד מחברת

סקירה כללית

מדריך זה מראה כיצד להשתמש בקורא BigQuery TensorFlow לאימוני רשת עצבית באמצעות ממשק ה- API הסדרתי של Keras.

מערך נתונים

במדריך זה נעשה שימוש בנתונים של הכנסות מפקד האוכלוסין בארצות הברית המסופקת על ידי מאגר הלמידה של מכונות Irvine Machine . מערך נתונים זה מכיל מידע על אנשים ממסד נתונים של מפקד האוכלוסין בשנת 1994, כולל גיל, השכלה, מצב משפחתי, עיסוק והאם הם מרוויחים יותר מ- 50,000 $ לשנה.

להכין

הגדר את פרויקט ה- GCP שלך

השלבים הבאים נדרשים, ללא קשר לסביבת המחברת שלך.

  1. בחר או צור פרויקט GCP.
  2. וודא שהחיוב מופעל עבור הפרויקט שלך.
  3. הפעל את API לאחסון BigQuery
  4. הזן את מזהה הפרוייקט שלך בתא למטה. לאחר מכן הפעל את התא כדי לוודא ש- Cloud SDK משתמש בפרויקט המתאים לכל הפקודות במחברת זו.

התקן חבילות נדרשות והפעל מחדש את זמן הריצה

 try:
  # Use the Colab's preinstalled TensorFlow 2.x
  %tensorflow_version 2.x 
except:
  pass
 
pip install fastavro
pip install tensorflow-io==0.9.0
pip install google-cloud-bigquery-storage

לְאַמֵת

 from google.colab import auth
auth.authenticate_user()
print('Authenticated')
 

הגדר את מזהה ה- PROJECT שלך

 PROJECT_ID = "<YOUR PROJECT>" 
! gcloud config set project $PROJECT_ID
%env GCLOUD_PROJECT=$PROJECT_ID
 

ייבא ספריות פיתון, הגדר קבועים

 from __future__ import absolute_import, division, print_function, unicode_literals

import os
from six.moves import urllib
import tempfile

import numpy as np
import pandas as pd
import tensorflow as tf

from google.cloud import bigquery
from google.api_core.exceptions import GoogleAPIError

LOCATION = 'us'

# Storage directory
DATA_DIR = os.path.join(tempfile.gettempdir(), 'census_data')

# Download options.
DATA_URL = 'https://storage.googleapis.com/cloud-samples-data/ml-engine/census/data'
TRAINING_FILE = 'adult.data.csv'
EVAL_FILE = 'adult.test.csv'
TRAINING_URL = '%s/%s' % (DATA_URL, TRAINING_FILE)
EVAL_URL = '%s/%s' % (DATA_URL, EVAL_FILE)

DATASET_ID = 'census_dataset'
TRAINING_TABLE_ID = 'census_training_table'
EVAL_TABLE_ID = 'census_eval_table'

CSV_SCHEMA = [
      bigquery.SchemaField("age", "FLOAT64"),
      bigquery.SchemaField("workclass", "STRING"),
      bigquery.SchemaField("fnlwgt", "FLOAT64"),
      bigquery.SchemaField("education", "STRING"),
      bigquery.SchemaField("education_num", "FLOAT64"),
      bigquery.SchemaField("marital_status", "STRING"),
      bigquery.SchemaField("occupation", "STRING"),
      bigquery.SchemaField("relationship", "STRING"),
      bigquery.SchemaField("race", "STRING"),
      bigquery.SchemaField("gender", "STRING"),
      bigquery.SchemaField("capital_gain", "FLOAT64"),
      bigquery.SchemaField("capital_loss", "FLOAT64"),
      bigquery.SchemaField("hours_per_week", "FLOAT64"),
      bigquery.SchemaField("native_country", "STRING"),
      bigquery.SchemaField("income_bracket", "STRING"),
  ]

UNUSED_COLUMNS = ["fnlwgt", "education_num"]
 

ייבא נתוני מפקד לתוך BigQuery

הגדירו שיטות עוזר לטעינת נתונים ל- BigQuery

 def create_bigquery_dataset_if_necessary(dataset_id):
  # Construct a full Dataset object to send to the API.
  client = bigquery.Client(project=PROJECT_ID)
  dataset = bigquery.Dataset(bigquery.dataset.DatasetReference(PROJECT_ID, dataset_id))
  dataset.location = LOCATION

  try:
    dataset = client.create_dataset(dataset)  # API request
    return True
  except GoogleAPIError as err:
    if err.code != 409: # http_client.CONFLICT
      raise
  return False

 
 def load_data_into_bigquery(url, table_id):
  create_bigquery_dataset_if_necessary(DATASET_ID)
  client = bigquery.Client(project=PROJECT_ID)
  dataset_ref = client.dataset(DATASET_ID)
  table_ref = dataset_ref.table(table_id)
  job_config = bigquery.LoadJobConfig()
  job_config.write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE
  job_config.source_format = bigquery.SourceFormat.CSV
  job_config.schema = CSV_SCHEMA

  load_job = client.load_table_from_uri(
      url, table_ref, job_config=job_config
  )
  print("Starting job {}".format(load_job.job_id))

  load_job.result()  # Waits for table load to complete.
  print("Job finished.")

  destination_table = client.get_table(table_ref)
  print("Loaded {} rows.".format(destination_table.num_rows))
 

טען נתוני מפקד ב- BigQuery.

 load_data_into_bigquery(TRAINING_URL, TRAINING_TABLE_ID)
load_data_into_bigquery(EVAL_URL, EVAL_TABLE_ID)
 
Starting job 2ceffef8-e6e4-44bb-9e86-3d97b0501187
Job finished.
Loaded 32561 rows.
Starting job bf66f1b3-2506-408b-9009-c19f4ae9f58a
Job finished.
Loaded 16278 rows.

אשר כי הנתונים יובאו

TODO: החלף את <PROJECT> שלך ב- PROJECT_ID שלך

 %%bigquery --use_bqstorage_api
SELECT * FROM `<YOUR PROJECT>.census_dataset.census_training_table` LIMIT 5
 

טען נתוני מפקד ב- TensorFlow DataSet באמצעות קורא BigQuery

קרא והפך נתונים של Cesnus מ- BigQuery ל- TensorFlow DataSet

 from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
from tensorflow_io.bigquery import BigQueryClient
from tensorflow_io.bigquery import BigQueryReadSession
  
def transofrom_row(row_dict):
  # Trim all string tensors
  trimmed_dict = { column:
                  (tf.strings.strip(tensor) if tensor.dtype == 'string' else tensor) 
                  for (column,tensor) in row_dict.items()
                  }
  # Extract feature column
  income_bracket = trimmed_dict.pop('income_bracket')
  # Convert feature column to 0.0/1.0
  income_bracket_float = tf.cond(tf.equal(tf.strings.strip(income_bracket), '>50K'), 
                 lambda: tf.constant(1.0), 
                 lambda: tf.constant(0.0))
  return (trimmed_dict, income_bracket_float)

def read_bigquery(table_name):
  tensorflow_io_bigquery_client = BigQueryClient()
  read_session = tensorflow_io_bigquery_client.read_session(
      "projects/" + PROJECT_ID,
      PROJECT_ID, table_name, DATASET_ID,
      list(field.name for field in CSV_SCHEMA 
           if not field.name in UNUSED_COLUMNS),
      list(dtypes.double if field.field_type == 'FLOAT64' 
           else dtypes.string for field in CSV_SCHEMA
           if not field.name in UNUSED_COLUMNS),
      requested_streams=2)
  
  dataset = read_session.parallel_read_rows()
  transformed_ds = dataset.map (transofrom_row)
  return transformed_ds

 
 BATCH_SIZE = 32

training_ds = read_bigquery(TRAINING_TABLE_ID).shuffle(10000).batch(BATCH_SIZE)
eval_ds = read_bigquery(EVAL_TABLE_ID).batch(BATCH_SIZE)
 

הגדר עמודות תכונות

 def get_categorical_feature_values(column):
  query = 'SELECT DISTINCT TRIM({}) FROM `{}`.{}.{}'.format(column, PROJECT_ID, DATASET_ID, TRAINING_TABLE_ID)
  client = bigquery.Client(project=PROJECT_ID)
  dataset_ref = client.dataset(DATASET_ID)
  job_config = bigquery.QueryJobConfig()
  query_job = client.query(query, job_config=job_config)
  result = query_job.to_dataframe()
  return result.values[:,0]
 
 from tensorflow import feature_column

feature_columns = []

# numeric cols
for header in ['capital_gain', 'capital_loss', 'hours_per_week']:
  feature_columns.append(feature_column.numeric_column(header))

# categorical cols
for header in ['workclass', 'marital_status', 'occupation', 'relationship',
               'race', 'native_country', 'education']:
  categorical_feature = feature_column.categorical_column_with_vocabulary_list(
        header, get_categorical_feature_values(header))
  categorical_feature_one_hot = feature_column.indicator_column(categorical_feature)
  feature_columns.append(categorical_feature_one_hot)

# bucketized cols
age = feature_column.numeric_column('age')
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
feature_columns.append(age_buckets)

feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
 

בנה ואמן מודל

בנה דגם

 Dense = tf.keras.layers.Dense
model = tf.keras.Sequential(
  [
    feature_layer,
      Dense(100, activation=tf.nn.relu, kernel_initializer='uniform'),
      Dense(75, activation=tf.nn.relu),
      Dense(50, activation=tf.nn.relu),
      Dense(25, activation=tf.nn.relu),
      Dense(1, activation=tf.nn.sigmoid)
  ])

# Compile Keras model
model.compile(
    loss='binary_crossentropy', 
    metrics=['accuracy'])
 

דגם רכבת

 model.fit(training_ds, epochs=5)
 
WARNING:tensorflow:Layer sequential is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

Warning:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4276: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4331: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
Epoch 1/5
1018/1018 [==============================] - 17s 17ms/step - loss: 0.5985 - accuracy: 0.8105
Epoch 2/5
1018/1018 [==============================] - 10s 10ms/step - loss: 0.3670 - accuracy: 0.8324
Epoch 3/5
1018/1018 [==============================] - 11s 10ms/step - loss: 0.3487 - accuracy: 0.8393
Epoch 4/5
1018/1018 [==============================] - 11s 10ms/step - loss: 0.3398 - accuracy: 0.8435
Epoch 5/5
1018/1018 [==============================] - 11s 11ms/step - loss: 0.3377 - accuracy: 0.8455

<tensorflow.python.keras.callbacks.History at 0x7f978f5b91d0>

הערכת המודל

הערכת המודל

 loss, accuracy = model.evaluate(eval_ds)
print("Accuracy", accuracy)
 
509/509 [==============================] - 8s 15ms/step - loss: 0.3338 - accuracy: 0.8398
Accuracy 0.8398452

הערכו מספר דוגמאות אקראיות

 sample_x = {
    'age' : np.array([56, 36]), 
    'workclass': np.array(['Local-gov', 'Private']), 
    'education': np.array(['Bachelors', 'Bachelors']), 
    'marital_status': np.array(['Married-civ-spouse', 'Married-civ-spouse']), 
    'occupation': np.array(['Tech-support', 'Other-service']), 
    'relationship': np.array(['Husband', 'Husband']), 
    'race': np.array(['White', 'Black']), 
    'gender': np.array(['Male', 'Male']), 
    'capital_gain': np.array([0, 7298]), 
    'capital_loss': np.array([0, 0]), 
    'hours_per_week': np.array([40, 36]), 
    'native_country': np.array(['United-States', 'United-States'])
  }

model.predict(sample_x)
 
array([[0.5541261],
       [0.6209938]], dtype=float32)

אֶמְצָעִי