Questa pagina è stata tradotta dall'API Cloud Translation.
Switch to English

Esempio end-to-end per il lettore BigQuery TensorFlow

Visualizza su TensorFlow.org Esegui in Google Colab Visualizza sorgente su GitHub Scarica il quaderno

Panoramica

Questo tutorial mostra come usare il lettore BigQuery TensorFlow per addestrare la rete neurale usando l'API sequenziale di Keras.

dataset

In questa esercitazione viene utilizzato il set di dati sul reddito degli censimenti degli Stati Uniti fornito da UC Irvine Machine Learning Repository . Questo set di dati contiene informazioni su persone provenienti da un database del censimento del 1994, tra cui età, istruzione, stato civile, occupazione e se guadagnano più di $ 50.000 all'anno.

Impostare

Imposta il tuo progetto GCP

Sono richiesti i seguenti passaggi, indipendentemente dall'ambiente del notebook.

  1. Seleziona o crea un progetto GCP.
  2. Assicurati che la fatturazione sia abilitata per il tuo progetto.
  3. Abilita l'API di archiviazione di BigQuery
  4. Inserisci il tuo ID progetto nella cella sottostante. Quindi esegui la cella per assicurarti che Cloud SDK utilizzi il progetto giusto per tutti i comandi di questo notebook.

Installa i pacchetti richiesti e riavvia il runtime

 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

Autenticare

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

Imposta il tuo ID PROGETTO

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

Importa le librerie Python, definisci le costanti

 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"]
 

Importa i dati del censimento in BigQuery

Definire i metodi di supporto per caricare i dati in 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))
 

Carica i dati del censimento in 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.

Conferma che i dati sono stati importati

TODO: sostituisci <YOUR PROJECT> con PROJECT_ID

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

Carica i dati del censimento in TensorFlow DataSet usando il lettore BigQuery

Leggi e trasforma i dati cesnus da BigQuery in 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)
 

Definire le colonne delle caratteristiche

 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)
 

Costruisci e addestra il modello

Costruisci modello

 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'])
 

Modello di treno

 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>

Valuta il modello

Valuta il modello

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

Valuta un paio di campioni casuali

 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)

risorse