Pengantar Komponen demi Komponen untuk TensorFlow Extended (TFX)
Tutorial berbasis Colab ini akan memandu secara interaktif setiap komponen bawaan TensorFlow Extended (TFX).
Ini mencakup setiap langkah dalam alur machine learning ujung ke ujung, mulai dari penyerapan data hingga mendorong model hingga penyajian.
Setelah selesai, konten notebook ini dapat diekspor secara otomatis sebagai kode sumber saluran pipa TFX, yang dapat Anda atur dengan Apache Airflow dan Apache Beam.
Latar belakang
Notebook ini menunjukkan cara menggunakan TFX di lingkungan Jupyter/Colab. Di sini, kami menelusuri contoh Taksi Chicago dalam buku catatan interaktif.
Bekerja di notebook interaktif adalah cara yang berguna untuk mengenal struktur saluran TFX. Ini juga berguna saat melakukan pengembangan saluran Anda sendiri sebagai lingkungan pengembangan yang ringan, tetapi Anda harus menyadari bahwa ada perbedaan dalam cara buku catatan interaktif diatur, dan bagaimana mereka mengakses artefak metadata.
Orkestrasi
Dalam penerapan produksi TFX, Anda akan menggunakan orkestra seperti Apache Airflow, Kubeflow Pipelines, atau Apache Beam untuk mengatur grafik pipeline komponen TFX yang telah ditentukan sebelumnya. Dalam notebook interaktif, notebook itu sendiri adalah orkestra, menjalankan setiap komponen TFX saat Anda menjalankan sel notebook.
Metadata
Dalam penerapan produksi TFX, Anda akan mengakses metadata melalui ML Metadata (MLMD) API. MLMD menyimpan properti metadata dalam database seperti MySQL atau SQLite, dan menyimpan muatan metadata di penyimpanan persisten seperti pada sistem file Anda. Dalam sebuah notebook interaktif, baik sifat dan muatan disimpan dalam database SQLite singkat dalam /tmp
direktori pada notebook Jupyter atau server CoLab.
Mempersiapkan
Pertama, kami menginstal dan mengimpor paket yang diperlukan, mengatur jalur, dan mengunduh data.
Tingkatkan Pip
Untuk menghindari mengupgrade Pip di sistem saat berjalan secara lokal, periksa untuk memastikan bahwa kami berjalan di Colab. Sistem lokal tentu saja dapat ditingkatkan secara terpisah.
try:
import colab
!pip install --upgrade pip
except:
pass
Instal TFX
pip install -U tfx
Apakah Anda me-restart runtime?
Jika Anda menggunakan Google Colab, pertama kali menjalankan sel di atas, Anda harus memulai ulang runtime (Runtime > Restart runtime ...). Ini karena cara Colab memuat paket.
paket impor
Kami mengimpor paket yang diperlukan, termasuk kelas komponen TFX standar.
import os
import pprint
import tempfile
import urllib
import absl
import tensorflow as tf
import tensorflow_model_analysis as tfma
tf.get_logger().propagate = False
pp = pprint.PrettyPrinter()
from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip
Mari kita periksa versi perpustakaan.
print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.7.0 TFX version: 1.5.0
Siapkan jalur pipa
# This is the root directory for your TFX pip package installation.
_tfx_root = tfx.__path__[0]
# This is the directory containing the TFX Chicago Taxi Pipeline example.
_taxi_root = os.path.join(_tfx_root, 'examples/chicago_taxi_pipeline')
# This is the path where your model will be pushed for serving.
_serving_model_dir = os.path.join(
tempfile.mkdtemp(), 'serving_model/taxi_simple')
# Set up logging.
absl.logging.set_verbosity(absl.logging.INFO)
Unduh contoh data
Kami mengunduh contoh kumpulan data untuk digunakan dalam saluran TFX kami.
Dataset yang kami gunakan adalah taksi Trips dataset yang dirilis oleh City of Chicago. Kolom dalam kumpulan data ini adalah:
pickup_community_area | tarif | trip_start_month |
trip_start_hour | trip_start_day | trip_start_timestamp |
pickup_latitude | pickup_longitude | dropoff_latitude |
dropoff_longitude | trip_miles | pickup_sensus_trak |
dropoff_sensus_trak | tipe pembayaran | perusahaan |
perjalanan_detik | dropoff_community_area | tips |
Dengan dataset ini, kita akan membangun sebuah model yang memprediksi tips
dari perjalanan.
_data_root = tempfile.mkdtemp(prefix='tfx-data')
DATA_PATH = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv'
_data_filepath = os.path.join(_data_root, "data.csv")
urllib.request.urlretrieve(DATA_PATH, _data_filepath)
('/tmp/tfx-datacz9xjro6/data.csv', <http.client.HTTPMessage at 0x7f889af49250>)
Lihat sekilas file CSV.
head {_data_filepath}
pickup_community_area,fare,trip_start_month,trip_start_hour,trip_start_day,trip_start_timestamp,pickup_latitude,pickup_longitude,dropoff_latitude,dropoff_longitude,trip_miles,pickup_census_tract,dropoff_census_tract,payment_type,company,trip_seconds,dropoff_community_area,tips ,12.45,5,19,6,1400269500,,,,,0.0,,,Credit Card,Chicago Elite Cab Corp. (Chicago Carriag,0,,0.0 ,0,3,19,5,1362683700,,,,,0,,,Unknown,Chicago Elite Cab Corp.,300,,0 60,27.05,10,2,3,1380593700,41.836150155,-87.648787952,,,12.6,,,Cash,Taxi Affiliation Services,1380,,0.0 10,5.85,10,1,2,1382319000,41.985015101,-87.804532006,,,0.0,,,Cash,Taxi Affiliation Services,180,,0.0 14,16.65,5,7,5,1369897200,41.968069,-87.721559063,,,0.0,,,Cash,Dispatch Taxi Affiliation,1080,,0.0 13,16.45,11,12,3,1446554700,41.983636307,-87.723583185,,,6.9,,,Cash,,780,,0.0 16,32.05,12,1,1,1417916700,41.953582125,-87.72345239,,,15.4,,,Cash,,1200,,0.0 30,38.45,10,10,5,1444301100,41.839086906,-87.714003807,,,14.6,,,Cash,,2580,,0.0 11,14.65,1,1,3,1358213400,41.978829526,-87.771166703,,,5.81,,,Cash,,1080,,0.0
Penafian: Situs ini menyediakan aplikasi yang menggunakan data yang telah dimodifikasi untuk digunakan dari sumber aslinya, www.cityofchicago.org, situs resmi Kota Chicago. City of Chicago tidak mengklaim konten, akurasi, ketepatan waktu, atau kelengkapan data apa pun yang disediakan di situs ini. Data yang disediakan di situs ini dapat berubah sewaktu-waktu. Dapat dipahami bahwa data yang disediakan di situs ini digunakan dengan risiko sendiri.
Buat Konteks Interaktif
Terakhir, kami membuat InteractiveContext, yang memungkinkan kami menjalankan komponen TFX secara interaktif di notebook ini.
# Here, we create an InteractiveContext using default parameters. This will
# use a temporary directory with an ephemeral ML Metadata database instance.
# To use your own pipeline root or database, the optional properties
# `pipeline_root` and `metadata_connection_config` may be passed to
# InteractiveContext. Calls to InteractiveContext are no-ops outside of the
# notebook.
context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq as root for pipeline outputs. WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/metadata.sqlite.
Jalankan komponen TFX secara interaktif
Di sel berikutnya, kami membuat komponen TFX satu per satu, menjalankan masing-masing komponen, dan memvisualisasikan artefak keluarannya.
ContohGen
The ExampleGen
komponen biasanya pada awal pipa TFX. Itu akan:
- Pisahkan data menjadi set pelatihan dan evaluasi (secara default, 2/3 pelatihan + 1/3 evaluasi)
- Data mengkonversi ke dalam
tf.Example
Format (pelajari lebih lanjut di sini ) - Menyalin data ke dalam
_tfx_root
direktori untuk komponen lain untuk mengakses
ExampleGen
mengambil sebagai masukan path ke sumber data Anda. Dalam kasus kami, ini adalah _data_root
jalan yang berisi CSV yang didownload.
example_gen = tfx.components.CsvExampleGen(input_base=_data_root)
context.run(example_gen)
INFO:absl:Running driver for CsvExampleGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:select span and version = (0, None) INFO:absl:latest span and version = (0, None) INFO:absl:Running executor for CsvExampleGen INFO:absl:Generating examples. WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features. INFO:absl:Processing input csv data /tmp/tfx-datacz9xjro6/* to TFExample. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be. INFO:absl:Examples generated. INFO:absl:Running publisher for CsvExampleGen INFO:absl:MetadataStore with DB connection initialized
Mari kita memeriksa artefak output ExampleGen
. Komponen ini menghasilkan dua artefak, contoh pelatihan dan contoh evaluasi:
artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/CsvExampleGen/examples/1
Kita juga dapat melihat tiga contoh pelatihan pertama:
# Get the URI of the output artifact representing the training examples, which is a directory
train_uri = os.path.join(example_gen.outputs['examples'].get()[0].uri, 'Split-train')
# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
for name in os.listdir(train_uri)]
# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
serialized_example = tfrecord.numpy()
example = tf.train.Example()
example.ParseFromString(serialized_example)
pp.pprint(example)
features { feature { key: "company" value { bytes_list { value: "Chicago Elite Cab Corp. (Chicago Carriag" } } } feature { key: "dropoff_census_tract" value { int64_list { } } } feature { key: "dropoff_community_area" value { int64_list { } } } feature { key: "dropoff_latitude" value { float_list { } } } feature { key: "dropoff_longitude" value { float_list { } } } feature { key: "fare" value { float_list { value: 12.449999809265137 } } } feature { key: "payment_type" value { bytes_list { value: "Credit Card" } } } feature { key: "pickup_census_tract" value { int64_list { } } } feature { key: "pickup_community_area" value { int64_list { } } } feature { key: "pickup_latitude" value { float_list { } } } feature { key: "pickup_longitude" value { float_list { } } } feature { key: "tips" value { float_list { value: 0.0 } } } feature { key: "trip_miles" value { float_list { value: 0.0 } } } feature { key: "trip_seconds" value { int64_list { value: 0 } } } feature { key: "trip_start_day" value { int64_list { value: 6 } } } feature { key: "trip_start_hour" value { int64_list { value: 19 } } } feature { key: "trip_start_month" value { int64_list { value: 5 } } } feature { key: "trip_start_timestamp" value { int64_list { value: 1400269500 } } } } features { feature { key: "company" value { bytes_list { value: "Taxi Affiliation Services" } } } feature { key: "dropoff_census_tract" value { int64_list { } } } feature { key: "dropoff_community_area" value { int64_list { } } } feature { key: "dropoff_latitude" value { float_list { } } } feature { key: "dropoff_longitude" value { float_list { } } } feature { key: "fare" value { float_list { value: 27.049999237060547 } } } feature { key: "payment_type" value { bytes_list { value: "Cash" } } } feature { key: "pickup_census_tract" value { int64_list { } } } feature { key: "pickup_community_area" value { int64_list { value: 60 } } } feature { key: "pickup_latitude" value { float_list { value: 41.836151123046875 } } } feature { key: "pickup_longitude" value { float_list { value: -87.64878845214844 } } } feature { key: "tips" value { float_list { value: 0.0 } } } feature { key: "trip_miles" value { float_list { value: 12.600000381469727 } } } feature { key: "trip_seconds" value { int64_list { value: 1380 } } } feature { key: "trip_start_day" value { int64_list { value: 3 } } } feature { key: "trip_start_hour" value { int64_list { value: 2 } } } feature { key: "trip_start_month" value { int64_list { value: 10 } } } feature { key: "trip_start_timestamp" value { int64_list { value: 1380593700 } } } } features { feature { key: "company" value { bytes_list { } } } feature { key: "dropoff_census_tract" value { int64_list { } } } feature { key: "dropoff_community_area" value { int64_list { } } } feature { key: "dropoff_latitude" value { float_list { } } } feature { key: "dropoff_longitude" value { float_list { } } } feature { key: "fare" value { float_list { value: 16.450000762939453 } } } feature { key: "payment_type" value { bytes_list { value: "Cash" } } } feature { key: "pickup_census_tract" value { int64_list { } } } feature { key: "pickup_community_area" value { int64_list { value: 13 } } } feature { key: "pickup_latitude" value { float_list { value: 41.98363494873047 } } } feature { key: "pickup_longitude" value { float_list { value: -87.72357940673828 } } } feature { key: "tips" value { float_list { value: 0.0 } } } feature { key: "trip_miles" value { float_list { value: 6.900000095367432 } } } feature { key: "trip_seconds" value { int64_list { value: 780 } } } feature { key: "trip_start_day" value { int64_list { value: 3 } } } feature { key: "trip_start_hour" value { int64_list { value: 12 } } } feature { key: "trip_start_month" value { int64_list { value: 11 } } } feature { key: "trip_start_timestamp" value { int64_list { value: 1446554700 } } } }
Sekarang ExampleGen
selesai menelan data, langkah selanjutnya adalah analisis data.
StatistikGen
The StatisticsGen
Toedjoe menghitung komponen statistik lebih dataset Anda untuk analisis data, serta untuk digunakan dalam komponen hilir. Ia menggunakan TensorFlow Validasi data perpustakaan.
StatisticsGen
mengambil input dataset kita hanya tertelan menggunakan ExampleGen
.
statistics_gen = tfx.components.StatisticsGen(
examples=example_gen.outputs['examples'])
context.run(statistics_gen)
INFO:absl:Excluding no splits because exclude_splits is not set. INFO:absl:Running driver for StatisticsGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for StatisticsGen INFO:absl:Generating statistics for split train. INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/StatisticsGen/statistics/2/Split-train. INFO:absl:Generating statistics for split eval. INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/StatisticsGen/statistics/2/Split-eval. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. INFO:absl:Running publisher for StatisticsGen INFO:absl:MetadataStore with DB connection initialized
Setelah StatisticsGen
selesai berjalan, kita dapat memvisualisasikan statistik yang dikeluarkan. Cobalah bermain dengan plot yang berbeda!
context.show(statistics_gen.outputs['statistics'])
SkemaGen
The SchemaGen
komponen menghasilkan skema berdasarkan statistik data Anda. (Skema menentukan batas-batas yang diharapkan, jenis, dan sifat dari fitur dalam dataset Anda.) Hal ini juga menggunakan TensorFlow Validasi data perpustakaan.
SchemaGen
akan mengambil sebagai masukan statistik yang kami dihasilkan dengan StatisticsGen
, melihat perpecahan pelatihan secara default.
schema_gen = tfx.components.SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Excluding no splits because exclude_splits is not set. INFO:absl:Running driver for SchemaGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for SchemaGen INFO:absl:Processing schema from statistics for split train. INFO:absl:Processing schema from statistics for split eval. INFO:absl:Schema written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/SchemaGen/schema/3/schema.pbtxt. INFO:absl:Running publisher for SchemaGen INFO:absl:MetadataStore with DB connection initialized
Setelah SchemaGen
selesai berjalan, kita dapat memvisualisasikan skema yang dihasilkan sebagai meja.
context.show(schema_gen.outputs['schema'])
Setiap fitur dalam kumpulan data Anda muncul sebagai baris dalam tabel skema, di samping propertinya. Skema juga menangkap semua nilai yang diambil oleh fitur kategorikal, dilambangkan sebagai domainnya.
Untuk mempelajari lebih lanjut tentang skema, lihat dokumentasi SchemaGen .
Contoh Validator
The ExampleValidator
komponen mendeteksi anomali dalam data Anda, berdasarkan pada harapan yang ditetapkan oleh skema. Hal ini juga menggunakan TensorFlow Validasi data perpustakaan.
ExampleValidator
akan mengambil sebagai masukan statistik dari StatisticsGen
, dan skema dari SchemaGen
.
example_validator = tfx.components.ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
context.run(example_validator)
INFO:absl:Excluding no splits because exclude_splits is not set. INFO:absl:Running driver for ExampleValidator INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for ExampleValidator INFO:absl:Validating schema against the computed statistics for split train. INFO:absl:Validation complete for split train. Anomalies written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/ExampleValidator/anomalies/4/Split-train. INFO:absl:Validating schema against the computed statistics for split eval. INFO:absl:Validation complete for split eval. Anomalies written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/ExampleValidator/anomalies/4/Split-eval. INFO:absl:Running publisher for ExampleValidator INFO:absl:MetadataStore with DB connection initialized
Setelah ExampleValidator
selesai berjalan, kita dapat memvisualisasikan anomali sebagai meja.
context.show(example_validator.outputs['anomalies'])
Pada tabel anomali, kita dapat melihat bahwa tidak ada anomali. Inilah yang kami harapkan, karena ini adalah kumpulan data pertama yang kami analisis dan skemanya disesuaikan untuk itu. Anda harus meninjau skema ini -- segala sesuatu yang tidak terduga berarti anomali dalam data. Setelah ditinjau, skema dapat digunakan untuk menjaga data di masa mendatang, dan anomali yang dihasilkan di sini dapat digunakan untuk men-debug kinerja model, memahami bagaimana data Anda berkembang dari waktu ke waktu, dan mengidentifikasi kesalahan data.
Mengubah
The Transform
Melakukan komponen rekayasa fitur untuk kedua pelatihan dan melayani. Ia menggunakan TensorFlow Transform perpustakaan.
Transform
akan mengambil sebagai masukan data dari ExampleGen
, skema dari SchemaGen
, serta modul yang berisi mengubah kode yang ditetapkan pengguna.
Mari kita lihat contoh-ditetapkan pengguna Transform kode di bawah ini (untuk pengenalan TensorFlow Transform API, lihat tutorial ). Pertama, kami mendefinisikan beberapa konstanta untuk rekayasa fitur:
_taxi_constants_module_file = 'taxi_constants.py'
%%writefile {_taxi_constants_module_file}
# Categorical features are assumed to each have a maximum value in the dataset.
MAX_CATEGORICAL_FEATURE_VALUES = [24, 31, 12]
CATEGORICAL_FEATURE_KEYS = [
'trip_start_hour', 'trip_start_day', 'trip_start_month',
'pickup_census_tract', 'dropoff_census_tract', 'pickup_community_area',
'dropoff_community_area'
]
DENSE_FLOAT_FEATURE_KEYS = ['trip_miles', 'fare', 'trip_seconds']
# Number of buckets used by tf.transform for encoding each feature.
FEATURE_BUCKET_COUNT = 10
BUCKET_FEATURE_KEYS = [
'pickup_latitude', 'pickup_longitude', 'dropoff_latitude',
'dropoff_longitude'
]
# Number of vocabulary terms used for encoding VOCAB_FEATURES by tf.transform
VOCAB_SIZE = 1000
# Count of out-of-vocab buckets in which unrecognized VOCAB_FEATURES are hashed.
OOV_SIZE = 10
VOCAB_FEATURE_KEYS = [
'payment_type',
'company',
]
# Keys
LABEL_KEY = 'tips'
FARE_KEY = 'fare'
Writing taxi_constants.py
Berikutnya, kita menulis preprocessing_fn
yang mengambil data mentah sebagai masukan, dan kembali fitur berubah bahwa model kami dapat melatih pada:
_taxi_transform_module_file = 'taxi_transform.py'
%%writefile {_taxi_transform_module_file}
import tensorflow as tf
import tensorflow_transform as tft
import taxi_constants
_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_FARE_KEY = taxi_constants.FARE_KEY
_LABEL_KEY = taxi_constants.LABEL_KEY
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in _DENSE_FLOAT_FEATURE_KEYS:
# If sparse make it dense, setting nan's to 0 or '', and apply zscore.
outputs[key] = tft.scale_to_z_score(
_fill_in_missing(inputs[key]))
for key in _VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[key] = tft.compute_and_apply_vocabulary(
_fill_in_missing(inputs[key]),
top_k=_VOCAB_SIZE,
num_oov_buckets=_OOV_SIZE)
for key in _BUCKET_FEATURE_KEYS:
outputs[key] = tft.bucketize(
_fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT)
for key in _CATEGORICAL_FEATURE_KEYS:
outputs[key] = _fill_in_missing(inputs[key])
# Was this passenger a big tipper?
taxi_fare = _fill_in_missing(inputs[_FARE_KEY])
tips = _fill_in_missing(inputs[_LABEL_KEY])
outputs[_LABEL_KEY] = tf.where(
tf.math.is_nan(taxi_fare),
tf.cast(tf.zeros_like(taxi_fare), tf.int64),
# Test if the tip was > 20% of the fare.
tf.cast(
tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))
return outputs
def _fill_in_missing(x):
"""Replace missing values in a SparseTensor.
Fills in missing values of `x` with '' or 0, and converts to a dense tensor.
Args:
x: A `SparseTensor` of rank 2. Its dense shape should have size at most 1
in the second dimension.
Returns:
A rank 1 tensor where missing values of `x` have been filled in.
"""
if not isinstance(x, tf.sparse.SparseTensor):
return x
default_value = '' if x.dtype == tf.string else 0
return tf.squeeze(
tf.sparse.to_dense(
tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),
default_value),
axis=1)
Writing taxi_transform.py
Sekarang, kita lulus dalam kode rekayasa fitur ini ke Transform
komponen dan menjalankannya untuk mengubah data Anda.
transform = tfx.components.Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=os.path.abspath(_taxi_transform_module_file))
context.run(transform)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py' (including modules: ['taxi_transform', 'taxi_constants']). INFO:absl:User module package has hash fingerprint version f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp9qnpryw9/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmppaskl3va', '--dist-dir', '/tmp/tmpr6oorqji'] /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools. setuptools.SetuptoolsDeprecationWarning, INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'; target user module is 'taxi_transform'. INFO:absl:Full user module path is 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' INFO:absl:Running driver for Transform INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for Transform INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set. INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn' INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbvbj9r5b', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'] running bdist_wheel running build running build_py creating build creating build/lib copying taxi_transform.py -> build/lib copying taxi_constants.py -> build/lib running install running install_lib running install_egg_info running egg_info creating tfx_user_code_Transform.egg-info writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt' writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt' Copying tfx_user_code_Transform.egg-info to /tmp/tmppaskl3va/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3.7.egg-info running install_scripts Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'. INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn' INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbzwdie1a', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'] Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424 Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'. INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp09euava5', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'] Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424 Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424 INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:289: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Use ref() instead. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2 WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2 INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2 WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2 WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. 2021-12-21 10:10:18.679569: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them. INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5/.temp_path/tftransform_tmp/80dbc09e6ded4a93b5c506e252c8f536/assets INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5/.temp_path/tftransform_tmp/572eacb7c64f4f6e9262f7d496a95f86/assets INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. INFO:absl:Running publisher for Transform INFO:absl:MetadataStore with DB connection initialized
Mari kita memeriksa artefak output Transform
. Komponen ini menghasilkan dua jenis output:
-
transform_graph
adalah grafik yang dapat melakukan operasi preprocessing (grafik ini akan dimasukkan dalam porsi dan evaluasi model). -
transformed_examples
mewakili data pelatihan dan evaluasi preprocessed.
transform.outputs
{'transform_graph': Channel( type_name: TransformGraph artifacts: [Artifact(artifact: id: 5 type_id: 22 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5" custom_properties { key: "name" value { string_value: "transform_graph" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 22 name: "TransformGraph" )] additional_properties: {} additional_custom_properties: {} ), 'transformed_examples': Channel( type_name: Examples artifacts: [Artifact(artifact: id: 6 type_id: 14 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transformed_examples/5" properties { key: "split_names" value { string_value: "[\"train\", \"eval\"]" } } custom_properties { key: "name" value { string_value: "transformed_examples" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 14 name: "Examples" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } properties { key: "version" value: INT } base_type: DATASET )] additional_properties: {} additional_custom_properties: {} ), 'updated_analyzer_cache': Channel( type_name: TransformCache artifacts: [Artifact(artifact: id: 7 type_id: 23 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/updated_analyzer_cache/5" custom_properties { key: "name" value { string_value: "updated_analyzer_cache" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 23 name: "TransformCache" )] additional_properties: {} additional_custom_properties: {} ), 'pre_transform_schema': Channel( type_name: Schema artifacts: [Artifact(artifact: id: 8 type_id: 18 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/pre_transform_schema/5" custom_properties { key: "name" value { string_value: "pre_transform_schema" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 18 name: "Schema" )] additional_properties: {} additional_custom_properties: {} ), 'pre_transform_stats': Channel( type_name: ExampleStatistics artifacts: [Artifact(artifact: id: 9 type_id: 16 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/pre_transform_stats/5" custom_properties { key: "name" value { string_value: "pre_transform_stats" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 16 name: "ExampleStatistics" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } base_type: STATISTICS )] additional_properties: {} additional_custom_properties: {} ), 'post_transform_schema': Channel( type_name: Schema artifacts: [Artifact(artifact: id: 10 type_id: 18 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_schema/5" custom_properties { key: "name" value { string_value: "post_transform_schema" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 18 name: "Schema" )] additional_properties: {} additional_custom_properties: {} ), 'post_transform_stats': Channel( type_name: ExampleStatistics artifacts: [Artifact(artifact: id: 11 type_id: 16 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_stats/5" custom_properties { key: "name" value { string_value: "post_transform_stats" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 16 name: "ExampleStatistics" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } base_type: STATISTICS )] additional_properties: {} additional_custom_properties: {} ), 'post_transform_anomalies': Channel( type_name: ExampleAnomalies artifacts: [Artifact(artifact: id: 12 type_id: 20 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_anomalies/5" custom_properties { key: "name" value { string_value: "post_transform_anomalies" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 20 name: "ExampleAnomalies" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } )] additional_properties: {} additional_custom_properties: {} )}
Ambil mengintip di transform_graph
artefak. Ini menunjuk ke direktori yang berisi tiga subdirektori.
train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata']
The transformed_metadata
subdirektori berisi skema data preprocessed. The transform_fn
subdirektori berisi grafik preprocessing yang sebenarnya. The metadata
subdirektori berisi skema dari data asli.
Kita juga dapat melihat tiga contoh transformasi pertama:
# Get the URI of the output artifact representing the transformed examples, which is a directory
train_uri = os.path.join(transform.outputs['transformed_examples'].get()[0].uri, 'Split-train')
# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
for name in os.listdir(train_uri)]
# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
serialized_example = tfrecord.numpy()
example = tf.train.Example()
example.ParseFromString(serialized_example)
pp.pprint(example)
features { feature { key: "company" value { int64_list { value: 8 } } } feature { key: "dropoff_census_tract" value { int64_list { value: 0 } } } feature { key: "dropoff_community_area" value { int64_list { value: 0 } } } feature { key: "dropoff_latitude" value { int64_list { value: 0 } } } feature { key: "dropoff_longitude" value { int64_list { value: 9 } } } feature { key: "fare" value { float_list { value: 0.061060599982738495 } } } feature { key: "payment_type" value { int64_list { value: 1 } } } feature { key: "pickup_census_tract" value { int64_list { value: 0 } } } feature { key: "pickup_community_area" value { int64_list { value: 0 } } } feature { key: "pickup_latitude" value { int64_list { value: 0 } } } feature { key: "pickup_longitude" value { int64_list { value: 9 } } } feature { key: "tips" value { int64_list { value: 0 } } } feature { key: "trip_miles" value { float_list { value: -0.15886741876602173 } } } feature { key: "trip_seconds" value { float_list { value: -0.7118487358093262 } } } feature { key: "trip_start_day" value { int64_list { value: 6 } } } feature { key: "trip_start_hour" value { int64_list { value: 19 } } } feature { key: "trip_start_month" value { int64_list { value: 5 } } } } features { feature { key: "company" value { int64_list { value: 0 } } } feature { key: "dropoff_census_tract" value { int64_list { value: 0 } } } feature { key: "dropoff_community_area" value { int64_list { value: 0 } } } feature { key: "dropoff_latitude" value { int64_list { value: 0 } } } feature { key: "dropoff_longitude" value { int64_list { value: 9 } } } feature { key: "fare" value { float_list { value: 1.2521240711212158 } } } feature { key: "payment_type" value { int64_list { value: 0 } } } feature { key: "pickup_census_tract" value { int64_list { value: 0 } } } feature { key: "pickup_community_area" value { int64_list { value: 60 } } } feature { key: "pickup_latitude" value { int64_list { value: 0 } } } feature { key: "pickup_longitude" value { int64_list { value: 3 } } } feature { key: "tips" value { int64_list { value: 0 } } } feature { key: "trip_miles" value { float_list { value: 0.532160758972168 } } } feature { key: "trip_seconds" value { float_list { value: 0.5509493350982666 } } } feature { key: "trip_start_day" value { int64_list { value: 3 } } } feature { key: "trip_start_hour" value { int64_list { value: 2 } } } feature { key: "trip_start_month" value { int64_list { value: 10 } } } } features { feature { key: "company" value { int64_list { value: 48 } } } feature { key: "dropoff_census_tract" value { int64_list { value: 0 } } } feature { key: "dropoff_community_area" value { int64_list { value: 0 } } } feature { key: "dropoff_latitude" value { int64_list { value: 0 } } } feature { key: "dropoff_longitude" value { int64_list { value: 9 } } } feature { key: "fare" value { float_list { value: 0.3873794376850128 } } } feature { key: "payment_type" value { int64_list { value: 0 } } } feature { key: "pickup_census_tract" value { int64_list { value: 0 } } } feature { key: "pickup_community_area" value { int64_list { value: 13 } } } feature { key: "pickup_latitude" value { int64_list { value: 9 } } } feature { key: "pickup_longitude" value { int64_list { value: 0 } } } feature { key: "tips" value { int64_list { value: 0 } } } feature { key: "trip_miles" value { float_list { value: 0.21955277025699615 } } } feature { key: "trip_seconds" value { float_list { value: 0.0019067146349698305 } } } feature { key: "trip_start_day" value { int64_list { value: 3 } } } feature { key: "trip_start_hour" value { int64_list { value: 12 } } } feature { key: "trip_start_month" value { int64_list { value: 11 } } } }
Setelah Transform
komponen telah mengubah data Anda ke dalam fitur, dan langkah berikutnya adalah untuk melatih model.
Pelatih
The Trainer
komponen akan melatih model yang Anda tetapkan di TensorFlow. Standar dukungan Trainer Pengukur API, menggunakan Keras API, Anda perlu menentukan Generic Trainer oleh pengaturan custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor)
di contructor Pelatih.
Trainer
mengambil sebagai masukan skema dari SchemaGen
, data ditransformasikan dan grafik dari Transform
, pelatihan parameter, serta modul yang berisi kode model yang ditetapkan pengguna.
Mari kita lihat contoh model yang ditetapkan pengguna kode di bawah ini (untuk pengenalan TensorFlow Keras API, lihat tutorial ):
_taxi_trainer_module_file = 'taxi_trainer.py'
%%writefile {_taxi_trainer_module_file}
from typing import List, Text
import os
from absl import logging
import datetime
import tensorflow as tf
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx_bsl.public import tfxio
import taxi_constants
_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_MAX_CATEGORICAL_FEATURE_VALUES = taxi_constants.MAX_CATEGORICAL_FEATURE_VALUES
_LABEL_KEY = taxi_constants.LABEL_KEY
def _get_tf_examples_serving_signature(model, tf_transform_output):
"""Returns a serving signature that accepts `tensorflow.Example`."""
# We need to track the layers in the model in order to save it.
# TODO(b/162357359): Revise once the bug is resolved.
model.tft_layer_inference = tf_transform_output.transform_features_layer()
@tf.function(input_signature=[
tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
])
def serve_tf_examples_fn(serialized_tf_example):
"""Returns the output to be used in the serving signature."""
raw_feature_spec = tf_transform_output.raw_feature_spec()
# Remove label feature since these will not be present at serving time.
raw_feature_spec.pop(_LABEL_KEY)
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer_inference(raw_features)
logging.info('serve_transformed_features = %s', transformed_features)
outputs = model(transformed_features)
# TODO(b/154085620): Convert the predicted labels from the model using a
# reverse-lookup (opposite of transform.py).
return {'outputs': outputs}
return serve_tf_examples_fn
def _get_transform_features_signature(model, tf_transform_output):
"""Returns a serving signature that applies tf.Transform to features."""
# We need to track the layers in the model in order to save it.
# TODO(b/162357359): Revise once the bug is resolved.
model.tft_layer_eval = tf_transform_output.transform_features_layer()
@tf.function(input_signature=[
tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
])
def transform_features_fn(serialized_tf_example):
"""Returns the transformed_features to be fed as input to evaluator."""
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer_eval(raw_features)
logging.info('eval_transformed_features = %s', transformed_features)
return transformed_features
return transform_features_fn
def _input_fn(file_pattern: List[Text],
data_accessor: tfx.components.DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
"""Generates features and label for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
tfxio.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_LABEL_KEY),
tf_transform_output.transformed_metadata.schema)
def _build_keras_model(hidden_units: List[int] = None) -> tf.keras.Model:
"""Creates a DNN Keras model for classifying taxi data.
Args:
hidden_units: [int], the layer sizes of the DNN (input layer first).
Returns:
A keras Model.
"""
real_valued_columns = [
tf.feature_column.numeric_column(key, shape=())
for key in _DENSE_FLOAT_FEATURE_KEYS
]
categorical_columns = [
tf.feature_column.categorical_column_with_identity(
key, num_buckets=_VOCAB_SIZE + _OOV_SIZE, default_value=0)
for key in _VOCAB_FEATURE_KEYS
]
categorical_columns += [
tf.feature_column.categorical_column_with_identity(
key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
for key in _BUCKET_FEATURE_KEYS
]
categorical_columns += [
tf.feature_column.categorical_column_with_identity( # pylint: disable=g-complex-comprehension
key,
num_buckets=num_buckets,
default_value=0) for key, num_buckets in zip(
_CATEGORICAL_FEATURE_KEYS,
_MAX_CATEGORICAL_FEATURE_VALUES)
]
indicator_column = [
tf.feature_column.indicator_column(categorical_column)
for categorical_column in categorical_columns
]
model = _wide_and_deep_classifier(
# TODO(b/139668410) replace with premade wide_and_deep keras model
wide_columns=indicator_column,
deep_columns=real_valued_columns,
dnn_hidden_units=hidden_units or [100, 70, 50, 25])
return model
def _wide_and_deep_classifier(wide_columns, deep_columns, dnn_hidden_units):
"""Build a simple keras wide and deep model.
Args:
wide_columns: Feature columns wrapped in indicator_column for wide (linear)
part of the model.
deep_columns: Feature columns for deep part of the model.
dnn_hidden_units: [int], the layer sizes of the hidden DNN.
Returns:
A Wide and Deep Keras model
"""
# Following values are hard coded for simplicity in this example,
# However prefarably they should be passsed in as hparams.
# Keras needs the feature definitions at compile time.
# TODO(b/139081439): Automate generation of input layers from FeatureColumn.
input_layers = {
colname: tf.keras.layers.Input(name=colname, shape=(), dtype=tf.float32)
for colname in _DENSE_FLOAT_FEATURE_KEYS
}
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _VOCAB_FEATURE_KEYS
})
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _BUCKET_FEATURE_KEYS
})
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _CATEGORICAL_FEATURE_KEYS
})
# TODO(b/161952382): Replace with Keras preprocessing layers.
deep = tf.keras.layers.DenseFeatures(deep_columns)(input_layers)
for numnodes in dnn_hidden_units:
deep = tf.keras.layers.Dense(numnodes)(deep)
wide = tf.keras.layers.DenseFeatures(wide_columns)(input_layers)
output = tf.keras.layers.Dense(1)(
tf.keras.layers.concatenate([deep, wide]))
model = tf.keras.Model(input_layers, output)
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(lr=0.001),
metrics=[tf.keras.metrics.BinaryAccuracy()])
model.summary(print_fn=logging.info)
return model
# TFX Trainer will call this function.
def run_fn(fn_args: tfx.components.FnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
# Number of nodes in the first layer of the DNN
first_dnn_layer_size = 100
num_dnn_layers = 4
dnn_decay_factor = 0.7
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, fn_args.data_accessor,
tf_transform_output, 40)
eval_dataset = _input_fn(fn_args.eval_files, fn_args.data_accessor,
tf_transform_output, 40)
model = _build_keras_model(
# Construct layers sizes with exponetial decay
hidden_units=[
max(2, int(first_dnn_layer_size * dnn_decay_factor**i))
for i in range(num_dnn_layers)
])
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=fn_args.model_run_dir, update_freq='batch')
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
callbacks=[tensorboard_callback])
signatures = {
'serving_default':
_get_tf_examples_serving_signature(model, tf_transform_output),
'transform_features':
_get_transform_features_signature(model, tf_transform_output),
}
model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
Writing taxi_trainer.py
Sekarang, kita lulus dalam model ini kode untuk Trainer
komponen dan menjalankannya untuk melatih model.
trainer = tfx.components.Trainer(
module_file=os.path.abspath(_taxi_trainer_module_file),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=tfx.proto.TrainArgs(num_steps=10000),
eval_args=tfx.proto.EvalArgs(num_steps=5000))
context.run(trainer)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py' (including modules: ['taxi_transform', 'taxi_constants', 'taxi_trainer']). INFO:absl:User module package has hash fingerprint version ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpzxd5b1yc/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpbg9ly6tr', '--dist-dir', '/tmp/tmpx43qh690'] /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools. setuptools.SetuptoolsDeprecationWarning, INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'; target user module is 'taxi_trainer'. INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl' INFO:absl:Running driver for Trainer INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for Trainer INFO:absl:Train on the 'train' split when train_args.splits is not set. INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set. WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE INFO:absl:udf_utils.get_fn {'train_args': '{\n "num_steps": 10000\n}', 'eval_args': '{\n "num_steps": 5000\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'taxi_trainer@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'} 'run_fn' INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp1osq6e1x', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'] running bdist_wheel running build running build_py creating build creating build/lib copying taxi_transform.py -> build/lib copying taxi_constants.py -> build/lib copying taxi_trainer.py -> build/lib running install running install_lib running install_egg_info running egg_info creating tfx_user_code_Trainer.egg-info writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt' writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt' Copying tfx_user_code_Trainer.egg-info to /tmp/tmpbg9ly6tr/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3.7.egg-info running install_scripts Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'. INFO:absl:Training model. INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. Installing collected packages: tfx-user-code-Trainer Successfully installed tfx-user-code-Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead. super(Adam, self).__init__(name, **kwargs) INFO:absl:Model: "model" INFO:absl:__________________________________________________________________________________________________ INFO:absl: Layer (type) Output Shape Param # Connected to INFO:absl:================================================================================================== INFO:absl: company (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: dropoff_census_tract (InputLay [(None,)] 0 [] INFO:absl: er) INFO:absl: INFO:absl: dropoff_community_area (InputL [(None,)] 0 [] INFO:absl: ayer) INFO:absl: INFO:absl: dropoff_latitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: dropoff_longitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: fare (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: payment_type (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: pickup_census_tract (InputLaye [(None,)] 0 [] INFO:absl: r) INFO:absl: INFO:absl: pickup_community_area (InputLa [(None,)] 0 [] INFO:absl: yer) INFO:absl: INFO:absl: pickup_latitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: pickup_longitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_miles (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_seconds (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_start_day (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_start_hour (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_start_month (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: dense_features (DenseFeatures) (None, 3) 0 ['company[0][0]', INFO:absl: 'dropoff_census_tract[0][0]', INFO:absl: 'dropoff_community_area[0][0]', INFO:absl: 'dropoff_latitude[0][0]', INFO:absl: 'dropoff_longitude[0][0]', INFO:absl: 'fare[0][0]', INFO:absl: 'payment_type[0][0]', INFO:absl: 'pickup_census_tract[0][0]', INFO:absl: 'pickup_community_area[0][0]', INFO:absl: 'pickup_latitude[0][0]', INFO:absl: 'pickup_longitude[0][0]', INFO:absl: 'trip_miles[0][0]', INFO:absl: 'trip_seconds[0][0]', INFO:absl: 'trip_start_day[0][0]', INFO:absl: 'trip_start_hour[0][0]', INFO:absl: 'trip_start_month[0][0]'] INFO:absl: INFO:absl: dense (Dense) (None, 100) 400 ['dense_features[0][0]'] INFO:absl: INFO:absl: dense_1 (Dense) (None, 70) 7070 ['dense[0][0]'] INFO:absl: INFO:absl: dense_2 (Dense) (None, 48) 3408 ['dense_1[0][0]'] INFO:absl: INFO:absl: dense_3 (Dense) (None, 34) 1666 ['dense_2[0][0]'] INFO:absl: INFO:absl: dense_features_1 (DenseFeature (None, 2127) 0 ['company[0][0]', INFO:absl: s) 'dropoff_census_tract[0][0]', INFO:absl: 'dropoff_community_area[0][0]', INFO:absl: 'dropoff_latitude[0][0]', INFO:absl: 'dropoff_longitude[0][0]', INFO:absl: 'fare[0][0]', INFO:absl: 'payment_type[0][0]', INFO:absl: 'pickup_census_tract[0][0]', INFO:absl: 'pickup_community_area[0][0]', INFO:absl: 'pickup_latitude[0][0]', INFO:absl: 'pickup_longitude[0][0]', INFO:absl: 'trip_miles[0][0]', INFO:absl: 'trip_seconds[0][0]', INFO:absl: 'trip_start_day[0][0]', INFO:absl: 'trip_start_hour[0][0]', INFO:absl: 'trip_start_month[0][0]'] INFO:absl: INFO:absl: concatenate (Concatenate) (None, 2161) 0 ['dense_3[0][0]', INFO:absl: 'dense_features_1[0][0]'] INFO:absl: INFO:absl: dense_4 (Dense) (None, 1) 2162 ['concatenate[0][0]'] INFO:absl: INFO:absl:================================================================================================== INFO:absl:Total params: 14,706 INFO:absl:Trainable params: 14,706 INFO:absl:Non-trainable params: 0 INFO:absl:__________________________________________________________________________________________________ 10000/10000 [==============================] - 100s 10ms/step - loss: 0.2372 - binary_accuracy: 0.8605 - val_loss: 0.2222 - val_binary_accuracy: 0.8709 INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f88b5e27910>> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f88b5e27910>> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert INFO:absl:serve_transformed_features = {'pickup_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:9' shape=(None,) dtype=int64>, 'trip_start_hour': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:15' shape=(None,) dtype=int64>, 'fare': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:5' shape=(None,) dtype=float32>, 'trip_miles': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:12' shape=(None,) dtype=float32>, 'trip_start_day': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:14' shape=(None,) dtype=int64>, 'dropoff_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:3' shape=(None,) dtype=int64>, 'trip_start_month': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:16' shape=(None,) dtype=int64>, 'dropoff_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:2' shape=(None,) dtype=int64>, 'dropoff_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:4' shape=(None,) dtype=int64>, 'payment_type': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:6' shape=(None,) dtype=int64>, 'pickup_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:10' shape=(None,) dtype=int64>, 'pickup_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:8' shape=(None,) dtype=int64>, 'company': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:0' shape=(None,) dtype=int64>, 'pickup_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:7' shape=(None,) dtype=int64>, 'dropoff_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:1' shape=(None,) dtype=int64>, 'trip_seconds': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:13' shape=(None,) dtype=float32>} INFO:absl:eval_transformed_features = {'pickup_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:9' shape=(None,) dtype=int64>, 'trip_start_hour': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:15' shape=(None,) dtype=int64>, 'fare': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:5' shape=(None,) dtype=float32>, 'trip_miles': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:12' shape=(None,) dtype=float32>, 'trip_start_day': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:14' shape=(None,) dtype=int64>, 'dropoff_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:3' shape=(None,) dtype=int64>, 'trip_start_month': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:16' shape=(None,) dtype=int64>, 'dropoff_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:2' shape=(None,) dtype=int64>, 'dropoff_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:4' shape=(None,) dtype=int64>, 'payment_type': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:6' shape=(None,) dtype=int64>, 'pickup_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:10' shape=(None,) dtype=int64>, 'pickup_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:8' shape=(None,) dtype=int64>, 'company': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:0' shape=(None,) dtype=int64>, 'pickup_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:7' shape=(None,) dtype=int64>, 'tips': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:11' shape=(None,) dtype=int64>, 'dropoff_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:1' shape=(None,) dtype=int64>, 'trip_seconds': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:13' shape=(None,) dtype=float32>} INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving/assets INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model_run/6 INFO:absl:Running publisher for Trainer INFO:absl:MetadataStore with DB connection initialized
Analisis Pelatihan dengan TensorBoard
Intip artefak pelatih. Ini menunjuk ke direktori yang berisi subdirektori model.
model_artifact_dir = trainer.outputs['model'].get()[0].uri
pp.pprint(os.listdir(model_artifact_dir))
model_dir = os.path.join(model_artifact_dir, 'Format-Serving')
pp.pprint(os.listdir(model_dir))
['Format-Serving'] ['variables', 'assets', 'keras_metadata.pb', 'saved_model.pb']
Secara opsional, kami dapat menghubungkan TensorBoard ke Pelatih untuk menganalisis kurva pelatihan model kami.
model_run_artifact_dir = trainer.outputs['model_run'].get()[0].uri
%load_ext tensorboard
%tensorboard --logdir {model_run_artifact_dir}
Penilai
The Evaluator
komponen menghitung metrik kinerja model atas evaluasi set. Ia menggunakan Analisis Model TensorFlow perpustakaan. The Evaluator
juga dapat opsional memvalidasi bahwa model baru dilatih lebih baik dari model sebelumnya. Ini berguna dalam pengaturan saluran produksi di mana Anda dapat melatih dan memvalidasi model secara otomatis setiap hari. Dalam notebook ini, kita hanya melatih satu model, sehingga Evaluator
otomatis akan label model sebagai "baik".
Evaluator
akan mengambil sebagai masukan data dari ExampleGen
, model terlatih dari Trainer
, dan konfigurasi mengiris. Konfigurasi slicing memungkinkan Anda untuk mengiris metrik Anda pada nilai fitur (misalnya bagaimana performa model Anda pada perjalanan taksi yang dimulai pada jam 8 pagi versus jam 8 malam?). Lihat contoh konfigurasi di bawah ini:
eval_config = tfma.EvalConfig(
model_specs=[
# This assumes a serving model with signature 'serving_default'. If
# using estimator based EvalSavedModel, add signature_name: 'eval' and
# remove the label_key.
tfma.ModelSpec(
signature_name='serving_default',
label_key='tips',
preprocessing_function_names=['transform_features'],
)
],
metrics_specs=[
tfma.MetricsSpec(
# The metrics added here are in addition to those saved with the
# model (assuming either a keras model or EvalSavedModel is used).
# Any metrics added into the saved model (for example using
# model.compile(..., metrics=[...]), etc) will be computed
# automatically.
# To add validation thresholds for metrics saved with the model,
# add them keyed by metric name to the thresholds map.
metrics=[
tfma.MetricConfig(class_name='ExampleCount'),
tfma.MetricConfig(class_name='BinaryAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.5}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
]
)
],
slicing_specs=[
# An empty slice spec means the overall slice, i.e. the whole dataset.
tfma.SlicingSpec(),
# Data can be sliced along a feature column. In this case, data is
# sliced along feature column trip_start_hour.
tfma.SlicingSpec(feature_keys=['trip_start_hour'])
])
Berikutnya, kami memberikan konfigurasi ini untuk Evaluator
dan menjalankannya.
# Use TFMA to compute a evaluation statistics over features of a model and
# validate them against a baseline.
# The model resolver is only required if performing model validation in addition
# to evaluation. In this case we validate against the latest blessed model. If
# no model has been blessed before (as in this case) the evaluator will make our
# candidate the first blessed model.
model_resolver = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
model_blessing=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
'latest_blessed_model_resolver')
context.run(model_resolver)
evaluator = tfx.components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
context.run(evaluator)
INFO:absl:Running driver for latest_blessed_model_resolver INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running publisher for latest_blessed_model_resolver INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running driver for Evaluator INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for Evaluator INFO:absl:Nonempty beam arg extra_packages already includes dependency INFO:absl:udf_utils.get_fn {'eval_config': '{\n "metrics_specs": [\n {\n "metrics": [\n {\n "class_name": "ExampleCount"\n },\n {\n "class_name": "BinaryAccuracy",\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.5\n }\n }\n }\n ]\n }\n ],\n "model_specs": [\n {\n "label_key": "tips",\n "preprocessing_function_names": [\n "transform_features"\n ],\n "signature_name": "serving_default"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "trip_start_hour"\n ]\n }\n ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model' INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config= model_specs { signature_name: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } } INFO:absl:Using /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving as model. WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87bc0f5e50> and <keras.engine.input_layer.InputLayer object at 0x7f87bc0f5b50>). INFO:absl:The 'example_splits' parameter is not set, using 'eval' split. INFO:absl:Evaluating model. INFO:absl:udf_utils.get_fn {'eval_config': '{\n "metrics_specs": [\n {\n "metrics": [\n {\n "class_name": "ExampleCount"\n },\n {\n "class_name": "BinaryAccuracy",\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.5\n }\n }\n }\n ]\n }\n ],\n "model_specs": [\n {\n "label_key": "tips",\n "preprocessing_function_names": [\n "transform_features"\n ],\n "signature_name": "serving_default"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "trip_start_hour"\n ]\n }\n ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors' INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config= model_specs { signature_name: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } model_names: "" } INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config= model_specs { signature_name: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } model_names: "" } INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config= model_specs { signature_name: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } model_names: "" } WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87b0102150> and <keras.engine.input_layer.InputLayer object at 0x7f875454e810>). WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87b06c9d50> and <keras.engine.input_layer.InputLayer object at 0x7f87d4041290>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f874c8d6a10> and <keras.engine.input_layer.InputLayer object at 0x7f874c8ac0d0>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f830dcf9fd0> and <keras.engine.input_layer.InputLayer object at 0x7f830dd87110>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f830dc8cad0> and <keras.engine.input_layer.InputLayer object at 0x7f830cf892d0>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87b041add0> and <keras.engine.input_layer.InputLayer object at 0x7f874d6b6d50>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f830c42a5d0> and <keras.engine.input_layer.InputLayer object at 0x7f830c3037d0>). INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/evaluation/8. INFO:absl:Checking validation results. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:107: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version. Instructions for updating: Use eager execution and: `tf.data.TFRecordDataset(path)` INFO:absl:Blessing result True written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/blessing/8. INFO:absl:Running publisher for Evaluator INFO:absl:MetadataStore with DB connection initialized
Sekarang mari kita memeriksa artefak output Evaluator
.
evaluator.outputs
{'evaluation': Channel( type_name: ModelEvaluation artifacts: [Artifact(artifact: id: 15 type_id: 29 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/evaluation/8" custom_properties { key: "name" value { string_value: "evaluation" } } custom_properties { key: "producer_component" value { string_value: "Evaluator" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 29 name: "ModelEvaluation" )] additional_properties: {} additional_custom_properties: {} ), 'blessing': Channel( type_name: ModelBlessing artifacts: [Artifact(artifact: id: 16 type_id: 30 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/blessing/8" custom_properties { key: "blessed" value { int_value: 1 } } custom_properties { key: "current_model" value { string_value: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6" } } custom_properties { key: "current_model_id" value { int_value: 13 } } custom_properties { key: "name" value { string_value: "blessing" } } custom_properties { key: "producer_component" value { string_value: "Evaluator" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 30 name: "ModelBlessing" )] additional_properties: {} additional_custom_properties: {} )}
Menggunakan evaluation
output yang kita dapat menunjukkan visualisasi default metrik global seluruh set evaluasi.
context.show(evaluator.outputs['evaluation'])
Untuk melihat visualisasi metrik evaluasi irisan, kita bisa langsung memanggil library Analisis Model TensorFlow.
import tensorflow_model_analysis as tfma
# Get the TFMA output result path and load the result.
PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
tfma_result = tfma.load_eval_result(PATH_TO_RESULT)
# Show data sliced along feature column trip_start_hour.
tfma.view.render_slicing_metrics(
tfma_result, slicing_column='trip_start_hour')
SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'trip_start_hour:19',…
Visualisasi ini menunjukkan metrik yang sama, tetapi dihitung pada setiap nilai fitur trip_start_hour
bukan pada seluruh himpunan evaluasi.
Analisis Model TensorFlow mendukung banyak visualisasi lainnya, seperti Indikator Kewajaran dan merencanakan rangkaian waktu kinerja model. Untuk mempelajari lebih lanjut, lihat tutorial .
Karena kami menambahkan ambang batas ke konfigurasi kami, keluaran validasi juga tersedia. The precence dari blessing
artefak menunjukkan bahwa model kami lulus validasi. Karena ini adalah validasi pertama yang dilakukan, kandidat secara otomatis diberkati.
blessing_uri = evaluator.outputs['blessing'].get()[0].uri
!ls -l {blessing_uri}
total 0 -rw-rw-r-- 1 kbuilder kbuilder 0 Dec 21 10:13 BLESSED
Sekarang juga dapat memverifikasi keberhasilan dengan memuat catatan hasil validasi:
PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
print(tfma.load_validation_result(PATH_TO_RESULT))
validation_ok: true validation_details { slicing_details { slicing_spec { } num_matching_slices: 25 } }
pendorong
The Pusher
komponen biasanya pada akhir pipa TFX. Ia memeriksa apakah model telah lulus validasi, dan jika demikian, ekspor model untuk _serving_model_dir
.
pusher = tfx.components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
context.run(pusher)
INFO:absl:Running driver for Pusher INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for Pusher INFO:absl:Model version: 1640081600 INFO:absl:Model written to serving path /tmp/tmpkvhhk5j5/serving_model/taxi_simple/1640081600. INFO:absl:Model pushed to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Pusher/pushed_model/9. INFO:absl:Running publisher for Pusher INFO:absl:MetadataStore with DB connection initialized
Mari kita memeriksa artefak output Pusher
.
pusher.outputs
{'pushed_model': Channel( type_name: PushedModel artifacts: [Artifact(artifact: id: 17 type_id: 32 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Pusher/pushed_model/9" custom_properties { key: "name" value { string_value: "pushed_model" } } custom_properties { key: "producer_component" value { string_value: "Pusher" } } custom_properties { key: "pushed" value { int_value: 1 } } custom_properties { key: "pushed_destination" value { string_value: "/tmp/tmpkvhhk5j5/serving_model/taxi_simple/1640081600" } } custom_properties { key: "pushed_version" value { string_value: "1640081600" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 32 name: "PushedModel" )] additional_properties: {} additional_custom_properties: {} )}
Secara khusus, Pusher akan mengekspor model Anda dalam format SavedModel, yang terlihat seperti ini:
push_uri = pusher.outputs['pushed_model'].get()[0].uri
model = tf.saved_model.load(push_uri)
for item in model.signatures.items():
pp.pprint(item)
('serving_default', <ConcreteFunction signature_wrapper(*, examples) at 0x7F82F31FDE50>) ('transform_features', <ConcreteFunction signature_wrapper(*, examples) at 0x7F82F31AC410>)
Kami telah menyelesaikan tur komponen TFX bawaan kami!