Saat mendesain kode pemodelan TensorFlow untuk TFX, ada beberapa item yang harus diperhatikan, termasuk pilihan API pemodelan.
- Mengkonsumsi: SavedModel dari Transform , dan data dari ExampleGen
- Memancarkan: Model terlatih dalam format Model Tersimpan
Input layer model Anda harus mengkonsumsi dari SavedModel yang diciptakan oleh Transform komponen, dan lapisan Transform model yang harus disertakan dengan model Anda sehingga ketika Anda ekspor SavedModel Anda dan EvalSavedModel mereka akan mencakup transformasi yang diciptakan oleh Transform komponen.
Desain model TensorFlow khas untuk TFX terlihat seperti ini:
def _build_estimator(tf_transform_dir,
config,
hidden_units=None,
warm_start_from=None):
"""Build an estimator for predicting the tipping behavior of taxi riders.
Args:
tf_transform_dir: directory in which the tf-transform model was written
during the preprocessing step.
config: tf.contrib.learn.RunConfig defining the runtime environment for the
estimator (including model_dir).
hidden_units: [int], the layer sizes of the DNN (input layer first)
warm_start_from: Optional directory to warm start from.
Returns:
Resulting DNNLinearCombinedClassifier.
"""
metadata_dir = os.path.join(tf_transform_dir,
transform_fn_io.TRANSFORMED_METADATA_DIR)
transformed_metadata = metadata_io.read_metadata(metadata_dir)
transformed_feature_spec = transformed_metadata.schema.as_feature_spec()
transformed_feature_spec.pop(_transformed_name(_LABEL_KEY))
real_valued_columns = [
tf.feature_column.numeric_column(key, shape=())
for key in _transformed_names(_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 _transformed_names(_VOCAB_FEATURE_KEYS)
]
categorical_columns += [
tf.feature_column.categorical_column_with_identity(
key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
for key in _transformed_names(_BUCKET_FEATURE_KEYS)
]
categorical_columns += [
tf.feature_column.categorical_column_with_identity(
key, num_buckets=num_buckets, default_value=0)
for key, num_buckets in zip(
_transformed_names(_CATEGORICAL_FEATURE_KEYS), #
_MAX_CATEGORICAL_FEATURE_VALUES)
]
return tf.estimator.DNNLinearCombinedClassifier(
config=config,
linear_feature_columns=categorical_columns,
dnn_feature_columns=real_valued_columns,
dnn_hidden_units=hidden_units or [100, 70, 50, 25],
warm_start_from=warm_start_from)