Comments (5)
To make sure things are fully compatible pleas use TFT version 0.3.0 which we just released yesterday.
https://github.com/tensorflow/transform/releases/tag/v0.3.0
That should help with the InputFnOps problem, please let me know if it works.
from transform.
Tried this bundle:
tensorflow==1.3
tensorflow_transform==0.3
six==1.10.0
Got the same error:
AttributeError: 'InputFnOps' object has no attribute 'receiver_tensors'
experiment fn:
estimator = tf.estimator.LinearClassifier(feature_columns=[weighted_reviews],
n_classes=LABEL_SIZE,
model_dir=output_dir,
config=tf.contrib.learn.RunConfig(save_checkpoints_steps=100))
transformed_metadata = metadata_io.read_metadata(transformed_metadata_dir)
raw_metadata = metadata_io.read_metadata(raw_metadata_dir)
train_input_fn = input_fn_maker.build_training_input_fn(
transformed_metadata,
transformed_train_filepattern,
training_batch_size=TRAIN_BATCH_SIZE,
label_keys=[LABEL_COLUMN])
eval_input_fn = input_fn_maker.build_training_input_fn(
transformed_metadata,
transformed_test_filepattern,
training_batch_size=1,
label_keys=[LABEL_COLUMN])
serving_input_fn = input_fn_maker.build_default_transforming_serving_input_fn(
raw_metadata=raw_metadata,
transform_savedmodel_dir=transform_fn_dir + "/transform_fn",
raw_label_keys=[],
raw_feature_keys=[KEYWORDS_COLUMN])
export_strategy = saved_model_export_utils.make_export_strategy(
serving_input_fn,
exports_to_keep=5,
default_output_alternative_key=None)
max_steps = MAX_STEPS if MAX_STEPS > 0 else \
TRAIN_NUM_EPOCHS * NUM_TRAIN_INSTANCES / TRAIN_BATCH_SIZE
return tf.contrib.learn.Experiment(
estimator=estimator,
train_steps=max_steps,
eval_steps=NUM_TEST_INSTANCES,
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
export_strategies=export_strategy,
min_eval_frequency=5)
from transform.
I see, yes we need to upload a fix to support the core estimators, will update this bug with more info as soon as we push it to github.
from transform.
Yep, that's a known issue; will fix soon.
from transform.
Fixed in 0.3.1
from transform.
Related Issues (20)
- Segmentation Fault on tft.compute_and_apply_vocabulary HOT 8
- Release new version that's compatible with `tfx[kfp]==1.10.0` HOT 7
- Can't install due to dependency on numpy HOT 5
- Could tft support embedding transform function? HOT 11
- Python 3.10 support HOT 19
- TypeError: an integer is required [while running 'AnalyzeAndTransformDataset/TransformDataset/InstrumentInputBytes[Transform] HOT 6
- Allow Identity "Transformation" HOT 4
- Beam AnalyzeAndTransformDataset runs expensive transformation _InstanceDictInputToTFXIOInput Twice HOT 3
- Install fails on Apple M2 Ventura HOT 3
- Apple Silicon support for tensorflow-transform not available as tfx-bsl does not support it? HOT 3
- Raise pyarrow upper bound? HOT 12
- apart from the batch dimension, all dimensions must have known size [while running 'AnalyzeAndTransformDataset/AnalyzeDataset/CreateSavedModel[tf_v2_only]/CreateSavedModel'] HOT 2
- Transform component and tf.function HOT 4
- Transform graph returns an empty dictionary at serve_tf_examples_fn function HOT 4
- Graph error when using TFHub Universal-sentence-encoder model HOT 2
- Wheel missing py.typed HOT 2
- universal sentence encoder batch pipeline failing HOT 3
- tfx.components.Transform returns invalid results HOT 1
- TFX Transform layer returns dict with missing keys HOT 2
- Pip fails to install TFT on Python 3.11 on Linux in a clean environment.
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from transform.