I've already successfully fit a TFTransformOutput to some data (in this case, the Census dataset from UCI common amongst the TF and TFX examples.) I try to apply the transformer with the method transform_raw_features(raw_features) but keep getting the error:
ValueError: Node 'transform/transform/inputs/workclass_copy' has an
_output_shapes attribute inconsistent with the GraphDef for output #0: Shapes must be equal rank, but are 0 and 1
Digging into the source code, it seems the error originates in saved_transform_io in the method _partially_apply_saved_transform_impl while doing:
saver = tf_saver.import_meta_graph(meta_graph_def, import_scope=import_scope,
input_map=input_map)
I examined the meta_graph_def produced by TFX TFTransform and Beam and notice that the graph indeed has a series of copied variables with input/output rank differences. However, that is nothing I have control over.
The column in the error message is "workclass" which is a simple categorical column. What might I be doing incorrectly? What is the best way to debug this? At this point, I've already dug deep into the TF source code but the error seems to originate with how the TFTransform graph was written, not sure what levers I have to change/fix that.
This is using TF Transform v0.9 and the corresponding TF v1.9
Traceback (most recent call last): File
"/home/sahmed/workspace/ml_playground/TFX-TFT/trainers.py", line 449,
in parse_csv
transformed_stuff=xformer.transform_raw_features(raw_features) File
"/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow_transform/output_wrapper.py",
line 122, in transform_raw_features
self.transform_savedmodel_dir, raw_features)) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow_transform/saved/saved_transform_io.py",
line 360, in partially_apply_saved_transform_internal
saved_model_dir, logical_input_map, tensor_replacement_map) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow_transform/saved/saved_transform_io.py",
line 218, in _partially_apply_saved_transform_impl
input_map=input_map) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow/python/training/saver.py",
line 1960, in import_meta_graph
**kwargs) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow/python/framework/meta_graph.py",
line 744, in import_scoped_meta_graph
producer_op_list=producer_op_list) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py",
line 432, in new_func
return func(*args, **kwargs) File "/home/sahmed/miniconda3/envs/kml2/lib/python2.7/site-packages/tensorflow/python/framework/importer.py",
line 422, in import_graph_def
raise ValueError(str(e)) ValueError: Node 'transform/transform/inputs/workclass_copy' has an _output_shapes
attribute inconsistent with the GraphDef for output #0: Shapes must be
equal rank, but are 0 and 1
The issue is likely that the shape of the workclass tensor is incompatible with what transform_raw_features expects.
TFTransformOutput.transform_raw_features() expects these features to have the same characteristics as described in the metadata given to tft.AnalyzeDataset() similarly to how it's done in this example:
https://github.com/tensorflow/transform/blob/master/examples/simple_example.py#L63
Could you take a look at the metadata used in your pipeline and see that it is compatible with the data fed into TFTransformOutput.transform_raw_features()?
Related
I ran default code of Temporal fusion transformer in google colab which downloaded at github.
After clone, when I ran the step 2, there's no way to test training.
python3 -m script_train_fixed_params volatility outputs yes
The problem is shape error in the below.
Computing best validation loss
Computing test loss
/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py:2079: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
updates=self.state_updates,
Traceback (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/content/drive/MyDrive/tft_tf2/script_train_fixed_params.py", line 239, in <module>
use_testing_mode=True) # Change to false to use original default params
File "/content/drive/MyDrive/tft_tf2/script_train_fixed_params.py", line 156, in main
targets = data_formatter.format_predictions(output_map["targets"])
File "/content/drive/MyDrive/tft_tf2/data_formatters/volatility.py", line 183, in format_predictions
output[col] = self._target_scaler.inverse_transform(predictions[col])
File "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_data.py", line 1022, in inverse_transform
force_all_finite="allow-nan",
File "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py", line 773, in check_array
"if it contains a single sample.".format(array)
ValueError: Expected 2D array, got 1D array instead:
array=[-1.43120418 1.58885804 0.28558148 ... -1.50945972 -0.16713021
-0.57365613].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
I've tried to modify code which is predict dataframe shpae of 'data_formatters/volatility.py", line 183, in format_predictions' because I guessed that's where the problem arises.), but I can't handle that.
You have to change line
183 in volatitlity.py
output[col] = self._target_scaler.inverse_transform(predictions[col].values.reshape(-1, 1))
and line 216 in electricity.py
sliced_copy[col] = target_scaler.inverse_transform(sliced_copy[col].values.reshape(-1, 1))
Afterwards the example electricity works fine. And I guess this should be the same with volatility.
I want to convert trained python model (.pb) to tensorflowjs model. To accomplish this, first I saved the model with estimator.export_savedmodel function, then I run the tensorflowjs_converter command on Google Colab. However, no file is created for tensorflowjs. The conversion also gives a lot of warning and ends with an error.
Here is the full code, and please run to see the full output:
https://colab.research.google.com/drive/19k2s8eHpQY9Trps9dyaxPp0HqHWp5qpb
What is the reason of the problem and how can I fix it?
Part of the output:
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
Traceback (most recent call last):
File "/usr/local/bin/tensorflowjs_converter", line 8, in <module>
sys.exit(pip_main())
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/converter.py", line 638, in pip_main
main([' '.join(sys.argv[1:])])
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/converter.py", line 642, in main
convert(argv[0].split(' '))
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/converter.py", line 591, in convert
strip_debug_ops=args.strip_debug_ops)
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py", line 435, in convert_tf_saved_model
strip_debug_ops=strip_debug_ops)
File "/usr/local/lib/python3.6/dist-packages/tensorflowjs/converters/tf_saved_model_conversion_v2.py", line 141, in optimize_graph
', '.join(unsupported))
ValueError: Unsupported Ops in the model before optimization
ParallelDynamicStitch, StringSplit, Unique, RegexReplace, DynamicPartition, StringToHashBucketFast, ParseExample, LookupTableFindV2, LookupTableSizeV2, SparseFillEmptyRows, StringJoin, AsString, SparseSegmentSqrtN, HashTableV2
Edit:
Seems like it isn't supported:
https://github.com/tensorflow/tfjs/issues/2322
This is because your model has ops that are not supported by tensorflow.js yet. And seems like you missed the missing op name in the output you pasted. Please feel free to update the output with missing op name or file a feature request in the tensorflow.js repo with more details.
I've been trying to adapt the reddit_tft example from the cloud-ml github samples repo to my needs.
I've been able to get it running as per the tutorial readme.
However what i want to use it for is a binary classification problem and also output keys in batch prediction.
So i have made copy of the tutorial code here and have changed it in a few places to be able to have a model type of deep_classifier that would use a DNNClasifier instead of a DNNRegressor.
I've changed the score variable to be
if(score>0,1,0) as score
It's training fine, deploys to cloud ml but i'm not sure how to now get keys back from my predictions. `
I've updated the sql pulling from BigQuery to include id as example_id here
It seems the code from the tutorial had some sort of placeholder for example_id so i'm trying to leverage that.
It all seems to work but when i get batch predictions all i get is json like this:
{"classes": ["0", "1"], "scores": [0.20427155494689941, 0.7957285046577454]}
{"classes": ["0", "1"], "scores": [0.14911963045597076, 0.8508803248405457]}
...
So example_id does not seem to be making it into the serving functions like i need.
I've tried to follow the approach here which is based on adapting the census example for keys.
I just cant figure out how to finish adapting this reddit example to also output keys in the predictions as they look a bit different to me in terms of design and functions being used.
Update 1
My latest attempt is here Trying to use the approach outlined here.
However this is giving errors:
NotFoundError (see above for traceback): /tmp/tmp2jllvb/model.ckpt-1_temp_9530d2c5823d4462be53fa5415e429fd; No such file or directory
[[Node: save/SaveV2 = SaveV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT64], _device="/job:ps/replica:0/task:0/device:CPU:0"](save/ShardedFilename, save/SaveV2/tensor_names, save/SaveV2/shape_and_slices, dnn/hiddenlayer_0/kernel/part_2/read, dnn/dnn/hiddenlayer_0/kernel/part_2/Adagrad/read, dnn/hiddenlayer_1/kernel/part_2/read, dnn/dnn/hiddenlayer_1/kernel/part_2/Adagrad/read, dnn/input_from_feature_columns/input_layer/subreddit_id_embedding/weights/part_0/read, dnn/dnn/input_from_feature_columns/input_layer/subreddit_id_embedding/weights/part_0/Adagrad/read, dnn/logits/bias/part_0/read, dnn/dnn/logits/bias/part_0/Adagrad/read, global_step)]]
Update 2
My latest attempt and details are here.
I'm now getting a error from tensorflow-fransform (run_preprocess.sh works fine in tft 0.1)
File "/usr/local/lib/python2.7/dist-packages/tensorflow_transform/tf_metadata/dataset_schema.py", line 282, in __setstate__
self._dtype = tf.as_dtype(state['dtype'])
TypeError: string indices must be integers, not str
Update 3
I have changed things to just use beam + csv and avoid tft. Also i'm now using the approach as outlined here for extending the canned estimator to get the key back with the predictions.
However when following this post to try get the comments in as features i'm now running into a new error.
The replica worker 3 exited with a non-zero status of 1. Termination reason: Error. Traceback (most recent call last): [...] File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/estimator/python/estimator/extenders.py", line 87, in new_model_fn spec = estimator.model_fn(features, labels, mode, config) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 203, in public_model_fn return self._call_model_fn(features, labels, mode, config) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 694, in _call_model_fn model_fn_results = self._model_fn(features=features, **kwargs) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/canned/dnn_linear_combined.py", line 520, in _model_fn config=config) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/canned/dnn_linear_combined.py", line 158, in _dnn_linear_combined_model_fn dnn_logits = dnn_logit_fn(features=features, mode=mode) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/canned/dnn.py", line 89, in dnn_logit_fn features=features, feature_columns=feature_columns) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/feature_column/feature_column.py", line 226, in input_layer with variable_scope.variable_scope(None, default_name=column.name): File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 1826, in __enter__ current_name_scope_name = self._current_name_scope.__enter__() File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 4932, in __enter__ return self._name_scope.__enter__() File "/usr/lib/python2.7/contextlib.py", line 17, in __enter__ return self.gen.next() File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3514, in name_scope raise ValueError("'%s' is not a valid scope name" % name) ValueError: 'Tensor("Slice:0", shape=(?, 20), dtype=int64)_embedding' is not a valid scope name
My repo for this attempt/approach is here. This all runs fine if i just use subreddit as a feature, it's adding in the comment feature that seems to be causing the problems. Lines 103 to 111 is where i have followed this approach.
Not sure what's triggering the error in my code from reading the trace. Anyone any ideas?
Or can anyone point me towards another approach to go from text to bow to embedding feature in TF?
See:
https://medium.com/#lakshmanok/how-to-extend-a-canned-tensorflow-estimator-to-add-more-evaluation-metrics-and-to-pass-through-ddf66cd3047d
Here's what the code looks like to pass through keys:
def forward_key_to_export(estimator):
estimator = tf.contrib.estimator.forward_features(estimator, KEY_COLUMN)
## This shouldn't be necessary (I've filed CL/187793590 to update extenders.py with this code)
config = estimator.config
def model_fn2(features, labels, mode):
estimatorSpec = estimator._call_model_fn(features, labels, mode, config=config)
if estimatorSpec.export_outputs:
for ekey in ['predict', 'serving_default']:
estimatorSpec.export_outputs[ekey] = \
tf.estimator.export.PredictOutput(estimatorSpec.predictions)
return estimatorSpec
return tf.estimator.Estimator(model_fn=model_fn2, config=config)
##
# Create estimator to train and evaluate
def train_and_evaluate(output_dir):
estimator = tf.estimator.DNNLinearCombinedRegressor(...)
estimator = forward_key_to_export(estimator)
...
tf.estimator.train_and_evaluate(estimator, ...)
We have plans, but haven't moved the changes into Census yet for the output keys. In the mean time can you please see if this gist helps https://gist.github.com/andrewm4894/ebd3ac3c87e2ab4af8a10740e85073bb#file-with_keys_model-py
Please feel free to send a PR if you get to it sooner and we will merge your contribution.
I try to follow this nice MXNet Tutorial. I create an extremely simple neural network (two input unit, no hidden units and one output unit) doing this:
from mxnet import gluon
net = gluon.nn.Dense(1, in_units=2)
After that I try to take a look at the shape of the weight matrix (the same way as it is described in the tutorial):
print(net.weight)
As a result I expect to see this:
Parameter dense4_weight (shape=(1, 2), dtype=None)
However, I see the following error message:
Traceback (most recent call last):
File "tmp.py", line 5, in <module>
print(net.weight)
File "/usr/local/lib/python3.6/site-packages/mxnet/gluon/parameter.py", line 120, in __repr__
return s.format(**self.__dict__)
KeyError: 'shape'
Am I doing something wrong?
This is a regression that happened here and has since been fixed on master branch here. Expect it to be fixed in the next MXNet release.
I want to view statistics of my model by saving my graph to a file then running graph_metrics.py.
I have tried a few different things to write the file, my best effort is:
tf.train.write_graph( session.graph_def, ".", "my_graph", as_text=True )
But here's what happens:
$ python ./util/graph_metrics.py --noinput_binary --graph my_graph
Traceback (most recent call last):
File "./util/graph_metrics.py", line 137, in <module>
tf.app.run()
File ".virtualenv/local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 30, in run
sys.exit(main(sys.argv))
File "./util/graph_metrics.py", line 85, in main
FLAGS.batch_size)
File "./util/graph_metrics.py", line 109, in calculate_graph_metrics
input_tensor = sess.graph.get_tensor_by_name(input_layer)
File ".virtualenv/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2531, in get_tensor_by_name
return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
File ".virtualenv/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2385, in as_graph_element
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
File ".virtualenv/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2427, in _as_graph_element_locked
"graph." % (repr(name), repr(op_name)))
KeyError: "The name 'Mul:0' refers to a Tensor which does not exist. The operation, 'Mul', does not exist in the graph."
Is there a complete working example of saving a graph, then analyzing it with graph_metrics.py?
This process seems to involve a magic incantation that I haven't yet discovered.
The error you're hitting is because you need to specify the name of your own input node with --input_layer= (it just defaults to Mul:0 because that's what we use in one of our Inception models):
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/graph_metrics.py#L51
The graph_metrics script is still very much a work in progress unfortunately, and you may hit problems with shape inference, but hopefully this should get you past the initial hurdle.