Hi Monika! Can you let us know what error message you are seeing in the output? Thanks!
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Hi Monika! The problem is you have a #grade-up-to-here tag in one of the cells. That’s why the grader is ignoring the rest of your code even if they are correct. Remove that and you will get the perfect score. Hope it also works on your end!
hi chris,
i am also stuck at the same step, i did everything correct , and i am not gettign any error , still this step is not been graded. i am attaching this code below. kindly guide
moderator edit: removed code
Hi Abhishek. Sorry this got buried in my notifications! Have you already resolved this?
Hi Please help i am facing error for C2 W3 and C2 W2 Assignments
Hi @Prasad_Sonar
What’s the exact error you have?
Hello @Prasad_Sonar
Sorry I have been away for some days, but I have seen another mentor is looking into the issue: Assignments not getting submitted - #3 by Th_o_Vy_Le_Nguy_n
Also please edit the post and delete your notebooks, It’s against our community guidelines to share assignments publicly
Hi! I’m also having error for preprocessing_fn with or without _fill_in_missing() function. Here is my code:
%%writefile {_traffic_transform_module_file}
import tensorflow as tf
import tensorflow_transform as tft
import traffic_constants
# Unpack the contents of the constants module
_DENSE_FLOAT_FEATURE_KEYS = traffic_constants.DENSE_FLOAT_FEATURE_KEYS
_RANGE_FEATURE_KEYS = traffic_constants.RANGE_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = traffic_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = traffic_constants.VOCAB_SIZE
_OOV_SIZE = traffic_constants.OOV_SIZE
_CATEGORICAL_FEATURE_KEYS = traffic_constants.CATEGORICAL_FEATURE_KEYS
_BUCKET_FEATURE_KEYS = traffic_constants.BUCKET_FEATURE_KEYS
_FEATURE_BUCKET_COUNT = traffic_constants.FEATURE_BUCKET_COUNT
_VOLUME_KEY = traffic_constants.VOLUME_KEY
_transformed_name = traffic_constants.transformed_name
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 = {}
### START CODE HERE
# Scale these features to the z-score.
for key in _DENSE_FLOAT_FEATURE_KEYS:
# Scale these features to the z-score.
outputs[_transformed_name(key)] = tft.scale_to_z_score(inputs[key])
# Scale these feature/s from 0 to 1
for key in _RANGE_FEATURE_KEYS:
outputs[_transformed_name(key)] = tft.scale_to_0_1(inputs[key])
# Transform the strings into indices
# hint: use the VOCAB_SIZE and OOV_SIZE to define the top_k and num_oov parameters
for key in _VOCAB_FEATURE_KEYS:
outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(inputs[key],
top_k = _VOCAB_SIZE, num_oov_buckets = _OOV_SIZE)
# Bucketize the feature
for key in _BUCKET_FEATURE_KEYS:
outputs[_transformed_name(key)] = tft.bucketize(
inputs[key], _FEATURE_BUCKET_COUNT[key])
# Keep the features as is. No tft function needed.
for key in _CATEGORICAL_FEATURE_KEYS:
outputs[_transformed_name(key)] = inputs[key]
# Use `tf.cast` to cast the label key to float32
traffic_volume = tf.cast(inputs[_VOLUME_KEY], dtype=tf.float32)
# Create a feature that shows if the traffic volume is greater than the mean and cast to an int
outputs[_transformed_name(_VOLUME_KEY)] = tf.cast(
# Use `tf.greater` to check if the traffic volume in a row is greater than the mean of the entire traffic volumn column
# Hint: Use a `tft` function to compute the mean.
tf.greater(None, None(tf.cast(inputs[_VOLUME_KEY], tf.float32))),
tf.int64)
### END CODE HERE
return outputs
# Test your preprocessing_fn
import traffic_transform
from testing_values import feature_description, raw_data
# NOTE: These next two lines are for reloading your traffic_transform module in case you need to
# update your initial solution and re-run this cell. Please do not remove them especially if you
# have revised your solution. Else, your changes will not be detected.
import importlib
importlib.reload(traffic_transform)
raw_data_metadata = dataset_metadata.DatasetMetadata(schema_utils.schema_from_feature_spec(feature_description))
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
transformed_dataset, _ = (
(raw_data, raw_data_metadata) | tft_beam.AnalyzeAndTransformDataset(traffic_transform.preprocessing_fn))
transformed_data, transformed_metadata = transformed_dataset
Getting the next error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-66-e2a9c8e67836> in <module>
14 with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
15 transformed_dataset, _ = (
---> 16 (raw_data, raw_data_metadata) | tft_beam.AnalyzeAndTransformDataset(traffic_transform.preprocessing_fn))
17
18 transformed_data, transformed_metadata = transformed_dataset
/opt/conda/lib/python3.8/site-packages/apache_beam/transforms/ptransform.py in __ror__(self, left, label)
613 pvalueish = _SetInputPValues().visit(pvalueish, replacements)
614 self.pipeline = p
--> 615 result = p.apply(self, pvalueish, label)
616 if deferred:
617 return result
/opt/conda/lib/python3.8/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
696 transform.type_check_inputs(pvalueish)
697
--> 698 pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
699
700 if type_options is not None and type_options.pipeline_type_check:
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/runner.py in apply(self, transform, input, options)
183 m = getattr(self, 'apply_%s' % cls.__name__, None)
184 if m:
--> 185 return m(transform, input, options)
186 raise NotImplementedError(
187 'Execution of [%s] not implemented in runner %s.' % (transform, self))
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/runner.py in apply_PTransform(self, transform, input, options)
213 def apply_PTransform(self, transform, input, options):
214 # The base case of apply is to call the transform's expand.
--> 215 return transform.expand(input)
216
217 def run_transform(self,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/beam/impl.py in expand(self, dataset)
1269 # e.g. caching the values of expensive computations done in AnalyzeDataset.
1270 transform_fn = (
-> 1271 dataset | 'AnalyzeDataset' >> AnalyzeDataset(self._preprocessing_fn))
1272
1273 if Context.get_use_deep_copy_optimization():
/opt/conda/lib/python3.8/site-packages/apache_beam/transforms/ptransform.py in __ror__(self, pvalueish, _unused)
1089
1090 def __ror__(self, pvalueish, _unused=None):
-> 1091 return self.transform.__ror__(pvalueish, self.label)
1092
1093 def expand(self, pvalue):
/opt/conda/lib/python3.8/site-packages/apache_beam/transforms/ptransform.py in __ror__(self, left, label)
613 pvalueish = _SetInputPValues().visit(pvalueish, replacements)
614 self.pipeline = p
--> 615 result = p.apply(self, pvalueish, label)
616 if deferred:
617 return result
/opt/conda/lib/python3.8/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
650 try:
651 old_label, transform.label = transform.label, label
--> 652 return self.apply(transform, pvalueish)
653 finally:
654 transform.label = old_label
/opt/conda/lib/python3.8/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
696 transform.type_check_inputs(pvalueish)
697
--> 698 pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
699
700 if type_options is not None and type_options.pipeline_type_check:
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/runner.py in apply(self, transform, input, options)
183 m = getattr(self, 'apply_%s' % cls.__name__, None)
184 if m:
--> 185 return m(transform, input, options)
186 raise NotImplementedError(
187 'Execution of [%s] not implemented in runner %s.' % (transform, self))
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/runner.py in apply_PTransform(self, transform, input, options)
213 def apply_PTransform(self, transform, input, options):
214 # The base case of apply is to call the transform's expand.
--> 215 return transform.expand(input)
216
217 def run_transform(self,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/beam/impl.py in expand(self, dataset)
1201 def expand(self, dataset):
1202 input_values, input_metadata = dataset
-> 1203 result, cache = super().expand((input_values, None, None, input_metadata))
1204 assert not cache
1205 return result
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/beam/impl.py in expand(self, dataset)
1002 # need to be serialized to SavedModel.
1003 graph, structured_inputs, structured_outputs = (
-> 1004 impl_helper.trace_preprocessing_function(self._preprocessing_fn, specs,
1005 self._use_tf_compat_v1,
1006 base_temp_dir))
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/impl_helper.py in trace_preprocessing_function(preprocessing_fn, input_specs, use_tf_compat_v1, base_temp_dir)
714 return _trace_preprocessing_fn_v1(preprocessing_fn, input_specs)
715 else:
--> 716 return _trace_preprocessing_fn_v2(preprocessing_fn, input_specs,
717 base_temp_dir)
718
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/impl_helper.py in _trace_preprocessing_fn_v2(preprocessing_fn, specs, base_temp_dir)
680 evaluated_replacements=None)
681 with annotators.object_tracker_scope(annotators.ObjectTracker()):
--> 682 concrete_fn = get_traced_transform_fn(
683 preprocessing_fn, specs, tf_graph_context).get_concrete_function()
684 return (concrete_fn.graph,
/opt/conda/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in get_concrete_function(self, *args, **kwargs)
1231 def get_concrete_function(self, *args, **kwargs):
1232 # Implements GenericFunction.get_concrete_function.
-> 1233 concrete = self._get_concrete_function_garbage_collected(*args, **kwargs)
1234 concrete._garbage_collector.release() # pylint: disable=protected-access
1235 return concrete
/opt/conda/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _get_concrete_function_garbage_collected(self, *args, **kwargs)
1211 if self._stateful_fn is None:
1212 initializers = []
-> 1213 self._initialize(args, kwargs, add_initializers_to=initializers)
1214 self._initialize_uninitialized_variables(initializers)
1215
/opt/conda/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
757 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
758 self._concrete_stateful_fn = (
--> 759 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
760 *args, **kwds))
761
/opt/conda/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3064 args, kwargs = None, None
3065 with self._lock:
-> 3066 graph_function, _ = self._maybe_define_function(args, kwargs)
3067 return graph_function
3068
/opt/conda/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3461
3462 self._function_cache.missed.add(call_context_key)
-> 3463 graph_function = self._create_graph_function(args, kwargs)
3464 self._function_cache.primary[cache_key] = graph_function
3465
/opt/conda/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3296 arg_names = base_arg_names + missing_arg_names
3297 graph_function = ConcreteFunction(
-> 3298 func_graph_module.func_graph_from_py_func(
3299 self._name,
3300 self._python_function,
/opt/conda/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)
1005 _, original_func = tf_decorator.unwrap(python_func)
1006
-> 1007 func_outputs = python_func(*func_args, **func_kwargs)
1008
1009 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/conda/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
666 # the function a weak reference to itself to avoid a reference cycle.
667 with OptionalXlaContext(compile_with_xla):
--> 668 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
669 return out
670
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/impl_helper.py in transform_fn(inputs)
637 inputs_copy = tf_utils.copy_tensors(inputs)
638 with tf_graph_context:
--> 639 transformed_features = preprocessing_fn(inputs_copy)
640 # An empty `TENSOR_REPLACEMENTS` collection symbolizes that there is no
641 # analyzer left for Transform to evaluate. Either if this collection is
~/work/traffic_transform.py in preprocessing_fn(inputs)
68 # Use `tf.greater` to check if the traffic volume in a row is greater than the mean of the entire traffic volumn column
69 # Hint: Use a `tft` function to compute the mean.
---> 70 tf.greater(None, None(tf.cast(inputs[_VOLUME_KEY], tf.float32))),
71
72 tf.int64)
TypeError: 'NoneType' object is not callable
1 Like
I think in your line tf.greater(None, None(tf.cast(inputs[_VOLUME_KEY], tf.float32))),
replace None with the required attribues