I'm currently working in a collaboration. My task is to convert an h5-file, which was generated by a neural network with tensorflow, to an coreML. Additionally I should implement it to my Xcode Project.
The Input is a two dimensional array of 21 Floats:
input = [[0.5, 0.4, ...]]
The output should be a Float between 0 and 1.
I've tried a lot but as far as I know the main issue is that coreML supports just the classification of a picture. I didn't find any clue how to convert an h5 to an coreML with this specific type of input and output as mentioned. Can anybody help?
Thanks a lot!
Edit
This is my code. I'm confused because once I read that I just have to name the input and output instead of defining the variable as an MLMultiArray. I guess this is my main issue. But didn't catch how to define the input as an MLMultiArray.
from keras.models import load_model
import coremltools
coreml_model = coremltools.converters.keras.convert('modelv.h5',
input_names=['data'],
output_names=['output'],
)
coreml_model.save('PredictionModel.mlmodel')
When I run the code I'm getting this following message from the compiler.
runfile('/Path/Neuronal Network')
Traceback (most recent call last):
File "/Path/ Neuronal Network/Converter.py", line 20, in <module>
output_names='output',
File "/path/", line 804, in convert
use_float_arraytype=use_float_arraytype)
File "/Path/opt/anaconda3/lib/python3.7/site-packages/coremltools/converters/keras/_keras_converter.py", line 585, in convertToSpec
use_float_arraytype=use_float_arraytype)
File "/Path/opt/anaconda3/lib/python3.7/site-packages/coremltools/converters/keras/_keras2_converter.py", line 328, in _convert
graph.build()
File "/Path/opt/anaconda3/lib/python3.7/site-packages/coremltools/converters/keras/_topology2.py", line 740, in build
self.make_input_layers()
File "/Path/opt/anaconda3/lib/python3.7/site-packages/coremltools/converters/keras/_topology2.py", line 169, in make_input_layers
if isinstance(kl, InputLayer) and kl.input == ts:
File "/Path/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 765, in __bool__
self._disallow_bool_casting()
File "/Path/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 534, in _disallow_bool_casting
self._disallow_in_graph_mode("using a `tf.Tensor` as a Python `bool`")
File "/Path/opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 523, in _disallow_in_graph_mode
" this function with #tf.function.".format(task))
OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed in Graph execution. Use Eager execution or decorate this function with #tf.function.
Related
1: problem:
I have the need to use a custom data set in a tff simulation. I have built on the tff/python/research/compression example "run_experiment.py".
The error:
File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\IPython\core\interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-2-47998fd56829>", line 1, in <module>
runfile('B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/train_v04.py', args=['--experiment_name=temp', '--client_batch_size=20', '--client_optimizer=sgd', '--client_learning_rate=0.2', '--server_optimizer=sgd', '--server_learning_rate=1.0', '--total_rounds=200', '--rounds_per_eval=1', '--rounds_per_checkpoint=50', '--rounds_per_profile=0', '--root_output_dir=B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/logs/fed_out/'], wdir='B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection')
File "B:\tools and software\PyCharm 2020.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "B:\tools and software\PyCharm 2020.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/train_v04.py", line 292, in <module>
app.run(main)
File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\absl\app.py", line 299, in run
_run_main(main, args)
File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\absl\app.py", line 250, in _run_main
sys.exit(main(argv))
File "B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/train_v04.py", line 285, in main
train_main()
File "B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/train_v04.py", line 244, in train_main
input_spec=input_spec),
File "B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/train_v04.py", line 193, in model_builder
metrics=[tf.keras.metrics.Accuracy()]
File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\tensorflow_federated\python\learning\keras_utils.py", line 125, in from_keras_model
if len(input_spec) != 2:
TypeError: object of type 'TensorSpec' has no len()
highlighting: TypeError: object of type 'TensorSpec' has no len()
2: have tried:
I have looked at the response to: TensorFlow Federated: How can I write an Input Spec for a model with more than one input
describing what would be needed to produce a custom input spec for.
I might be miss understanding input spec.
If I don't need to do this, and there is a better way, please tell.
3: source:
df = get_train_data(sysarg)
x_train, x_opt, x_test = np.split(df.sample(frac=1,
random_state=17),
[int(1 / 3 * len(df)), int(2 / 3 * len(df))])
x_train, x_opt, x_test = create_scalar(x_opt, x_test, x_train)
input_spec = tf.nest.map_structure(tf.TensorSpec.from_tensor, tf.convert_to_tensor(x_train))
TFF's models declare a slightly different input specification than you may be expecting; they generally are expecting both the x and the y values as parameters (IE, data and labels). It is unfortunate that you're hitting that AttributeError, as the ValueError TFF would be raising is probably more helpful in this case. Inlining the operative parts of the message here:
The top-level structure in `input_spec` must contain exactly two elements,
as it must specify type information for both inputs to and predictions from the model.
The TLDR in your particular example is: if you have access to the labels as well (y_train below), simply change your input_spec definition to:
input_spec = tf.nest.map_structure(
tf.TensorSpec.from_tensor,
[tf.convert_to_tensor(x_train), tf.convert_to_tensor(y_train)])
I'm trying to launch dask.cluster.Kmeans with the huge amount of data.
Working with CPU is OK since i wrap numpy arrays with dask.array.
Working with GPU doesn't seem to be possible due to not implemented functionalities in cupy.
I've tried to reproduce Mattew Rocklin example (https://blog.dask.org/2019/01/03/dask-array-gpus-first-steps) on generating random dask array from CuPy random generator - and it works, but it's not the case I want to use.
Wrapping cupy with dask.array - doesn't work.
>>> import dask.array as da
>>> import cupy as cp
>>> da.from_array(cp.arange(100000)).sum().compute()
I expect the sum of this array but get the following error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ubuntu/miniconda3/envs/cupy/lib/python3.6/site-packages/dask/base.py", line 175, in compute
(result,) = compute(self, traverse=False, **kwargs)
File "/home/ubuntu/miniconda3/envs/cupy/lib/python3.6/site-packages/dask/base.py", line 446, in compute
results = schedule(dsk, keys, **kwargs)
File "/home/ubuntu/miniconda3/envs/cupy/lib/python3.6/site-packages/dask/threaded.py", line 82, in get
**kwargs
File "/home/ubuntu/miniconda3/envs/cupy/lib/python3.6/site-packages/dask/local.py", line 491, in get_async
raise_exception(exc, tb)
File "/home/ubuntu/miniconda3/envs/cupy/lib/python3.6/site-packages/dask/compatibility.py", line 130, in reraise
raise exc
File "/home/ubuntu/miniconda3/envs/cupy/lib/python3.6/site-packages/dask/local.py", line 233, in execute_task
result = _execute_task(task, data)
File "/home/ubuntu/miniconda3/envs/cupy/lib/python3.6/site-packages/dask/core.py", line 119, in _execute_task
return func(*args2)
File "/home/ubuntu/miniconda3/envs/cupy/lib/python3.6/site-packages/dask/array/core.py", line 100, in getter
c = np.asarray(c)
File "/home/ubuntu/miniconda3/envs/cupy/lib/python3.6/site-packages/numpy/core/numeric.py", line 538, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: object __array__ method not producing an array
So how could I manage the work with CuPy through the dask array?
When creating the Dask Array from a CuPy array, you need to supply da.from_array the keyword argument asarray=False. So your code would look like the following.
>>> import dask.array as da
>>> import cupy as cp
>>> da.from_array(cp.arange(100000), asarray=False).sum().compute()
I have installed elm library of python. There is an example provided in this link http://elm.readthedocs.io/en/latest/usage.html. The code is:
import elm
# download an example dataset from
# https://github.com/acba/elm/tree/develop/tests/data
# load dataset
data = elm.read("iris.data")
# create a classifier
elmk = elm.ELMKernel()
# search for best parameter for this dataset
# define "kfold" cross-validation method, "accuracy" as a objective function
# to be optimized and perform 10 searching steps.
# best parameters will be saved inside 'elmk' object
elmk.search_param(data, cv="kfold", of="accuracy", eval=10)
# split data in training and testing sets
# use 80% of dataset to training and shuffle data before splitting
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)
#train and test
# results are Error objects
tr_result = elmk.train(tr_set)
te_result = elmk.test(te_set)
print(te_result.get_accuracy)
When I run the code I am shown this error. It would be great help for me if someone could point out what is causing the problem. I have downloaded the dataset from the given URL provided in the link. My elm package's version is 0.1.1 and python version is 3.5.2. Thanks in advance.
Error is:
Traceback (most recent call last):
File "F:\7th semester\machine language\thesis work\python\Applying ELM in iris dataset\elm1.py", line 17, in <module>
elmk.search_param(data, cv="kfold", of="accuracy", eval=10)
File "C:\Users\maisha\AppData\Local\Programs\Python\Python35\lib\site-packages\elm\elmk.py", line 489, in search_param
param_kernel=param_ranges[1])
File "C:\Users\maisha\AppData\Local\Programs\Python\Python35\lib\site-packages\optunity\api.py", line 212, in minimize
pmap=pmap)
File "C:\Users\maisha\AppData\Local\Programs\Python\Python35\lib\site-packages\optunity\api.py", line 245, in optimize
solution, report = solver.optimize(f, maximize, pmap=pmap)
File "C:\Users\maisha\AppData\Local\Programs\Python\Python35\lib\site-packages\optunity\solvers\CMAES.py", line 139, in optimize
sigma=self.sigma)
File "C:\Users\maisha\AppData\Local\Programs\Python\Python35\lib\site-packages\deap\cma.py", line 90, in __init__
self.dim = len(self.centroid)
TypeError: len() of unsized object
I have a feature like a post tag. So for each observation the post_tag feature might be a selection of tags like "oscars,brad-pitt,awards". I'd like to be able to pass this as a feature to a tensorflow model build using the estimator api running on google cloud machine learning (as per this example but adapted for my own problem).
I'm just not sure how to transform this into a multi-hot encoded feature in tensorflow. I'm trying to get something similar to MultiLabelBinarizer in sklearn ideally.
I think this is sort of related but not quite what i need.
So say i have data like:
id,post_tag
1,[oscars,brad-pitt,awards]
2,[oscars,film,reviews]
3,[matt-damon,bourne]
I want to featurize it, as part of preprocessing within tensorflow, as:
id,post_tag_oscars,post_tag_brad_pitt,post_tag_awards,post_tag_film,post_tag_reviews,post_tag_matt_damon,post_tag_bourne
1,1,1,1,0,0,0,0
2,1,0,0,1,1,0,0
3,0,0,0,0,0,1,1
Update
If i have post_tag_list be a string like "oscars,brad-pitt,awards" in the input csv. And if i try then do:
INPUT_COLUMNS = [
...
tf.contrib.lookup.HashTable(tf.contrib.lookup.KeyValueTensorInitializer('post_tag_list',
tf.range(0, 10, dtype=tf.int64),
tf.string, tf.int64),
default_value=10, name='post_tag_list'),
...]
I get this error:
Traceback (most recent call last):
File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/home/andrew_maguire/localDev/codeBase/pmc-analytical-data-mart/clickmodel/trainer/task.py", line 4, in <module>
import model
File "trainer/model.py", line 49, in <module>
default_value=10, name='post_tag_list'),
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/lookup_ops.py", line 276, in __init__
super(HashTable, self).__init__(table_ref, default_value, initializer)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/lookup_ops.py", line 162, in __init__
self._init = initializer.initialize(self)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/lookup_ops.py", line 348, in initialize
table.table_ref, self._keys, self._values, name=scope)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_lookup_ops.py", line 205, in _initialize_table_v2
values=values, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2632, in create_op
set_shapes_for_outputs(ret)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1911, in set_shapes_for_outputs
shapes = shape_func(op)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1861, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/common_shapes.py", line 595, in call_cpp_shape_fn
require_shape_fn)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/common_shapes.py", line 659, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Shape must be rank 1 but is rank 0 for 'key_value_init' (op: 'InitializeTableV2') with input shapes: [], [], [10].
If i was to pad each post_tag_list to be like "oscars,brad-pitt,awards,OTHER,OTHER,OTHER,OTHER,OTHER,OTHER,OTHER" so it's always 10 long. Would that be a potential solution here.
Or do i need to in some way know the size of all post tags i might ever be passing in here (kinda ill defined as new ones created all the time).
Have you tried tf.contrib.lookup.Hashtable?
Here is an example usage from my own use: https://github.com/TensorLab/tensorfx/blob/master/src/data/_transforms.py#L160 and a made up example snippet based on that:
import tensorflow as tf
session = tf.InteractiveSession()
entries = ['red', 'blue', 'green']
table = tf.contrib.lookup.HashTable(
tf.contrib.lookup.KeyValueTensorInitializer(entries,
tf.range(0, len(entries), dtype=tf.int64),
tf.string, tf.int64),
default_value=len(entries), name='entries')
tf.tables_initializer().run()
value = tf.constant([['blue', 'red'], ['green', 'red']])
print(table.lookup(value).eval())
I believe lookup works for both regular tensors and SparseTensors (you might end up with the latter given your variable length list of values).
There are a couple of issues to tackle here. First, is the question about a tag set which keeps growing. You would also like to know how to parse variable-length data from CSV.
To handle a growing tag set, you'll need to use an OOV or feature hashing. Nikhil showed the latter, so I'll show the former.
How to parse variable-length data from CSV
Let's suppose the column with variable length data uses | as a separator, e.g.
csv = [
"1,oscars|brad-pitt|awards",
"2,oscars|film|reviews",
"3,matt-damon|bourne",
]
You can use code like this to convert those to a SparseTensor.
import tensorflow as tf
# Purposefully omitting "bourne" to demonstrate OOV mappings.
TAG_SET = ["oscars", "brad-pitt", "awards", "film", "reviews", "matt-damon"]
NUM_OOV = 1
def sparse_from_csv(csv):
ids, post_tags_str = tf.decode_csv(csv, [[-1], [""]])
table = tf.contrib.lookup.index_table_from_tensor(
mapping=TAG_SET, num_oov_buckets=NUM_OOV, default_value=-1)
split_tags = tf.string_split(post_tags_str, "|")
return ids, tf.SparseTensor(
indices=split_tags.indices,
values=table.lookup(split_tags.values),
dense_shape=split_tags.dense_shape)
# Optionally create an embedding for this.
TAG_EMBEDDING_DIM = 3
ids, tags = sparse_from_csv(csv)
embedding_params = tf.Variable(tf.truncated_normal([len(TAG_SET) + NUM_OOV, TAG_EMBEDDING_DIM]))
embedded_tags = tf.nn.embedding_lookup_sparse(embedding_params, sp_ids=tags, sp_weights=None)
# Test it out
with tf.Session() as s:
s.run([tf.global_variables_initializer(), tf.tables_initializer()])
print(s.run([ids, embedded_tags]))
You'll see output like so (since the embedding is random, exact numbers will change):
[array([1, 2, 3], dtype=int32), array([[ 0.16852427, 0.26074541, -0.4237918 ],
[-0.38550434, 0.32314634, 0.858069 ],
[ 0.19339906, -0.24429649, -0.08393878]], dtype=float32)]
You can see that each column in the CSV is represented as an ndarray, where the tags are now 3-dimensional embeddings.
I was trying to use tensorflow. The input attributes are similar to census example except that the LABEL Column is a continuous value. I executed the below command:
test-server#:~/aaaml-samples/arbitrator$ gcloud ml-engine local train --module-name trainer.task --package-path trainer/ -- --train-files $TRAIN_DATA --eval-files $EVAL_DATA --train-steps 1000 --job-dir
$MODEL_DIR
Filename: ['/home/madhukar_mhraju/aaaml-samples/arbitrator/data/aaa.data.csv']
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
Filename: ['/home/madhukar_mhraju/aaaml-samples/arbitrator/data/aaa.test.csv']
Filename: ['/home/madhukar_mhraju/aaaml-samples/arbitrator/data/aaa.test.csv']
Traceback (most recent call last):
File "/usr/lib/python2.7/runpy.py", line 162, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/home/madhukar_mhraju/aaaml-samples/arbitrator/trainer/task.py", line 193, in <module>
learn_runner.run(generate_experiment_fn(**arguments), job_dir)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/learn_runner.py", line 106, in run
return task()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/experiment.py", line 465, in train_and_evaluate
export_results = self._maybe_export(eval_result)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/experiment.py", line 484, in _maybe_export
compat.as_bytes(strategy.name))))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/export_strategy.py", line 32, in export
return self.export_fn(estimator, export_path)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py", line 283, in export_fn
exports_to_keep=exports_to_keep)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/framework/experimental.py", line 64, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1264, in export_savedmodel
model_fn_lib.ModeKeys.INFER)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1133, in _call_model_fn
model_fn_results = self._model_fn(features, labels, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py", line 268, in _dnn_linear_combined_model_fn
scope=scope)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 531, in weighted_sum_from_feature_columns
transformed_tensor = transformer.transform(column)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 879, in transform
feature_column.insert_transformed_feature(self._columns_to_tensors)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column.py", line 528, in insert_transformed_feature
sparse_values = string_ops.as_string(input_tensor.values)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_string_ops.py", line 51, in as_string
width=width, fill=fill, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 585, in apply_op
param_name=input_name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 61, in _SatisfiesTypeConstraint
", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
TypeError: Value passed to parameter 'input' has DataType string not
in list of allowed values: int32, int64, complex64, float32, float64,
bool, int8
Am new to tensorflow. I understand that this issue is occurring while processing the evaluation file(aaa.test.csv). The evaluation file data and format is correctly defined. And also the column data type have been mapped correctly as well.But i am not sure why the error is occurring.
1) The training data csv had column headings in them. When I generated the data, i was reordering them randomly, which resulted in the column headings being moved to somewhere in the middle. Hence the type error. It was difficult to find out as the training data was huge.