Intro
I am using a modified version of the Tensorflow tutorial "Deep MNIST for experts" with the Python API for a medical images classification project using convolutionnal networks.
I want to artificially increase the size of my training set by applying random modifications on the images of my training set.
Problem
When I run the line :
flipped_images = tf.image.random_flip_left_right(images)
I get de following error :
AttributeError: 'numpy.ndarray' object has no attribute 'get_shape'
My Tensor "images" is an ndarray (shape=[batch, im_size, im_size, channels]) of "batch" ndarrays (shape=[im_size, im_size, channels]).
Just to check if my input data was packed in the right shape and type, I have tried to apply this simple function in the (not modified) tutorial "Tensorflow Mechanics 101" and I get the same error.
Finally, I still get the same error trying to use the following functions :
tf.image.random_flip_up_down()
tf.image.random_brightness()
tf.image.random_contrast()
Questions
As input data is usually carried in Tensorflow as ndarrays, I would like to know :
Is it a bug of Tensorflow Python API or is it my "fault" because
of the type/shape of my input data?
How could I get it to work and be able to apply tf.image.random_flip_left_right to my training set?
This seems like an inconsistency in the TensorFlow API, since almost all other op functions accept NumPy arrays wherever a tf.Tensor is expected. I've filed an issue to track the fix.
Fortunately, there is a simple workaround, using tf.convert_to_tensor(). Replace your code with the following:
flipped_images = tf.image.random_flip_left_right(tf.convert_to_tensor(images))
Related
I've deployed a Pipeline model in AWS and am now trying to use ModelMonitor to assess incoming data behavior, but it failes when generating monitoring report
The pipeline consists of a preprocessing step and then a regular XGBoost container. The model is invoked with Content-type: application/json.
For that I set up as stated in the docs, but it fails with the following error
Exception in thread "main" com.amazonaws.sagemaker.dataanalyzer.exception.CustomerError: Error: Encoding mismatch: Encoding is JSON for endpointInput, but Encoding is CSV for endpointOutput. We currently only support the same type of input and output encoding at the moment.
I've found this issue at GitHub, but didn't help me.
Digging depper into how XGBoost outputs, I've found out that it's CSV encoded, hence the error makes sense, but even deploying the model enforcing the serializers fails (code in the section below)
I'm configuring the schedule as recommended by AWS, I've just changed the location of my constraints (had to manually adjust'em)
---> Tried so far (all attempts fail with the exact same error)
As mentioned in the issue, but since I'm expecting a json payload, I've used
data_capture_config=DataCaptureConfig(
enable_capture = True,
sampling_percentage=100,
json_content_types = ['application/json'],
destination_s3_uri=MY_BUCKET)
Tried enforcing the (de)serializer of the predictor (I'm not sure if that even makes sense)
predictor = Predictor(
endpoint_name=MY_ENDPOINT,
# Hoping that I could force the output to be a JSON
deserializer=sagemaker.deserializers.JSONDeserializer)
and later
predictor = Predictor(
endpoint_name=MY_ENDPOINT,
# Hoping that I could force the input to be a CSV
serializer=sagemaker.serializers.CSVSerializer)
Setting (de)serializer during deploy
p_modle = pipeline_model.deploy(
initial_instance_count=1,
instance_type='ml.m4.xlarge',
endpoint_name=MY_ENDPOINT,
serializer = sagemaker.serializers.JSONSerializer(),
deserializer= sagemaker.deserializers.JSONDeserializer(),
wait = True)
I have come across a similar issue earlier while invoking the endpoint using boto3 sagemaker runtime. Try adding the 'Accept' parameter in invoke_endpoint function with value as 'application/json'.
refer for more help https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html#API_runtime_InvokeEndpoint_RequestSyntax
Could I get rid of the pipeline.Fit(trainingData) method if I load a fully trained model (e.g. from an onnx file)?
What does the fit method do anyway? I read in some sources the method would performing a training step, in other sources I read it fits the pipeline (whatever that should mean). I also read that the fit method just performs the steps defined in the pipeline before.
But do I need this steps from the pipeline if I load a fully trained model?
When I load a model from a .zip file I don`t need the fit method.
To clarify my question I added some code...
(The code doesn`t run without errors... I suggest some problems with the naming of some input and output columns... but thats not the part of the question. ;) )
I want to call the CreatePredictionEngine without the .fit method.
(As said before it would be possible with saved .zip models)
Thanks for clarification in advance. ;)
var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "image", imageFolder: "", inputColumnName: nameof(ImageData.ImagePath))
.Append(mlContext.Transforms.ResizeImages(outputColumnName: "image", imageWidth: ImageNetSettings.imageWidth, imageHeight: ImageNetSettings.imageHeight, inputColumnName: "image"))
.Append(mlContext.Transforms.ExtractPixels(outputColumnName: "inception_v3_input", inputColumnName: "image"))
.Append(mlContext.Transforms.ApplyOnnxModel(modelFile: modelLocation, outputColumnNames: new[] { TinyYoloModelSettings.ModelOutput }, inputColumnNames: new[] { TinyYoloModelSettings.ModelInput }))
.Append(mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: "LabelKey", inputColumnName: "Label"))
.Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(labelColumnName: "LabelKey", featureColumnName: TinyYoloModelSettings.ModelOutput))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabelValue", "PredictedLabel"))
.AppendCacheCheckpoint(mlContext);
IDataView trainingData = mlContext.Data.LoadFromTextFile<ImageData>(path: _trainTagsTsv, hasHeader: false);
ITransformer model = pipeline.Fit(trainingData);
var imageData = new ImageData()
{
ImagePath = _url
};
var predictor = mlContext.Model.CreatePredictionEngine<ImageData, ImagePrediction>(model);
var prediction = predictor.Predict(imageData);
I would highly recommend you to read this document on high-level concepts of ML.NET. As a fellow developer, this may speak to you better than the derived docs and recipes :)
That doc is unfortunately a little bit outdated: I wrote it before we finalized the API on prediction engines, so the code in 'prediction function' will not compile. The rest of the document appears to still hold.
In ML.NET API design, we followed the set of Spark naming conventions. Unfortunately for us, sklearn uses the same names with completely different semantics. So, ML.NET does what Spark does, not what sklearn does.
In short, the 'pipeline' is an Estimator. Estimators have only one operation: Fit, which takes data and produces a Transformer.
Transformers, on the other hand, take data and produce data. The ZIP file that you save the model in contains the transformer.
PredictionEngine is constructed out of a Transformer.
Typically, an Estimator is a 'pipeline' or 'chain' of trainable and non-trainable operators, that include a ML algorithm. However, this is not a requirement: you can build a pipeline out of only non-trainable operators (such as loading an ONNX model from a file). It will still be an Estimator (and therefore you have to call Fit to get the Transformer, even though in this case Fit will be a no-op).
The MLContext's Append methods, by design, only create Estimators. Call it the price of strong typing, but Fit is a requirement.
In this explanation I deliberately didn't use the term 'model': unfortunately, it has become so loaded that it's hard to tell whether 'model' refers to 'the ML algorithm', or 'a mutable object that can train itself', or 'the result of such training'.
What is a difference between tf.GraphDef.FromString and tf.GraphDef.ParseFromString ? I cannot find anything for FromString in tensorflow documentaion.
I am trying to run a model protobuf file in C++ but the prediction output is empty. In python example the model is loaded using tf.GraphDef.FromString function. In C++ I am using ReadBinaryProto method, I am wondering if there is some other way to load the model in C++ that corresponds to tf.GraphDef.FromString .
I started using Tensorflow recently and I try to get use to tf.estimator.Estimator objects. I would like to do something a priori quite natural: after having trained my classifier, i.e. an instance of tf.estimator.Estimator (with the train method), I would like to save it in a file (whatever the extension) and then reload it later to predict the labels for some new data. Since the official documentation recommends to use Estimator APIs, I guess something as important as that should be implemented and documented.
I saw on some other page that the method to do that is export_savedmodel (see the official documentation) but I simply don't understand the documentation. There is no explanation of how to use this method. What is the argument serving_input_fn? I never encountered it in the Creating Custom Estimators tutorial or in any of the tutorials that I read. By doing some googling, I discovered that around a year ago the estimators where defined using an other class (tf.contrib.learn.Estimator) and it looks like the tf.estimator.Estimator is reusing some of the previous APIs. But I don't find clear explanations in the documentation about it.
Could someone please give me a toy example? Or explain me how to define/find this serving_input_fn?
And then how would be load the trained classifier again?
Thank you for your help!
Edit: I discovered that one doesn't necessarily need to use export_savemodel to save the model. It is actually done automatically. Then if we define later a new estimator having the same model_dir argument, it will also automatically restore the previous estimator, as explained here.
As you figured out, estimator automatically saves an restores the model for you during the training. export_savemodel might be useful if you want to deploy you model to the field (for example providing the best model for Tensorflow Serving).
Here is a simple example:
est.export_savedmodel(export_dir_base=FLAGS.export_dir, serving_input_receiver_fn=serving_input_fn)
def serving_input_fn():
inputs = {'features': tf.placeholder(tf.float32, [None, 128, 128, 3])}
return tf.estimator.export.ServingInputReceiver(inputs, inputs)
Basically serving_input_fn is responsible for replacing dataset pipelines with a placeholder. In the deployment you can feed data to this placeholder as the input to your model for inference or prediction.
I tried to load the GoogleNews-vectors-negative300.bin and try the predict_output_word method,
I tested three ways, but every failed, the code and error of each way are shown below.
import gensim
from gensim.models import Word2Vec
The first:
I first used this line:
model=Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin',binary=True)
print(model.wv.predict_output_word(['king','man'],topn=10))
error:
DeprecationWarning: Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead.
The second:
Then I tried:
model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin',binary=True)
print(model.wv.predict_output_word(['king','man'],topn=10))
error:
AttributeError: 'Word2VecKeyedVectors' object has no attribute 'predict_output_word'
The third:
model = gensim.models.Word2Vec.load('GoogleNews-vectors-negative300.bin')
print(model.wv.predict_output_word(['king','man'],topn=10))
error:
_pickle.UnpicklingError: invalid load key, '3'.
I read the document at
https://radimrehurek.com/gensim/models/word2vec.html
but still have no idea the namespace where the predict_output_word would be in.
Anybody can help?
Thanks.
The GoogleNews set of vectors is just the raw vectors – without a full trained model (including internal weights). So it:
can't be loaded as a fully-functional gensim Word2Vec model
can be loaded as a lookup-only KeyedVectors, but that object alone doesn't have the data or protocols necessary for further model training or other functionality
Google hasn't released the full model that was used to create the GoogleNews vector set.
Note also that the predict_output_word() function in gensim should be considered an experimental curiosity. It doesn't work in hierarchical-softmax models (because it's not as simple to generate ranked predictions). It doesn't quite match the same context-window weighting as is used during training.
Predicting words isn't really the point of the word2vec algorithm – and many imeplementations don't offer any interface for making individual word-predictions outside of the sparse bulk training process. Rather, word2vec uses the exercise of (sloppily) trying to make predictions to train word-vectors that turn out to be useful for other, non-word-prediction, purposes.