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.
Related
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'.
I've solved a model and output the results to filename
from pyomo.environ import *
model = ConcreteModel()
# declared variables
...
# solved model
...
# display results
model.display(filename)
Now, this program has finished running. I'd like to do some post-processing of the results in filename. Is there an easy way to read filename and put all the solution information back into model for post-processing of the solution?
I'm trying to plot many of the variables that I have solved for with matplotlib. I'd like to separate the "solution of the model" code and the "post-processing of the model" code, because I'd like to be able to post-process the model in many different ways that I won't be able to decide at runtime. So, I'd like to solve model, call model.display(filename), read all the data from filename and input back into the pyomo model, and do some plotting of the results.
I am currently writing my own parser for filename, but I wanted to know if there is an available method with pyomo to do this.
A good way to do what you want is to pickle (i.e., serialize) the model after solution, then subsequent programs can restore the model and use it. For some discussion of pickling a Pyomo model, see this Stackoverflow post:
How to save (pickle) a model instance in pyomo
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.
How do I create a model dynamically upon uploading a csv file? I have done the part where it can read the csv file.
This doc explains very well how to dynamically create models at runtime in django. It also links to an example of doing so.
However, as you will see after looking at the document, it is quite complex and cumbersome to do this. I would not recommend doing this and believe it is quite likely you can determine a model ahead of time that is flexible enough to handle the CSV. This would be much better practice since dynamically changing the schema of your database as your application is running is a recipe for a ton of bugs in your code.
I understand that you want to create new schema's on the fly based on fields in the those in a CSV. While thats a valid use case and could be the absolute right call. I doubt it though - it lends itself to a data model for a single tenet SaaS application that could have goofy performance and migration issues.
I'd try using Mongo/ some other NoSQL solutions as others have mentioned. But a simpler approach may be a modified Star Schema implemented in SQL. In this case you create a dimensions tables that stores each header, then create an instance of each data element that has a foreign key to dimension and records the value of that dimension.
If you read the csv the psuedo code would look something like this:
for row in DictReader(file):
for k in row.keys():
try:
dim = Dimension.objects.get(name=k)
except:
dim = Dimension(name=k)
dim.save()
DimensionRecord(dimension=dim, value=row[k]
Obviously you could better handle reading the headers and error trapping if dimensions already exist, but this would be an example of how you could dynamically load variable headered CSV's into a SQL db.
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))