I am trying to understand the Large-scale Linear Models with TensorFlow documentation. The docs motivate these models as follows:
Linear model can be interpreted and debugged more easily than neural
nets. You can examine the weights assigned to each feature to figure
out what's having the biggest impact on a prediction.
So I ran the extended code example from the accompanying TensorFlow Linear Model Tutorial. In particular, I ran the example code from GitHub with the model-type flag set to wide. This correctly ran and produced accuracy: 0.833733, similar to the accuracy: 0.83557522 on the Tensorflow web page.
The example uses a tf.estimator.LinearClassifier to train the weights. However, in contrast to the quoted motivation of being able to examine the weights, I can't find any function to actually extract the trained weights in the LinearClassifier documentation.
Question: how do I access the trained weights for the various feature columns in a tf.estimator.LinearClassifier? I'd prefer to be able to extract all the weights in a NumPy array.
Note: I am coming from an R environment where linear regression / classification models have a coefs method to extract learned weights. I want to be able to compare linear models in both R and TensorFlow on the same datasets.
After training the model with Estimator, you could use the tf.train.load_variable to retrieve the weights from checkpoint. You can use tf.train.list_variables to find the names for model weights.
There are plans to add this support in Estimator directly also.
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I am trying to build a machine learning model in python. I used pytorch and sklearn to make the model. My model is a bit complicated: I have one input feature but several target variables. My target variables are values making a curve and I used each value of the curve as a different feature. I showed five different curves in the upladed figure.
I used algorithms like DecisionTreeRegressor and RandomeForestRegressor to fit the only input variable to several target variables. But the prediction of trained model is not so well for extrapolation. The trained model can create the a series of data but not so accure. Does anyone know such trained model in Python? I tried hyperparameter tuning using GridSearchCV but it did not help me.
In advance I do appreciate your help and feedback.
In my previous project, I need to frame an image classification task as a regression problem. I implement the regression model using Tensorflow, with standard Sequential model with a 1 node Dense layer with no activation function as the last layer. In order to measure the performance, I need to use standard classification metrics, such as accuracy and cohen kappa.
However, I can't directly use those metrics because my model is a regression model, so I need to clip and round the output before feeding them to the metrics. I use a workaround by defining my own metric, however that workaround is not practical. Therefore, I'm thinking about contributing to Tensorflow by implementing a custom transformation_function to transform y_pred by a Tensor lambda function before storing them in the __update_state method. After reading the source code, I get doubts regarding this idea. So, I'm asking out to you, fellow Tensorflow user/contributors, what is the best practice of transforming y_pred before feeding it to a metric? Is this functionality already implemented in the newest version?
Thank you!
I am following this tutorial:
https://www.tensorflow.org/tutorials/keras/text_classification_with_hub
It only goes up to fitting the model but I couldn't find how to use the model on a new dataset to classify unlabeled data. I tried following other tutorials but I couldn't get them to work since they might not be text based.
model.add(tf.keras.layers.Dense(1))
I run into an issue where I try to set the layer to 2 for positive, negative but that doesn't work either.
I think you misunderstood the purpose of that tutorial. That tutorial is applying the use of what is known as "transfer learning". Transfer Learning is when you take an already trained model, and train it with other data. What you are doing is creating an entirely new model, which is not the purpose of that specific tutorial. Furthermore, for that model you need a labeled dataset, which is provided in the tutorial using the Tensorflow Datasets library. To accomplish what you are trying to do, you must look at a different tutorial explaining how to train an LSTM model for text classification from scratch.
I have a support vector machine trained on ~300,000 examples, and it takes roughly 1.5-2 hours to train this model, and I pickled(serialized) it. Currently, I want to add/remove a couple of the parameters of the model. Is there a way to do this without having to retrain the entire model? I am using sklearn in python.
If you are using SVC from sklearn then the answer is no. There is no way to do it, this implementation is purely batch training based. If you are training linear SVM using SGDClassifier from sklearn then the answer is yes as you can simply start the optimization from the previous solution (when removing feature - simply with removed corresponding weight, and when adding - with added any weight there).
I want to build a Deep Believe Network with scikit-learn. As I know one should train many Restricted Boltzmann Machines (RBM) individually. Then one should create a Multilayer Perceptron (MLP) that has the same number of layers as the number of (RBMs), and the weights of the MLP should be initialized with the weights of the RBMs. However I'm unable to find a way to get the weights of the RBMs from scikit-learn's BernoulliRBM. Also it doesn't seem to be a way also to initialize the weights of a MLP in scikit-learn.
Is there a way to do what I described?
scikit-learn does not currently have an MLP implemented which you can initialize via an RBM, but you can still access the weights which are stored in the components_ attribute and the bias which is stored in the intercept_hidden_ attribute.
If you're interested in using modern MLPs, torch7, pylearn2, and deepnet are all modern libraries and most of them contain pretraining routines like you describe.