skorch | learn compatible neural network library that wraps PyTorch | Machine Learning library
kandi X-RAY | skorch Summary
kandi X-RAY | skorch Summary
A scikit-learn compatible neural network library that wraps PyTorch
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Top functions reviewed by kandi - BETA
- Load model parameters
- Set the value of the array
- Get module by name
- Check if the requested device is available
- Calculates loss scoring
- Unpack data
- Get parameters for a given prefix
- Get the dataset
- Called when epoch is finished
- Saves model parameters
- Tokenize the input
- Predict if X is higher than threshold
- Predict for each test
- Compute the accuracy of the classifier
- Predict probabilities for X
- Compute the likelihood for the given batch
- Train an MLP classifier
- Evaluate the training step
- Transform a Pandas dataframe
- Train a single step
- Set parameters
- Compute performance history
- Save model parameters
- Forward computation
- Sample from the forward distribution
- Fit the ExactGP module
- Predict the posterior distribution
skorch Key Features
skorch Examples and Code Snippets
Lines of Code : 280
License : No License
Copy|build| |coverage| |docs| |powered|
A scikit-learn compatible neural network library that wraps PyTorch.
.. |build| image:: https://github.com/skorch-dev/skorch/workflows/tests/badge.svg
:alt: Test Status
:scale: 100%
.. |coverage| image:: """This script trains a NeuralNetClassifier on MNIST data, once with
skorch, once with pure PyTorch.
Apart from that change, both approaches are as close to eachother as
possible (e.g. performing the same validation steps).
At the end, we assert th """Benchmark to test time and memory performance of History.
Before #312, the timing would be roughly 5 sec and memory usage would
triple. After #312, the timing would be roughly 2 sec and memory usage
roughly constant.
For the reasons, see #306.
"""Benchmark to test runtime and memory performance of
different freezing approaches.
Test A is done by setting `requires_grad=False` manually
while filtering these parameters from the optimizer using
``skorch.helper.filtered_optimizer``.
Test B us list(net.module.parameters())
class RegressionModule(torch.nn.Module):
def __init__(self, input_dim=80):
super().__init__()
self.l0 = torch.nn.Linear(input_dim, 10)
self.l1 = torch.nn.Linear(10, 5)
def forward(self, X):
X = X.to# Creates an instance of the model
outputs = classificadorFinal(inputs)
# Call the instance of the model, not the class
outputs = classificador(inputs)
self.layers = [nn.Linear(i,p) for i,p in zip(layer_units,layer_units[1:])]
self.layers = nn.ModuleList([nn.Linear(i,p) for i,p in zip(layer_units,layer_units[1:])])
class InputShapeSetter(skorch.callbacks.Callback):
def on_train_begin(self, net, X, y):
net.set_params(module__input_dim=X.shape[-1])
import torch
import skorch
from sklearn.datasets import make_classif# (n, 512, 512, 3)
X = my_data
# (n, 4096, 64, 64, 3)
X = sliding_window(X, 64, 64)
# (n * 4096, 64, 64, 3)
X = X.reshape(-1, 64, 64, 3)
y = net.predict(X)
Community Discussions
Trending Discussions on skorch
QUESTION
I want to use skorch to do multi-output regression. I've created a small toy example as can be seen below. In the example, the NN should predict 5 outputs. I also want to use a preprocessing step that is incorporated using sklearn pipelines (in this example PCA is used, but it could be any other preprocessor). When executing this example I get the following error in the Variable._execution_engine.run_backward step of torch:
...ANSWER
Answered 2021-Apr-12 at 16:05By default OneHotEncoder returns numpy array of dtype=float64. So one could simply cast the input-data X when being fed into forward() of the model:
QUESTION
I'm trying to develop an image segmentation model. In the below code I keep hitting a RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same. I'm not sure why as I've tried to load both my data and my UNet model to the GPU using .cuda() (although not the skorch model-- not sure how to do that). I'm using a library for active learning, modAL, which wraps skorch.
...ANSWER
Answered 2020-Dec-09 at 06:59cv2.imread gives you np.uint8 data type which will be converted to PyTorch's byte. The byte type cannot be used with the float type (which is most probably used by your model).
You need to convert the byte type to float type (and to Tensor), by modifying the dataset
QUESTION
I was training a model that contains 8 features that allows us to predict the probability of a room been sold.
Region: The region the room belongs to (an integer, taking value between 1 and 10)
Date:The date of stay (an integer between 1‐365, here we consider only one‐day request)
Weekday: Day of week (an integer between 1‐7)
Apartment: Whether the room is a whole apartment (1) or just a room (0)
#beds:The number of beds in the room (an integer between 1‐4)
Review: Average review of the seller (a continuous variable between 1 and 5)
Pic Quality: Quality of the picture of the room (a continuous variable between 0 and 1)
Price: he historic posted price of the room (a continuous variable)
Accept:Whether this post gets accepted (someone took it, 1) or not (0) in the end
Column Accept is the "y". Hence, this is a binary classification.
We have plot the data and some of the data were skewed so we applied power transform. We tried a neural network, ExtraTrees, XGBoost, Gradient boost, Random forest. They all gave about 0.77 AUC. However, when we tried them on the test set, the AUC dropped to 0.55 with a precision of 27%.
I am not sure where when wrong but my thinking was that the reason may due to the mixing of discrete and continuous data. Especially some of them are either 0 or 1. Can anyone help?
...ANSWER
Answered 2020-Jul-31 at 13:38Without deeply exploring all the data you are using it is hard to say for certain what is causing the drop in accuracy (or AUC) when moving from your training set to the testing set. It is unlikely to be caused by the mixed discrete/continuous data.
The drop just suggests that your models are over-fitting to your training data (and therefore not transferring well). This could be caused by too many learned parameters (given the amount of data you have)--more often a problem with neural networks than with some of the other methods you mentioned. Or, the problem could be with the way the data was split into training/testing. If the distribution of the data has a significant difference (that's maybe not obvious) then you wouldn't expect the testing performance to be as good. If it were me, I'd look carefully at how the data was split into training/testing (assuming you have a reasonably large set of data). You may try repeating your experiments with a number of random training/testing splits (search k-fold cross validation if you're not familiar with it).
QUESTION
I'm learning to use pytorch and I got an error that won't let me continue programming.
My code:
...ANSWER
Answered 2020-Jun-12 at 12:25The outputs are not actually the output of the model, but rather the model itself. classificadorFinal is the class, calling it creates an object/instance of that class, and inputs will be the first argument to the __init__ method, namely activation.
QUESTION
So, I am used to use PyTorch and now decided to give Skorch a shot.
Here they define the network as
...ANSWER
Answered 2020-Apr-21 at 22:29Pytorch will look for subclasses of nn.Module, so changing
QUESTION
I am trying to incorporate PyTorch functionalities into a scikit-learn environment (in particular Pipelines and GridSearchCV) and therefore have been looking into skorch. The standard documentation example for neural networks looks like
ANSWER
Answered 2020-Feb-16 at 16:14This is a very good question and I'm afraid that there is best practice answer to this as PyTorch is normally written in a way where initialization and execution are separate steps which is exactly what you don't want in this case.
There are several ways forward which are all going in the same direction, namely introspecting the input data and re-initializing the network before fitting. The simplest way I can think of is writing a callback that sets the corresponding parameters during training begin:
QUESTION
I want to apply cross validation in Pytorch using skorch, so I prepared my model and my tensorDataset which returns (image,caption and captions_length) and so it has X and Y, so I'll not be able to set Y in the method
...ANSWER
Answered 2019-Jun-19 at 14:54You are (implicitly) using the internal CV split of skorch which uses a stratified split in case of the NeuralNetClassifier which in turn needs information about the labels beforehand.
When passing X and y to fit separately this works fine since y is accessible at all times. The problem is that you are using torch.dataset.Dataset which is lazy and does not give you access to y directly, hence the error.
Your options are the following.
Settrain_split=None to disable the internal CV split
QUESTION
I want to create my own dataset class based on Dataset class of Skorch because I want to differentiate categorical columns and continuous columns. These categorical columns will be passed through the embedding layers in the model. The result is weird because it show NAN like this:
...ANSWER
Answered 2019-Jun-19 at 11:58The problem is not with skorch but your data. You have to scale your inputs and, in this case, especially the targets to avoid huge losses and exploding gradients. As a start I suggest using, for example, sklearn.preprocessing.StandardScaler:
QUESTION
I have extended nn.Module to implement my network whose forward function is like this ...
ANSWER
Answered 2019-Mar-27 at 10:26The fit_params parameter is intended for passing information that is relevant to data splits and the model alike, like split groups.
In your case, you are passing additional data to the module via fit_params which is not what it is intended for. In fact, you could easily run into trouble doing this if you, for example, enable batch shuffling on the train data loader since then your lengths and your data are misaligned.
The best way to do this is already described in the answer to your question on the issue tracker:
QUESTION
I'm trying to use skorch class to execut GridSearch on a classifier.
I tried running with the vanilla NeuralNetClassifier object, but I haven't found a way to pass the Adam optimizer only the trainable weights (I'm using pre-trained embeddings and I would like to keep them frozen). It's doable if a module is initialized, and then pass those weights with the optimizer__params option, but module needs an uninitialized model. Is there a way around this?
ANSWER
Answered 2018-Sep-17 at 14:36but
moduleneeds an uninitialized model
That is not correct, you can pass an initialized model as well. The documentation of the model parameter states:
It is, however, also possible to pass an instantiated module, e.g. a PyTorch Sequential instance.
The problem is that when passing an initialized model you cannot pass any module__ parameters to the NeuralNet as this would require the module to be re-initialized. But of course that's problematic if you want to do a grid search over module parameters.
A solution for this would be to overwrite initialize_model and after creating a new instance loading and freezing the parameters (by setting the parameter's requires_grad attribute to False):
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