pytorch-examples | train models in pytorch , Learn to Rank , Collaborative Filter | Recommender System library
kandi X-RAY | pytorch-examples Summary
kandi X-RAY | pytorch-examples Summary
train models in pytorch, Learn to Rank, Collaborative Filter, Heterogeneous Treatment Effect, Uplift Modeling, etc
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Top functions reviewed by kandi - BETA
- Train a ranking model
- Apply a scaler to the feature
- Generate batch per query
- Dump the weight of each layer
- Train a rank network
- Perform baseline training
- Generate query pairs
- Evaluate a model
- Evaluate the criterion
- Calculate the gain
- Return the discount at k
- Make a log - likelihood
- Calculates the uplift per - quantile per unit test
- Calculate the sum of per - quantile per quantile
- Calculate the difference between two DataFrames
- Calculates the uplift per - quantile per - round
- Get argument parser
- Evaluate NDCG
pytorch-examples Key Features
pytorch-examples Examples and Code Snippets
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Trending Discussions on pytorch-examples
QUESTION
Following the example from:
https://github.com/jcjohnson/pytorch-examples
This code trains successfully:
...ANSWER
Answered 2018-Jun-27 at 08:28The code you posted is a simple demo trying to reveal the inner mechanism of such deep learning frameworks. These frameworks, including PyTorch, Keras, Tensorflow and many more automatically handle the forward calculation, the tracking and applying gradients for you as long as you defined the network structure. However, the code you showed still try to do these stuff manually. That's the reason why you feel cumbersome when predicting one example, because you are still doing it from scratch.
In practice, we will define a model class inherited from torch.nn.Module and initialize all the network components (like neural layer, GRU, LSTM layer etc.) in the __init__ function, and define how these components interact with the network input in the forward function.
Taken the example from the page you've provided:
QUESTION
So I have this MNIST example for PyTorch. I wanted to replace conv2d with functional method. But got unexpected error.
I replace self.conv1 = nn.Conv2d(1, 32, 5, padding=2) with self.w_conv1 = Variable(torch.randn(1, 32, 5))
In the forward method I replace x = F.max_pool2d(F.relu(self.conv1(x)), 2) with x = F.max_pool2d(F.relu(F.conv2d(x, self.w_conv1, padding=2),2))
And then it will give me an error:
Expected 4-dimensional input for 4-dimensional weight [1, 32, 5], but got input of size [50, 1, 28, 28] instead
The code worked before, and I thought I'd replace the class with it's functional equivalent.
...ANSWER
Answered 2018-Apr-18 at 12:00albanD answerd the question in https://discuss.pytorch.org/t/pytorch-replace-torch-nn-conv2d-with-torch-nn-functional-conv2d/16596
Hi,
The error message is not very clear I’m afraid because it comes from deep within the C backend. The problem here is that when you do a convolution on a 2D image with size (batch, in_chan, width, height), and you want an output of size (batch, out_chan, width’, height’), your weights for the convolution should be (out_chan, in_chan, width_kern_size, height_kern_size), basically when you use a kernel size of 5 for the Conv2d function, it is the same as having a kernel of width 5 and height 5. Thus you should have self.w_conv1 = Variable(torch.randn(32, 1, 5, 5)). See the doc for more details.
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You can use pytorch-examples like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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