VAE | Tensorflow implementation of Auto-Encoding Variational Bayes | Machine Learning library

 by   dillonalaird Python Version: Current License: No License

kandi X-RAY | VAE Summary

kandi X-RAY | VAE Summary

VAE is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow applications. VAE has no bugs, it has no vulnerabilities and it has low support. However VAE build file is not available. You can download it from GitHub.

Tensorflow implementation of Auto-Encoding Variational Bayes
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            kandi-support Support

              VAE has a low active ecosystem.
              It has 8 star(s) with 3 fork(s). There are 2 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 1 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of VAE is current.

            kandi-Quality Quality

              VAE has 0 bugs and 0 code smells.

            kandi-Security Security

              VAE has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              VAE code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              VAE does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              VAE releases are not available. You will need to build from source code and install.
              VAE has no build file. You will be need to create the build yourself to build the component from source.
              It has 189 lines of code, 13 functions and 2 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed VAE and discovered the below as its top functions. This is intended to give you an instant insight into VAE implemented functionality, and help decide if they suit your requirements.
            • Train the model
            • Build the model
            • Encodes a tensor
            • Decode tensor
            • Sample the manifold
            • Sample from the feed
            • Returns the path to the model
            • Test the loss
            • Loads the checkpoint
            • Sample image
            Get all kandi verified functions for this library.

            VAE Key Features

            No Key Features are available at this moment for VAE.

            VAE Examples and Code Snippets

            No Code Snippets are available at this moment for VAE.

            Community Discussions

            QUESTION

            Why does my variational autoencoder only produce positive values?
            Asked 2022-Apr-05 at 12:05

            I copied this example to build a variational autoencoder (VAE). The example uses images, but I use it for a signal that contains negative values. After training, the autoencoder only reconstructs the positive part of the signal, it does not produce negative values. Can anyone spot where the problem is or explain why this is the case?

            ...

            ANSWER

            Answered 2022-Apr-05 at 12:05

            If you used the exact code as the one shown in the example you put the link in, then at the end of the decoder you have x = torch.sigmoid(self.decConv2(x)) which take the real number line and outputs numbers between [0, 1]. This is why the network is unable to output negative numbers. If you want to change the model to output negative numbers as well, remove the sigmoid function.

            This means of course that you also have to change the loss function with which you train your model since the BCE loss is only good for outputs in the range of [0, 1].

            As a recommendation I would suggest anyone to use the BCE with logits loss and avoid using the sigmoid in the decoder since this method incorporates the sigmoid and the BCE loss in a more numerically stable manner.

            Source https://stackoverflow.com/questions/71749870

            QUESTION

            When loading a JSON body receiving error: "expecting ',' delimiter: line 1 column 18 (char 17)"?
            Asked 2022-Apr-02 at 20:14
            import json
            a_json = '{"some_body":" 
            [{"someId":"189353945391","EId":"09358039485","someUID":10,"LegalId":"T743","cDate":"202452","rAmount":{"aPa":{"am":1500,"currId":"UD"},"cost":{"amount":1000,"currId":"US"},"lPrice":{"amount":100,"currId":"DD"}},"tes":{"ant":0,"currId":"US"},"toount":{"amnt":0,"currId":"US"},"toount":{"amt":210,"currId":"US"},"bry":"US","pay":[{"pId":"7111","axt":{"amt":2000,"currId":"US"},"mKey":"CSD"}],"oItems":[{"iIndex":0,"rId":"69823","provId":"001","segEntityId":"C001","per":{"vae":1,"ut":"MOS"},"pct":{"prod":"748"},"revType":"REW","rAmount":{"aPaid":{"amt":90000,"currId":"US"},"xt":{"amt":0,"currId":"USD"},"lPrice":{"amt":90000,"currId":"US"}},"stion":{"sLocal":"094u5304","eLocal":"3459340"},"tx":{"adt":{"adet":0,"currId":"US"},"era":"werTIC"}}}]"}'
            
            ...

            ANSWER

            Answered 2022-Apr-02 at 20:14

            It seems that you're treating the content of some_body as a string since it's enclosed with double quotes. But inside of that string there's also quotation marks and now it's interpreted that the content of some_body is [{ and then it breaks because directly after that is someId rather than a comma. Thus the error:

            expecting ',' delimiter: line 1 column 18 (char 17)

            If the content of some_body was actually meant to be a string then all the double quotes inside of it should be preceded by a double backslash (\\) although in this case you'd have to parse the JSON twice - first the entire a_json string and then the content of some_body. However I think it would be easier to just remove the double quotes around the content of some_body.

            Source https://stackoverflow.com/questions/71720114

            QUESTION

            Input 0 of layer "conv1d_3" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, 3)
            Asked 2022-Apr-02 at 10:38

            I am trying to develop a VAE using this dataset, I have created and encoder and decoder by myself using keras tutorial, I only used Dense layers but now I wanted to add Conv1D layers too, however, after adding 1 conv layer to the encoder I get: Input 0 of layer "conv1d_3" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, 3)

            I have found many questions like this but haven't found the exact answer, I want to add more Conv1D layers to the encoder and decoder, what do I need to change in both of them to add Con1D layers?

            The code:

            ...

            ANSWER

            Answered 2022-Apr-01 at 11:48

            The problem is that data is missing the feature dimension necessary for a Conv1D layer, which needs the input_shape=(timesteps, features). You can try adding an additional dimension with tf.expand_dims:

            Source https://stackoverflow.com/questions/71706347

            QUESTION

            Invalid reduction dimension 1 for input with 1 dimensions
            Asked 2022-Mar-29 at 10:45

            I am developing a VAE using this dataset. I have used keras tutorial code and developed my own VAE. However, when I run fit() function I get: Invalid reduction dimension 1 for input with 1 dimensions. for '{{node Sum}} = Sum[T=DT_FLOAT, Tidx=DT_INT32, keep_dims=false](Mean, Sum/reduction_indices)' with input shapes: [?], [2] and with computed input tensors: input[1] = <1 2>. What do I have to change?

            The code:

            ...

            ANSWER

            Answered 2022-Mar-29 at 10:45

            The error is coming from tf.reduce_mean and tf.reduce_sum. In the train_step method of the VAE model, change this line:

            Source https://stackoverflow.com/questions/71659817

            QUESTION

            'int' object is not subscriptable in vae.fit() function
            Asked 2022-Mar-28 at 19:03

            I am developing a VAE using this: dataset

            I have used keras tutorial code and I have developed my own encoder and decoder, the problem is that when I run vae.fit() I get 'int' object is not subscriptable. What am I doing wrong?

            ...

            ANSWER

            Answered 2022-Mar-28 at 19:03

            The encoder and decoder functions expect an input_shape sequence. But with

            Source https://stackoverflow.com/questions/71652404

            QUESTION

            ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors on a VQVAE
            Asked 2022-Mar-21 at 06:09

            I am training a VQVAE with this dataset (64x64x3). I have downloaded it locally and loaded it with keras in Jupyter notebook. The problem is that when I ran fit() to train the model I get this error: ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors. Inputs received: [, ] . I have taken most of the code from here and adapted it myself. But for some reason I can't make it work for other datasets. You can ignore most of the code here and check it in the page, help is much appreciated.

            The code I have so far:

            ...

            ANSWER

            Answered 2022-Mar-21 at 06:09

            This kind of model does not work with labels. Try running:

            Source https://stackoverflow.com/questions/71540034

            QUESTION

            C function for combining an array of strings into a single string in a loop and return the string after freeing the allocated memory
            Asked 2022-Mar-18 at 07:54

            I'm working on a procfs kernel extension for macOS and trying to implement a feature that emulates Linux’s /proc/cpuinfo similar to what FreeBSD does with its linprocfs. Since I'm trying to learn, and since not every bit of FreeBSD code can simply be copied over to XNU and be expected to work right out of the jar, I'm writing this feature from scratch, with FreeBSD and NetBSD's linux-based procfs features as a reference. Anyways...

            Under Linux, $cat /proc/cpuinfo showes me something like this:

            ...

            ANSWER

            Answered 2022-Mar-18 at 07:54

            There is no need to allocate memory for this task: pass a pointer to a local array along with its size and use strlcat properly:

            Source https://stackoverflow.com/questions/71518714

            QUESTION

            Pretrained lightning-bolts VAE not doing proper inference on training dataset
            Asked 2022-Feb-01 at 20:11

            I'm using the CIFAR-10 pre-trained VAE from lightning-bolts. It should be able to regenerate images with the quality shown on this picture taken from the docs (LHS are the real images, RHS are the generated)

            However, when I write a simple script that loads the model, the weights, and tests it over the training set, I get a much worse reconstruction (top row are real images, bottom row are the generated ones):

            Here is a link to a self-contained colab notebook that reproduces the steps I've followed to produce the pictures.

            Am I doing something wrong on my inference process? Could it be that the weights are not as "good" as the docs claim?

            Thanks!

            ...

            ANSWER

            Answered 2022-Feb-01 at 20:11

            First, the image from the docs you show is for the AE, not the VAE. The results for the VAE look much worse:

            https://pl-bolts-weights.s3.us-east-2.amazonaws.com/vae/vae-cifar10/vae_output.png

            Second, the docs state "Both input and generated images are normalized versions as the training was done with such images." So when you load the data you should specify normalize=True. When you plot your data, you will need to 'unnormalize' the data as well:

            Source https://stackoverflow.com/questions/70197274

            QUESTION

            Div split pane, with min-width
            Asked 2022-Jan-25 at 21:06

            I want to have two 50% divs, but the content of the first div got a min-size.

            ...

            ANSWER

            Answered 2021-Oct-15 at 13:37

            You could use CSS Grid on the parent div and set it to two equal columns.

            Source https://stackoverflow.com/questions/69585284

            QUESTION

            Custom keras callbacks and changing weight (beta) of regularization term in variational autoencoder loss function
            Asked 2021-Dec-02 at 08:18

            The variational autoencoder loss function is this: Loss = Loss_reconstruction + Beta * Loss_kld. I am trying to efficiently implement Kullback-Liebler Divergence Cyclic Annealing--that is changing the weight of beta dynamically during training. I subclass the tf.keras.callbacks.Callback class as a start, but I don't know how I can update a tf.keras.Model variable from a custom keras callback. Furthermore, I would like to track how the betas change at the end of each training step (on_train_batch_end), and right now I have a list in the callback class, but I know python lists don't play well with TensorFlow. When I fit the model, I get a warning that my on_train_batch_end function is slower than the processing of the batch itself. I think I should use a tf.TensorArray instead of python lists, but then the tf.TensorArray method write cannot use a tf.Variable for the index (i.e., as the number of steps changes, the index in the tf.TensorArray to which a new beta for that step should be written changes)... is there a better way to store value changes? It looks like this github shows a solution that doesn't involve a custom tf.keras.Model and that uses a different kind of KL annealing. Below is a callback function and dummy VAE.

            ...

            ANSWER

            Answered 2021-Oct-23 at 14:01

            Concerning your first question: It depends how you plan to update your gradients with your optimizer (e.g. ADAM). When training a VAE with Tensorflow / Keras, I usually use the @tf.functiondecorator to calculate the loss of my model and based on that update my model's parameters:

            Source https://stackoverflow.com/questions/68636987

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install VAE

            You can download it from GitHub.
            You can use VAE 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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            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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