All Questions
Tagged with keras autoencoder
716 questions
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gradient tape for custom loss function
I'm currently working with an autoencoder in hopes to test its accuracy vs pca. My tutor asked me to add a custom loss function that involves the derivatives of the decoder output with respect to the ...
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1
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26
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Test step of VAE returns only 0s
I'm jumping back into a project I stopped working on last year (mostly). I had already encountered this issue, and this answer solved it back then. I am currently running basically the exact script ...
- 507
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12
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custom callback "BetaAnnealing" isn't updating the beta value correctly for my VAE kl_loss
I have a custom layer in my VAE which handles the loss calculation. I have a custom callback which is meant to update the beta value at the beginning of every epoch. The calculation is correct because ...
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3
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371
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Workaround for removal of add_loss()
I'm working through a Keras/Tensorflow course that uses Keras 2 to build a variational autoencoder and I'm trying to get it working in Keras 3. I've managed to overcome a lot of issues but I'm stuck ...
- 174
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99
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Convolutional Variational Autoencoder
I'm creating a Convolutional Variational Autoencoder with Tensorflow in Python code, with some images I created myself (64x64 pixels).
My problem is, that if I use anything else than Binary ...
1
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0
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36
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Incompatible shape Keras Autoencoder
I'm relatively new and am trying to build an autoencoder for anomaly detection on an unlabelled dataset that only contains categorical columns.
The error I get is:
Incompatible shapes: [64,1,5346] vs. ...
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1
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56
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Autoencoder shaping issue
I am having an issue with my autoencoder as I am shaping the ouput incorrectly. Currently the autoencoder is coded lke this.
I Got This Error :
ValueError: Dimensions must be equal, but are 2000 and ...
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1
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156
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Test_step returns no loss values
I have been working for some time with a VAE model based off of thisexample, working with binary data so it has been modified.
Recently, the computing cluster I was working on suffered a fault and my ...
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1
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52
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Dimension error in Denoising AutoEncoder model Dimensions must be equal, but are 9252 and 129 for with input shapes: [?,9252,32], [?,129,32]
I am trying to build an Autoencoder model using TensorFlow. The input data shape: (339, 129, 32)
The model architecture is as follows:
input_data = tf.keras.layers.Input(shape=train_sample.shape[1:])
...
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2
answers
788
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Debugging autoencoder training (loss is low but reconstructed image is all black)
I am implementing a (variational) autoencoder (VAE) in Keras following this guide.
The VAE reads data from topomaps folder and labels from labels folder. They both contain .npy files, which are ...
- 451
1
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0
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91
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keras VAE custom training step ValueError: No loss to optimize
I am trying to create a VAE for tabular data - I am using the general structure provided in the keras documentation: https://keras.io/examples/generative/vae/
I have two networks the encoder and ...
- 312
0
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2
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131
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Keras Variational Autoencoder with ImageDataGenerator returns InvalidArgumentError: Graph execution error
I try to build an autoencoder for image generation, but my first prototype refuse to work.
The main problem gives the data generator in vae.fit(image_generator). It should fill training and validation ...
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1
vote
1
answer
38
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some values of reconstructed images turn in to zero
I'm new in NN field and trying to use autoencoder for image compression using keras library. most of the time I get some zero values in reconstructed vector that cause some black dot in reconstructed ...
user22042543
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1
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89
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Tensorflow variational autoencoder, how is decoder connected?
In the keras example at https://blog.keras.io/building-autoencoders-in-keras.html, the section "Variational autoencoder (VAE)" writes
z = layers.Lambda(sampling)([z_mean, z_log_sigma])
# ...
- 441
-1
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1
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439
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Defining loss function for autoencoder in Tensorflow
I am trying to create autoencoder (CVAE) on similar lines like one given here: Use Conditional Variational Autoencoder for Regression (CVAE). However, in vae_loss() and in KL_loss(), different ...
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