All Questions
177 questions
1
vote
0
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36
views
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. ...
- 11
0
votes
1
answer
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:])
...
0
votes
2
answers
788
views
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
vote
0
answers
91
views
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
votes
2
answers
131
views
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 ...
- 3
0
votes
1
answer
89
views
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
answer
439
views
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 ...
- 441
-1
votes
1
answer
1k
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Whats the difference between a convolutional autoencoder (CAE) and a convolutional neural network (CNN)
I'm working on a bachelor's project that involves using a convolutional autoencoder [1]. I used the code from this blog. Now the goal was to make a model that could take as input a pixelated image ...
- 21
0
votes
1
answer
58
views
Incompatible shapes when training a custom autoencoder. ([batch_size, img_height, img_widht, 3] vs. [batch_size])
I am currently working on an autoencoder for anomaly detection, and I keep running into the same error no matter what I try:
Node: 'gradient_tape/mean_squared_error/BroadcastGradientArgs'
...
- 1
0
votes
1
answer
316
views
I am trying to build a variational autoencoder. I am getting an error while running model.fit which I don't understand
Epoch 1/10
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-28-f82b6d9aa841> ...
0
votes
1
answer
169
views
Why VAE in Keras has an exponential?
In Keras VAE implementation:
class Sampling(layers.Layer):
"""Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""
def call(self, inputs):
...
- 23
0
votes
1
answer
67
views
Python autoencoder returns mixed up image
I'm trying to create Python autoencoder image compression but all I'm getting is the mixed up images.
Here's my code:
path1 = 'C:\\Users\\klaud\\Desktop\\images\\'
all_images = []
subjects = os....
- 37
2
votes
1
answer
386
views
Generating new data using VAE in keras
I have built the following function which takes as input some data and runs a VAE on them:
def VAE(data, original_dim, latent_dim, test_size, epochs):
x_train, x_test = train_test_split(data, ...
- 4,492
0
votes
1
answer
74
views
Preprocessing of own handwritten numbers in machine learning
Denoising Autoencoder was created to remove noise from noisy handwritten numbers.
Next, I would like to input noisy handwritten numbers that I have prepared myself into the model I created and remove ...
- 51
0
votes
1
answer
520
views
How do I get the Latent Space Representation from an LSTM based Autoencoder?
I'm trying to construct an encoder to get the latent space in order to plot it. I don't really know if I can get it from the RepeatVector or if I have to add a Dense layer.
Here is my code:
model = ...
- 3