I have written a code that computes Choquet pooling in a Custom Layer in Keras. Below the Colab link to the notebook:
https://colab.research.google.com/drive/1lCrUb2Jm680JRnACPxWpxkOSkP_DlHGj
As you can the code crashes in gradient computation, precisely inside the function custom_grad. This is impossible because I'm returning 0 gradients with the same shape as the previous layer.
So I have 2 questions:
Is in Keras (or in Tensorflow) a way to compute gradient between the layer input and its output?
If I have passed a Tensor with the same shape as the previous layer, but filled with 0s, why the code is not working?
Thanks for your attention and I'm waiting for your help.
Thanks in advance
No one is interested in that question.
After several trials, I have found a solution. The problem is that, as posted by Mainak431 in this GitHub repo:
link to diff and non-diff ops in tensorflow
There are differentiable TensorFlow operations and non-differentiable operations. In the Colab notebook, I used, as an example, scatter_nd_update that is non-differentiable.
So I suggest, if you want to create your own Custom Layer in Keras to take a look at the above lists in order to use operations that allow Keras to auto-differentiate for you.
Anyway, I'm working on it to inform as much as possible on that open research topic. I remember that with the neural network the "LEGO-ing" is borderline, and I know for sure that many of you are interested in adding your operations(aggregation or something else) in a deep neural network model.
Special Thanks to Maniak431, I love you <3
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I am using convolutional neural networks to predict vegetation growth. My input is a (n,51,51,1) terrain elevation tensor, and the label is a (n,51,51,1) vegetation tensor.
Since flow from directory uses foldernames as labels, this is a bit of a problem. My network is performing well, but having to have all the data in memory is a bit limiting. If anyone knows how to setup a flow from directory for this problem I would appreciate it. I'm using R as an interface to keras and tensorflow, but solutions in python are welcome too. Included the picture in case it wasn't clear what I'm doing. Thanks!
This is a complex problem you are trying to solve. Image creation is another can of worms than classification (which is what you are talking about)
You can check this article that talks more in depth about the generational networks.
Another way to think about it, is to have the last output layer with 51*51 hidden units and do regression. By this I mean to treat it as a regression problem where you do regression on each pixel individually.
I am creating a model somewhat similar to the one mentioned below:
model
I am using Keras to create such model but have struck a dead end as I have not been able find a way to add SoftMax to outputs of the LSTM units. So far all the tutorials and helping material provides with information about outputting a single class even like in the case of image captioning as provided in this link.
So is it possible to apply SoftMax to every unit of LSTM (where return sequence is true) or do I have to move to pytorch.
The answer is: yes, it is possible to apply to each unit of LSTM and no, you do not have to move to PyTorch.
While in Keras 1.X you needed to explicitly state that you add a TimeDistributed layer, in Keras 2.X you can just write:
model.add(LSTM(50,activation='relu',return_sequences=False))
model.add(Dense(number_of_classes,activation='softmax'))
I wrote a custom layer that is part of a neural network and it contains some operations that I am using for the first time such as tf.scan and tf.slice.
I can easily test that the forward pass works and it makes sense, but how do I know that it will still work during the learning, when it has to do backpropagation? Can I safely assume that everything is going to be fine because the results I get make sense in the forward pass?
I was thinking that one possibility might be to create a neural network, replace one or two layers with the custom ones I have just created, train it, and see what happens. However, despite this would take quite a long time, the network may learn in the other layers whereas in my custom layer it may not work well anyway.
In conclusion, is there any way I can see that back-propagation will work well and I won't have any problems during the learning in this layer?
As far as I know, almost all TensorFlow ops are differentiable, including ops such as tf.abs or tf.where and gradient flows correctly through them. TensorFlow has an automatic differentiation engine, that takes any TensorFlow graph and computes derivatives w.r.t. desired variables.
So if your graph is composed of TensorFlow ops I wouldn't worry about the gradients being wrong (if you would post the code of your layer, I could expand further). However, there are still issues like numerical stability which can make otherwise mathematically sound operation still fail in practice (e.g. naive softmax computation, or tf.exp in your graph in general). Apart from that, TensorFlow differentiation should be correct and taken care of, from the user's point of view.
If you still want to examine your gradients by hand, you can compute the derivatives in your graph using tf.gradients op, which will get you the gradients that you wish and you can check by hand if TensorFlow did the differentiation correctly. (See https://www.tensorflow.org/api_docs/python/tf/gradients)
I need to implement a neural network which is NOT layer based, meaning that ANY neuron may be connected to any other neuron, and that there's no way to logically organize them in consecutive layers.
What I'm asking for is an example or a reference to proper and clear documentation about how to implement the following:
Originally I had my own implementation in matlab, however, I've been using TensorFlow and Keras to test simple models and it allows to tune your networks very fast and the implementations are pretty efficient, so I decided to try out more complex models, however, I just got stuck creating this type of network.
HINT: It MAY be OK to create single-neuron layers, as long as you can connect a layer to ANY layer (without caring if it is not adjacent) and to MORE THAN ONE LAYER.
I'm new to Tf and Keras, so a simple python example would be appreciated, althought, pointing me in the right direction would be OK.
This is an example network (¡loops are intentional!):
I dont need to train at the moment, just to evaluate models, however, keep in mind that evaluation of this kind of network is different too, one possible way is to keep with the signal sending until output stabilices, but it is just an example.
I'm trying to implement SegNet in Keras (tf backend) to do semantic segmentation.
The most impressived trick of SgeNet is to pass max-pooling indices to the upsampling layers. However, there are many implementations of SegNet in Keras(e.g.) I find on github just using simple UpSampling (called SegNet-Basic).
I notice that it can be achieved in Tensorflow with " tf.nn.max_pool_with_argmax ". So I want to know is there any similar method to get the max-pooling indices and put them back in upsampling in Keras.
Thanks in advance.
Well, I think I've found the answer.