I wanted to use the layer Conv2Dtranspose and Conv3Dtranspose of Keras in order to do deconvolution (upsampling and convolution at the same time). I can get my model built and compiled, but the upsampling part does not seem to work, even when I modify the parameter dilate_rate.
Any idea how to do the upsampling part using ConvXDtranspose?
Or am I misunderstanding how this layer works?
I misunderstood how this works. The dilate_rate parameter is about the convolution radius to allow "a trou" convolutions.
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I am currently pursuing undergraduation, I am working on CNN model to recognize Telegu characters.
This Questions has two parts,
I have a (32,32,1) shape Telegu character images, I want to train my CNN model to recognize the character. So, what should be my model architecture and how to decide the architecture, no of parameters and hidden layers. I know that my case is exactly same as handwritten digit recognition, but I want to know how to decide those parameters. Is there any common practice in building such architecture.
Operation Conv2D (32, (5,5)) means 32 filters of size 5x5 are applied on to the input, my question is are these filters all same or different, if different what kind of filters are initialized and who decides them?
I tried to surf internet but everywhere I go, the answer I get is Conv2D operation applies filters on input and does the convolution operation.
To decide which model architecture would be best, you need to experiment. Thats the only way. As you want to classify, VGG architecture would be a good starting point I believe. You need to experiment with number of parameters as it depends on your problem. You can use Keras Tuner for it: https://keras.io/keras_tuner/
For kernel initialization, as far as I know convolutional layers in Keras uses Glorot Uniform Initialization but you can change that by using kernel_initializer parameter. Long story short, convolutional layers are initialized with a distribution function and as training goes filters change the values inside, which is learning process. https://keras.io/api/layers/initializers
Edit: I forgot to inform you that I suggest VGG architecture but in a way you downsize the models a lot. Your input shape is little so if your model is too much deep, you will overfit really quickly.
I am currently turning my Binary Classification Model to a multi-class classification Model. Bare with me.. I am very knew to pytorch and Machine Learning.
Most of what I state here, I know from the following video.
https://www.youtube.com/watch?v=7q7E91pHoW4&t=654s
What I read / know is that the CrossEntropyLoss already has the Softmax function implemented, thus my output layer is linear.
What I then read / saw is that I can just choose my Model prediction by taking the torch.max() of my model output (Which comes from my last linear output. This feels weird because I Have some negative outputs and i thought I need to apply the SOftmax function first, but It seems to work right without it.
So know the big confusing question I have is, when would I use the Softmax function? Would I only use it when my loss doesnt have it implemented? BUT then I would choose my prediction based on the outputs of the SOftmax layer which wouldnt be the same as with the linear output layer.
Thank you guys for every answer this gets.
For calculating the loss using CrossEntropy you do not need softmax because CrossEntropy already includes it. However to turn model outputs to probabilities you still need to apply softmax to turn them into probabilities.
Lets say you didnt apply softmax at the end of you model. And trained it with crossentropy. And then you want to evaluate your model with new data and get outputs and use these outputs for classification. At this point you can manually apply softmax to your outputs. And there will be no problem. This is how it is usually done.
Traning()
MODEL ----> FC LAYER --->raw outputs ---> Crossentropy Loss
Eval()
MODEL ----> FC LAYER --->raw outputs --> Softmax -> Probabilites
Yes you need to apply softmax on the output layer. When you are doing binary classification you are free to use relu, sigmoid,tanh etc activation function. But when you are doing multi class classification softmax is required because softmax activation function distributes the probability throughout each output node. So that you can easily conclude that the output node which has the highest probability belongs to a particular class. Thank you. Hope this is useful!
I'm new at keras lstm could you please explain to me this model.summary()
in rasa core training
![model after training][1]
Also, what is the Masking layer doing and what does the value -1 in it mean?
A Masking layer is meant to "ignore steps" in sequences.
Your LSTM is working with sequences of 5 steps and 42 features per step.
If all features in a step have the same value defined in Masking (-1 in the example), that step will be ignored during training.
The idea is to simulate variable length sequences.
Not sure exactly, what exactly you don't understand but model.summary()
prints a summary representation of your model. (keras.io)
It lists all layers used in the given model with its respective size.
This particular model obviously starts with a masking layer for input sequences (I guess because of padding) and is followed by the simplest LSTM model possible.
In a CNN, the convolution operation 'convolves' a kernel matrix over an input matrix. Now, I know how a fully connected layer makes use of gradient descent and backpropagation to get trained. But how does the kernel matrix change over time?
There are multiple ways in which the kernel matrix is initialized as mentioned here, in the Keras documentation. However, I am interested to know how it is trained? If it uses backpropagation too, then is there any paper that describes in detail the training process?
This post also raises a similar question, but it is unanswered.
Here you have a well explained post about backpropagation for Convolutional layer. In short, it is also gradient descent just like with FC layer. In fact, you can effectively turn a Convolutional layer into a Fuly Connected layer as explained here.
I want to know if the filters' weights in a, for example, 2D convolution layer in Keras are shared along the spatial dimensions by default. If yes, is there any way to have not shared weights?
I found that LocallyConnected2D does what I am looking for.
The LocallyConnected2D layer works similarly to the Conv2D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.
I'm not clear on what your asking but:
The weights in the a single convolutional layer are shared. That is, the filters share the same weights with each stride.
However The weights between two convolutonal layers are not shared by default in keras.
There is no getting around shared wiegths in the filters within the conv layer. Since the execution of the convolution if offloaded to C++ libraries.
See this answer for further reference, in particular:
The implementation of tf.nn.conv2d() is written in C++, which invokes
optimized code using either Eigen (on CPU) or the cuDNN library (on
GPU). You can find the implementation here.