Backpropagation | Implementing multilayer neural networks | Machine Learning library
kandi X-RAY | Backpropagation Summary
kandi X-RAY | Backpropagation Summary
Using Java Swing to implement backpropagation neural network. Learning algorithm can refer to this Wikipedia page. Input consists of several groups of multi-dimensional data set, The data were cut into three parts (each number roughly equal to the same group), 2/3 of the data given to training function, and the remaining 1/3 of the data given to testing function. The purpose of program is training to cut a number of groups of hyperplanes and synaptic weights, and display the results in the graphical interface.
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Top functions reviewed by kandi - BETA
- Load the selected file
- Creates a string representation of the errors
- Train neural network
- Apply backpropagation to the output layer
- Utility method for debugging purposes
- Gets the weight
- Get a list of all connections
- The main menu
- Change the look and feel
- Reset the current frame
- Create the UI components
- Returns the kind of output kind for the given outputs
- Add neurons
- Set the background color of the text field
- Get a random number
Backpropagation Key Features
Backpropagation Examples and Code Snippets
Lines of Code : 9License : Permissive (MIT License)
Copyprivate void backPropogation(Node nodeToExplore, int playerNo) {
Node tempNode = nodeToExplore;
while (tempNode != null) {
tempNode.getState().incrementVisit();
if (tempNode.getState().getPlayerNo() == playerNo Community Discussions
Trending Discussions on Backpropagation
QUESTION
I have a concern in understanding the Cartpole code as an example for Deep Q Learning. The DQL Agent part of the code as follow:
...ANSWER
Answered 2021-May-31 at 22:21self.model.predict(state) will return a tensor of shape of (1, 2) containing the estimated Q values for each action (in cartpole the action space is {0,1}).
As you know the Q value is a measure of the expected reward.
By setting self.model.predict(state)[0][action] = target (where target is the expected sum of rewards) it is creating a target Q value on which to train the model. By then calling model.fit(state, train_target) it is using the target Q value to train said model to approximate better Q values for each state.
I don't understand why you are saying that the loss becomes 0: the target is set to the discounted sum of rewards plus the current reward
QUESTION
I have a list of more than 100 points. I'd like to plot a figure like this picture. The lines connect any two points whose distance is less than 3.
...ANSWER
Answered 2021-May-24 at 18:06You probably have to check every point against every other point whether the distance is less than your threshold. So, create a table with all these points, the vector between them and plot them with vectors. The following example creates some random points with random sizes and random colors.
Code:
QUESTION
I need to create a Neural Network where I use binary gates to zero-out certain tensors, which are the output of disabled circuits.
To improve runtime speed, I was looking forward to use torch.bool binary gates to stop backpropagation along disabled circuits in the network. However, I created a small experiment using the official PyTorch example for the CIFAR-10 dataset, and the runtime speed is exactly the same for any values for gate_A and gate_B: (this means that the idea is not working)
ANSWER
Answered 2021-May-13 at 18:28You could use torch.no_grad (the code below can probably be made more concise):
QUESTION
Currently, I'm working on an image motion deblurring problem with PyTorch. I have two kinds of images: Blurry images (variable = blur_image) that are the input image and the sharp version of the same images (variable = shar_image), which should be the output. Now I wanted to try out transfer learning, but I can't get it to work.
Here is the code for my dataloaders:
...ANSWER
Answered 2021-May-13 at 16:00Here your you can't use alexnet for this task. becouse output from your model and sharp_image should be shame. because convnet encode your image as enbeddings you and fully connected layers can not convert these images to its normal size you can not use fully connected layers for decoding, for obtain the same size you need to use ConvTranspose2d() for this task.
your encoder should be:
QUESTION
I'm playing around a bit with OpenCL and I have a problem which can be simplified as follows. I'm sure this is a common problem but I cannot find many references or examples that would show me how this is usually done Suppose for example you have a function (writing in CStyle syntax)
...ANSWER
Answered 2021-May-10 at 04:31If your gradient function only has 5 components, it does not make sense to parallelize it in a way that one thread does one component. As you mentioned, GPU parallelization does not work if the mathematical structure of each components is different (multiple instructionsmultiple data, MIMD).
If you would need to compute the 5-dimensional gradient at 100k different coordinates however, then each thread would do all 5 components for each coordinate and parallelization would work efficiently.
In the backpropagation example, you have one gradient function with thousands of dimensions. In this case you would indeed parallelize the gradient function itself such that one thread computes one component of the gradient. However in this case all gradient components have the same mathematical structure (with different weighting factors in global memory), so branching is not required. Each gradient component is the same equation with different numbers (single instruction multiple data, SIMD). GPUs are designed to only handle SIMD; this is also why they are so energy efficient (~30TFLOPs @ 300W) compared to CPUs (which can do MIMD, ~2-3TFLOPs @ 150W).
Finally, note that backpropagation / neural nets are specifically designed to be SIMD. Not every new algorithm you come across can be parallelize in this manner.
Coming back to your 5-dimensional gradient example: There are ways to make it SIMD-compatible without branching. Specifically bit-maskimg: You would compute 2 cosines (for componet 1 express the sine through cosine) and one exponent and add all the terms up with a factor in front of each. The terms that you don't need, you multiply by a factor 0. Lastly, the factors are functions of the component ID. However as mentioned above, this only makes sense if you have many thousands to millions of dimensions.
Edit: here the SIMD-compatible version with bit masking:
QUESTION
I am trying to implement a neural net in PyTorch but it doesn't seem to work. The problem seems to be in the training loop. I've spend several hours into this but can't get it right. Please help, thanks.
I haven't added the data preprocessing parts.
...ANSWER
Answered 2021-May-09 at 10:03You need the data type of the data to match the data type of the model.
Either convert the model to double (recommended for simple nets with no serious performance problems such as yours)
QUESTION
I have a tensor of shape (3072,1000) which represents the weights in my neural network. I want to:
- randomly set 60% of its elements to zero.
- After updating the weights, keep 60% of the elements equal to zero but again randomly i.e., not the same previous elements.
Note: my network is not the usual artificial neural network which uses backpropagation algorithm but it is a biophysical model of the neurons in the brain so I am using special weight updating rules. Therefore, I think the ready functions in pytorch, if any, might not be helpful.
I tried the follwoing code, it is working but it takes so long because after every time I update my weight tensor, I have to run that code to set the weight tensor again to be 60% zeros
...ANSWER
Answered 2021-Apr-27 at 15:14You can use the dropout function for this:
QUESTION
I'm working on my backpropagation for a basic neural network, and for each example I must calculate the new weights. I save my weights in a 2D numpy array called weights looking like:
ANSWER
Answered 2021-Apr-09 at 18:33weights.shape is a tuple, so you can't include it as it is as the dimensions must be integers. You can use * to unpack the tuple:
QUESTION
In my model, I'm using tf.keras.losses.MSE to calculate the mean squared error of my BATCH_SIZE x 256 x 256 x 3 output and my BATCH_SIZE x 256 x 256 x 3 input.
The output of this function appears to be (None,256,256).
I then use tf.GradientTape.gradients, with the MSE output as the "target" attribute. In the documentation, it says that this attribute can be a tensor.
My understanding is that loss is a scalar number which is differentiated against each of the weights during backpropagation.
Therefore, my question is: What happens when a multi-dimensional tensor is passed into the gradients function? Is the sum of all elements in the tensor simple calculated?
I ask this because my model is not training at the moment, with loss reading at 1.0 at every epoch. My assumption is that I am not calculating the gradients correctly, as all my gradients are reading as 0.0 for each weight.
...ANSWER
Answered 2021-Apr-04 at 23:10import tensorflow as tf
x = tf.Variable([3.0, 2.0])
with tf.GradientTape() as g:
g.watch(x)
y = x * x
dy_dx = g.gradient(y, x)
print(dy_dx)
print(y)
Result:
tf.Tensor([6. 4.], shape=(2,), dtype=float32)
tf.Tensor([9. 4.], shape=(2,), dtype=float32)
QUESTION
I have a very long time series I want to feed into an LSTM for classification per-frame.
My data is labeled per frame, and I know some rare events happen that influence the classification heavily ever since they occur.
Thus, I have to feed the entire sequence to get meaningful predictions.
It is known that just feeding very long sequences into LSTM is sub-optimal, since the gradients vanish or explode just like normal RNNs.
I wanted to use a simple technique of cutting the sequence to shorter (say, 100-long) sequences, and run the LSTM on each, then pass the final LSTM hidden and cell states as the start hidden and cell state of the next forward pass.
Here is an example I found of someone who did just that. There it is called "Truncated Back propagation through time". I was not able to make the same work for me.
My attempt in Pytorch lightning (stripped of irrelevant parts):
...ANSWER
Answered 2021-Apr-01 at 11:40Apparently, I missed the trailing _ for detach():
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