neuralnet | Deep Lerning with Convolutional Neural Net | Machine Learning library
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kandi X-RAY | neuralnet Summary
Deep Lerning with Convolutional Neural Net
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- Demonstrates how to create an image
- To double array
- Creates an image of a graph
- Creates the JFrame
- Main method for testing
- To double array
- Creates an image of a graph
- Creates the JFrame
- Entry point
- supervised learning algorithm
- Main method for testing
- Benchmark a Runnable
- Performs a backward pass on the input
- Performs backword filtering
- Little test program
- Normalize image
- Creates an image graph
- Creates a new line graph from the data
- Performs a forward - forward convolution
- Convert an array of pixels to a BufferedImage
- Get forward
- Creates a graph from a float array
- Forward through the input channels
- Main method
- Approximates the forward direction
- Main method
- Creates a backward value from the input channels
- Main entry point
- Performs local normalization
neuralnet Key Features
neuralnet Examples and Code Snippets
Community Discussions
Trending Discussions on neuralnet
QUESTION
I'm trying to train a neural network in PyTorch with some input signals. The layers are conv1d. The shape of my input is [100, 10], meaning 100 signals of a length of 10.
But when I execute the training, I have this error: Given groups=1, weight of size [100, 10, 1], expected input[1, 1, 10] to have 10 channels, but got 1 channels instead
...ANSWER
Answered 2022-Apr-08 at 17:55nn.Conv1d expects input with shape of form (batch_size, num_of_channels, seq_length). It's parameters allow to directly set number of ouput channels (out_channels) and change length of output using, for example, stride. For conv1d layer to work correctly it should know number of input channels (in_channels), which is not the case on first convolution: input.shape == (batch_size, 1, 10), therefore num_of_channels = 1, while convolution in self.layers[0] expects this value to be equal 10 (because in_channels set by self.config[0] and self.config[0] == 10). Hence to fix this append one more value to config:
QUESTION
I want to save pytorch model in one .py file and use it in another .py file, but I get
AttributeError: 'function' object has no attribute 'copy'
Here is what I have:
train.py
...ANSWER
Answered 2022-Feb-19 at 08:33In fact, what you have done is save a function (AKA method in OOP) then load it in a variable utilizing deepcopy which is not available for functions!
The only change you need to make is adding () to state_dict. Let's assume this is the model
QUESTION
The code below opens the mnist dataset as a csv
...ANSWER
Answered 2022-Feb-18 at 15:07There are two problems here. (1) You need to skip the first row because they are labels. (1x1), (1x2) and etc. (2) You need int64 data type. The code below will solve both. next(csvreader) skips the first row.
QUESTION
I'm working on a NeuralNet and I decided to store the edges (connections) of the network in format (int,int) edge.
I used it because it's very easy to add it to a list List<(int,int)> listOfConnections
I've already implemented this variable in multiple places in my code and only now I've realized I'm not sure how to access each int separately.
When I try edge[0] i get an error that I can't use indexing in this type of variable.
Any ideas how can I pull out the first and second ints separately? Also what's the name of this form of variable? It's not Touple, not a Vector, if I knew the name maybe I could find more information on how to use it.
...ANSWER
Answered 2022-Feb-18 at 13:39You've created a ValueTuple.
You can access the first item with edge.Item1 and the second with edge.Item2.
QUESTION
I have a multiple layer Neural Network, that uses two variables from the database (Alcohol and Malic.Acid)
My Code
...ANSWER
Answered 2022-Jan-20 at 13:57I can't see your data, but I'm assuming this is a classification problem (you're predicting a binary outcome), and the predicted results you show above are probabilities produced by your neural network model.
The next step would be to apply a decision threshold to those probabilities. i.e. which of them should be 1 and which should be 0. e.g. you could keep the ratio of 0:1 the same as in your training data. Note that applying decisions to probabilities falls outside the modelling process.
Once you have a binary classification for each row in your test data, you can compare those predicted classifications to the actual classifications. This can be done in the form of a confusion matrix. In classification problems, accuracy is defined as the number of correct predictions divided by the total number of predictions.
Note that accuracy is not normally a very good indicator of model performance, especially with imbalanced datasets. It's better to assess your model based on the probabilities it produces rather than the classifications that follow your decision-making process. e.g. Look into using Brier scores as an alternative.
QUESTION
I am using a binary crossentropy model with non binary Y values & a sigmoid activation layer.
I have created my first custom loss function but when I execute it I get the error "ValueError: No gradients provided for any variable: [....]"
This is my loss function. It is used for cryptocurrency prediction. The y_true are the price change values and the y_pred values are rounded to 0/1 (sigmoid). It penalizes false positives with price_change * 3 and false negatives with price_change. I know my loss function is not like the regular loss functions but I wouldn't know how to achieve this goal with those functions.
ANSWER
Answered 2022-Jan-05 at 08:08I found the correct differentiable code for the loss function i wanted to use.
QUESTION
I found this GitHub rep about image classification (Male and female images) using an artificial neural network, the training data are 2 directories "./male", and "./female", each one contains a list of txt files.
these text files contain rows of numbers, each row of 16 numbers. a preview
...ANSWER
Answered 2021-Dec-28 at 12:03Yes the files contain grayscale images. It is obvious when you look at the actual code.
QUESTION
I know there are some questions that are like this question, but when I follow them it seems to lead me down a rabbit hole. As if The problem I just fixed causes another problem.
Here are 2 of the rabbit hole solutions I have kept because they have seemed to fix their problems. I doubt they would be of any help but here they are just in case.
one:
...ANSWER
Answered 2021-Dec-22 at 06:32Why do you cast X and Y to int64? Mainly, this is the problem.
QUESTION
I am currently trying to fit a neural network with 3 hidden layers using the neuralnet package in R. This is a classification problem.
I wish to test a series of possible hidden layer parameters, and take the one which returns the lowest classification error rate - using 5-Fold-CV. Please find the code and dput output below:
...ANSWER
Answered 2021-Dec-14 at 00:59Well, you're using the neuralnet package, and in some iteration, with the parameters passed, the algorithm used by the neuralnet() function didn't converge and therefore the function didn't return the net weights. Right after the error you can inspect the model and see that the weights are not there. For example, I changed the threshold used to .1 and the algorithm converged for all iterations, as per the code below.
QUESTION
I'm getting the error Error in eval(predvars, data, env) : object 'B' not found, I'm not sure how to do this line:
nn <- neuralnet(B+M~ area+texture+smoothness, data=cancertrain, hidden=3,
B+M are the two potential values, either benign or malignant, and the three attributes that have more impact in the determination are area, texture, and smoothness. I'm assuming I just have the parameters in the function neuralnet done incorrectly, does anyone know? Here's the cancer dataset in a public Google spreadsheet.
...ANSWER
Answered 2021-Nov-20 at 05:53You can use the following code
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Install neuralnet
You can use neuralnet like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the neuralnet component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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