neural | neural networks , sparse encoders | Machine Learning library
kandi X-RAY | neural Summary
kandi X-RAY | neural Summary
This is a rewrite of the tutorial about unsupervised feature learning and deep learning in Python using numpy and scipy. I use numpy for matrices, and scipy.optimize package for the L-BFGS minimization algorithm. Deep learning learns low and high-level features from large amounts of unlabeled data, improving classification on different, labeled, datasets. Deep learning can achieve an accuracy of 98% on the MNIST dataset.
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Top functions reviewed by kandi - BETA
- Calculate the cost function
- Reshape op
- Binary KL divergence
- Flatten nested arrays
- Unflattens theta and b
- Sample from images
- Return a random range
- Generate a random image
- Normalize patches
- Display a network of images
- Convert an array to a file
- Normalize an array
- Compute the accuracy of the predicted and gold
- Compute the confusion matrix
- Compute the principal components of the covariance matrix
- Compute the covariance matrix
- Reads the MNIST dataset
- Read the MNIST dataset
- Scatter plot
- Unflattens the input parameters
- Load data from a file
- Load images from a matlab file
- Calculate the cosine coefficient of a cartesian domain
- Read a MNIST file
- Calculate the Pca correlation coefficient
neural Key Features
neural Examples and Code Snippets
Lines of Code : 140
License : No License
Copydef fit(self, trees, learning_rate=3*1e-3, mu=0.99, reg=1e-4, epochs=15, activation=T.nnet.relu, train_inner_nodes=False):
D = self.D
V = self.V
K = self.K
self.f = activation
N = len(trees)
We = init_ def fit(self, X, Y, learning_rate=1e-4, mu=0.99, epochs=30, show_fig=True, activation=T.nnet.relu, RecurrentUnit=GRU, normalize=False):
D = self.D
V = self.V
N = len(X)
We = init_weight(V, D)
self.hidden_layer public static NeuralNetwork assembleNeuralNetwork() {
Layer inputLayer = new Layer();
inputLayer.addNeuron(new Neuron());
inputLayer.addNeuron(new Neuron());
Layer hiddenLayerOne = new Layer();
hiddenLayerOne Community Discussions
Trending Discussions on neural
QUESTION
I'd like to run a simple neural network model which uses Keras on a Rasperry microcontroller. I get a problem when I use a layer. The code is defined like this:
...ANSWER
Answered 2021-May-25 at 01:08I had the same problem, man. I want to transplant tflite to the development board of CEVA. There is no problem in compiling. In the process of running, there is also an error in AddBuiltin(full_connect). At present, the only possible situation I guess is that some devices can not support tflite.
QUESTION
I am programming in Python 3.8 with Tensorflow installed along with my natural language processing project. When I want to begin the training phase, I get this message right before I begin...
...ANSWER
Answered 2021-Mar-10 at 14:44I would suggest you to use conda (Ananconda/Miniconda) to create a separate environment and install tensorflow-gpu, cudnn and cudatoolkit. Miniconda has a much smaller footprint than Anaconda. I would suggest you to install Miniconda if you do not have conda already.
QUESTION
I have to design a neural network that takes two inputs X_1 and X_2. The layer transforms them to fixed-size vectors(10D) and then sums them in the following manner
ANSWER
Answered 2021-Jun-13 at 07:26If you've two input of such layer, then you can simply initialize your weights something like as follows
QUESTION
I have the data in the following format. I am using a neural network to predict three parameters downtime, latency and accuracy using neural network regression.
...ANSWER
Answered 2021-Jun-14 at 00:47I can't run your code so I created something similar and I get this error when pre_norms has values NaN.
I get pre_norms with NaN because predictors has columns No_Model,Technique which have strings and predictors-predictors.mean()/predictors.std()) convert them to NaN
Solution could be removing columns No_Model,Technique but this create empty data - so it is useless.
I don't know you full code but you should check what you have in variables and if you have NaN then you have wrong calculations.
QUESTION
I'm new with recurrent neural network and I have to apply LSTM (KERAS) to predict parking Availability from my dataset. I have a dataset with two features, timestamp (Y-M-D H-M-S) and the parking availability (number of free parking spaces). Each 5 minutes, for each day starting from 00:03 AM to 23:58 PM (188 samples for each day) was sampled the parking Availability for a duration of 25 weeks. I need some help to understand how to apply LSTM (what timestep to select ect).
...ANSWER
Answered 2021-Jun-13 at 12:15It seems that you want to understand that how could you use your dataset and apply LSTMs over it to get some meaningful out of your data.
Now here you can reframe your data set to create more features from your present data set for eg.
Features That could be derived out of Data
- Take Out day of the month (which day is it 1-31)
- Week of the month (which week of month it is 1-4)
- Day of the week (Monday - Saturday)
- what is the time ( you can have any of the value out of 188)
Features that could be added from opensource data
- What is the wheather of the day
- Is there any holiday nearby(days remaining for next holiday/function etc.)
Now let's Assume for each row you have K features in your data and you have a target that you have to predict which is what is the availability of parking. P(#parking_space|X)
Now just just keep your timesteps as a variable while creating your model and reshape your data from X.shape-->(Examples, Features) to the format X.shape-->(examples,Timesteps,Features). You can use below code and define your own look_back
Here your architecture will be many to many with Tx=Ty
QUESTION
I have a text file that contains abbreviations like so (simplified example):
...ANSWER
Answered 2021-Jun-11 at 10:22Here’s a ‘tidyverse’ solution:
QUESTION
I have a dataset of 100000 binary 3D arrays of shape (6, 4, 4) so the shape of my input is (10000, 6, 4, 4). I'm trying to set up a 3D Convolutional Neural Network (CNN) using Keras; however, there seems to be a problem with the input_shape that I enter. My first layer is:
...ANSWER
Answered 2021-Jun-11 at 21:50Example with dummy data:
QUESTION
Whenever I fetch data from any server to display it in ag-grid, ag-grid does not maintain the column order for the column that uses valueGetter to choose the value and puts that column automatically at the end.
The problem is replicated in the following code sandbox link: https://codesandbox.io/s/ag-grid-column-ordering-bug-bz055 as a minimum reproducible example
The data received from the server is in the following format
...ANSWER
Answered 2021-Jun-11 at 14:48Since the column does not have a field supplied, I'd recommend either supplying a field or colID to the column. This would be the simplest approach without having to use any API calls to move the column:
QUESTION
İ am working on transfer learning for multiclass classification of image datasets that consists of 12 classes. As a result, İ am using VGG19. However, the accuracy of the model is as much lower than the expectation. İn addition train and valid accuracy do not increase. Besides that İ ma trying to decrease the batch size which is still 383
My code:
...ANSWER
Answered 2021-Jun-10 at 15:05383 on the log is not the batch size. It's the number of steps which is data_size / batch_size.
The problem that training does not work properly is probably because of very low or high learning rate. Try adjusting the learning rate.
QUESTION
I'm trying to learn Recurrent Neural Networks (RNN) with Flux.jl in Julia by following along some tutorials, like Char RNN from the FluxML/model-zoo.
I managed to build and train a model containing some RNN cells, but am failing to evaluate the model after training.
Can someone point out what I'm missing for this code to evaluate a simple (untrained) RNN?
...ANSWER
Answered 2021-Jun-11 at 12:27Turns out it's just a problem with the input type.
Doing something like this will work:
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Install neural
You can use neural like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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