RNN | RNN示例集合

by TolicWang Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | RNN Summary

RNN is a Python library. RNN has no bugs, it has no vulnerabilities and it has low support. However RNN build file is not available. You can download it from GitHub.

RNN示例集合

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Support

RNN has a low active ecosystem.

It has 32 star(s) with 24 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of RNN is current.

Quality

RNN has 0 bugs and 0 code smells.

Security

RNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

RNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

RNN does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

RNN releases are not available. You will need to build from source code and install.

RNN has no build file. You will be need to create the build yourself to build the component from source.

RNN saves you 344 person hours of effort in developing the same functionality from scratch.

It has 824 lines of code, 42 functions and 11 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed RNN and discovered the below as its top functions. This is intended to give you an instant insight into RNN implemented functionality, and help decide if they suit your requirements.

Train the network
Generate a batch
Process all words in fild_dir
Train the model
LSTM
Train the optimizer
Compose a poem
Call the convolution function
Convolutional convolution layer
Transform data into index
Load data files
Transform data to index
Loads a dataset
Creates the index tables from src_vocab_file
Cache variables in a file
Transform a list of words into a perfect poem
Generate batch
Add padding to sentences
Infer the translations
Transform index to data
Build the embedding
Create embedding
Create or load an embedding variable

Get all kandi verified functions for this library.

RNN Key Features

No Key Features are available at this moment for RNN.

RNN Examples and Code Snippets

No Code Snippets are available at this moment for RNN.

Community Discussions

Trending Discussions on RNN

Why GPU is much slower than cpu in google colab?

How to reset the state of an LSTM RNN after each epoch within Keras?

How to process .csv for time series classification

tf2.0: Gradient Tape returns None gradient in RNN model

ValueError: Unexpected result of `train_function` (Empty logs). for RNN

Input 0 is incompatible with layer repeat_vector_40: expected ndim=2, found ndim=1

Using RNN Trained Model without pytorch installed

How would one use a RNN when predicting temperature?

Advisable ways to shape my data as input for a RNN

tensorflow: Obtain RNN hidden states gradients with respect to input

QUESTION

Why GPU is much slower than cpu in google colab?

Asked 2022-Apr-03 at 10:13

I'm training a RNN on google colab and this is my first time using gpu to train a neural network. From my point of view, GPU should be much faster than cpu, and changing device from cpu to gpu only need to add .to('cuda') in the definition of model/loss/variable and set google colab 'running on gpu'.

When I train it on cpu, the average speed is 650 iteration/s

Training on cpu in google colab

But when I train it on gpu, the average speed is only 340 iterations/s, only half of the cpu

Training on gpu in google colab

and this happened on every epoch

Here is my code.

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ANSWER

Answered 2022-Feb-06 at 02:23

My brother says that when the tensor is very big, such as 1 million dimension, gpu can be faster than cpu, otherwise we don't even need parallel computing because computing are not mainly on tensor multiply, but on copy tensors and other things like that.

My RNN has about 256x256+256x8 parameters and batch_size is 100, and the dimention of that is much lower than 1 million. So gpu is much slower.

And, when I change my batch_size to 10000, gpu is 145 iteration/s while cpu is only 15iterations/s. This time gpu is much faster.

A CNN, with stride one, in gpu we can calculate filter_size *image_size * batch_size, about 2,415,919,104 times multiply simultaneously. So in this kind of computing, gpu is much faster.

Source https://stackoverflow.com/questions/71003586

QUESTION

How to reset the state of an LSTM RNN after each epoch within Keras?

Asked 2022-Apr-01 at 10:14

I have defined a stateful LSTM RNN, and I want to reset the state of the RNN after each epoch. I have found that one way to do this would be:

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ANSWER

Answered 2022-Mar-27 at 00:09

🧸💬 For experiments only, everyone knows when working for multiple steps and you set all input values back to 0 for all DATA ( long potential enough or the same number as input ) in the batch that reset all memories of LSTM. 🐑💬 That is the behavior of LSTM since they are sensitive to input because it contains comparison units and summation units.

🐑💬 The picture is from Internet.

...

Source https://stackoverflow.com/questions/71630895

QUESTION

How to process .csv for time series classification

Asked 2022-Mar-30 at 09:32

I was wondering how to handle the recorded time series data to feed it into a RNN.

I want to take the data of 16 time steps and the labels of 15 to make the RNN classify the 16th time step(if that makes any sense). By using every third entry for the batch I can cover about 3 Seconds of data with a reasonable amount of entries per second.

Here is a smaller .csv of the recorded data. The Columns "Time" and "Mayday" are just for reference to make sure that everything is labeled correctly and can therefore be dropped.
This is what my data looks like after dropping the unrelated columns

Here is what I have tried so far in google colab unfortunately this approach doesnt work and I get an "AttributeError: 'tuple' object has no attribute 'shape'" when calling model.fit.

Alternatively I have also tried this:

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ANSWER

Answered 2022-Mar-30 at 08:26

Your idea is fine. The problem is that train_target and test_target are returning tuples, since as the docs state:

Returns a tf.data.Dataset instance. If targets was passed, the dataset yields tuple (batch_of_sequences, batch_of_targets). If not, the dataset yields only batch_of_sequences.

Since you are only interested in the targets in this case, you can run:

Source https://stackoverflow.com/questions/71667965

QUESTION

tf2.0: Gradient Tape returns None gradient in RNN model

Asked 2022-Mar-27 at 23:56

In a model with an embedding layer and SimpleRNN layer, I would like to compute the partial derivative dh_t/dh_0 for each step t.

The structure of my model, including imports and data preprocessing.
Toxic comment train data available: https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification/data?select=jigsaw-toxic-comment-train.csv
GloVe 6B 100d embeddings available: https://nlp.stanford.edu/projects/glove/

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ANSWER

Answered 2022-Feb-18 at 14:02

You could maybe try using tf.gradients. Also rather use tf.Variable for h0:

Source https://stackoverflow.com/questions/71153292

QUESTION

ValueError: Unexpected result of `train_function` (Empty logs). for RNN

Asked 2022-Mar-14 at 10:06

I am reproducing the examples of the chapter 16 of the book Hands-On Machine Learning of Aurélien Géron and found an error while trying to train a simple RNN model.

The error is the following:

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ANSWER

Answered 2022-Mar-14 at 10:06

The problem is that tokenizer.document_count considers the whole text as one data entry, which is why dataset_size equals 1 and train_size therefore equals 0, resulting in an empty data set. Try using the encoded array to get the true number of data entries:

Source https://stackoverflow.com/questions/71242821

QUESTION

Input 0 is incompatible with layer repeat_vector_40: expected ndim=2, found ndim=1

Asked 2022-Mar-09 at 19:59

I am developing an LSTM autoencoder model for anomaly detection. I have my keras model setup as below:

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ANSWER

Answered 2022-Mar-09 at 19:59

I think that the problem lies in this line:

Source https://stackoverflow.com/questions/71413194

QUESTION

Using RNN Trained Model without pytorch installed

Asked 2022-Feb-28 at 20:17

I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.

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ANSWER

Answered 2022-Feb-17 at 10:47

You should try to export the model using torch.onnx. The page gives you an example that you can start with.

An alternative is to use TorchScript, but that requires torch libraries.

Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

A running example

Just modifying a little your example to go over the errors I found

Notice that via tracing any if/elif/else, for, while will be unrolled

Source https://stackoverflow.com/questions/71146140

QUESTION

How would one use a RNN when predicting temperature?

Asked 2022-Feb-19 at 10:09

Let's assume I have a dataframe with several features, like humidity, pressure, and so on. One of these columns, would be temperature.

At each row, I have the data for one day. I would like to predict the temperature for the next day, with past data only.

How would I shape the dataframe so that it could be used in a RNN with Keras?

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ANSWER

Answered 2022-Feb-19 at 10:09

Let's assume you have the following data structure and we want to predict the temperature given 1 day in the past:

Source https://stackoverflow.com/questions/71180881

QUESTION

Advisable ways to shape my data as input for a RNN

Asked 2022-Feb-19 at 09:19

I have a dataframe X, where each row is a data point in time and each column is a feature. The label/target variable Y is univariate. One of the columns of X is the lagged values of Y.

The RNN input is of the shape (batch_size, n_timesteps, n_feature).

From what I've been reading on this site, batch_size should be as big as possible without running out of memory. My main doubt is about n_timesteps. and n_features.

I think n_feature is the number of columns in the X dataframe.

What about the n_timesteps?

...

ANSWER

Answered 2022-Feb-19 at 09:19

Consider the following dataframe with the features temperature, pressure, and humidity:

Source https://stackoverflow.com/questions/71178099

QUESTION

tensorflow: Obtain RNN hidden states gradients with respect to input

Asked 2022-Feb-14 at 09:49

My model consists of an Embedding layer and a SimpleRNN layer. I have obtained the hidden states at all steps with model.predict, and plotted them against the steps. I find that the hidden states converge to zero but I am not sure if I can infer anything from that. Therefore plotting their gradients with respect to the model inputs might provide me some further insights. I would like some help with obtaining these gradients.

My model:

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ANSWER

Answered 2022-Feb-14 at 08:13

The problem is tf.GradientTape() doesn't propagate the gradients through integer inputs. That is probably the reason you are getting None gradients. What you can do is calculate the gradients with respect to the output of the Embedding layer like this:

Source https://stackoverflow.com/questions/71107377

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install RNN

You can download it from GitHub.
You can use RNN 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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