gru | HTTP interaction testing framework with out-of-box support | Unit Testing library
kandi X-RAY | gru Summary
kandi X-RAY | gru Summary
Gru is HTTP interaction testing framework with out-of-box support for Grails and REST interfaces.
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Top functions reviewed by kandi - BETA
- This method executes a route proxy
- Returns the full path
- Finds a mapping for the route and url
- Get the HTTP request HTTP method
- Serialize this request
- Serialize the given configuration to JSON
- Waits for a great circle to become moon
- Sends a toast response to the application
- Returns the redirect URL
- Get multi - value headers
- This method is called before the Unit tests
- Set the request body
- Set the path parameters
- Set request body
- Add a parameter
- A convenience method to steal the Gateway proxy
- Add a query parameter
- Eliminates a person from a moon
- Returns a redirect URL
- Sets the payload
- Get the response body as String
- Get response status
- Calls the given callable
- Sets the multipart
- Maps specified HTTP methods to this route
- Build ok http request
gru Key Features
gru Examples and Code Snippets
Community Discussions
Trending Discussions on gru
QUESTION
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/
ANSWER
Answered 2022-Feb-18 at 14:02You could maybe try using tf.gradients
. Also rather use tf.Variable
for h0
:
QUESTION
For a university work, we have some geographic coordinates of world locations in a pandas dataframe:
...ANSWER
Answered 2022-Mar-17 at 11:51You can use str.replace
:
QUESTION
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:
...ANSWER
Answered 2022-Mar-14 at 10:06The 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:
QUESTION
I train the following model based on GRU, note that I am passing the argument stateful=True
to the GRU builder.
ANSWER
Answered 2022-Feb-22 at 09:55You could try defining a custom GRU
layer with a trainable variable for the states but not sure how the performance will be:
QUESTION
I train the following model based on GRU, note that I am passing the argument stateful=True
to the GRU builder.
ANSWER
Answered 2022-Feb-22 at 08:05You can try using tf.random.normal
:
QUESTION
I train the following model based on GRU, note that I am passing the argument stateful=True
to the GRU builder.
ANSWER
Answered 2022-Feb-21 at 15:16You can try resetting the states in a custom Callback
:
QUESTION
UPDATE: To solve this, I kept the checkpoint structure the same but wrote a custom train_step function, with the help of the repo linked in the accepted answer of the question linked below, which calculated the gradients and used apply_weights rather than compiling the model and using train_on_batch. This lets the full GAN state be restored. Sadly, with this method I'm fairly sure the dropout layers no longer work as the discriminator is able to work perfectly very early in the training which prevents the model from training properly. Nevertheless, the original problem is solved.
Original:
I am currently training a GAN in keras and trying to make it so that I can save the model and resume training later. Ordinarily in keras you'd simply use model.save(), however for a GAN if the discriminator and GAN (combined generator and discriminator, with discriminator weights not trainable) models are saved and loaded separately then the link between them is broken and the GAN will not function as expected. Someone asked a similar question here, How to save and resume training a GAN with multiple model parts with Tensorflow 2/ Keras, and was told to use tf.train.Checkpoint instead to save the full model at once as a checkpoint.
I've tried implementing this as follows:
...ANSWER
Answered 2021-Nov-16 at 14:46If you have the following checkpoint structure, your model should work properly:
QUESTION
I have created a very simple API based on two files as follows:
api.js
...ANSWER
Answered 2021-Nov-13 at 09:51@Bob, going by the use case, you can expect the iata
from the params of the API.
In handlers.js file in getWhereToApi() function you could extract it using const iata = req.params.iata;
way, as following
QUESTION
I recently implemented a two-layer GRU network in Jax and was disappointed by its performance (it was unusable).
So, i tried a little speed comparison with Pytorch. Minimal working exampleThis is my minimal working example and the output was created on Google Colab with GPU-runtime. notebook in colab
...ANSWER
Answered 2021-Oct-29 at 13:49The reason the JAX code compiles slowly is that during JIT compilation JAX unrolls loops. So in terms of XLA compilation, your function is actually very large: you call rnn_jax.apply()
1000 times, and compilation times tend to be roughly quadratic in the number of statements.
By contrast, your pytorch function uses no Python loops, and so under the hood it is relying on vectorized operations that run much faster.
Any time you use a for
loop over data in Python, a good bet is that your code will be slow: this is true whether you're using JAX, torch, numpy, pandas, etc. I'd suggest finding an approach to the problem in JAX that relies on vectorized operations rather than relying on slow Python looping.
QUESTION
I have implemented a recurrent neural network autoencoder as below:
...ANSWER
Answered 2021-Sep-28 at 16:26This may be because of the dying relu problem. The relu is 0 for negative values. Have a look at this (https://towardsdatascience.com/the-dying-relu-problem-clearly-explained-42d0c54e0d24) explanation of the problem.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install gru
You can use gru 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 gru 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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