hyperas | simple wrapper for convenient hyperparameter optimization | Machine Learning library
kandi X-RAY | hyperas Summary
kandi X-RAY | hyperas Summary
A very simple convenience wrapper around hyperopt for fast prototyping with keras models. Hyperas lets you use the power of hyperopt without having to learn the syntax of it. Instead, just define your keras model as you are used to, but use a simple template notation to define hyper-parameter ranges to tune.
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Top functions reviewed by kandi - BETA
- Minimizes the hyperparameters
- Base method for hyperparameters
- Return a list of the names of the parts
- Returns the string representation of the given model
- Loads the cifar10 dataset
- Visualize visualization
- Generate random pairwise pairs
- Create training and test pair
- Create a model
- Create base network
- Predict using the model
- Returns a voting model for the given model
- Return a list of models with best fit
- Predict predictions for the model
- Visit an ImportFrom node
- Insert an import node
hyperas Key Features
hyperas Examples and Code Snippets
Lines of Code : 17License : Permissive (MIT)
Copy [2.039406419881185, 0.13577102455894152], u'epoch': [0, 1], u'time': 20.692888021469116, u'accuracy': 0.984}
(worker available gpu0) launching new model to worker...
1475215229.75 Model returned results: gpu1 392261103527195121 1174066377352204012 { brew install lame
brew reinstall sox --with-lame # for mp3 compatibility
pip install -r requirements.txt
def score_trained_model(params, args):
# Get the model from the fixed args.
model = args[0]
# Run the model on the params, return the output.
return model_predict(model, params)
# Nelder-Mead is myfrom __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from keras.datasets import mnist
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Sequential
from keras.utils import np_utiprint('Best performing model chosen hyper-parameters:')
print(best_run)
model.add(LSTM({{choice([32,64,128,256,512])}},W_regularizer=l2({{uniform(0, 1)}})))
model.add(LSTM(units={{choice([32,64,128,256,512])}},...)
model.add(Dropout(rate=..))
choice(32, 64, 128, 256, 512, 1024)
choice([32, 64, 128, 256, 512, 1024])
conda update --strict-channel-priority --all
datagen, X_train, Y_train, X_test, Y_test = data()
datagen = ImageDataGenerator()
train_list = []
for input in X_train:
train_list.append(datagen.fit(input))
best_run, best_model = optim.minimize(model=create_model,
data=data,
functions=[processing], # <<
algo=tpe.suggest,
Community Discussions
Trending Discussions on hyperas
QUESTION
I am trying to build a classifier based on a Dense Network with Keras. My input are (26,1) vectors and I want to get a binary classification 1 or 0 as output.
Using a Dense network and some optimisation with hyperas I manage to reach 80% accuracy which is not bad but I am trying to improve the accuracy of the network using Residual networks.
I found on various forums a lot of examples of residual networks for convolutionary networks but I did not find examples of residual networks.
I tried the following code to generate the residual net :
...
ANSWER
Answered 2021-Jan-30 at 10:14Firstly, resnets are intended to be used in much deep networks. Your network seems too shallow to get maximum adding benefit.
More generally, resnets are kind of an evolved version of simple architectures like VGGNet, with the intend to be able to "go deeper". That does not mean that residual layers will always increase your accuracy, if the network is too shallow.
Adding more layers should help. The key idea is to avoid vanishing gradients in the deeper layers of the network by allowing to skip connections.
QUESTION
I want to make use of a promising NN I found at towardsdatascience for my case study.
The data shapes I have are:
...ANSWER
Answered 2020-Aug-17 at 18:14I cannot reproduce your error, check if the following code works for you:
QUESTION
tl;dr I try to optimize and cross-validate my hyperparameters with Hyperas but can't make a preprocessing (scaling, over/undersampling) pipeline with KerasClassifier work
I use Hyperas (wrapper for hyperopt) to tune my Neural Network's (built with Keras/Tensorflow) hyperparameters and try to implement a kfold-cross validation for optimal parameters. However, I also do preprocessing on the data (Standardscaler & MinMaxScaler) and then Over/undersampling using SMOTETOMEK).
I read that one should not do the feature scaling and resampling on the whole dataset but only on the parts that are used for training to avoid spillovers. Trying to implement this inside hyperopt only for the train folds of the cross-validation is somewhat difficult, because when using a pipeline like imblearn, the pipeline only works with KerasClassifier which only takes a model-function. I can't give him that model function because the whole validation process in hyperopt takes place in one function.
Do you have any suggestions on how to make something like this work? Can I do all the preprocessing in def data() and optimize/cross validate the parameters on the whole dataset or does this hurt the correct parameter finding process? (I do have an additional test dataset for the final model)
Is there a way to make it work manually?
...ANSWER
Answered 2020-May-19 at 21:25solved it. This is the solution if anybody is interested:
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