keras-tuner | A Hyperparameter Tuning Library for Keras | Machine Learning library
kandi X-RAY | keras-tuner Summary
kandi X-RAY | keras-tuner Summary
Import KerasTuner and TensorFlow:. Write a function that creates and returns a Keras model. Use the hp argument to define the hyperparameters during model creation. Initialize a tuner (here, RandomSearch). We use objective to specify the objective to select the best models, and we use max_trials to specify the number of different models to try.
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Top functions reviewed by kandi - BETA
- Connects the model
- Context manager for conditional scope
- Create a Choice instance
- Return a fully qualified name
- Runs a single trial
- Builds the hyperparametric hyperparameters
- Build and fit the given hyperparameters
- Builds a keras model
- Update a trial
- Check if the hyperparameter is active
- Populates the hyperparameters
- Construct hyperparameters from keras_pb2
- Register a transform
- Converts the hyperparameters to a protobuf
- End a trial
- Fit the Gaussian model
- Update the hyperparameters
- Prints elapsed time
- Return a list of the best fit models
- Populates a trial space
- Build the keras model
- Write a path to a file
- Run oracle search
- Create a trial for the given trial
- Populates a random trial allocation
- Fills the Gaussian distribution with the given trial
keras-tuner Key Features
keras-tuner Examples and Code Snippets
Lines of Code : 48License : Permissive (MIT)
Copypython keras_tuner.py \
--data_dir path/to/dataset \
--num_epochs 20 \
--frame_sizes 16 64 \
--frame_sizes 32 128 \
--batch_size 32 64 128 \
--seq_len 512 1024 \
--num_rnn_layers 2 4
[Trial summary]
|-Trial ID: b366a8c644b6b0a28ef0c37 # import this
from kerastuner_tensorboard_logger import (
TensorBoardLogger,
setup_tb # Optional
)
tuner = Hyperband(
build_model,
objective="val_acc",
max_epochs=5,
directory="logs/tuner",
project_name="tf_test",
lo $ pip install kerastuner-tensorboard-logger
hypermodel = tuner.hypermodel.build(best_hps)
# Retrain the model
hypermodel.fit(img_train, label_train, epochs=best_epoch, validation_split=0.2)
import os
checkpoint_path = "training_1/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoin# ...
tuner = kt.Hyperband(
self.build_auto_encoder_model,
objective='val_loss',
max_epochs=30,
hyperband_iterations=20,
overwrite = True) # here
# ...
BaseTuner.get_best_models(num_models=1)
best_hp = tuner.get_best_hyperparameters()[0]
model = tuner.hypermodel.build(best_hp)
outputs = tf.keras.layers.Dense(1, activation='sigmoid')(x)
change your model compile code from
model.compile(
optimizer=opt,
loss='binary_crossentropy',
metrics=['accuracy'])
hp_optimizer = hp.Choice('optimizer', values=['sgd', 'rmsprop', 'adam'])
if hp_optimizer == 'sgd':
optimizer = SGD(learning_rate=hp_lr)
elif hp_optimizer == 'rmsprop':
optimizer = RMSprop(learning_rate=hp_lr)
elif hp_optimzier == tuner.search(train_data_gen,
epochs=50,
validation_data=test_data_gen,
callbacks=[stop_early])
def get_annotation(fnmtxt, verbose=True):
if verbose:
print("_" * 70)
print(fnmtxt)
rfile = open(fnmtxt, 'r')
texts = rfile.read().split("\n")
rfile.close()
columns = np.array(texts[1].split(" "))
columns = columns[columns != ""]Community Discussions
Trending Discussions on keras-tuner
QUESTION
My original MLP model is as follows:
...ANSWER
Answered 2021-Oct-17 at 07:00I can imagine you are getting this warning and error because you have to use exactly the same metric from your model and Keras Tuner model in your RandomSearch target, which is categorical_accuracy. So, perhaps, try:
QUESTION
from sklearn import ensemble
from sklearn import linear_model
def build_model(hp):
model_type = hp.Choice('model_type', ['random_forest', 'ridge'])
if model_type == 'random_forest':
with hp.conditional_scope('model_type', 'random_forest'):
model = ensemble.RandomForestClassifier(
n_estimators=hp.Int('n_estimators', 10, 50, step=10),
max_depth=hp.Int('max_depth', 3, 10))
elif model_type == 'ridge':
with hp.conditional_scope('model_type', 'ridge'):
model = linear_model.RidgeClassifier(
alpha=hp.Float('alpha', 1e-3, 1, sampling='log'))
else:
raise ValueError('Unrecognized model_type')
return model
tuner = kt.tuners.Sklearn(
oracle=kt.oracles.BayesianOptimization(
objective=kt.Objective('score', 'max'),
max_trials=10),
hypermodel=build_model,
directory=".")
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.2)
tuner.search(X_train, y_train)
best_model = tuner.get_best_models(num_models=1)[0]
ANSWER
Answered 2021-Sep-10 at 17:18Adding
import sklearn.pipeline would temporarily fix the problem.
This is a very recent issue and will be fixed in the next release.
You can find more about it here https://github.com/keras-team/keras-tuner/issues/600
QUESTION
I'm trying to use keras_tuner.RandomSearch to find the best parameters that fit my model. I installed keras_tuner in my anaconda command prompt using the following command:
conda install -c conda-forge keras-tuner
I then imported the package as follows: import keras_tuner as kt
but when I call kt.RandomSearch, I got the following error message:
tuner_search= kt.RandomSearch(build_model, AttributeError: partially initialized module 'keras_tuner' has no attribute 'RandomSearch' (most likely due to a circular import).
following is my code:
...ANSWER
Answered 2021-Aug-26 at 09:48It is likely that you have a local file (the current one) with the exact name of the library (the module you are trying to import), hence the circular reference, since Python thinks is is a module).
Change the name of the file in which you run the code (avoid namings which overlap perfectly with the library/module names) and see if it works.
QUESTION
I am trying to use hyperparameter optimization in FER2013 test set but I am not able to overcome this problem ...any help or suggestion.
...ANSWER
Answered 2021-Jun-27 at 18:25I just banged my head for 10 straight hours for a silly mistake
wrong : tuner_search.search(X_train,X_test,epochs=3,validation_split=0.1)
correct: tuner_search.search(X_train,y_train,epochs=3,validation_split=0.1)
QUESTION
I'm using Keras-Tuner to auto-identify the best parameters for my CNN. I am using Celeb_a dataset.
you can find the code I am using here When I try to run it I get the error below.
InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [1,10] and labels shape [40] [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at C:\Users\admin-sepr\anaconda3\envs\tf\lib\site-packages\kerastuner\engine\tuner.py:141) ]] [Op:__inference_train_function_953]
I have seen in other places that this could be down to using loss="categorical_crossentropy", when I compile the code and they recomended to try loss="sparse_categorical_crossentropy", instead.
when I do this I get the following error.
InvalidArgumentError: logits and labels must be broadcastable: logits_size=[64,380192] labels_size=[64,40] [[node categorical_crossentropy/softmax_cross_entropy_with_logits (defined at C:\Users\admin-sepr\anaconda3\envs\tf\lib\site-packages\kerastuner\engine\tuner.py:141) ]] [Op:__inference_train_function_6830]
The function call stack is the following for both errors.
...ANSWER
Answered 2021-Apr-14 at 19:30It looks like your data you have loaded from the Celeb_A dataset is not compatible with the model that you are trying to train. From just scanning the code it looks like you need to change the number of nodes in your output dense layer. This number (which you currently have at 10 nodes) should match the number of labels you expect to predict. From the error, it looks like there are 40 labels that you are predicting but the model thinks there are only 10.
My best guess is to keep your loss as categorical cross-entropy and change your output dense layer to Dense(40) rather than Dense(10).
QUESTION
I'm trying to come up with a way to use Keras-Tuner to auto-identify the best parameters for my CNN. I am using Celeb_a dataset
I tried a similar project where I used fashion_mnist and this worked perfectly but my experience with python isn't enough to do what I want to achieve. When I tried with fashion_mnist I managed to create this table of results
My code is here.
I am hoping to produce a similar table using the Celeb_a dataset. This is for a report I'm doing for college. In the report, my college used AWS Rekognition to produce the table below.
I am hoping to be able to train the data so I can save this model to a pickle and generate a similar table of results to compare them.
Any recommendations on how to approach this? My queries at the moment are:
- How to load the dataset correctly?
- how can i train the model to give me accuracy on "Moustache", "Beard", "Emotion" (like on the table of results above)
I tried loading the data using:
...ANSWER
Answered 2021-Apr-01 at 20:00I managed to do this by creating a function to collect all annotations like this:
QUESTION
I'm getting following error and I'm not able to figure out why:
RuntimeError: Model-building function did not return a valid Keras Model instance, found (, )
I have read the answers here and here which seem to telling to import keras from tensorflow instead of stand alone keras which I'm doing but still getting the error. I would very much appreciate your help in figuring this out. Below is my entire code:
ANSWER
Answered 2021-Feb-21 at 09:13RuntimeError: Model-building function did not return a valid Keras Model instance, found (, )
(, )
As you can see this a tuple of two Keras Model instance. This is output of create_autoencoder(hp, input_dim, output_dim).
QUESTION
I have a setup.py which contains the following:
ANSWER
Answered 2020-Sep-01 at 13:41You can only use PEP 508 - Dependency specification for Python Software Packages requirements. git://github.com/BioGeek/tta_wrapper.git@master#egg=tta_wrapper is not valid syntax according to that standard.
setuptools does accept the name@ url direct reference syntax:
QUESTION
I am trying to make a CNN model and I get a the following error
...ANSWER
Answered 2020-Dec-09 at 17:37keras.layers.MaxPooling2D(pool_size = (2,2), padding= "same"),
keras.layers.Dropout(0.2),
QUESTION
I am looking at Keras Tuner as a way of doing hyperparameter optimization, but all of the examples I have seen show an entirely fresh model being defined. For example, from the Keras Tuner Hello World:
...ANSWER
Answered 2020-Sep-23 at 15:24Indeed this is possible, as this GitHub issue makes clear...
However rather than passing the hp object through the hyperparameters arg to the Tuner, instead I override the Tuner run_trial method in the manner suggested here.
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