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Does the tune_new_entries parameter really do what the documentation says? #40

mikulskibartosz opened this issue Jul 30, 2019 · 0 comments


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commented Jul 30, 2019


I have been training HyperResNet using the Hyperband tuner, and I have noticed that when I set the tune_new_entries parameter to True, the parameters which I did not specify manually are not even listed in the tuner's output. After reading the documentation, I was the under impression that the tuner is supposed to add those parameters to the search space and tune them.

On the other hand, when I set the tune_new_entries to False, the tuner lists all parameters of the HyperResNet and chooses a different value every time I run it.

Steps to reproduce the problem

from tensorflow import keras

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

train_images = train_images / 255.0
test_images = test_images / 255.0

train_images = train_images.reshape(len(train_images), 28, 28, 1)
test_images = test_images.reshape(len(test_images), 28, 28, 1)

from keras.utils import to_categorical
train_labels_binary = to_categorical(train_labels)

from kerastuner.applications import HyperResNet
from kerastuner.tuners import Hyperband

hypermodel = HyperResNet(input_shape=(28, 28, 1), classes=10)

from kerastuner import HyperParameters
hp = HyperParameters()
hp.Choice('learning_rate', values=[1e-3, 1e-4])
hp.Fixed('optimizer', value='adam')

tuner = Hyperband(
    project_name='FashionMNIST'), train_labels_binary, validation_split=0.1)

Expected results

The list of tuned arguments printed to the output should also contain values not explicitly specified as the instance of HyperParameters, because according to the documentation tune_new_entries=False prevents unlisted parameters from being tuned. I set it to True, so I expect the unlisted parameters to be tuned. That does not seem to happen.

Actual results

This is printed:

Hp values:
|-learning_rate: 0.0001
|-optimizer: adam
|-tuner/epochs: 3

When I set tune_new_entries=False, it prints this:

|-learning_rate: 0.0001
|-optimizer: adam
|-pooling: max
|-tuner/epochs: 3
|-v2/conv3_depth: 4
|-v2/conv4_depth: 36
|-version: v2

I guess it should be the other way around.


Keras: 2.2.4-tf
Tensorflow: 2.0.0-beta1
Keras tuner: installed when cfc6e20956cb8554ee29ef2a1ba4635da7d0228b commit was the most recent one

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