How to tune hyperparameters for keras model?
This recipe helps you tune hyperparameters for keras model
Recipe Objective
How to tune hyperparameters for keras model.
The process of selecting the right hyperparameters in a Deep Learning or Machine Learning Model is called hyperparameter tuning.
Hyperparameters are the variables that control the training of the dataset, they have a huge impact on the learning of the model, and tuning of these hyperparameters controls the accuracy.
Step 1- Importing Libraries.
import tensorflow as tf
from tensorflow import keras
import kerastuner as kt
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Layer
#!pip install -U keras-tuner
Step 2- Creating a function.
Create a function for hypertuning, defining all the hyper parameters
def model_tune(new):
model = keras.Sequential()
model.add(keras.layers.Flatten(input_shape=(28, 28)))
units = new.Int('units', min_value = 16, max_value = 128, step = 16)
model.add(keras.layers.Dense(units = units, activation = 'sigmoid'))
model.add(keras.layers.Dense(10))
learning_rate = new.Choice('learning_rate', values = [1e-1,1e-2, 1e-3, 1e-4])
model.compile(optimizer = keras.optimizers.Adam(learning_rate = learning_rate),
loss = keras.losses.SparseCategoricalCrossentropy,
metrics = ['mse'])
return model
Step 3-Instantiating the hypertuning model.
We will instantiate the hypertuning model here.
tuning = kt.Hyperband(model_tune,
objective = 'val_accuracy',
max_epochs = 100,
factor = 3,
directory = 'my_dir',
project_name = 'learning_hyperparameter')
print(tuning)
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