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Typical Keras sweep: complete code

Train two small Iris classifiers with different activations. This page is the standalone companion to the walkthrough.

Prerequisites and execution​

Use Python 3.11–3.13 with the TensorFlow extra (talos[tensorflow]). The dataset is an offline scikit-learn fixture. From a writable experiment directory, save the following program as iris_example.py and execute python iris_example.py.

The walkthrough owns the data split, callback explanation and interpretation of metrics. The program is bounded to two trials; successful execution passes its result-row assertion.

Program​

import talos
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Input, Dense, Dropout, Conv2D, Flatten, concatenate

x, y = talos.templates.datasets.iris()
x_train, x_val, y_train, y_val = train_test_split(
    x.astype('float32'), y.astype('float32'), test_size=.2, random_state=17,
    stratify=y.argmax(axis=1))

def iris_model(x_train, y_train, x_val, y_val, params):
    model = Sequential([Input(shape=(4,)),
                        Dense(params['first_neuron'], activation=params['activation']),
                        Dense(3, activation='softmax')])
    model.compile(optimizer=params['optimizer'], loss=params['losses'],
                  metrics=['accuracy', talos.utils.metrics.f1score])
    out = model.fit(x_train, y_train, batch_size=params['batch_size'],
                    epochs=params['epochs'], validation_data=(x_val, y_val),
                    verbose=0)
    return out, model

p = {'activation': ['relu', 'elu'],
     'first_neuron': [8], 'optimizer': ['adam'],
     'losses': ['categorical_crossentropy'],
     'batch_size': [16], 'epochs': [2]}

scan_object = talos.Scan(x=x_train, y=y_train, x_val=x_val, y_val=y_val,
                         model=iris_model, params=p, experiment_name='iris',
                         round_limit=2, seed=17, backend='tensorflow')
assert len(scan_object.data) == 2

Scan() always needs to have x, y, model, and params arguments declared. Find the description for all Scan() arguments Scan arguments.

Result and failure boundaries​

scan_object.data contains two completed rows. Each row records the selected activation and final training/validation metrics. The model returns a probability vector for each of the three Iris classes. The program writes result and checkpoint artifacts to its experiment run directory.

The Iris labels are one-hot vectors, so the output has three units and uses categorical cross-entropy. Keep this encoding, the output shape and the loss consistent when replacing the dataset.

Return to the walkthrough for the procedure and failure diagnosis. Analyze results, then evaluate candidates on data held out from tuning.