infinite-training 1.0.0

The python package for training ML models base on loss, metrics and timeout.

Infinite Training

The python package for training ML models base on loss, metrics and timeout. We can create target for training or using Ctrl + C to interrupt the training session. This package can store the last weight and optimize weight for us to continue training after interrupting.

Using as example:

"""
    Apply in example from https://www.tensorflow.org/datasets/keras_example
"""
import tensorflow_datasets as tfds
import tensorflow as tf
from infinite_training import InfinityTraining, Target

(ds_train, ds_test), ds_info = tfds.load(
    'mnist',
    split=['train', 'test'],
    shuffle_files=True,
    as_supervised=True,
    with_info=True,
)


def normalize_img(image, label):
    """Normalizes images: `uint8` -> `float32`."""
    return tf.cast(image, tf.float32) / 255., label

ds_train = ds_train.map(
    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_train = ds_train.cache()
ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
ds_train = ds_train.batch(128)
ds_train = ds_train.prefetch(tf.data.AUTOTUNE)

ds_test = ds_test.map(
    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_test = ds_test.batch(128)
ds_test = ds_test.cache()
ds_test = ds_test.prefetch(tf.data.AUTOTUNE)

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

it = InfinityTraining(model=model, target=Target(
    name="val_sparse_categorical_accuracy", smaller_is_better=False, target_value=0.98), timeout=100)
it.compile(optimizer=tf.keras.optimizers.Adam(0.001),
           loss=tf.keras.losses.SparseCategoricalCrossentropy(
               from_logits=True),
           metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],)
it.train(ds_train, validation_data=ds_test)
it.show_result(ds_train)