TensorFlow: Tabular Classify Binary๏ƒ

Detecting Naval Mines with Binary Classification of Sonar Data.

๐Ÿ’พ Data๏ƒ

Reference Example Datasets for more information.

This dataset is comprised of:

  • Features = sonar readings that have been bounced off a distant object.

  • Label = either a rock or metal structure (potentially a naval mine).

[2]:

from aiqc import datum
file_path = datum.get_path('sonar.csv')

[5]:

from aiqc.orm import Dataset
shared_dataset = Dataset.Tabular.from_path(file_path=file_path)

๐Ÿšฐ Pipeline๏ƒ

Reference High-Level API Docs for more information.

[4]:

from aiqc.mlops import Pipeline, Input, Target, Stratifier
from sklearn.preprocessing import LabelBinarizer, PowerTransformer

[6]:

pipeline = Pipeline(
    Input(
        dataset  = shared_dataset,
        encoders = Input.Encoder(
            PowerTransformer(method='yeo-johnson', copy=False),
            dtypes = ['float64']
        )
    ),

    Target(
        dataset   = shared_dataset
        , column  = 'object'
        , encoder = Target.Encoder(LabelBinarizer(sparse_output=False))
    ),

    Stratifier(
        size_test         = 0.12
        , size_validation = 0.22
    )
)

๐Ÿงช Experiment๏ƒ

Reference High-Level API Docs for more information.

[7]:

from aiqc.mlops import Experiment, Architecture, Trainer
import tensorflow as tf
from tensorflow.keras import layers as l

[8]:

def fn_build(features_shape, label_shape, **hp):
    m = tf.keras.models.Sequential()
    m.add(l.Input(shape=features_shape))
    m.add(l.Dense(hp['neuron_count'], activation='relu', kernel_initializer='he_uniform'))
    m.add(l.Dropout(0.40))
    m.add(l.Dense(hp['neuron_count'], activation='relu', kernel_initializer='he_uniform'))
    m.add(l.Dropout(0.40))
    m.add(l.Dense(units=label_shape[0], activation='sigmoid', kernel_initializer='glorot_uniform'))
    return m

[9]:

def fn_train(
    model, loser, optimizer,
    train_features, train_label,
    eval_features, eval_label,
    **hp
):
    model.compile(
        loss        = loser
        , optimizer = optimizer
        , metrics   = ['accuracy']
    )
    model.fit(
        train_features, train_label
        , validation_data = (eval_features, eval_label)
        , verbose         = 0
        , batch_size      = 3
        , epochs          = hp['epochs']
        , callbacks       = [tf.keras.callbacks.History()]
    )
    return model

[10]:

hyperparameters = dict(
    neuron_count=[24, 32],
    epochs=[25, 50]
)

[11]:

experiment = Experiment(
    Architecture(
        library           = "keras"
        , analysis_type   = "classification_binary"
        , fn_build        = fn_build
        , fn_train        = fn_train
        , hyperparameters = hyperparameters
    ),

    Trainer(
        pipeline       = pipeline
        , repeat_count = 2
    )
)

[12]:

experiment.run_jobs()

๐Ÿ“ฆ Caching Splits ๐Ÿ“ฆ: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 3/3 [00:00<00:00, 360.06it/s]
๐Ÿ”ฎ Training Models ๐Ÿ”ฎ: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [01:38<00:00, 12.29s/it]

๐Ÿ“Š Visualization & Interpretation๏ƒ

For more information on visualization of performance metrics, reference the Dashboard documentation.