🏃‍♀️ Introduction

Use Weights & Biases for machine learning experiment tracking, dataset versioning, and project collaboration.

This colab notebook introduces the WandbMetricsLogger callback. Use this callback for Experiment Tracking. It will log your training and validation metrics along with system metrics to Weights and Biases.

🌴 Setup and Installation

First, let us install the latest version of Weights and Biases. We will then authenticate this colab instance to use W&B.

!pip install -qq -U wandb

import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models
import tensorflow_datasets as tfds

# Weights and Biases related imports
import wandb
from wandb.keras import WandbMetricsLogger

If this is your first time using W&B or you are not logged in, the link that appears after running wandb.login() will take you to sign-up/login page. Signing up for a free account is as easy as a few clicks.

wandb.login()

🌳 Hyperparameters

Use of proper config system is a recommended best practice for reproducible machine learning. We can track the hyperparameters for every experiment using W&B. In this colab we will be using simple Python dict as our config system.

configs = dict(
    num_classes = 10,
    shuffle_buffer = 1024,
    batch_size = 64,
    image_size = 28,
    image_channels = 1,
    earlystopping_patience = 3,
    learning_rate = 1e-3,
    epochs = 3
)

🍁 Dataset

In this colab, we will be using CIFAR100 dataset from TensorFlow Dataset catalog. We aim to build a simple image classification pipeline using TensorFlow/Keras.

train_ds, valid_ds = tfds.load('fashion_mnist', split=['train', 'test'])

AUTOTUNE = tf.data.AUTOTUNE


def parse_data(example):
    # Get image
    image = example["image"]
    # image = tf.image.convert_image_dtype(image, dtype=tf.float32)

    # Get label
    label = example["label"]
    label = tf.one_hot(label, depth=configs["num_classes"])

    return image, label


def get_dataloader(ds, configs, dataloader_type="train"):
    dataloader = ds.map(parse_data, num_parallel_calls=AUTOTUNE)

    if dataloader_type=="train":
        dataloader = dataloader.shuffle(configs["shuffle_buffer"])
      
    dataloader = (
        dataloader
        .batch(configs["batch_size"])
        .prefetch(AUTOTUNE)
    )

    return dataloader

trainloader = get_dataloader(train_ds, configs)
validloader = get_dataloader(valid_ds, configs, dataloader_type="valid")

🎄 Model

def get_model(configs):
    backbone = tf.keras.applications.mobilenet_v2.MobileNetV2(weights='imagenet', include_top=False)
    backbone.trainable = False

    inputs = layers.Input(shape=(configs["image_size"], configs["image_size"], configs["image_channels"]))
    resize = layers.Resizing(32, 32)(inputs)
    neck = layers.Conv2D(3, (3,3), padding="same")(resize)
    preprocess_input = tf.keras.applications.mobilenet.preprocess_input(neck)
    x = backbone(preprocess_input)
    x = layers.GlobalAveragePooling2D()(x)
    outputs = layers.Dense(configs["num_classes"], activation="softmax")(x)

    return models.Model(inputs=inputs, outputs=outputs)

tf.keras.backend.clear_session()
model = get_model(configs)
model.summary()

🌿 Compile Model

model.compile(
    optimizer = "adam",
    loss = "categorical_crossentropy",
    metrics = ["accuracy", tf.keras.metrics.TopKCategoricalAccuracy(k=5, name='top@5_accuracy')]
)

🌻 Train

# Initialize a W&B run
run = wandb.init(
    project = "report-to-notebook",
    config = configs
)

# Train your model
model.fit(
    trainloader,
    epochs = configs["epochs"],
    validation_data = validloader,
    callbacks = [WandbMetricsLogger(log_freq=10)] # Notice the use of WandbMetricsLogger here
)

# Close the W&B run
run.finish()