Keras models

Use W&B for machine learning experiment tracking, dataset versioning, and project collaboration.

This Colab notebook introduces the WandbModelCheckpoint callback. Use this callback to log your model checkpoints to W&B Artifacts.

Setup and Installation

First, let us install the latest version of W&B. 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

# W&B related imports
import wandb
from wandb.integration.keras import WandbMetricsLogger
from wandb.integration.keras import WandbModelCheckpoint

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 = 10
)

Dataset

In this colab, we will be using Fashion-MNIST 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 = "intro-keras",
    config = configs
)

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

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