Keras and Tensorflow

The package SciKeras brings a Scikit-learn API to Keras. This allows Dask-ML to be used seamlessly with Keras models.

Installation

Following the Tensorflow install directions and SciKeras install guide, these packages need to be installed:

$ pip install tensorflow>=2.3.0
$ pip install scikeras>=0.1.8

These are the minimum versions that Dask-ML requires to use Tensorflow/Keras.

Usage

First, let’s start by defining normal function to create our model. This is the normal way to create a Keras Sequential model

import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential

def build_model(lr=0.01, momentum=0.9):
    layers = [Dense(512, input_shape=(784,), activation="relu"),
              Dense(10, input_shape=(512,), activation="softmax")]
    model = Sequential(layers)

    opt = tf.keras.optimizers.SGD(
        learning_rate=lr, momentum=momentum, nesterov=True,
    )
    model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
    return model

Now, we can use the SciKeras to create a Scikit-learn compatible model:

from scikeras.wrappers import KerasClassifier
niceties = dict(verbose=False)
model = KerasClassifier(build_fn=build_model, lr=0.1, momentum=0.9, **niceties)

This model will work with all of Dask-ML: it can use NumPy arrays as inputs and obeys the Scikit-learn API. For example, it’s possible to use Dask-ML to do the following:

If we want to tune lr and momentum, SciKeras requires that we pass lr and momentum at initialization:

SciKeras supports more model creation methods, including some that are backwards-compatible with Tensorflow. Refer to their documentation for details.

Example: Hyperparameter Optimization

If we wanted to, we could use the model above with HyperbandSearchCV. Let’s tune this model on the MNIST dataset:

from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
import numpy as np
from typing import Tuple

def get_mnist() -> Tuple[np.ndarray, np.ndarray]:
    (X_train, y_train), _ = mnist.load_data()
    X_train = X_train.reshape(X_train.shape[0], 784)
    X_train = X_train.astype("float32")
    X_train /= 255
    return X_train, y_train

And let’s perform the basic task of tuning our SGD implementation:

from scipy.stats import loguniform, uniform
params = {"lr": loguniform(1e-3, 1e-1), "momentum": uniform(0, 1)}
X, y = get_mnist()

Now, the search can be run:

from dask.distributed import Client
client = Client()

from dask_ml.model_selection import HyperbandSearchCV
search = HyperbandSearchCV(model, params, max_iter=27)
search.fit(X, y)