In rstudio/keras: R Interface to 'Keras'

Introduction

Keras Core is a full implementation of the Keras API that works with TensorFlow, JAX, and PyTorch interchangeably. This notebook will walk you through key Keras Core workflows.

First, let's install Keras Core:

pip install -q keras-core

Setup

We're going to be using the JAX backend here -- but you can edit the string below to "tensorflow" or "torch" and hit "Restart runtime", and the whole notebook will run just the same! This entire guide is backend-agnostic.

import numpy as np
import os

os.environ["KERAS_BACKEND"] = "jax"

# Note that keras_core should only be imported after the backend
# has been configured. The backend cannot be changed once the
# package is imported.
import keras_core as keras

A first example: A MNIST convnet

Let's start with the Hello World of ML: training a convnet to classify MNIST digits.

Here's the data:

# Load the data and split it between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
print("x_train shape:", x_train.shape)
print("y_train shape:", y_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")

Here's our model.

Different model-building options that Keras offers include:

The Sequential API (what we use below)
The Functional API (most typical)
Writing your own models yourself via subclassing (for advanced use cases)

# Model parameters
num_classes = 10
input_shape = (28, 28, 1)

model = keras.Sequential(
    [
        keras.layers.Input(shape=input_shape),
        keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        keras.layers.MaxPooling2D(pool_size=(2, 2)),
        keras.layers.Conv2D(128, kernel_size=(3, 3), activation="relu"),
        keras.layers.Conv2D(128, kernel_size=(3, 3), activation="relu"),
        keras.layers.GlobalAveragePooling2D(),
        keras.layers.Dropout(0.5),
        keras.layers.Dense(num_classes, activation="softmax"),
    ]
)

Here's our model summary:

model.summary()

We use the compile() method to specify the optimizer, loss function, and the metrics to monitor. Note that with the JAX and TensorFlow backends, XLA compilation is turned on by default.

model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(),
    optimizer=keras.optimizers.Adam(learning_rate=1e-3),
    metrics=[
        keras.metrics.SparseCategoricalAccuracy(name="acc"),
    ],
)

Let's train and evaluate the model. We'll set aside a validation split of 15% of the data during training to monitor generalization on unseen data.

batch_size = 128
epochs = 20

callbacks = [
    keras.callbacks.ModelCheckpoint(filepath="model_at_epoch_{epoch}.keras"),
    keras.callbacks.EarlyStopping(monitor="val_loss", patience=2),
]

model.fit(
    x_train,
    y_train,
    batch_size=batch_size,
    epochs=epochs,
    validation_split=0.15,
    callbacks=callbacks,
)
score = model.evaluate(x_test, y_test, verbose=0)

During training, we were saving a model at the end of each epoch. You can also save the model in its latest state like this:

model.save("final_model.keras")

And reload it like this:

model = keras.saving.load_model("final_model.keras")

Next, you can query predictions of class probabilities with predict():

predictions = model.predict(x_test)

That's it for the basics!

Writing cross-framework custom components

Keras Core enables you to write custom Layers, Models, Metrics, Losses, and Optimizers that work across TensorFlow, JAX, and PyTorch with the same codebase. Let's take a look at custom layers first.

If you're already familiar with writing custom layers in tf.keras -- well, nothing has changed. Except one thing: instead of using functions from the tf namespace, you should use functions from keras.ops.*.

The keras.ops namespace contains:

An implementation of the NumPy API, e.g. keras.ops.stack or keras.ops.matmul.
A set of neural network specific ops that are absent from NumPy, such as keras.ops.conv or keras.ops.binary_crossentropy.

Let's make a custom Dense layer that works with all backends:

class MyDense(keras.layers.Layer):
    def __init__(self, units, activation=None, name=None):
        super().__init__(name=name)
        self.units = units
        self.activation = keras.activations.get(activation)

    def build(self, input_shape):
        input_dim = input_shape[-1]
        self.w = self.add_weight(
            shape=(input_dim, self.units),
            initializer=keras.initializers.GlorotNormal(),
            name="kernel",
            trainable=True,
        )

        self.b = self.add_weight(
            shape=(self.units,),
            initializer=keras.initializers.Zeros(),
            name="bias",
            trainable=True,
        )

    def call(self, inputs):
        # Use Keras ops to create backend-agnostic layers/metrics/etc.
        x = keras.ops.matmul(inputs, self.w) + self.b
        return self.activation(x)

Next, let's make a custom Dropout layer that relies on the keras.random namespace:

class MyDropout(keras.layers.Layer):
    def __init__(self, rate, name=None):
        super().__init__(name=name)
        self.rate = rate
        # Use seed_generator for managing RNG state.
        # It is a state element and its seed variable is
        # tracked as part of `layer.variables`.
        self.seed_generator = keras.random.SeedGenerator(1337)

    def call(self, inputs):
        # Use `keras_core.random` for random ops.
        return keras.random.dropout(inputs, self.rate, seed=self.seed_generator)

Next, let's write a custom subclassed model that uses our two custom layers:

class MyModel(keras.Model):
    def __init__(self, num_classes):
        super().__init__()
        self.conv_base = keras.Sequential(
            [
                keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
                keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
                keras.layers.MaxPooling2D(pool_size=(2, 2)),
                keras.layers.Conv2D(128, kernel_size=(3, 3), activation="relu"),
                keras.layers.Conv2D(128, kernel_size=(3, 3), activation="relu"),
                keras.layers.GlobalAveragePooling2D(),
            ]
        )
        self.dp = MyDropout(0.5)
        self.dense = MyDense(num_classes, activation="softmax")

    def call(self, x):
        x = self.conv_base(x)
        x = self.dp(x)
        return self.dense(x)

Let's compile it and fit it:

model = MyModel(num_classes=10)
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(),
    optimizer=keras.optimizers.Adam(learning_rate=1e-3),
    metrics=[
        keras.metrics.SparseCategoricalAccuracy(name="acc"),
    ],
)

model.fit(
    x_train,
    y_train,
    batch_size=batch_size,
    epochs=1,  # For speed
    validation_split=0.15,
)

Training models on arbitrary data sources

All Keras models can be trained and evaluated on a wide variety of data sources, independently of the backend you're using. This includes:

NumPy arrays
Pandas dataframes
TensorFlowtf.data.Dataset objects
PyTorch DataLoader objects
Keras PyDataset objects

They all work whether you're using TensorFlow, JAX, or PyTorch as your Keras backend.

Let's try it out with PyTorch DataLoaders:

import torch

# Create a TensorDataset
train_torch_dataset = torch.utils.data.TensorDataset(
    torch.from_numpy(x_train), torch.from_numpy(y_train)
)
val_torch_dataset = torch.utils.data.TensorDataset(
    torch.from_numpy(x_test), torch.from_numpy(y_test)
)

# Create a DataLoader
train_dataloader = torch.utils.data.DataLoader(
    train_torch_dataset, batch_size=batch_size, shuffle=True
)
val_dataloader = torch.utils.data.DataLoader(
    val_torch_dataset, batch_size=batch_size, shuffle=False
)

model = MyModel(num_classes=10)
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(),
    optimizer=keras.optimizers.Adam(learning_rate=1e-3),
    metrics=[
        keras.metrics.SparseCategoricalAccuracy(name="acc"),
    ],
)
model.fit(train_dataloader, epochs=1, validation_data=val_dataloader)

Now let's try this out with tf.data:

import tensorflow as tf

train_dataset = (
    tf.data.Dataset.from_tensor_slices((x_train, y_train))
    .batch(batch_size)
    .prefetch(tf.data.AUTOTUNE)
)
test_dataset = (
    tf.data.Dataset.from_tensor_slices((x_test, y_test))
    .batch(batch_size)
    .prefetch(tf.data.AUTOTUNE)
)

model = MyModel(num_classes=10)
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(),
    optimizer=keras.optimizers.Adam(learning_rate=1e-3),
    metrics=[
        keras.metrics.SparseCategoricalAccuracy(name="acc"),
    ],
)
model.fit(train_dataset, epochs=1, validation_data=test_dataset)