Developer guides / Migrating Keras 2 code to multi-backend Keras 3

Migrating Keras 2 code to multi-backend Keras 3

Author: Divyashree Sreepathihalli
Date created: 2023/10/23
Last modified: 2023/10/30
Description: Instructions & troubleshooting for migrating your Keras 2 code to multi-backend Keras 3.

View in Colab GitHub source

This guide will help you migrate TensorFlow-only Keras 2 code to multi-backend Keras 3 code. The overhead for the migration is minimal. Once you have migrated, you can run Keras workflows on top of either JAX, TensorFlow, or PyTorch.

This guide has two parts:

  1. Migrating your legacy Keras 2 code to Keras 3, running on top of the TensorFlow backend. This is generally very easy, though there are minor issues to be mindful of, that we will go over in detail.
  2. Further migrating your Keras 3 + TensorFlow code to multi-backend Keras 3, so that it can run on JAX and PyTorch.

Let's get started.


Setup

First, lets install keras-nightly.

This example uses the TensorFlow backend (os.environ["KERAS_BACKEND"] = "tensorflow"). After you've migrated your code, you can change the "tensorflow" string to "jax" or "torch" and click "Restart runtime" in Colab, and your code will run on the JAX or PyTorch backend.

!pip install -q keras-nightly
import os

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

import keras
import tensorflow as tf
import numpy as np

Going from Keras 2 to Keras 3 with the TensorFlow backend

First, replace your imports:

  1. Replace from tensorflow import keras to import keras
  2. Replace from tensorflow.keras import xyz (e.g. from tensorflow.keras import layers) to from keras import xyz (e.g. from keras import layers)
  3. Replace tf.keras.* to keras.*

Next, start running your tests. Most of the time, your code will execute on Keras 3 just fine. All issues you might encounter are detailed below, with their fixes.

jit_compile is set to True by default on GPU.

The default value of the jit_compile argument to the Model constructor has been set to True on GPU in Keras 3. This means that models will be compiled with Just-In-Time (JIT) compilation by default on GPU.

JIT compilation can improve the performance of some models. However, it may not work with all TensorFlow operations. If you are using a custom model or layer and you see an XLA-related error, you may need to set the jit_compile argument to False. Here is a list of known issues encountered when using XLA with TensorFlow. In addition to these issues, there are some ops that are not supported by XLA.

The error message you could encounter would be as follows:

Detected unsupported operations when trying to compile graph
__inference_one_step_on_data_125[] on XLA_GPU_JIT

For example, the following snippet of code will reproduce the above error:

class MyModel(keras.Model):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def call(self, inputs):
        string_input = tf.strings.as_string(inputs)
        return tf.strings.to_number(string_input)


subclass_model = MyModel()
x_train = np.array([[1, 2, 3], [4, 5, 6]])
subclass_model.compile(optimizer="sgd", loss="mse")
subclass_model.predict(x_train)

How to fix it: set jit_compile=False in model.compile(..., jit_compile=False), or set the jit_compile attribute to False, like this:

class MyModel(keras.Model):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def call(self, inputs):
        # tf.strings ops aren't support by XLA
        string_input = tf.strings.as_string(inputs)
        return tf.strings.to_number(string_input)


subclass_model = MyModel()
x_train = np.array([[1, 2, 3], [4, 5, 6]])
subclass_model.jit_compile = False
subclass_model.predict(x_train)
 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step

array([[1., 2., 3.],
       [4., 5., 6.]], dtype=float32)

Saving a model in the TF SavedModel format

Saving to the TF SavedModel format via model.save() is no longer supported in Keras 3.

The error message you could encounter would be as follows:

>>> model.save("mymodel")
ValueError: Invalid filepath extension for saving. Please add either a `.keras` extension
for the native Keras format (recommended) or a `.h5` extension. Use
`tf.saved_model.save()` if you want to export a SavedModel for use with
TFLite/TFServing/etc. Received: filepath=saved_model.

The following snippet of code will reproduce the above error:

sequential_model = keras.Sequential([
    keras.layers.Dense(2)
])
sequential_model.save("saved_model")

How to fix it: use tf.saved_model.save instead of model.save

sequential_model = keras.Sequential([keras.layers.Dense(2)])
sequential_model(np.random.rand(3, 5))
tf.saved_model.save(sequential_model, "saved_model")
INFO:tensorflow:Assets written to: saved_model/assets

INFO:tensorflow:Assets written to: saved_model/assets

Loading a TF SavedModel

Loading a TF SavedModel file via keras.models.load_model() is no longer supported If you try to use keras.models.load_model() with a TF SavedModel, you will get the following error:

ValueError: File format not supported: filepath=saved_model. Keras 3 only supports V3
`.keras` files and legacy H5 format files (`.h5` extension). Note that the legacy
SavedModel format is not supported by `load_model()` in Keras 3. In order to reload a
TensorFlow SavedModel as an inference-only layer in Keras 3, use
`keras.layers.TFSMLayer(saved_model, call_endpoint='serving_default')` (note that your
`call_endpoint` might have a different name).

The following snippet of code will reproduce the above error:

keras.models.load_model("saved_model")

How to fix it: Use keras.layers.TFSMLayer(filepath, call_endpoint="serving_default") to reload a TF SavedModel as a Keras layer. This is not limited to SavedModels that originate from Keras – it will work with any SavedModel, e.g. TF-Hub models.

keras.layers.TFSMLayer("saved_model", call_endpoint="serving_default")
<TFSMLayer name=tfsm_layer, built=True>

Using deeply nested inputs in Functional Models

Model() can no longer be passed deeply nested inputs/outputs (nested more than 1 level deep, e.g. lists of lists of tensors).

You would encounter errors as follows:

ValueError: When providing `inputs` as a dict, all values in the dict must be
KerasTensors. Received: inputs={'foo': <KerasTensor shape=(None, 1), dtype=float32,
sparse=None, name=foo>, 'bar': {'baz': <KerasTensor shape=(None, 1), dtype=float32,
sparse=None, name=bar>}} including invalid value {'baz': <KerasTensor shape=(None, 1),
dtype=float32, sparse=None, name=bar>} of type <class 'dict'>

The following snippet of code will reproduce the above error:

inputs = {
    "foo": keras.Input(shape=(1,), name="foo"),
    "bar": {
        "baz": keras.Input(shape=(1,), name="bar"),
    },
}
outputs = inputs["foo"] + inputs["bar"]["baz"]
keras.Model(inputs, outputs)

How to fix it: replace nested input with either dicts, lists, and tuples of input tensors.

inputs = {
    "foo": keras.Input(shape=(1,), name="foo"),
    "bar": keras.Input(shape=(1,), name="bar"),
}
outputs = inputs["foo"] + inputs["bar"]
keras.Model(inputs, outputs)
<Functional name=functional_2, built=True>

TF autograph

In Keras 2, TF autograph is enabled by default on the call() method of custom layers. In Keras 3, it is not. This means you may have to use cond ops if you're using control flow, or alternatively you can decorate your call() method with @tf.function.

You would encounter an error as follows:

OperatorNotAllowedInGraphError: Exception encountered when calling MyCustomLayer.call().

Using a symbolic [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor) as a Python `bool` is not allowed. You can attempt the
following resolutions to the problem: If you are running in Graph mode, use Eager
execution mode or decorate this function with @tf.function. If you are using AutoGraph,
you can try decorating this function with @tf.function. If that does not work, then you
may be using an unsupported feature or your source code may not be visible to AutoGraph.
Here is a [link for more information](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/ref
erence/limitations.md#access-to-source-code).

The following snippet of code will reproduce the above error:

class MyCustomLayer(keras.layers.Layer):

  def call(self, inputs):
    if tf.random.uniform(()) > 0.5:
      return inputs * 2
    else:
      return inputs / 2


layer = MyCustomLayer()
data = np.random.uniform(size=[3, 3])
model = keras.models.Sequential([layer])
model.compile(optimizer="adam", loss="mse")
model.predict(data)

How to fix it: decorate your call() method with @tf.function

class MyCustomLayer(keras.layers.Layer):
    @tf.function()
    def call(self, inputs):
        if tf.random.uniform(()) > 0.5:
            return inputs * 2
        else:
            return inputs / 2


layer = MyCustomLayer()
data = np.random.uniform(size=[3, 3])
model = keras.models.Sequential([layer])
model.compile(optimizer="adam", loss="mse")
model.predict(data)
 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step

array([[0.69081205, 1.0757748 , 0.06216738],
       [0.86100876, 0.92610997, 1.7946503 ],
       [1.0368572 , 1.0535108 , 1.1335285 ]], dtype=float32)

Calling TF ops with a KerasTensor

Using a TF op on a Keras tensor during functional model construction is disallowed: "A KerasTensor cannot be used as input to a TensorFlow function".

The error you would encounter would be as follows:

ValueError: A KerasTensor cannot be used as input to a TensorFlow function. A KerasTensor
is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional
models or Keras Functions. You can only use it as input to a Keras layer or a Keras
operation (from the namespaces `keras.layers` and `keras.operations`).

The following snippet of code will reproduce the error:

input = keras.layers.Input([2, 2, 1])
tf.squeeze(input)

How to fix it: use an equivalent op from keras.ops.

input = keras.layers.Input([2, 2, 1])
keras.ops.squeeze(input)
<KerasTensor shape=(None, 2, 2), dtype=float32, sparse=None, name=keras_tensor_6>

Multi-output model evaluate()

The evaluate() method of a multi-output model no longer returns individual output losses separately. Instead, you should utilize the metrics argument in the compile() method to keep track of these losses.

When dealing with multiple named outputs, such as output_a and output_b, the legacy tf.keras would include _loss, _loss, and similar entries in metrics. However, in keras 3.0, these entries are not automatically added to metrics. They must be explicitly provided in the metrics list for each individual output.

The following snippet of code will reproduce the above behavior:

from keras import layers
# A functional model with multiple outputs
inputs = layers.Input(shape=(10,))
x1 = layers.Dense(5, activation='relu')(inputs)
x2 = layers.Dense(5, activation='relu')(x1)
output_1 = layers.Dense(5, activation='softmax', name="output_1")(x1)
output_2 = layers.Dense(5, activation='softmax', name="output_2")(x2)
model = keras.Model(inputs=inputs, outputs=[output_1, output_2])
model.compile(optimizer='adam', loss='categorical_crossentropy')
# dummy data
x_test = np.random.uniform(size=[10, 10])
y_test = np.random.uniform(size=[10, 5])

model.evaluate(x_test, y_test)
from keras import layers

# A functional model with multiple outputs
inputs = layers.Input(shape=(10,))
x1 = layers.Dense(5, activation="relu")(inputs)
x2 = layers.Dense(5, activation="relu")(x1)
output_1 = layers.Dense(5, activation="softmax", name="output_1")(x1)
output_2 = layers.Dense(5, activation="softmax", name="output_2")(x2)
# dummy data
x_test = np.random.uniform(size=[10, 10])
y_test = np.random.uniform(size=[10, 5])
multi_output_model = keras.Model(inputs=inputs, outputs=[output_1, output_2])
multi_output_model.compile(
    optimizer="adam",
    loss="categorical_crossentropy",
    metrics=["categorical_crossentropy", "categorical_crossentropy"],
)
multi_output_model.evaluate(x_test, y_test)
 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 111ms/step - loss: 3.7628 - output_1_categorical_crossentropy: 3.7628

[3.762784481048584, 3.762784481048584]

TensorFlow variables tracking

Setting a tf.Variable as an attribute of a Keras 3 layer or model will not automatically track the variable, unlike in Keras 2. The following snippet of code will show that the tf.Variables are not being tracked.

class MyCustomLayer(keras.layers.Layer):
    def __init__(self, units):
        super().__init__()
        self.units = units

    def build(self, input_shape):
        input_dim = input_shape[-1]
        self.w = tf.Variable(initial_value=tf.zeros([input_dim, self.units]))
        self.b = tf.Variable(initial_value=tf.zeros([self.units,]))

    def call(self, inputs):
        return keras.ops.matmul(inputs, self.w) + self.b


layer = MyCustomLayer(3)
data = np.random.uniform(size=[3, 3])
model = keras.models.Sequential([layer])
model.compile(optimizer="adam", loss="mse")
model.predict(data)
# The model does not have any trainable variables
for layer in model.layers:
    print(layer.trainable_variables)

You will see the following warning:

UserWarning: The model does not have any trainable weights.
  warnings.warn("The model does not have any trainable weights.")

How to fix it: use self.add_weight() method or opt for a keras.Variable instead. If you are currently using tf.variable, you can switch to keras.Variable.

class MyCustomLayer(keras.layers.Layer):
    def __init__(self, units):
        super().__init__()
        self.units = units

    def build(self, input_shape):
        input_dim = input_shape[-1]
        self.w = self.add_weight(
            shape=[input_dim, self.units],
            initializer="zeros",
        )
        self.b = self.add_weight(
            shape=[
                self.units,
            ],
            initializer="zeros",
        )

    def call(self, inputs):
        return keras.ops.matmul(inputs, self.w) + self.b


layer = MyCustomLayer(3)
data = np.random.uniform(size=[3, 3])
model = keras.models.Sequential([layer])
model.compile(optimizer="adam", loss="mse")
model.predict(data)
# Verify that the variables are now being tracked
for layer in model.layers:
    print(layer.trainable_variables)
 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step
[<KerasVariable shape=(3, 3), dtype=float32, path=sequential_2/my_custom_layer_1/variable>, <KerasVariable shape=(3,), dtype=float32, path=sequential_2/my_custom_layer_1/variable_1>]

None entries in nested call() arguments

None entries are not allowed as part of nested (e.g. list/tuples) tensor arguments in Layer.call(), nor as part of call()'s nested return values.

If the None in the argument is intentional and serves a specific purpose, ensure that the argument is optional and structure it as a separate parameter. For example, consider defining the call method with optional argument.

The following snippet of code will reproduce the error.

class CustomLayer(keras.layers.Layer):
    def __init__(self):
        super().__init__()

    def call(self, inputs):
        foo = inputs["foo"]
        baz = inputs["bar"]["baz"]
        if baz is not None:
            return foo + baz
        return foo

layer = CustomLayer()
inputs = {
    "foo": keras.Input(shape=(1,), name="foo"),
    "bar": {
        "baz": None,
    },
}
layer(inputs)

How to fix it:

Solution 1: Replace None with a value, like this:

class CustomLayer(keras.layers.Layer):
    def __init__(self):
        super().__init__()

    def call(self, inputs):
        foo = inputs["foo"]
        baz = inputs["bar"]["baz"]
        return foo + baz


layer = CustomLayer()
inputs = {
    "foo": keras.Input(shape=(1,), name="foo"),
    "bar": {
        "baz": keras.Input(shape=(1,), name="bar"),
    },
}
layer(inputs)
<KerasTensor shape=(None, 1), dtype=float32, sparse=False, name=keras_tensor_14>

Solution 2: Define the call method with an optional argument. Here is an example of this fix:

class CustomLayer(keras.layers.Layer):
    def __init__(self):
        super().__init__()

    def call(self, foo, baz=None):
        if baz is not None:
            return foo + baz
        return foo


layer = CustomLayer()
foo = keras.Input(shape=(1,), name="foo")
baz = None
layer(foo, baz=baz)
<KerasTensor shape=(None, 1), dtype=float32, sparse=False, name=keras_tensor_15>

State-building issues

Keras 3 is significantly stricter than Keras 2 about when state (e.g. numerical weight variables) can be created. Keras 3 wants all state to be created before the model can be trained. This is a requirement for using JAX (whereas TensorFlow was very lenient about state creation timing).

Keras layers should create their state either in their constructor (__init__() method) or in their build() method. They should avoid creating state in call().

If you ignore this recommendation and create state in call() anyway (e.g. by calling a previously unbuilt layer), then Keras will attempt to build the layer automatically by calling the call() method on symbolic inputs before training. However, this attempt at automatic state creation may fail in certain cases. This will cause an error that looks like like this:

Layer 'frame_position_embedding' looks like it has unbuilt state,
but Keras is not able to trace the layer `call()` in order to build it automatically.
Possible causes:
1. The `call()` method of your layer may be crashing.
Try to `__call__()` the layer eagerly on some test input first to see if it works.
E.g. `x = np.random.random((3, 4)); y = layer(x)`
2. If the `call()` method is correct, then you may need to implement
the `def build(self, input_shape)` method on your layer.
It should create all variables used by the layer
(e.g. by calling `layer.build()` on all its children layers).

You could reproduce this error with the following layer, when used with the JAX backend:

class PositionalEmbedding(keras.layers.Layer):
    def __init__(self, sequence_length, output_dim, **kwargs):
        super().__init__(**kwargs)
        self.position_embeddings = layers.Embedding(
            input_dim=sequence_length, output_dim=output_dim
        )
        self.sequence_length = sequence_length
        self.output_dim = output_dim

    def call(self, inputs):
        inputs = keras.ops.cast(inputs, self.compute_dtype)
        length = keras.ops.shape(inputs)[1]
        positions = keras.ops.arange(start=0, stop=length, step=1)
        embedded_positions = self.position_embeddings(positions)
        return inputs + embedded_positions

How to fix it: Do exactly what the error message asks. First, try to run the layer eagerly to see if the call() method is in fact correct (note: if it was working in Keras 2, then it is correct and does not need to be changed). If it is indeed correct, then you should implement a build(self, input_shape) method that creates all of the layer's state, including the state of sublayers. Here's the fix as applied for the layer above (note the build() method):

class PositionalEmbedding(keras.layers.Layer):
    def __init__(self, sequence_length, output_dim, **kwargs):
        super().__init__(**kwargs)
        self.position_embeddings = layers.Embedding(
            input_dim=sequence_length, output_dim=output_dim
        )
        self.sequence_length = sequence_length
        self.output_dim = output_dim

    def build(self, input_shape):
        self.position_embeddings.build(input_shape)

    def call(self, inputs):
        inputs = keras.ops.cast(inputs, self.compute_dtype)
        length = keras.ops.shape(inputs)[1]
        positions = keras.ops.arange(start=0, stop=length, step=1)
        embedded_positions = self.position_embeddings(positions)
        return inputs + embedded_positions

Removed features

A small number of legacy features with very low usage were removed from Keras 3 as a cleanup measure:

  • keras.layers.ThresholdedReLU is removed. Instead, you can simply use the ReLU layer with the argument threshold.
  • Symbolic Layer.add_loss(): Symbolic add_loss() is removed (you can still use add_loss() inside the call() method of a layer/model).
  • Locally connected layers (LocallyConnected1D, LocallyConnected2D are removed due to very low usage. To use locally connected layers, copy the layer implementation into your own codebase.
  • keras.layers.experimental.RandomFourierFeatures is removed due to very low usage. To use it, copy the layer implementation into your own codebase.
  • Removed layer attributes: Layer attributes metrics, dynamic are removed. metrics is still available on the Model class.
  • The constants and time_major arguments in RNN layers are removed. The constants argument was a remnant of Theano and had very low usage. The time_major argument also had very low usage.
  • reset_metrics argument: The reset_metrics argument is removed from model.*_on_batch() methods. This argument had very low usage.
  • The keras.constraints.RadialConstraint object is removed. This object had very low usage.

Transitioning to backend-agnostic Keras 3

Keras 3 code with the TensorFlow backend will work with native TensorFlow APIs. However, if you want your code to be backend-agnostic, you will need to:

  • Replace all of the tf.* API calls with their equivalent Keras APIs.
  • Convert your custom train_step/test_step methods to a multi-framework implementation.
  • Make sure you're using stateless keras.random ops correctly in your layers.

Let's go over each point in detail.

Switching to Keras ops

In many cases, this is the only thing you need to do to start being able to run your custom layers and metrics with JAX and PyTorch: replace any tf.*, tf.math*, tf.linalg.*, etc. with keras.ops.*. Most TF ops should be consistent with Keras 3. If the names different, they will be highlighted in this guide.

NumPy ops

Keras implements the NumPy API as part of keras.ops.

The table below only lists a small subset of TensorFlow and Keras ops; ops not listed are usually named the same in both frameworks (e.g. reshape, matmul, cast, etc.)

TensorFlow Keras 3.0
tf.abs keras.ops.absolute
tf.reduce_all keras.ops.all
tf.reduce_max keras.ops.amax
tf.reduce_min keras.ops.amin
tf.reduce_any keras.ops.any
tf.concat keras.ops.concatenate
tf.range keras.ops.arange
tf.acos keras.ops.arccos
tf.asin keras.ops.arcsin
tf.asinh keras.ops.arcsinh
tf.atan keras.ops.arctan
tf.atan2 keras.ops.arctan2
tf.atanh keras.ops.arctanh
tf.convert_to_tensor keras.ops.convert_to_tensor
tf.reduce_mean keras.ops.mean
tf.clip_by_value keras.ops.clip
tf.math.conj keras.ops.conjugate
tf.linalg.diag_part keras.ops.diagonal
tf.reverse keras.ops.flip
tf.gather keras.ops.take
tf.math.is_finite keras.ops.isfinite
tf.math.is_inf keras.ops.isinf
tf.math.is_nan keras.ops.isnan
tf.reduce_max keras.ops.max
tf.reduce_mean keras.ops.mean
tf.reduce_min keras.ops.min
tf.rank keras.ops.ndim
tf.math.pow keras.ops.power
tf.reduce_prod keras.ops.prod
tf.math.reduce_std keras.ops.std
tf.reduce_sum keras.ops.sum
tf.gather keras.ops.take
tf.gather_nd keras.ops.take_along_axis
tf.math.reduce_variance keras.ops.var

Others ops

TensorFlow Keras 3.0
tf.nn.sigmoid_cross_entropy_with_logits keras.ops.binary_crossentropy (mind the from_logits argument)
tf.nn.sparse_softmax_cross_entropy_with_logits keras.ops.sparse_categorical_crossentropy (mind the from_logits argument)
tf.nn.sparse_softmax_cross_entropy_with_logits keras.ops.categorical_crossentropy(target, output, from_logits=False, axis=-1)
tf.nn.conv1d, tf.nn.conv2d, tf.nn.conv3d, tf.nn.convolution keras.ops.conv
tf.nn.conv_transpose, tf.nn.conv1d_transpose, tf.nn.conv2d_transpose, tf.nn.conv3d_transpose keras.ops.conv_transpose
tf.nn.depthwise_conv2d keras.ops.depthwise_conv
tf.nn.separable_conv2d keras.ops.separable_conv
tf.nn.batch_normalization No direct equivalent; use keras.layers.BatchNormalization
tf.nn.dropout keras.random.dropout
tf.nn.embedding_lookup keras.ops.take
tf.nn.l2_normalize keras.utils.normalize (not an op)
x.numpy keras.ops.convert_to_numpy
tf.scatter_nd_update keras.ops.scatter_update
tf.tensor_scatter_nd_update keras.ops.slice_update
tf.signal.fft2d keras.ops.fft2
tf.signal.inverse_stft keras.ops.istft

Custom train_step() methods

Your models may include a custom train_step() or test_step() method, which rely on TensorFlow-only APIs – for instance, your train_step() method may leverage TensorFlow's tf.GradientTape. To convert such models to run on JAX or PyTorch, you will have a write a different train_step() implementation for each backend you want to support.

In some cases, you might be able to simply override the Model.compute_loss() method and make it fully backend-agnostic, instead of overriding train_step(). Here's an example of a layer with a custom compute_loss() method which works across JAX, TensorFlow, and PyTorch:

class MyModel(keras.Model):
    def compute_loss(self, x=None, y=None, y_pred=None, sample_weight=None):
        loss = keras.ops.sum(keras.losses.mean_squared_error(y, y_pred, sample_weight))
        return loss

If you need to modify the optimization mechanism itself, beyond the loss computation, then you will need to override train_step(), and implement one train_step method per backend, like below.

See the following guides for details on how each backend should be handled:

class MyModel(keras.Model):
    def train_step(self, *args, **kwargs):
        if keras.backend.backend() == "jax":
            return self._jax_train_step(*args, **kwargs)
        elif keras.backend.backend() == "tensorflow":
            return self._tensorflow_train_step(*args, **kwargs)
        elif keras.backend.backend() == "torch":
            return self._torch_train_step(*args, **kwargs)

    def _jax_train_step(self, state, data):
        pass  # See guide: keras.io/guides/custom_train_step_in_jax/

    def _tensorflow_train_step(self, data):
        pass  # See guide: keras.io/guides/custom_train_step_in_tensorflow/

    def _torch_train_step(self, data):
        pass  # See guide: keras.io/guides/custom_train_step_in_torch/

RNG-using layers

Keras 3 has a new keras.random namespace, containing:

These operations are stateless, which means that if you pass a seed argument, they will return the same result every time. Like this:

print(keras.random.normal(shape=(), seed=123))
print(keras.random.normal(shape=(), seed=123))
tf.Tensor(0.7832616, shape=(), dtype=float32)
tf.Tensor(0.7832616, shape=(), dtype=float32)

Crucially, this differs from the behavior of stateful tf.random ops:

print(tf.random.normal(shape=(), seed=123))
print(tf.random.normal(shape=(), seed=123))
tf.Tensor(2.4435377, shape=(), dtype=float32)
tf.Tensor(-0.6386405, shape=(), dtype=float32)

When you write a RNG-using layer, such as a custom dropout layer, you are going to want to use a different seed value at layer call. However, you cannot just increment a Python integer and pass it, because while this would work fine when executed eagerly, it would not work as expected when using compilation (which is available with JAX, TensorFlow, and PyTorch). When compiling the layer, the first Python integer seed value seen by the layer would be hardcoded into the compiled graph.

To address this, you should pass as the seed argument an instance of a stateful keras.random.SeedGenerator object, like this:

seed_generator = keras.random.SeedGenerator(1337)
print(keras.random.normal(shape=(), seed=seed_generator))
print(keras.random.normal(shape=(), seed=seed_generator))
tf.Tensor(0.6077996, shape=(), dtype=float32)
tf.Tensor(0.8211102, shape=(), dtype=float32)

So when writing a RNG using layer, you would use the following pattern:

class RandomNoiseLayer(keras.layers.Layer):
    def __init__(self, noise_rate, **kwargs):
        super().__init__(**kwargs)
        self.noise_rate = noise_rate
        self.seed_generator = keras.random.SeedGenerator(1337)

    def call(self, inputs):
        noise = keras.random.uniform(
            minval=0, maxval=self.noise_rate, seed=self.seed_generator
        )
        return inputs + noise

Such a layer is safe to use in any setting – in eager execution or in a compiled model. Each layer call will be using a different seed value, as expected.