keras

name	keras
description	Multi-backend deep learning library for building, training, running inference, and saving neural network models in Python.
version	3.13.2
ecosystem	python
license	MIT
generated_with	gpt-5.2

Imports

import keras
from keras import Model, layers

Core Patterns

Run inference with Model.predict() ✅ Current

import os

# Use a backend that is installed; here, "tensorflow" is most widely available.
os.environ["KERAS_BACKEND"] = "tensorflow"

# Keras 3 requires TensorFlow backend as an extra dependency for most environments.
import tensorflow as tf
import keras
from keras import layers
import numpy as np

model = keras.Sequential(
    [
        layers.Input(shape=(4,)),
        layers.Dense(8, activation="relu"),
        layers.Dense(3, activation="softmax"),
    ]
)

x = np.random.RandomState(0).randn(5, 4).astype("float32")
preds = model.predict(x, verbose=0)
print(preds.shape)

• Use keras.Model.predict(x) for forward-pass inference on NumPy arrays (and other backend-compatible inputs).
• Works with any supported backend; for OpenVINO, inference is the intended workflow.

Save a model with Model.save() to the native .keras format ✅ Current

import os
import tempfile

# Use a backend that supports saving; here, "tensorflow" is most widely available.
os.environ["KERAS_BACKEND"] = "tensorflow"

import tensorflow as tf
import keras
from keras import layers
import numpy as np

model = keras.Sequential(
    [
        layers.Input(shape=(4,)),
        layers.Dense(8, activation="relu"),
        layers.Dense(1),
    ]
)
# Save to temp path in .keras format
path = os.path.join(tempfile.gettempdir(), "example_model.keras")
model.save(path)
print("Saved to:", path)

• Prefer saving to a filename ending in .keras for the up-to-date Keras 3 native format (not legacy/ambiguous formats).

Configure backend via environment before import ✅ Current

import os

# Set backend before importing keras
os.environ["KERAS_BACKEND"] = "tensorflow"

import tensorflow as tf
import keras
import numpy as np

# Minimal model: single Dense layer, no training, just inference
model = keras.models.Sequential([
    keras.layers.Input(shape=(4,)),
    keras.layers.Dense(2, activation="relu")
])

# Create dummy input and do a forward pass
x = np.random.rand(3, 4).astype(np.float32)
y = model(x)

# Print backend name as in the example
print("Keras imported with backend:", os.environ["KERAS_BACKEND"])

• Backend selection is a pre-import configuration step; do not attempt to switch backends after import keras.

OpenVINO backend for inference-only Model.predict() ✅ Current

import os

# OpenVINO backend is intended for inference-only usage.
os.environ["KERAS_BACKEND"] = "openvino"

import numpy as np
import keras
from keras import layers

model = keras.Sequential(
    [
        layers.Input(shape=(4,)),
        layers.Dense(8, activation="relu"),
        layers.Dense(2),
    ]
)

x = np.random.RandomState(0).randn(3, 4).astype("float32")
y = model.predict(x, verbose=0)
print(y)

• Use OpenVINO backend to run predictions; do not use it for training workflows.

Configuration

• Backend selection (required for multi-backend):
  • Set KERAS_BACKEND before importing keras.
    • Valid values (per installation): tensorflow, jax, torch, openvino (inference-only).
  • Alternatively configure via ~/.keras/keras.json before import.
• Backend immutability:
  • Backend cannot be changed reliably after keras is imported; restart the process/kernel to switch.
• Installation convention:
  • Install Keras 3 from PyPI as keras.
  • Keras 2 remains separately available as tf-keras.
  • Install at least one backend package alongside keras: tensorflow, jax, torch (and optionally openvino for inference-only).
• GPU environments:
  • Prefer separate environments per backend to avoid CUDA version mismatches; use backend-provided CUDA requirements files when applicable.

Pitfalls

Wrong: Setting KERAS_BACKEND after importing keras

import keras
import os

os.environ["KERAS_BACKEND"] = "jax"  # too late; has no reliable effect

Right: Set KERAS_BACKEND before importing keras

import os

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

import keras

Wrong: Trying to train on the OpenVINO backend (inference-only)

import os
os.environ["KERAS_BACKEND"] = "openvino"

import numpy as np
import keras
from keras import layers

model = keras.Sequential([layers.Input(shape=(4,)), layers.Dense(1)])
x = np.random.RandomState(0).randn(8, 4).astype("float32")
y = np.random.RandomState(1).randn(8, 1).astype("float32")

# Inference-only backend: training workflows like fit() are not supported.
model.fit(x, y, epochs=1)

Right: Use OpenVINO backend for Model.predict() only

import os
os.environ["KERAS_BACKEND"] = "openvino"

import numpy as np
import keras
from keras import layers

model = keras.Sequential([layers.Input(shape=(4,)), layers.Dense(1)])
x = np.random.RandomState(0).randn(8, 4).astype("float32")

preds = model.predict(x, verbose=0)
print(preds.shape)

Wrong: Saving without an explicit .keras extension (ambiguous/legacy)

import os
import tempfile

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

import keras
from keras import layers

model = keras.Sequential([layers.Input(shape=(4,)), layers.Dense(1)])

# May not use the up-to-date native Keras format.
model.save(os.path.join(tempfile.gettempdir(), "model"))

Right: Save using the native .keras format

import os
import tempfile

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

import keras
from keras import layers

model = keras.Sequential([layers.Input(shape=(4,)), layers.Dense(1)])

model.save(os.path.join(tempfile.gettempdir(), "model.keras"))

Wrong: Expecting backend changes to apply within the same process

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

import keras

# Attempting to switch after import leads to inconsistent behavior.
os.environ["KERAS_BACKEND"] = "torch"

Right: Restart the process/kernel to change backend

# Process A:
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import keras

# To switch to torch, start a new Python process/kernel:
# Process B:
# import os
# os.environ["KERAS_BACKEND"] = "torch"
# import keras

References

• Home
• Repository

Migration from v2 (tf.keras / tf-keras)

• Packaging changed

  • Before (Keras 2): typically tf.keras (bundled with TensorFlow) or tf-keras on PyPI.
  • Now (Keras 3): install/import keras from PyPI; Keras 2 remains available as tf-keras.

• Backend selection is explicit and pre-import

  • Before: backend implicitly TensorFlow via tf.keras.
  • Now: choose backend via KERAS_BACKEND env var or ~/.keras/keras.json before importing keras.

• Prefer the native .keras saving format

  • Before: often SavedModel / H5 patterns.
  • Now: use model.save("path/model.keras") for the native Keras 3 format (especially when migrating).

Example (before/after):

# Before (TensorFlow + tf.keras)
import tensorflow as tf

model = tf.keras.Sequential([tf.keras.layers.Input(shape=(4,)), tf.keras.layers.Dense(1)])
model.save("model_path")  # legacy/TF-specific defaults

# After (Keras 3)
import os
os.environ["KERAS_BACKEND"] = "tensorflow"

import keras
from keras import layers

model = keras.Sequential([layers.Input(shape=(4,)), layers.Dense(1)])
model.save("model_path.keras")

Migration

Breaking changes from Keras 2.x to Keras 3.x:

• Default model save format is now .keras instead of legacy HDF5 (.h5).
  ⚠️ Update model.save() calls to use the .keras extension and format.
• Configuring backend must be done before importing keras; backend cannot be changed after import.
  ⚠️ Move all backend configuration (KERAS_BACKEND env var, config file) before any keras import statements.

Keras 3 is intended as a drop-in replacement for tf.keras when using the TensorFlow backend. For custom components, refactor to backend-agnostic implementations. Model saving should use the new .keras format. Configure backend before importing keras. See README and Keras 3 release announcement for more details.

API Reference

• keras - Top-level package for Keras 3 (multi-backend); backend configured pre-import.
• keras.Model - Model(inputs=None, outputs=None, name=None)
  • Base class for models; exposes inference and saving APIs.
• keras.Sequential - Sequential(layers=None, name=None)
  • Linear stack model constructor.
• keras.layers.Layer - Layer(name=None, trainable=True, dtype=None)
  • Base class for layers.
• keras.layers.Input - Input(shape=None, batch_size=None, name=None, dtype=None, sparse=None, tensor=None, ragged=None, batch_shape=None)
  • Defines input shape for models.
• keras.layers.InputSpec - InputSpec(dtype=None, shape=None, ndim=None, max_ndim=None, min_ndim=None, axes=None)
  • Used in layer input validation.
• keras.Model.predict(x, verbose=...) - Runs inference; key params: input x, verbosity.
• keras.Model.save(filepath) - Saves the model; prefer *.keras for native format.
• keras.Model.compile(...) - Configures training (backend-dependent); not supported for inference-only backends like OpenVINO.
• keras.Model.fit(...) - Training loop (when supported by backend).
• keras.KerasTensor - KerasTensor(shape, dtype, name=None, sparse=None, ragged=None, element_spec=None)
  • Symbolic tensor used internally and for model construction.
• keras.Variable - Variable(initial_value, name=None, dtype=None, trainable=True)
  • Backend variable abstraction.
• keras.Loss - Loss(reduction='auto', name=None)
• keras.Metric - Metric(name=None, dtype=None)
• keras.Optimizer - Optimizer(name, **kwargs)
• keras.Initializer - Initializer()
• keras.DTypePolicy - DTypePolicy(name_or_spec)
• keras.FloatDTypePolicy -

For additional layers, losses, optimizers, and utilities, see respective submodules:
keras.layers, keras.losses, keras.metrics, keras.optimizers, etc.

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