Convert Keras Core to Keras 3.

README.md

@@ -1,9 +1,9 @@
-[![](https://github.com/keras-team/keras-core/workflows/Tests/badge.svg?branch=main)](https://github.com/keras-team/keras-core/actions?query=workflow%3ATests+branch%3Amain)
-[![](https://badge.fury.io/py/keras-core.svg)](https://badge.fury.io/py/keras-core)
+[![](https://github.com/keras-team/keras/workflows/Tests/badge.svg?branch=main)](https://github.com/keras-team/keras/actions?query=workflow%3ATests+branch%3Amain)
+[![](https://badge.fury.io/py/keras.svg)](https://badge.fury.io/py/keras)
 
-# Keras Core: A new multi-backend Keras
+# Keras 3: A new multi-backend Keras
 
-Keras Core is a new multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.
+Keras 3 is a new multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.
 
 **WARNING:** At this time, this package is experimental.
 It has rough edges and not everything might work as expected.
@@ -13,7 +13,7 @@ Once ready, this package will become Keras 3.0 and subsume `tf.keras`.
 
 ## Local installation
 
-Keras Core is compatible with Linux and MacOS systems. To install a local development version:
+Keras 3 is compatible with Linux and MacOS systems. To install a local development version:
 
 1. Install dependencies:
 
@@ -28,7 +28,7 @@ python pip_build.py --install
 ```
 
 You should also install your backend of choice: `tensorflow`, `jax`, or `torch`.
-Note that `tensorflow` is required for using certain Keras Core features: certain preprocessing layers as
+Note that `tensorflow` is required for using certain Keras 3 features: certain preprocessing layers as
 well as `tf.data` pipelines.
 
 ## Configuring your backend
@@ -46,16 +46,16 @@ In Colab, you can do:
 import os
 os.environ["KERAS_BACKEND"] = "jax"
 
-import keras_core as keras
+import keras as keras
 ```
 
-**Note:** The backend must be configured before importing `keras_core`, and the backend cannot be changed after 
+**Note:** The backend must be configured before importing `keras`, and the backend cannot be changed after 
 the package has been imported.
 
 ## Backwards compatibility
 
-Keras Core is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your
-existing `tf.keras` code, change the `keras` imports to `keras_core`, make sure that your calls to `model.save()` are using
+Keras 3 is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your
+existing `tf.keras` code, change the `keras` imports to `keras`, make sure that your calls to `model.save()` are using
 the up-to-date `.keras` format, and you're done.
 
 If your `tf.keras` model does not include custom components, you can start running it on top of JAX or PyTorch immediately.
@@ -66,7 +66,7 @@ to a backend-agnostic implementation in just a few minutes.
 In addition, Keras models can consume datasets in any format, regardless of the backend you're using:
 you can train your models with your existing `tf.data.Dataset` pipelines or PyTorch `DataLoaders`.
 
-## Why use Keras Core?
+## Why use Keras 3?
 
 - Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework,
 e.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.

benchmarks/layer_benchmark/README.md

@@ -1,7 +1,7 @@
 # Benchmark the layer performance
 
 This directory contains benchmarks to compare the performance of
-`keras_core.layers.XXX` and `tf.keras.layers.XXX`. We compare the performance of
+`keras.layers.XXX` and `tf.keras.layers.XXX`. We compare the performance of
 both the forward pass and train step (forward & backward pass). 
 
 To run the benchmark, use the command below and change the flags according to

benchmarks/layer_benchmark/base_benchmark.py

@@ -4,7 +4,7 @@ import numpy as np
 import tensorflow as tf
 from absl import flags
 
-import keras_core
+import keras
 
 FLAGS = flags.FLAGS
 
@@ -66,7 +66,7 @@ class BenchmarkMetricsCallback:
             self.state["throughput"] = throughput
 
 
-class KerasCoreBenchmarkMetricsCallback(keras_core.callbacks.Callback):
+class KerasCoreBenchmarkMetricsCallback(keras.callbacks.Callback):
     def __init__(self, start_batch=1, stop_batch=None):
         self._callback = BenchmarkMetricsCallback(start_batch, stop_batch)
 
@@ -108,21 +108,21 @@ class LayerBenchmark:
         input_shape,
         flat_call_inputs=True,
         jit_compile=True,
-        keras_core_layer=None,
+        keras_layer=None,
         tf_keras_layer=None,
     ):
         self.layer_name = layer_name
-        _keras_core_layer_class = getattr(keras_core.layers, layer_name)
+        _keras_layer_class = getattr(keras.layers, layer_name)
         _tf_keras_layer_class = getattr(tf.keras.layers, layer_name)
 
-        if keras_core_layer is None:
-            # Sometimes you want to initialize the keras_core layer and tf_keras
+        if keras_layer is None:
+            # Sometimes you want to initialize the keras layer and tf_keras
             # layer in a different way. For example, `Bidirectional` layer,
-            # which takes in `keras_core.layers.Layer` and
+            # which takes in `keras.layers.Layer` and
             # `tf.keras.layer.Layer` separately.
-            self._keras_core_layer = _keras_core_layer_class(**init_args)
+            self._keras_layer = _keras_layer_class(**init_args)
         else:
-            self._keras_core_layer = keras_core_layer
+            self._keras_layer = keras_layer
 
         if tf_keras_layer is None:
             self._tf_keras_layer = _tf_keras_layer_class(**init_args)
@@ -130,14 +130,14 @@ class LayerBenchmark:
             self._tf_keras_layer = tf_keras_layer
 
         self.input_shape = input_shape
-        self._keras_core_model = self._build_keras_core_model(
+        self._keras_model = self._build_keras_model(
             input_shape, flat_call_inputs
         )
         self._tf_keras_model = self._build_tf_keras_model(
             input_shape, flat_call_inputs
         )
 
-        self._keras_core_model.compile(
+        self._keras_model.compile(
             loss="mse", optimizer="sgd", jit_compile=jit_compile
         )
         self._tf_keras_model.compile(
@@ -148,19 +148,19 @@ class LayerBenchmark:
         self.jit_compile = jit_compile
         self.input_shape = input_shape
 
-    def _build_keras_core_model(self, input_shape, flat_call_inputs=True):
+    def _build_keras_model(self, input_shape, flat_call_inputs=True):
         inputs = []
         if not isinstance(input_shape[0], (tuple, list)):
             input_shape = [input_shape]
 
         for shape in input_shape:
-            inputs.append(keras_core.Input(shape=shape))
+            inputs.append(keras.Input(shape=shape))
 
         if flat_call_inputs:
-            outputs = self._keras_core_layer(*inputs)
+            outputs = self._keras_layer(*inputs)
         else:
-            outputs = self._keras_core_layer(inputs)
-        return keras_core.Model(inputs=inputs, outputs=outputs)
+            outputs = self._keras_layer(inputs)
+        return keras.Model(inputs=inputs, outputs=outputs)
 
     def _build_tf_keras_model(self, input_shape, flat_call_inputs=True):
         inputs = []
@@ -195,7 +195,7 @@ class LayerBenchmark:
             stop_batch=num_iterations
         )
 
-        self._keras_core_model.predict(
+        self._keras_model.predict(
             data,
             batch_size=batch_size,
             callbacks=[callback],
@@ -207,15 +207,15 @@ class LayerBenchmark:
             callbacks=[tf_keras_callback],
         )
 
-        keras_core_throughput = (
+        keras_throughput = (
             callback._callback.state["throughput"] * batch_size
         )
         tf_keras_throughput = (
             tf_keras_callback._callback.state["throughput"] * batch_size
         )
         print(
-            f"Keras Core throughput of forward pass of {self.layer_name}: "
-            f"{keras_core_throughput:.2f} samples/sec."
+            f"Keras 3 throughput of forward pass of {self.layer_name}: "
+            f"{keras_throughput:.2f} samples/sec."
         )
         print(
             f"TF Keras throughput of forward pass of {self.layer_name}: "
@@ -240,15 +240,15 @@ class LayerBenchmark:
             if self.flat_call_inputs:
                 # Scale by a small factor to avoid zero gradients.
                 label = (
-                    keras_core.backend.convert_to_numpy(
-                        self._keras_core_layer(*data)
+                    keras.backend.convert_to_numpy(
+                        self._keras_layer(*data)
                     )
                     * 1.001
                 )
             else:
                 label = (
-                    keras_core.backend.convert_to_numpy(
-                        self._keras_core_layer(data)
+                    keras.backend.convert_to_numpy(
+                        self._keras_layer(data)
                     )
                     * 1.001
                 )
@@ -259,7 +259,7 @@ class LayerBenchmark:
             stop_batch=num_iterations
         )
 
-        self._keras_core_model.fit(
+        self._keras_model.fit(
             data,
             label,
             batch_size=batch_size,
@@ -272,15 +272,15 @@ class LayerBenchmark:
             callbacks=[tf_keras_callback],
         )
 
-        keras_core_throughput = (
+        keras_throughput = (
             callback._callback.state["throughput"] * batch_size
         )
         tf_keras_throughput = (
             tf_keras_callback._callback.state["throughput"] * batch_size
         )
         print(
-            f"Keras Core throughput of forward & backward pass of "
-            f"{self.layer_name}: {keras_core_throughput:.2f} samples/sec."
+            f"Keras 3 throughput of forward & backward pass of "
+            f"{self.layer_name}: {keras_throughput:.2f} samples/sec."
         )
         print(
             f"TF Keras  throughput of forward & backward pass of "

benchmarks/layer_benchmark/rnn_benchmark.py

@@ -16,7 +16,7 @@ import tensorflow as tf
 from absl import app
 from absl import flags
 
-import keras_core
+import keras
 from benchmarks.layer_benchmark.base_benchmark import LayerBenchmark
 
 FLAGS = flags.FLAGS
@@ -194,8 +194,8 @@ def benchmark_bidirectional(
 ):
     layer_name = "Bidirectional"
     init_args = {}
-    keras_core_layer = keras_core.layers.Bidirectional(
-        keras_core.layers.LSTM(32)
+    keras_layer = keras.layers.Bidirectional(
+        keras.layers.LSTM(32)
     )
     tf_keras_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32))
     benchmark = LayerBenchmark(
@@ -203,7 +203,7 @@ def benchmark_bidirectional(
         init_args,
         input_shape=[256, 256],
         jit_compile=jit_compile,
-        keras_core_layer=keras_core_layer,
+        keras_layer=keras_layer,
         tf_keras_layer=tf_keras_layer,
     )
 
@@ -225,8 +225,8 @@ def benchmark_time_distributed(
 ):
     layer_name = "TimeDistributed"
     init_args = {}
-    keras_core_layer = keras_core.layers.TimeDistributed(
-        keras_core.layers.Conv2D(16, (3, 3))
+    keras_layer = keras.layers.TimeDistributed(
+        keras.layers.Conv2D(16, (3, 3))
     )
     tf_keras_layer = tf.keras.layers.TimeDistributed(
         tf.keras.layers.Conv2D(16, (3, 3))
@@ -236,7 +236,7 @@ def benchmark_time_distributed(
         init_args,
         input_shape=[10, 32, 32, 3],
         jit_compile=jit_compile,
-        keras_core_layer=keras_core_layer,
+        keras_layer=keras_layer,
         tf_keras_layer=tf_keras_layer,
     )
 

benchmarks/model_benchmark/benchmark_utils.py

@@ -1,9 +1,9 @@
 import time
 
-import keras_core
+import keras
 
 
-class BenchmarkMetricsCallback(keras_core.callbacks.Callback):
+class BenchmarkMetricsCallback(keras.callbacks.Callback):
     def __init__(self, start_batch=1, stop_batch=None):
         self.start_batch = start_batch
         self.stop_batch = stop_batch

benchmarks/model_benchmark/bert_benchmark.py

@@ -21,7 +21,7 @@ from absl import flags
 from absl import logging
 from model_benchmark.benchmark_utils import BenchmarkMetricsCallback
 
-import keras_core as keras
+import keras as keras
 
 flags.DEFINE_string("model_size", "small", "The size of model to benchmark.")
 flags.DEFINE_string(

benchmarks/model_benchmark/image_classification_benchmark.py

@@ -27,7 +27,7 @@ from absl import flags
 from absl import logging
 from model_benchmark.benchmark_utils import BenchmarkMetricsCallback
 
-import keras_core as keras
+import keras as keras
 
 flags.DEFINE_string("model", "EfficientNetV2B0", "The model to benchmark.")
 flags.DEFINE_integer("epochs", 1, "The number of epochs.")

benchmarks/torch_ctl_benchmark/conv_model_benchmark.py

@@ -9,9 +9,9 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 
-import keras_core
+import keras
 from benchmarks.torch_ctl_benchmark.benchmark_utils import train_loop
-from keras_core import layers
+from keras import layers
 
 num_classes = 2
 input_shape = (3, 256, 256)
@@ -55,8 +55,8 @@ class TorchModel(torch.nn.Module):
         return x
 
 
-def run_keras_core_custom_training_loop():
-    keras_model = keras_core.Sequential(
+def run_keras_custom_training_loop():
+    keras_model = keras.Sequential(
         [
             layers.Input(shape=input_shape),
             layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
@@ -74,7 +74,7 @@ def run_keras_core_custom_training_loop():
         num_epochs=num_epochs,
         optimizer=optimizer,
         loss_fn=loss_fn,
-        framework="keras_core",
+        framework="keras",
     )
 
 
@@ -93,5 +93,5 @@ def run_torch_custom_training_loop():
 
 
 if __name__ == "__main__":
-    run_keras_core_custom_training_loop()
+    run_keras_custom_training_loop()
     run_torch_custom_training_loop()

benchmarks/torch_ctl_benchmark/dense_model_benchmark.py

@@ -9,9 +9,9 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 
-import keras_core
+import keras
 from benchmarks.torch_ctl_benchmark.benchmark_utils import train_loop
-from keras_core import layers
+from keras import layers
 
 num_classes = 2
 input_shape = (8192,)
@@ -55,8 +55,8 @@ class TorchModel(torch.nn.Module):
         return x
 
 
-def run_keras_core_custom_training_loop():
-    keras_model = keras_core.Sequential(
+def run_keras_custom_training_loop():
+    keras_model = keras.Sequential(
         [
             layers.Input(shape=input_shape),
             layers.Dense(64, activation="relu"),
@@ -73,7 +73,7 @@ def run_keras_core_custom_training_loop():
         num_epochs=num_epochs,
         optimizer=optimizer,
         loss_fn=loss_fn,
-        framework="keras_core",
+        framework="keras",
     )
 
 
@@ -92,5 +92,5 @@ def run_torch_custom_training_loop():
 
 
 if __name__ == "__main__":
-    run_keras_core_custom_training_loop()
+    run_keras_custom_training_loop()
     run_torch_custom_training_loop()

codecov.yml

@@ -26,10 +26,10 @@ flag_management:
       - type: patch
         target: auto
   individual_flags:
-    - name: keras_core
+    - name: keras
       paths:
-        - keras_core
-    - name: keras_core.applications
+        - keras
+    - name: keras.applications
       paths:
-        - keras_core/applications
+        - keras/applications
       carryforward: true

conftest.py

@@ -8,7 +8,7 @@ except ImportError:
 
 import pytest
 
-from keras_core.backend import backend
+from keras.backend import backend
 
 
 def pytest_configure(config):

examples/demo_custom_jax_workflow.py

@@ -7,12 +7,12 @@ os.environ["KERAS_BACKEND"] = "jax"
 import jax
 import numpy as np
 
-from keras_core import Model
-from keras_core import backend
-from keras_core import initializers
-from keras_core import layers
-from keras_core import ops
-from keras_core import optimizers
+from keras import Model
+from keras import backend
+from keras import initializers
+from keras import layers
+from keras import ops
+from keras import optimizers
 
 
 class MyDense(layers.Layer):

examples/demo_custom_layer_backend_agnostic.py

@@ -1,13 +1,13 @@
 import numpy as np
 
-import keras_core
-from keras_core import Model
-from keras_core import initializers
-from keras_core import layers
-from keras_core import losses
-from keras_core import metrics
-from keras_core import ops
-from keras_core import optimizers
+import keras
+from keras import Model
+from keras import initializers
+from keras import layers
+from keras import losses
+from keras import metrics
+from keras import ops
+from keras import optimizers
 
 
 class MyDense(layers.Layer):
@@ -43,11 +43,11 @@ class MyDropout(layers.Layer):
         # 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_core.random.SeedGenerator(1337)
+        self.seed_generator = keras.random.SeedGenerator(1337)
 
     def call(self, inputs):
-        # Use `keras_core.random` for random ops.
-        return keras_core.random.dropout(
+        # Use `keras.random` for random ops.
+        return keras.random.dropout(
             inputs, self.rate, seed=self.seed_generator
         )
 

examples/demo_custom_tf_workflow.py

@@ -7,12 +7,12 @@ os.environ["KERAS_BACKEND"] = "tensorflow"
 import numpy as np
 import tensorflow as tf
 
-from keras_core import Model
-from keras_core import backend
-from keras_core import initializers
-from keras_core import layers
-from keras_core import ops
-from keras_core import optimizers
+from keras import Model
+from keras import backend
+from keras import initializers
+from keras import layers
+from keras import ops
+from keras import optimizers
 
 
 class MyDense(layers.Layer):

examples/demo_custom_torch_workflow.py

@@ -7,8 +7,8 @@ os.environ["KERAS_BACKEND"] = "torch"
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from keras_core import layers
-import keras_core
+from keras import layers
+import keras
 import numpy as np
 
 # Model / data parameters
@@ -19,7 +19,7 @@ batch_size = 64
 num_epochs = 1
 
 # Load the data and split it between train and test sets
-(x_train, y_train), (x_test, y_test) = keras_core.datasets.mnist.load_data()
+(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
@@ -32,7 +32,7 @@ print(x_train.shape[0], "train samples")
 print(x_test.shape[0], "test samples")
 
 # Create the Keras model
-model = keras_core.Sequential(
+model = keras.Sequential(
     [
         layers.Input(shape=(28, 28, 1)),
         layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
@@ -102,7 +102,7 @@ train(model, train_loader, num_epochs, optimizer, loss_fn)
 class MyModel(nn.Module):
     def __init__(self):
         super().__init__()
-        self.model = keras_core.Sequential(
+        self.model = keras.Sequential(
             [
                 layers.Input(shape=(28, 28, 1)),
                 layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),

examples/demo_jax_distributed.py

@@ -11,7 +11,7 @@ pp = pprint.PrettyPrinter()
 import jax
 import jax.numpy as jnp
 import tensorflow as tf  # just for tf.data
-import keras_core as keras  # Keras multi-backend
+import keras as keras  # Keras multi-backend
 
 import numpy as np
 from tqdm import tqdm

examples/demo_mnist_convnet.py

@@ -1,14 +1,14 @@
 import numpy as np
-import keras_core
-from keras_core import layers
-from keras_core.utils import to_categorical
+import keras
+from keras import layers
+from keras.utils import to_categorical
 
 # Model / data parameters
 num_classes = 10
 input_shape = (28, 28, 1)
 
 # Load the data and split it between train and test sets
-(x_train, y_train), (x_test, y_test) = keras_core.datasets.mnist.load_data()
+(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
@@ -28,7 +28,7 @@ y_test = to_categorical(y_test, num_classes)
 batch_size = 128
 epochs = 3
 
-model = keras_core.Sequential(
+model = keras.Sequential(
     [
         layers.Input(shape=input_shape),
         layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),

examples/demo_subclass.py

@@ -1,10 +1,10 @@
 import numpy as np
 
-from keras_core import Model
-from keras_core import layers
-from keras_core import losses
-from keras_core import metrics
-from keras_core import optimizers
+from keras import Model
+from keras import layers
+from keras import losses
+from keras import metrics
+from keras import optimizers
 
 
 class MyModel(Model):

examples/demo_torch_multi_gpu.py

@@ -7,8 +7,8 @@ os.environ["KERAS_BACKEND"] = "torch"
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from keras_core import layers
-import keras_core
+from keras import layers
+import keras
 import numpy as np
 
 import torch.multiprocessing as mp
@@ -27,7 +27,7 @@ num_epochs = 1
 
 def get_data():
     # Load the data and split it between train and test sets
-    (x_train, y_train), (x_test, y_test) = keras_core.datasets.mnist.load_data()
+    (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
@@ -48,7 +48,7 @@ def get_data():
 
 def get_model():
     # Create the Keras model
-    model = keras_core.Sequential(
+    model = keras.Sequential(
         [
             layers.Input(shape=(28, 28, 1)),
             layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
@@ -66,7 +66,7 @@ def get_model():
 class MyModel(nn.Module):
     def __init__(self):
         super().__init__()
-        self.model = keras_core.Sequential(
+        self.model = keras.Sequential(
             [
                 layers.Input(shape=(28, 28, 1)),
                 layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),

examples/keras_io/generative/text_generation_gpt.py

@@ -41,7 +41,7 @@ import os
 os.environ["KERAS_BACKEND"] = "jax"
 
 import keras_nlp
-import keras_core as keras
+import keras as keras
 
 import tensorflow.data as tf_data
 import tensorflow.strings as tf_strings

examples/keras_io/generative/text_generation_with_miniature_gpt.py

@@ -42,10 +42,10 @@ with TensorFlow 2.3 or higher.
 import os
 os.environ['KERAS_BACKEND'] = 'tensorflow'
 
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
-from keras_core.layers import TextVectorization
+import keras as keras
+from keras import layers
+from keras import ops
+from keras.layers import TextVectorization
 import numpy as np
 import os
 import string

examples/keras_io/nlp/addition_rnn.py

@@ -46,8 +46,8 @@ Five digits (reversed):
 ## Setup
 """
 
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 import numpy as np
 
 # Parameters for the model and dataset.

examples/keras_io/nlp/bidirectional_lstm_imdb.py

@@ -11,8 +11,8 @@ Accelerator: GPU
 """
 
 import numpy as np
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 max_features = 20000  # Only consider the top 20k words
 maxlen = 200  # Only consider the first 200 words of each movie review

examples/keras_io/nlp/multiple_choice_task_with_transfer_learning.py

@@ -21,7 +21,7 @@ unlike question answering. We will use SWAG dataset to demonstrate this example.
 """
 
 """shell
-pip install -q keras-core --upgrade
+pip install -q keras --upgrade
 pip install -q keras-nlp --upgrade
 """
 
@@ -29,7 +29,7 @@ import os
 os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"
 
 import keras_nlp
-import keras_core as keras
+import keras as keras
 import tensorflow as tf
 
 import numpy as np
@@ -194,7 +194,7 @@ content of the options themselves, rather than being influenced by their positio
 
 **Note:** Even though `option_shuffle` function is written in pure
 tensorflow, it can be used with any backend (e.g. JAX, PyTorch) as it is only used
-in `tf.data.Dataset` pipeline which is compatible with Keras Core routines.
+in `tf.data.Dataset` pipeline which is compatible with Keras 3 routines.
 """
 
 

examples/keras_io/nlp/neural_machine_translation_with_keras_nlp.py

@@ -52,8 +52,8 @@ import keras_nlp
 import pathlib
 import random
 
-import keras_core as keras
-from keras_core import ops
+import keras as keras
+from keras import ops
 
 import tensorflow.data as tf_data
 from tensorflow_text.tools.wordpiece_vocab import (

examples/keras_io/nlp/neural_machine_translation_with_transformer.py

@@ -53,10 +53,10 @@ import numpy as np
 import tensorflow.data as tf_data
 import tensorflow.strings as tf_strings
 
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
-from keras_core.layers import TextVectorization
+import keras as keras
+from keras import layers
+from keras import ops
+from keras.layers import TextVectorization
 
 """
 ## Downloading the data
@@ -231,7 +231,7 @@ sure that it only uses information from target tokens 0 to N when predicting tok
 (otherwise, it could use information from the future, which would
 result in a model that cannot be used at inference time).
 """
-import keras_core.ops as ops
+import keras.ops as ops
 
 class TransformerEncoder(layers.Layer):
     def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):

examples/keras_io/nlp/pretraining_BERT.py

@@ -84,7 +84,7 @@ import nltk
 import random
 import logging
 
-import keras_core as keras
+import keras as keras
 
 nltk.download("punkt")
 # Set random seed

examples/keras_io/pytorch/torchvision_keras.py

@@ -21,7 +21,7 @@ Dataloaders](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html).
 ### References:
 
 - [Customizing what happens in `fit()` with
-PyTorch](https://keras.io/keras_core/guides/custom_train_step_in_torch/)
+PyTorch](https://keras.io/keras/guides/custom_train_step_in_torch/)
 - [PyTorch Datasets and
 Dataloaders](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html)
 - [Transfer learning for Computer Vision using
@@ -46,8 +46,8 @@ import torch.nn.functional as F
 import torchvision
 from torchvision import datasets, models, transforms
 
-import keras_core as keras
-from keras_core.layers import TorchModuleWrapper
+import keras as keras
+from keras.layers import TorchModuleWrapper
 
 """
 ## Define the Hyperparameters
@@ -165,9 +165,9 @@ resnet_18.fc = nn.Identity()
 
 """
 Even though Keras supports PyTorch as a backend, it does not mean that we can nest torch
-modules inside a [`keras_core.Model`](https://keras.io/keras_core/api/models/), because
+modules inside a [`keras.Model`](https://keras.io/keras/api/models/), because
 trainable variables inside a Keras Model is tracked exclusively via [Keras
-Layers](https://keras.io/keras_core/api/layers/).
+Layers](https://keras.io/keras/api/layers/).
 
 KerasCore provides us with a feature called `TorchModuleWrapper` which enables us to do
 exactly this. The `TorchModuleWrapper` is a Keras Layer that accepts a torch module and

examples/keras_io/structured_data/collaborative_filtering_movielens.py

@@ -37,9 +37,9 @@ import matplotlib.pyplot as plt
 import numpy as np
 from zipfile import ZipFile
 
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras import ops
 
 """
 ## First, load the data and apply preprocessing

examples/keras_io/structured_data/deep_neural_decision_forests.py

@@ -33,10 +33,10 @@ and 9 categorical features.
 ## Setup
 """
 
-import keras_core as keras
-from keras_core import layers
-from keras_core.layers import StringLookup
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras.layers import StringLookup
+from keras import ops
 
 
 from tensorflow import data as tf_data

examples/keras_io/structured_data/tabtransformer.py

@@ -21,9 +21,9 @@ into robust contextual embeddings to achieve higher predictive accuracy.
 
 ## Setup
 """
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras import ops
 
 import math
 import numpy as np

examples/keras_io/tensorflow/audio/speaker_recognition_using_cnn.py

@@ -1,7 +1,7 @@
 """
 Title: Speaker Recognition
 Author: [Fadi Badine](https://twitter.com/fadibadine)
-Converted to Keras Core by: [Fadi Badine](https://twitter.com/fadibadine)
+Converted to Keras 3 by: [Fadi Badine](https://twitter.com/fadibadine)
 Date created: 14/06/2020
 Last modified: 19/07/2023
 Description: Classify speakers using Fast Fourier Transform (FFT) and a 1D Convnet.
@@ -47,7 +47,7 @@ import shutil
 import numpy as np
 
 import tensorflow as tf
-import keras_core as keras
+import keras as keras
 
 from pathlib import Path
 from IPython.display import display, Audio

examples/keras_io/tensorflow/audio/uk_ireland_accent_recognition.py

@@ -1,7 +1,7 @@
 """
 Title: English speaker accent recognition using Transfer Learning
 Author: [Fadi Badine](https://twitter.com/fadibadine)
-Converted to Keras Core by: [Fadi Badine](https://twitter.com/fadibadine)
+Converted to Keras 3 by: [Fadi Badine](https://twitter.com/fadibadine)
 Date created: 2022/04/16
 Last modified: 2023/07/19
 Description: Training a model to classify UK & Ireland accents using feature extraction from Yamnet.
@@ -108,7 +108,7 @@ import pandas as pd
 import tensorflow as tf
 import tensorflow_hub as hub
 import tensorflow_io as tfio
-import keras_core as keras
+import keras as keras
 import matplotlib.pyplot as plt
 import seaborn as sns
 from scipy import stats

examples/keras_io/tensorflow/generative/cyclegan.py

@@ -27,9 +27,9 @@ using cycle-consistent adversarial networks.
 
 import numpy as np
 import matplotlib.pyplot as plt
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras import ops
 
 import tensorflow as tf
 import tensorflow_datasets as tfds
@@ -664,13 +664,13 @@ and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io
 # data and check the model's performance.
 
 """shell
-curl -LO https://github.com/freedomtan/cyclegan-keras-core/archive/refs/tags/2.0.zip
+curl -LO https://github.com/freedomtan/cyclegan-keras/archive/refs/tags/2.0.zip
 unzip -qq 2.0.zip
 """
 
 
 # Load the checkpoints
-weight_file = "./cyclegan-keras-core-2.0/model_checkpoints/cyclegan_checkpoints.090.weights.h5"
+weight_file = "./cyclegan-keras-2.0/model_checkpoints/cyclegan_checkpoints.090.weights.h5"
 cycle_gan_model.load_weights(weight_file)
 print("Weights loaded successfully")
 

examples/keras_io/tensorflow/generative/dcgan_overriding_train_step.py

@@ -11,8 +11,8 @@ Accelerator: GPU
 """
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 import matplotlib.pyplot as plt
 import os
 import gdown

examples/keras_io/tensorflow/generative/ddim.py

@@ -72,9 +72,9 @@ import matplotlib.pyplot as plt
 import tensorflow as tf
 import tensorflow_datasets as tfds
 
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras import ops
 
 """
 ## Hyperparameters

examples/keras_io/tensorflow/generative/ddpm.py

@@ -90,8 +90,8 @@ import matplotlib.pyplot as plt
 
 # Requires TensorFlow >=2.11 for the GroupNormalization layer.
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 import tensorflow_datasets as tfds
 
 """

examples/keras_io/tensorflow/generative/deep_dream.py

@@ -38,8 +38,8 @@ and compare the result to the (resized) original image.
 
 import numpy as np
 import tensorflow as tf
-import keras_core as keras
-from keras_core.applications import inception_v3
+import keras as keras
+from keras.applications import inception_v3
 
 base_image_path = keras.utils.get_file(
     "sky.jpg", "https://i.imgur.com/aGBdQyK.jpg"

examples/keras_io/tensorflow/generative/lstm_character_level_text_generation.py

@@ -25,8 +25,8 @@ has at least ~100k characters. ~1M is better.
 """
 ## Setup
 """
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 import numpy as np
 import random

examples/keras_io/tensorflow/generative/neural_style_transfer.py

@@ -40,8 +40,8 @@ keeping the generated image close enough to the original one.
 
 import numpy as np
 import tensorflow as tf
-import keras_core as keras
-from keras_core.applications import vgg19
+import keras as keras
+from keras.applications import vgg19
 
 base_image_path = keras.utils.get_file(
     "paris.jpg", "https://i.imgur.com/F28w3Ac.jpg"

examples/keras_io/tensorflow/generative/vae.py

@@ -13,8 +13,8 @@ Accelerator: GPU
 
 import numpy as np
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 ## Create a sampling layer

examples/keras_io/tensorflow/generative/wgan_gp.py

@@ -30,8 +30,8 @@ that keeps the L2 norm of the discriminator gradients close to 1.
 """
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 
 """

examples/keras_io/tensorflow/keras_recipes/antirectifier.py

@@ -23,8 +23,8 @@ features back to a space of the original size.
 """
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 ## The Antirectifier layer

examples/keras_io/tensorflow/keras_recipes/endpoint_layer_pattern.py

@@ -12,7 +12,7 @@ Accelerator: GPU
 """
 
 import tensorflow as tf
-import keras_core as keras
+import keras as keras
 import numpy as np
 
 """

examples/keras_io/tensorflow/keras_recipes/subclassing_conv_layers.py

@@ -27,8 +27,8 @@ Using this approach, we can quickly implement a
 [StandardizedConv2D](https://arxiv.org/abs/1903.10520) as shown below.
 """
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 import numpy as np
 
 

examples/keras_io/tensorflow/keras_recipes/tensorflow_numpy_models.py

@@ -29,8 +29,8 @@ TensorFlow NumPy requires TensorFlow 2.5 or later.
 
 import tensorflow as tf
 import tensorflow.experimental.numpy as tnp
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 Optionally, you can call `tnp.experimental_enable_numpy_behavior()` to enable type promotion in TensorFlow.

examples/keras_io/tensorflow/keras_recipes/tf_serving.py

@@ -60,7 +60,7 @@ import shutil
 import requests
 import numpy as np
 import tensorflow as tf
-import keras_core as keras
+import keras as keras
 import matplotlib.pyplot as plt
 
 """

examples/keras_io/tensorflow/keras_recipes/trainer_pattern.py

@@ -25,7 +25,7 @@ by putting the custom training step in the Trainer class definition.
 """
 
 import tensorflow as tf
-import keras_core as keras
+import keras as keras
 
 # Load MNIST dataset and standardize the data
 mnist = keras.datasets.mnist

examples/keras_io/tensorflow/nlp/end_to_end_mlm_with_bert.py

@@ -50,8 +50,8 @@ from pathlib import Path
 from dataclasses import dataclass
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 ## Configuration

examples/keras_io/tensorflow/nlp/lstm_seq2seq.py

@@ -47,7 +47,7 @@ models are more common in this domain.
 """
 
 import numpy as np
-import keras_core as keras
+import keras as keras
 import os
 from pathlib import Path
 

examples/keras_io/tensorflow/nlp/ner_transformers.py

@@ -34,8 +34,8 @@ wget https://raw.githubusercontent.com/sighsmile/conlleval/master/conlleval.py
 
 import os
 import numpy as np
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 from datasets import load_dataset
 from collections import Counter
 from conlleval import evaluate

examples/keras_io/tensorflow/nlp/pretrained_word_embeddings.py

@@ -13,7 +13,7 @@ Accelerator: GPU
 
 import numpy as np
 import tensorflow.data as tf_data
-import keras_core as keras
+import keras as keras
 
 """
 ## Introduction
@@ -123,7 +123,7 @@ Our layer will only consider the top 20,000 words, and will truncate or pad sequ
 be actually 200 tokens long.
 """
 
-from keras_core.layers import TextVectorization
+from keras.layers import TextVectorization
 
 vectorizer = TextVectorization(max_tokens=20000, output_sequence_length=200)
 text_ds = tf_data.Dataset.from_tensor_slices(train_samples).batch(128)
@@ -228,7 +228,7 @@ Note that we set `trainable=False` so as to keep the embeddings fixed (we don't
 update them during training).
 """
 
-from keras_core.layers import Embedding
+from keras.layers import Embedding
 
 embedding_layer = Embedding(
     num_tokens,
@@ -244,7 +244,7 @@ embedding_layer.set_weights([embedding_matrix])
 A simple 1D convnet with global max pooling and a classifier at the end.
 """
 
-from keras_core import layers
+from keras import layers
 
 int_sequences_input = keras.Input(shape=(None,), dtype="int64")
 embedded_sequences = embedding_layer(int_sequences_input)

examples/keras_io/tensorflow/nlp/text_classification_from_scratch.py

@@ -20,9 +20,9 @@ classification dataset (unprocessed version). We use the `TextVectorization` lay
 """
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core.layers import TextVectorization
-from keras_core import layers
+import keras as keras
+from keras.layers import TextVectorization
+from keras import layers
 import string
 import re
 import os

examples/keras_io/tensorflow/rl/actor_critic_cartpole.py

@@ -1,7 +1,7 @@
 """
 Title: Actor Critic Method
 Author: [Apoorv Nandan](https://twitter.com/NandanApoorv)
-Converted to Keras Core by: [Muhammad Anas Raza](https://anasrz.com)
+Converted to Keras 3 by: [Muhammad Anas Raza](https://anasrz.com)
 Date created: 2020/05/13
 Last modified: 2023/07/19
 Description: Implement Actor Critic Method in CartPole environment.
@@ -44,8 +44,8 @@ import os
 
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 import gym
 import numpy as np

examples/keras_io/tensorflow/structured_data/imbalanced_classification.py

@@ -20,7 +20,7 @@ to train a classification model on data with highly imbalanced classes.
 """
 
 import numpy as np
-import keras_core as keras
+import keras as keras
 
 # Get the real data from https://www.kaggle.com/mlg-ulb/creditcardfraud/
 fname = "/Users/fchollet/Downloads/creditcard.csv"

examples/keras_io/tensorflow/structured_data/structured_data_classification_from_scratch.py

@@ -51,8 +51,8 @@ Target | Diagnosis of heart disease (1 = true; 0 = false) | Target
 
 import tensorflow as tf
 import pandas as pd
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 ## Preparing the data
@@ -172,9 +172,9 @@ then one-hot encode these integer indices.
 - `encode_integer_categorical_feature` to one-hot encode integer categorical features.
 """
 
-from keras_core.layers import IntegerLookup
-from keras_core.layers import Normalization
-from keras_core.layers import StringLookup
+from keras.layers import IntegerLookup
+from keras.layers import Normalization
+from keras.layers import StringLookup
 
 
 def encode_numerical_feature(feature, name, dataset):

examples/keras_io/tensorflow/structured_data/structured_data_classification_with_feature_space.py

@@ -61,8 +61,8 @@ Target | Diagnosis of heart disease (1 = true; 0 = false) | Target
 
 import tensorflow as tf
 import pandas as pd
-import keras_core as keras
-from keras_core.utils import FeatureSpace
+import keras as keras
+from keras.utils import FeatureSpace
 
 keras.config.disable_traceback_filtering()
 

examples/keras_io/tensorflow/timeseries/eeg_signal_classification.py

@@ -65,8 +65,8 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import json
 import numpy as np
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 import tensorflow as tf
 from sklearn import preprocessing, model_selection
 import random

examples/keras_io/tensorflow/timeseries/timeseries_traffic_forecasting.py

@@ -47,9 +47,9 @@ import typing
 import matplotlib.pyplot as plt
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
-from keras_core.utils import timeseries_dataset_from_array
+import keras as keras
+from keras import layers
+from keras.utils import timeseries_dataset_from_array
 
 """
 ## Data preparation

examples/keras_io/tensorflow/timeseries/timeseries_weather_forecasting.py

@@ -14,7 +14,7 @@ This example requires TensorFlow 2.3 or higher.
 
 import pandas as pd
 import matplotlib.pyplot as plt
-import keras_core as keras
+import keras as keras
 
 """
 ## Climate Data Time-Series

examples/keras_io/tensorflow/vision/bit.py

@@ -42,7 +42,7 @@ import os
 
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
-import keras_core as keras
+import keras as keras
 import numpy as np
 import matplotlib.pyplot as plt
 

examples/keras_io/tensorflow/vision/cutmix.py

@@ -1,7 +1,7 @@
 """
 Title: CutMix data augmentation for image classification
 Author: [Sayan Nath](https://twitter.com/sayannath2350)
-Converted to Keras Core By: [Piyush Thakur](https://github.com/cosmo3769)
+Converted to Keras 3 By: [Piyush Thakur](https://github.com/cosmo3769)
 Date created: 2021/06/08
 Last modified: 2023/07/24
 Description: Data augmentation with CutMix for image classification on CIFAR-10.
@@ -48,10 +48,10 @@ where `rx, ry` are randomly drawn from a uniform distribution with upper bound.
 
 import numpy as np
 import pandas as pd
-import keras_core as keras
+import keras as keras
 import matplotlib.pyplot as plt
 
-from keras_core import layers
+from keras import layers
 
 # TF imports related to tf.data preprocessing
 from tensorflow import clip_by_value

examples/keras_io/tensorflow/vision/deit.py

@@ -49,8 +49,8 @@ os.environ["KERAS_BACKEND"] = "tensorflow"
 
 import tensorflow as tf
 import tensorflow_datasets as tfds
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 tfds.disable_progress_bar()
 keras.utils.set_random_seed(42)

examples/keras_io/tensorflow/vision/grad_cam.py

@@ -13,7 +13,7 @@ Adapted from Deep Learning with Python (2017).
 
 import numpy as np
 import tensorflow as tf
-import keras_core as keras
+import keras as keras
 
 # Display
 from IPython.display import Image, display

examples/keras_io/tensorflow/vision/image_captioning.py

@@ -17,10 +17,10 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
-from keras_core.applications import efficientnet
-from keras_core.layers import TextVectorization
+import keras as keras
+from keras import layers
+from keras.applications import efficientnet
+from keras.layers import TextVectorization
 
 
 seed = 111

examples/keras_io/tensorflow/vision/image_classification_from_scratch.py

@@ -23,8 +23,8 @@ we use Keras image preprocessing layers for image standardization and data augme
 """
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 import os
 from pathlib import Path
 import matplotlib.pyplot as plt

examples/keras_io/tensorflow/vision/integrated_gradients.py

@@ -58,9 +58,9 @@ from scipy import ndimage
 from IPython.display import Image, display
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
-from keras_core.applications import xception
+import keras as keras
+from keras import layers
+from keras.applications import xception
 
 keras.config.disable_traceback_filtering()
 

examples/keras_io/tensorflow/vision/involution.py

@@ -36,7 +36,7 @@ layer.
 """
 
 import tensorflow as tf
-import keras_core as keras
+import keras as keras
 import matplotlib.pyplot as plt
 
 # Set seed for reproducibility.

examples/keras_io/tensorflow/vision/metric_learning.py

@@ -32,9 +32,9 @@ import tensorflow as tf
 from collections import defaultdict
 from PIL import Image
 from sklearn.metrics import ConfusionMatrixDisplay
-import keras_core as keras
-from keras_core import layers
-from keras_core.datasets import cifar10
+import keras as keras
+from keras import layers
+from keras.datasets import cifar10
 
 """
 ## Dataset

examples/keras_io/tensorflow/vision/mirnet.py

@@ -1,7 +1,7 @@
 """
 Title: Low-light image enhancement using MIRNet
 Author: [Soumik Rakshit](http://github.com/soumik12345)
-Converted to Keras Core by: [Soumik Rakshit](http://github.com/soumik12345)
+Converted to Keras 3 by: [Soumik Rakshit](http://github.com/soumik12345)
 Date created: 2021/09/11
 Last modified: 2023/07/15
 Description: Implementing the MIRNet architecture for low-light image enhancement.
@@ -34,7 +34,7 @@ consists of a low-light input image and its corresponding well-exposed reference
 """
 
 """shell
-pip install -q git+https://github.com/keras-team/keras-core
+pip install -q git+https://github.com/keras-team/keras
 """
 
 import os
@@ -47,8 +47,8 @@ from glob import glob
 from PIL import Image, ImageOps
 import matplotlib.pyplot as plt
 
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 import tensorflow as tf
 

examples/keras_io/tensorflow/vision/mixup.py

@@ -37,10 +37,10 @@ processing, speech, and so on.
 """
 
 import numpy as np
-import keras_core as keras
+import keras as keras
 import matplotlib.pyplot as plt
 
-from keras_core import layers
+from keras import layers
 
 # TF imports related to tf.data preprocessing
 from tensorflow import data as tf_data

examples/keras_io/tensorflow/vision/perceiver_image_classification.py

@@ -43,8 +43,8 @@ pip install -U tensorflow-addons
 
 import numpy as np
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 ## Prepare the data

examples/keras_io/tensorflow/vision/reptile.py

@@ -1,7 +1,7 @@
 """
 Title: Few-Shot learning with Reptile
 Author: [ADMoreau](https://github.com/ADMoreau)
-Converted to Keras Core By: [Muhammad Anas Raza](https://anasrz.com)
+Converted to Keras 3 By: [Muhammad Anas Raza](https://anasrz.com)
 Date created: 2020/05/21
 Last modified: 2023/07/20
 Description: Few-shot classification on the Omniglot dataset using Reptile.
@@ -22,8 +22,8 @@ import os
 
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 import matplotlib.pyplot as plt
 import numpy as np

examples/keras_io/tensorflow/vision/semisupervised_simclr.py

@@ -77,8 +77,8 @@ import matplotlib.pyplot as plt
 import tensorflow as tf
 import tensorflow_datasets as tfds
 
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 ## Hyperparameter setup

examples/keras_io/tensorflow/vision/shiftvit.py

@@ -1,7 +1,7 @@
 """
 Title: A Vision Transformer without Attention
 Author: [Aritra Roy Gosthipaty](https://twitter.com/ariG23498), [Ritwik Raha](https://twitter.com/ritwik_raha)
-Converted to Keras Core: [Muhammad Anas Raza](https://anasrz.com)
+Converted to Keras 3: [Muhammad Anas Raza](https://anasrz.com)
 Date created: 2022/02/24
 Last modified: 2023/07/15
 Description: A minimal implementation of ShiftViT.
@@ -40,8 +40,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 
 # Setting seed for reproducibiltiy

examples/keras_io/tensorflow/vision/siamese_network.py

@@ -38,11 +38,11 @@ import numpy as np
 import os
 import tensorflow as tf
 from pathlib import Path
-from keras_core import layers
-from keras_core import optimizers
-from keras_core import metrics
-from keras_core import Model
-from keras_core.applications import resnet
+from keras import layers
+from keras import optimizers
+from keras import metrics
+from keras import Model
+from keras.applications import resnet
 
 
 target_shape = (200, 200)

examples/keras_io/tensorflow/vision/simsiam.py

@@ -49,9 +49,9 @@ versions of our dataset.
 import os
 os.environ['KERAS_BACKEND'] = 'tensorflow'
 
-from keras_core import layers
-from keras_core import regularizers
-import keras_core as keras
+from keras import layers
+from keras import regularizers
+import keras as keras
 import tensorflow as tf
 
 import matplotlib.pyplot as plt

examples/keras_io/tensorflow/vision/swim_transformers.py

@@ -29,8 +29,8 @@ This example requires TensorFlow 2.5 or higher.
 import matplotlib.pyplot as plt
 import numpy as np
 import tensorflow as tf
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 ## Prepare the data

examples/keras_io/tensorflow/vision/visualizing_what_convnets_learn.py

@@ -26,7 +26,7 @@ import os
 
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
-import keras_core as keras
+import keras as keras
 
 
 import numpy as np

examples/keras_io/tensorflow/vision/zero_dce.py

@@ -1,7 +1,7 @@
 """
 Title: Zero-DCE for low-light image enhancement
 Author: [Soumik Rakshit](http://github.com/soumik12345)
-Converted to Keras Core by: [Soumik Rakshit](http://github.com/soumik12345)
+Converted to Keras 3 by: [Soumik Rakshit](http://github.com/soumik12345)
 Date created: 2021/09/18
 Last modified: 2023/07/15
 Description: Implementing Zero-Reference Deep Curve Estimation for low-light image enhancement.
@@ -49,8 +49,8 @@ from glob import glob
 from PIL import Image, ImageOps
 import matplotlib.pyplot as plt
 
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 import tensorflow as tf
 

examples/keras_io/timeseries/timeseries_anomaly_detection.py

@@ -20,8 +20,8 @@ autoencoder model to detect anomalies in timeseries data.
 
 import numpy as np
 import pandas as pd
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 from matplotlib import pyplot as plt
 
 """

examples/keras_io/timeseries/timeseries_classification_from_scratch.py

@@ -19,7 +19,7 @@ CSV timeseries files on disk. We demonstrate the workflow on the FordA dataset f
 ## Setup
 
 """
-import keras_core as keras
+import keras as keras
 import numpy as np
 import matplotlib.pyplot as plt
 

examples/keras_io/timeseries/timeseries_classification_transformer.py

@@ -62,8 +62,8 @@ You can replace your classification RNN layers with this one: the
 inputs are fully compatible!
 """
 
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 We include residual connections, layer normalization, and dropout.

examples/keras_io/vision/attention_mil_classification.py

@@ -54,9 +54,9 @@ by TensorFlow.
 """
 
 import numpy as np
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras import ops
 from tqdm import tqdm
 from matplotlib import pyplot as plt
 

examples/keras_io/vision/autoencoder.py

@@ -24,9 +24,9 @@ by [François Chollet](https://twitter.com/fchollet).
 import numpy as np
 import matplotlib.pyplot as plt
 
-from keras_core import layers
-from keras_core.datasets import mnist
-from keras_core.models import Model
+from keras import layers
+from keras.datasets import mnist
+from keras.models import Model
 
 
 def preprocess(array):

examples/keras_io/vision/cct.py

@@ -1,7 +1,7 @@
 """
 Title: Compact Convolutional Transformers
 Author: [Sayak Paul](https://twitter.com/RisingSayak)
-Converted to Keras Core by: [Muhammad Anas Raza](https://anasrz.com), [Guillaume Baquiast](https://www.linkedin.com/in/guillaume-baquiast-478965ba/)
+Converted to Keras 3 by: [Muhammad Anas Raza](https://anasrz.com), [Guillaume Baquiast](https://www.linkedin.com/in/guillaume-baquiast-478965ba/)
 Date created: 2021/06/30
 Last modified: 2023/08/07
 Description: Compact Convolutional Transformers for efficient image classification.
@@ -39,8 +39,8 @@ code snippets from another example,
 ## Imports
 """
 
-from keras_core import layers
-import keras_core as keras
+from keras import layers
+import keras as keras
 
 import matplotlib.pyplot as plt
 import numpy as np

examples/keras_io/vision/conv_lstm.py

@@ -25,8 +25,8 @@ of predicting what video frames come next given a series of past frames.
 import numpy as np
 import matplotlib.pyplot as plt
 
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 import io
 import imageio

examples/keras_io/vision/deeplabv3_plus.py

@@ -1,7 +1,7 @@
 """
 Title: Multiclass semantic segmentation using DeepLabV3+
 Author: [Soumik Rakshit](http://github.com/soumik12345)
-Converted to Keras Core: [Muhammad Anas Raza](https://anasrz.com)
+Converted to Keras 3: [Muhammad Anas Raza](https://anasrz.com)
 Date created: 2021/08/31
 Last modified: 2023/07/19
 Description: Implement DeepLabV3+ architecture for Multi-class Semantic Segmentation.
@@ -32,9 +32,9 @@ This dataset can be used for the "human part segmentation" task.
 """
 
 
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras import ops
 
 import cv2
 import numpy as np

examples/keras_io/vision/eanet.py

@@ -1,7 +1,7 @@
 """
 Title: Image classification with EANet (External Attention Transformer)
 Author: [ZhiYong Chang](https://github.com/czy00000)
-Converted to Keras Core: [Muhammad Anas Raza](https://anasrz.com)
+Converted to Keras 3: [Muhammad Anas Raza](https://anasrz.com)
 Date created: 2021/10/19
 Last modified: 2023/07/18
 Description: Image classification with a Transformer that leverages external attention.
@@ -25,9 +25,9 @@ implicitly considers the correlations between all samples.
 ## Setup
 """
 
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras import ops
 
 import matplotlib.pyplot as plt
 

examples/keras_io/vision/fixres.py

@@ -27,8 +27,8 @@ to fix this discrepancy.
 ## Imports
 """
 
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 import tensorflow as tf  # just for image processing and pipeline
 
 import tensorflow_datasets as tfds

examples/keras_io/vision/gradient_centralization.py

@@ -1,7 +1,7 @@
 """
 Title: Gradient Centralization for Better Training Performance
 Author: [Rishit Dagli](https://github.com/Rishit-dagli)
-Converted to Keras Core by: [Muhammad Anas Raza](https://anasrz.com)
+Converted to Keras 3 by: [Muhammad Anas Raza](https://anasrz.com)
 Date created: 06/18/21
 Last modified: 07/25/23
 Description: Implement Gradient Centralization to improve training performance of DNNs.
@@ -34,10 +34,10 @@ pip install tensorflow-datasets
 
 from time import time
 
-import keras_core as keras
-from keras_core import layers
-from keras_core.optimizers import RMSprop
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras.optimizers import RMSprop
+from keras import ops
 
 from tensorflow import data as tf_data
 import tensorflow_datasets as tfds

examples/keras_io/vision/image_classification_with_vision_transformer.py

@@ -1,7 +1,7 @@
 """
 Title: Image classification with Vision Transformer
 Author: [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)
-Converted to Keras Core by: [divyasreepat](https://github.com/divyashreepathihalli), [Soumik Rakshit](http://github.com/soumik12345)
+Converted to Keras 3 by: [divyasreepat](https://github.com/divyashreepathihalli), [Soumik Rakshit](http://github.com/soumik12345)
 Date created: 2021/01/18
 Last modified: 2021/01/18
 Description: Implementing the Vision Transformer (ViT) model for image classification.
@@ -27,9 +27,9 @@ import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # @param ["tensorflow", "jax", "torch"]
 
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras import ops
 
 import numpy as np
 import matplotlib.pyplot as plt

examples/keras_io/vision/image_classifier.py

@@ -25,7 +25,7 @@ classification problems at three levels of complexity:
 ## Multi-Backend Support
 
 KerasCV's `ImageClassifier` model supports several backends like JAX, PyTorch,
-and TensorFlow with the help of `keras_core`. To enable multi-backend support
+and TensorFlow with the help of `keras`. To enable multi-backend support
 in KerasCV, set the `KERAS_CV_MULTI_BACKEND` environment variable. We can
 then switch between different backends by setting the `KERAS_BACKEND`
 environment variable. Currently, `"tensorflow"`, `"jax"`, and `"torch"` are
@@ -42,12 +42,12 @@ os.environ["KERAS_BACKEND"] = "jax"
 import json
 import math
 import keras_cv
-import keras_core as keras
-from keras_core import ops
-from keras_core import losses
-from keras_core import optimizers
-from keras_core.optimizers import schedules
-from keras_core import metrics
+import keras as keras
+from keras import ops
+from keras import losses
+from keras import optimizers
+from keras.optimizers import schedules
+from keras import metrics
 import tensorflow as tf
 from tensorflow import data as tf_data
 import tensorflow_datasets as tfds

examples/keras_io/vision/keypoint_detection.py

@@ -1,7 +1,7 @@
 """
 Title: Keypoint Detection with Transfer Learning
 Author: [Sayak Paul](https://twitter.com/RisingSayak)
-Converted to Keras Core by: [Muhammad Anas Raza](https://anasrz.com)
+Converted to Keras 3 by: [Muhammad Anas Raza](https://anasrz.com)
 Date created: 2021/05/02
 Last modified: 2023/07/19
 Description: Training a keypoint detector with data augmentation and transfer learning.
@@ -57,8 +57,8 @@ unzip -qq ~/stanfordextra_v12.zip
 """
 ## Imports
 """
-from keras_core import layers
-import keras_core as keras
+from keras import layers
+import keras as keras
 
 from imgaug.augmentables.kps import KeypointsOnImage
 from imgaug.augmentables.kps import Keypoint

examples/keras_io/vision/learnable_resizer.py

@@ -39,9 +39,9 @@ using the [DenseNet-121](https://arxiv.org/abs/1608.06993) architecture.
 ## Setup
 """
 
-from keras_core import layers
-import keras_core as keras
-from keras_core import ops
+from keras import layers
+import keras as keras
+from keras import ops
 
 from tensorflow import data as tf_data
 from tensorflow import image as tf_image

examples/keras_io/vision/mlp_image_classification.py

@@ -1,7 +1,7 @@
 """
 Title: Image classification with modern MLP models
 Author: [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)
-Converted to Keras Core by: [Guillaume Baquiast](https://www.linkedin.com/in/guillaume-baquiast-478965ba/), [divyasreepat](https://github.com/divyashreepathihalli)
+Converted to Keras 3 by: [Guillaume Baquiast](https://www.linkedin.com/in/guillaume-baquiast-478965ba/), [divyasreepat](https://github.com/divyashreepathihalli)
 Date created: 2021/05/30
 Last modified: 2023/08/03
 Description: Implementing the MLP-Mixer, FNet, and gMLP models for CIFAR-100 image classification.
@@ -29,8 +29,8 @@ main building blocks.
 """
 
 import numpy as np
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 ## Prepare the data

examples/keras_io/vision/mnist_convnet.py

@@ -12,8 +12,8 @@ Accelerator: GPU
 """
 
 import numpy as np
-import keras_core as keras
-from keras_core import layers
+import keras as keras
+from keras import layers
 
 """
 ## Prepare the data

examples/keras_io/vision/object_detection_using_vision_transformer.py

@@ -1,7 +1,7 @@
 """
 Title: Object detection with Vision Transformers
 Author: [Karan V. Dave](https://www.linkedin.com/in/karan-dave-811413164/)
-Converted to Keras Core by: [Gabriel Rasskin](https://github.com/grasskin), [Soumik Rakshit](http://github.com/soumik12345)
+Converted to Keras 3 by: [Gabriel Rasskin](https://github.com/grasskin), [Soumik Rakshit](http://github.com/soumik12345)
 Date created: 2022/03/27
 Last modified: 2022/03/27
 Description: A simple Keras implementation of object detection using Vision Transformers.
@@ -29,7 +29,7 @@ from which we import the `AdamW` optimizer.
 TensorFlow Addons can be installed via the following command:
 
 ```
-pip install -U git+https://github.com/keras-team/keras-core
+pip install -U git+https://github.com/keras-team/keras
 ```
 """
 
@@ -43,9 +43,9 @@ os.environ["KERAS_BACKEND"] = "jax"  # @param ["tensorflow", "jax", "torch"]
 
 
 import numpy as np
-import keras_core as keras
-from keras_core import layers
-from keras_core import ops
+import keras as keras
+from keras import layers
+from keras import ops
 import matplotlib.pyplot as plt
 import numpy as np
 import cv2

examples/keras_io/vision/oxford_pets_image_segmentation.py

@@ -58,7 +58,7 @@ for input_path, target_path in zip(input_img_paths[:10], target_img_paths[:10]):
 """
 
 from IPython.display import Image, display
-from keras_core.utils import load_img
+from keras.utils import load_img
 from PIL import ImageOps
 
 # Display input image #7
@@ -72,7 +72,7 @@ display(img)
 ## Prepare dataset to load & vectorize batches of data
 """
 
-import keras_core as keras
+import keras as keras
 import numpy as np
 from tensorflow import data as tf_data
 from tensorflow import image as tf_image
@@ -118,7 +118,7 @@ def get_dataset(
 ## Prepare U-Net Xception-style model
 """
 
-from keras_core import layers
+from keras import layers
 
 
 def get_model(img_size, num_classes):

examples/keras_io/vision/siamese_contrastive.py

@@ -26,8 +26,8 @@ the class segmentation of the training inputs.
 
 import random
 import numpy as np
-import keras_core as keras
-from keras_core import ops
+import keras as keras
+from keras import ops
 import matplotlib.pyplot as plt
 
 """