Remove import keras as keras (#18725)

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 as keras
+import 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 as keras
+import keras
 
 flags.DEFINE_string("model", "EfficientNetV2B0", "The model to benchmark.")
 flags.DEFINE_integer("epochs", 1, "The number of epochs.")

examples/demo_functional.py

@@ -5,7 +5,7 @@ from keras import layers
 from keras import losses
 from keras import metrics
 from keras import optimizers
-import keras as keras
+import keras
 
 keras.config.disable_traceback_filtering()
 

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 as keras  # Keras multi-backend
+import keras  # Keras multi-backend
 
 import numpy as np
 from tqdm import tqdm

examples/keras_io/generative/text_generation_gpt.py

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

examples/keras_io/generative/text_generation_with_miniature_gpt.py

@@ -42,7 +42,7 @@ with TensorFlow 2.3 or higher.
 import os
 os.environ['KERAS_BACKEND'] = 'tensorflow'
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 from keras.layers import TextVectorization

examples/keras_io/nlp/addition_rnn.py

@@ -46,7 +46,7 @@ Five digits (reversed):
 ## Setup
 """
 
-import keras as keras
+import keras
 from keras import layers
 import numpy as np
 

examples/keras_io/nlp/bidirectional_lstm_imdb.py

@@ -11,7 +11,7 @@ Accelerator: GPU
 """
 
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 
 max_features = 20000  # Only consider the top 20k words

examples/keras_io/nlp/multiple_choice_task_with_transfer_learning.py

@@ -29,7 +29,7 @@ import os
 os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"
 
 import keras_nlp
-import keras as keras
+import keras
 import tensorflow as tf
 
 import numpy as np

examples/keras_io/nlp/neural_machine_translation_with_keras_nlp.py

@@ -52,7 +52,7 @@ import keras_nlp
 import pathlib
 import random
 
-import keras as keras
+import keras
 from keras import ops
 
 import tensorflow.data as tf_data

examples/keras_io/nlp/neural_machine_translation_with_transformer.py

@@ -53,7 +53,7 @@ import numpy as np
 import tensorflow.data as tf_data
 import tensorflow.strings as tf_strings
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 from keras.layers import TextVectorization

examples/keras_io/nlp/pretraining_BERT.py

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

examples/keras_io/pytorch/torchvision_keras.py

@@ -46,7 +46,7 @@ import torch.nn.functional as F
 import torchvision
 from torchvision import datasets, models, transforms
 
-import keras as keras
+import keras
 from keras.layers import TorchModuleWrapper
 
 """

examples/keras_io/structured_data/collaborative_filtering_movielens.py

@@ -37,7 +37,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 from zipfile import ZipFile
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

examples/keras_io/structured_data/deep_neural_decision_forests.py

@@ -33,7 +33,7 @@ and 9 categorical features.
 ## Setup
 """
 
-import keras as keras
+import keras
 from keras import layers
 from keras.layers import StringLookup
 from keras import ops

examples/keras_io/structured_data/tabtransformer.py

@@ -21,7 +21,7 @@ into robust contextual embeddings to achieve higher predictive accuracy.
 
 ## Setup
 """
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

examples/keras_io/tensorflow/audio/speaker_recognition_using_cnn.py

@@ -47,7 +47,7 @@ import shutil
 import numpy as np
 
 import tensorflow as tf
-import keras as keras
+import keras
 
 from pathlib import Path
 from IPython.display import display, Audio

examples/keras_io/tensorflow/audio/uk_ireland_accent_recognition.py

@@ -108,7 +108,7 @@ import pandas as pd
 import tensorflow as tf
 import tensorflow_hub as hub
 import tensorflow_io as tfio
-import keras as keras
+import keras
 import matplotlib.pyplot as plt
 import seaborn as sns
 from scipy import stats

examples/keras_io/tensorflow/generative/cyclegan.py

@@ -27,7 +27,7 @@ using cycle-consistent adversarial networks.
 
 import numpy as np
 import matplotlib.pyplot as plt
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

examples/keras_io/tensorflow/generative/dcgan_overriding_train_step.py

@@ -11,7 +11,7 @@ Accelerator: GPU
 """
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 import matplotlib.pyplot as plt
 import os

examples/keras_io/tensorflow/generative/ddim.py

@@ -72,7 +72,7 @@ import matplotlib.pyplot as plt
 import tensorflow as tf
 import tensorflow_datasets as tfds
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

examples/keras_io/tensorflow/generative/ddpm.py

@@ -90,7 +90,7 @@ import matplotlib.pyplot as plt
 
 # Requires TensorFlow >=2.11 for the GroupNormalization layer.
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 import tensorflow_datasets as tfds
 

examples/keras_io/tensorflow/generative/deep_dream.py

@@ -38,7 +38,7 @@ and compare the result to the (resized) original image.
 
 import numpy as np
 import tensorflow as tf
-import keras as keras
+import keras
 from keras.applications import inception_v3
 
 base_image_path = keras.utils.get_file(

examples/keras_io/tensorflow/generative/lstm_character_level_text_generation.py

@@ -25,7 +25,7 @@ has at least ~100k characters. ~1M is better.
 """
 ## Setup
 """
-import keras as keras
+import keras
 from keras import layers
 
 import numpy as np

examples/keras_io/tensorflow/generative/neural_style_transfer.py

@@ -40,7 +40,7 @@ keeping the generated image close enough to the original one.
 
 import numpy as np
 import tensorflow as tf
-import keras as keras
+import keras
 from keras.applications import vgg19
 
 base_image_path = keras.utils.get_file(

examples/keras_io/tensorflow/generative/vae.py

@@ -13,7 +13,7 @@ Accelerator: GPU
 
 import numpy as np
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 
 """

examples/keras_io/tensorflow/generative/wgan_gp.py

@@ -30,7 +30,7 @@ that keeps the L2 norm of the discriminator gradients close to 1.
 """
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 
 

examples/keras_io/tensorflow/keras_recipes/antirectifier.py

@@ -23,7 +23,7 @@ features back to a space of the original size.
 """
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 
 """

examples/keras_io/tensorflow/keras_recipes/endpoint_layer_pattern.py

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

examples/keras_io/tensorflow/keras_recipes/subclassing_conv_layers.py

@@ -27,7 +27,7 @@ Using this approach, we can quickly implement a
 [StandardizedConv2D](https://arxiv.org/abs/1903.10520) as shown below.
 """
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 import numpy as np
 

examples/keras_io/tensorflow/keras_recipes/tensorflow_numpy_models.py

@@ -29,7 +29,7 @@ TensorFlow NumPy requires TensorFlow 2.5 or later.
 
 import tensorflow as tf
 import tensorflow.experimental.numpy as tnp
-import keras as keras
+import keras
 from keras import layers
 
 """

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 as keras
+import 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 as keras
+import keras
 
 # Load MNIST dataset and standardize the data
 mnist = keras.datasets.mnist

examples/keras_io/tensorflow/nlp/end_to_end_mlm_with_bert.py

@@ -50,7 +50,7 @@ from pathlib import Path
 from dataclasses import dataclass
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 
 """

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 as keras
+import keras
 import os
 from pathlib import Path
 

examples/keras_io/tensorflow/nlp/ner_transformers.py

@@ -34,7 +34,7 @@ wget https://raw.githubusercontent.com/sighsmile/conlleval/master/conlleval.py
 
 import os
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 from datasets import load_dataset
 from collections import Counter

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 as keras
+import keras
 
 """
 ## Introduction

examples/keras_io/tensorflow/nlp/text_classification_from_scratch.py

@@ -20,7 +20,7 @@ classification dataset (unprocessed version). We use the `TextVectorization` lay
 """
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras.layers import TextVectorization
 from keras import layers
 import string

examples/keras_io/tensorflow/rl/actor_critic_cartpole.py

@@ -44,7 +44,7 @@ import os
 
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
-import keras as keras
+import keras
 from keras import layers
 
 import gym

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 as keras
+import 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

@@ -51,7 +51,7 @@ Target | Diagnosis of heart disease (1 = true; 0 = false) | Target
 
 import tensorflow as tf
 import pandas as pd
-import keras as keras
+import keras
 from keras import layers
 
 """

examples/keras_io/tensorflow/structured_data/structured_data_classification_with_feature_space.py

@@ -61,7 +61,7 @@ Target | Diagnosis of heart disease (1 = true; 0 = false) | Target
 
 import tensorflow as tf
 import pandas as pd
-import keras as keras
+import keras
 from keras.utils import FeatureSpace
 
 keras.config.disable_traceback_filtering()

examples/keras_io/tensorflow/timeseries/eeg_signal_classification.py

@@ -65,7 +65,7 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import json
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 import tensorflow as tf
 from sklearn import preprocessing, model_selection

examples/keras_io/tensorflow/timeseries/timeseries_traffic_forecasting.py

@@ -47,7 +47,7 @@ import typing
 import matplotlib.pyplot as plt
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 from keras.utils import timeseries_dataset_from_array
 

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 as keras
+import 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 as keras
+import keras
 import numpy as np
 import matplotlib.pyplot as plt
 

examples/keras_io/tensorflow/vision/cutmix.py

@@ -48,7 +48,7 @@ where `rx, ry` are randomly drawn from a uniform distribution with upper bound.
 
 import numpy as np
 import pandas as pd
-import keras as keras
+import keras
 import matplotlib.pyplot as plt
 
 from keras import layers

examples/keras_io/tensorflow/vision/deit.py

@@ -49,7 +49,7 @@ os.environ["KERAS_BACKEND"] = "tensorflow"
 
 import tensorflow as tf
 import tensorflow_datasets as tfds
-import keras as keras
+import keras
 from keras import layers
 
 tfds.disable_progress_bar()

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 as keras
+import keras
 
 # Display
 from IPython.display import Image, display

examples/keras_io/tensorflow/vision/image_captioning.py

@@ -17,7 +17,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 from keras.applications import efficientnet
 from keras.layers import TextVectorization

examples/keras_io/tensorflow/vision/image_classification_from_scratch.py

@@ -23,7 +23,7 @@ we use Keras image preprocessing layers for image standardization and data augme
 """
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 import os
 from pathlib import Path

examples/keras_io/tensorflow/vision/integrated_gradients.py

@@ -58,7 +58,7 @@ from scipy import ndimage
 from IPython.display import Image, display
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 from keras.applications import xception
 

examples/keras_io/tensorflow/vision/involution.py

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

examples/keras_io/tensorflow/vision/mirnet.py

@@ -47,7 +47,7 @@ from glob import glob
 from PIL import Image, ImageOps
 import matplotlib.pyplot as plt
 
-import keras as keras
+import keras
 from keras import layers
 
 import tensorflow as tf

examples/keras_io/tensorflow/vision/mixup.py

@@ -37,7 +37,7 @@ processing, speech, and so on.
 """
 
 import numpy as np
-import keras as keras
+import keras
 import matplotlib.pyplot as plt
 
 from keras import layers

examples/keras_io/tensorflow/vision/perceiver_image_classification.py

@@ -43,7 +43,7 @@ pip install -U tensorflow-addons
 
 import numpy as np
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 
 """

examples/keras_io/tensorflow/vision/reptile.py

@@ -22,7 +22,7 @@ import os
 
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
-import keras as keras
+import keras
 from keras import layers
 
 import matplotlib.pyplot as plt

examples/keras_io/tensorflow/vision/semisupervised_simclr.py

@@ -77,7 +77,7 @@ import matplotlib.pyplot as plt
 import tensorflow as tf
 import tensorflow_datasets as tfds
 
-import keras as keras
+import keras
 from keras import layers
 
 """

examples/keras_io/tensorflow/vision/shiftvit.py

@@ -40,7 +40,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 
 

examples/keras_io/tensorflow/vision/simsiam.py

@@ -51,7 +51,7 @@ os.environ['KERAS_BACKEND'] = 'tensorflow'
 
 from keras import layers
 from keras import regularizers
-import keras as keras
+import keras
 import tensorflow as tf
 
 import matplotlib.pyplot as plt

examples/keras_io/tensorflow/vision/swim_transformers.py

@@ -29,7 +29,7 @@ This example requires TensorFlow 2.5 or higher.
 import matplotlib.pyplot as plt
 import numpy as np
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 
 """

examples/keras_io/tensorflow/vision/visualizing_what_convnets_learn.py

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

examples/keras_io/tensorflow/vision/zero_dce.py

@@ -49,7 +49,7 @@ from glob import glob
 from PIL import Image, ImageOps
 import matplotlib.pyplot as plt
 
-import keras as keras
+import keras
 from keras import layers
 
 import tensorflow as tf

examples/keras_io/timeseries/timeseries_anomaly_detection.py

@@ -20,7 +20,7 @@ autoencoder model to detect anomalies in timeseries data.
 
 import numpy as np
 import pandas as pd
-import keras as keras
+import 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 as keras
+import keras
 import numpy as np
 import matplotlib.pyplot as plt
 

examples/keras_io/timeseries/timeseries_classification_transformer.py

@@ -62,7 +62,7 @@ You can replace your classification RNN layers with this one: the
 inputs are fully compatible!
 """
 
-import keras as keras
+import keras
 from keras import layers
 
 """

examples/keras_io/vision/attention_mil_classification.py

@@ -54,7 +54,7 @@ by TensorFlow.
 """
 
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 from tqdm import tqdm

examples/keras_io/vision/cct.py

@@ -40,7 +40,7 @@ code snippets from another example,
 """
 
 from keras import layers
-import keras as keras
+import keras
 
 import matplotlib.pyplot as plt
 import numpy as np

examples/keras_io/vision/conv_lstm.py

@@ -25,7 +25,7 @@ of predicting what video frames come next given a series of past frames.
 import numpy as np
 import matplotlib.pyplot as plt
 
-import keras as keras
+import keras
 from keras import layers
 
 import io

examples/keras_io/vision/deeplabv3_plus.py

@@ -32,7 +32,7 @@ This dataset can be used for the "human part segmentation" task.
 """
 
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

examples/keras_io/vision/eanet.py

@@ -25,7 +25,7 @@ implicitly considers the correlations between all samples.
 ## Setup
 """
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

examples/keras_io/vision/fixres.py

@@ -27,7 +27,7 @@ to fix this discrepancy.
 ## Imports
 """
 
-import keras as keras
+import keras
 from keras import layers
 import tensorflow as tf  # just for image processing and pipeline
 

examples/keras_io/vision/gradient_centralization.py

@@ -34,7 +34,7 @@ pip install tensorflow-datasets
 
 from time import time
 
-import keras as keras
+import keras
 from keras import layers
 from keras.optimizers import RMSprop
 from keras import ops

examples/keras_io/vision/image_classification_with_vision_transformer.py

@@ -27,7 +27,7 @@ import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # @param ["tensorflow", "jax", "torch"]
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

examples/keras_io/vision/image_classifier.py

@@ -42,7 +42,7 @@ os.environ["KERAS_BACKEND"] = "jax"
 import json
 import math
 import keras_cv
-import keras as keras
+import keras
 from keras import ops
 from keras import losses
 from keras import optimizers

examples/keras_io/vision/keypoint_detection.py

@@ -58,7 +58,7 @@ unzip -qq ~/stanfordextra_v12.zip
 ## Imports
 """
 from keras import layers
-import keras as keras
+import keras
 
 from imgaug.augmentables.kps import KeypointsOnImage
 from imgaug.augmentables.kps import Keypoint

examples/keras_io/vision/learnable_resizer.py

@@ -40,7 +40,7 @@ using the [DenseNet-121](https://arxiv.org/abs/1608.06993) architecture.
 """
 
 from keras import layers
-import keras as keras
+import keras
 from keras import ops
 
 from tensorflow import data as tf_data

examples/keras_io/vision/mlp_image_classification.py

@@ -29,7 +29,7 @@ main building blocks.
 """
 
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 
 """

examples/keras_io/vision/mnist_convnet.py

@@ -12,7 +12,7 @@ Accelerator: GPU
 """
 
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 
 """

examples/keras_io/vision/object_detection_using_vision_transformer.py

@@ -43,7 +43,7 @@ os.environ["KERAS_BACKEND"] = "jax"  # @param ["tensorflow", "jax", "torch"]
 
 
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 import matplotlib.pyplot as plt

examples/keras_io/vision/oxford_pets_image_segmentation.py

@@ -72,7 +72,7 @@ display(img)
 ## Prepare dataset to load & vectorize batches of data
 """
 
-import keras as keras
+import keras
 import numpy as np
 from tensorflow import data as tf_data
 from tensorflow import image as tf_image

examples/keras_io/vision/siamese_contrastive.py

@@ -26,7 +26,7 @@ the class segmentation of the training inputs.
 
 import random
 import numpy as np
-import keras as keras
+import keras
 from keras import ops
 import matplotlib.pyplot as plt
 

examples/keras_io/vision/super_resolution_sub_pixel.py

@@ -26,7 +26,7 @@ dataset,
 ## Setup
 """
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 from keras.utils import load_img

examples/keras_io/vision/token_learner.py

@@ -49,7 +49,7 @@ references:
 ## Imports
 """
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 from tensorflow import data as tf_data

examples/keras_io/vision/video_transformers.py

@@ -47,7 +47,7 @@ import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # @param ["tensorflow", "jax", "torch"]
 
-import keras as keras
+import keras
 from keras import layers
 from keras.applications.densenet import DenseNet121
 

guides/custom_train_step_in_jax.py

@@ -48,7 +48,7 @@ import os
 os.environ["KERAS_BACKEND"] = "jax"
 
 import jax
-import keras as keras
+import keras
 import numpy as np
 
 """

guides/custom_train_step_in_tensorflow.py

@@ -48,7 +48,7 @@ import os
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
 import tensorflow as tf
-import keras as keras
+import keras
 from keras import layers
 import numpy as np
 

guides/custom_train_step_in_torch.py

@@ -48,7 +48,7 @@ import os
 os.environ["KERAS_BACKEND"] = "torch"
 
 import torch
-import keras as keras
+import keras
 from keras import layers
 import numpy as np
 

guides/distributed_training_with_jax.py

@@ -46,7 +46,7 @@ os.environ["KERAS_BACKEND"] = "jax"
 import jax
 import numpy as np
 import tensorflow as tf
-import keras as keras
+import keras
 
 from jax.experimental import mesh_utils
 from jax.sharding import Mesh

guides/distributed_training_with_tensorflow.py

@@ -42,7 +42,7 @@ import os
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
 import tensorflow as tf
-import keras as keras
+import keras
 
 """
 ## Single-host, multi-device synchronous training

guides/distributed_training_with_torch.py

@@ -46,7 +46,7 @@ os.environ["KERAS_BACKEND"] = "torch"
 
 import torch
 import numpy as np
-import keras as keras
+import keras
 
 
 def get_model():

guides/functional_api.py

@@ -11,7 +11,7 @@ Accelerator: GPU
 """
 
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

guides/making_new_layers_and_models_via_subclassing.py

@@ -29,7 +29,7 @@ Let's dive in.
 """
 
 import numpy as np
-import keras as keras
+import keras
 from keras import ops
 from keras import layers
 

guides/sequential_model.py

@@ -11,7 +11,7 @@ Accelerator: GPU
 
 """
 
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

guides/training_with_built_in_methods.py

@@ -17,7 +17,7 @@ import tensorflow as tf
 
 import os
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 from keras import ops
 

guides/transfer_learning.py

@@ -11,7 +11,7 @@ Accelerator: GPU
 """
 
 import numpy as np
-import keras as keras
+import keras
 from keras import layers
 import tensorflow_datasets as tfds
 import matplotlib.pyplot as plt

guides/understanding_masking_and_padding.py

@@ -10,7 +10,7 @@ Accelerator: None
 ## Setup
 """
 import numpy as np
-import keras as keras
+import keras
 from keras import ops
 from keras import layers
 

guides/writing_a_custom_training_loop_in_jax.py

@@ -19,7 +19,7 @@ import jax
 
 # We import TF so we can use tf.data.
 import tensorflow as tf
-import keras as keras
+import keras
 import numpy as np
 
 """

guides/writing_a_custom_training_loop_in_tensorflow.py

@@ -17,7 +17,7 @@ import os
 os.environ["KERAS_BACKEND"] = "tensorflow"
 
 import tensorflow as tf
-import keras as keras
+import keras
 import numpy as np
 
 """

guides/writing_a_custom_training_loop_in_torch.py

@@ -16,7 +16,7 @@ import os
 os.environ["KERAS_BACKEND"] = "torch"
 
 import torch
-import keras as keras
+import keras
 import numpy as np
 
 """