text stringlengths 5 261k | id stringlengths 16 106 | metadata dict | __index_level_0__ int64 0 266 |
|---|---|---|---|
from keras_core import backend
from keras_core import ops
from keras_core.api_export import keras_core_export
from keras_core.optimizers import optimizer
@keras_core_export(["keras_core.optimizers.Nadam"])
class Nadam(optimizer.Optimizer):
"""Optimizer that implements the Nadam algorithm.
Much like Adam is e... | keras-core/keras_core/optimizers/nadam.py/0 | {
"file_path": "keras-core/keras_core/optimizers/nadam.py",
"repo_id": "keras-core",
"token_count": 2677
} | 51 |
import math
from keras_core import ops
from keras_core.api_export import keras_core_export
from keras_core.utils.numerical_utils import normalize
@keras_core_export(
["keras_core.Regularizer", "keras_core.regularizers.Regularizer"]
)
class Regularizer:
"""Regularizer base class.
Regularizers allow you t... | keras-core/keras_core/regularizers/regularizers.py/0 | {
"file_path": "keras-core/keras_core/regularizers/regularizers.py",
"repo_id": "keras-core",
"token_count": 4658
} | 52 |
import numpy as np
from absl.testing import parameterized
from keras_core import backend
from keras_core import metrics as losses_module
from keras_core import metrics as metrics_module
from keras_core import testing
from keras_core.trainers.compile_utils import CompileLoss
from keras_core.trainers.compile_utils impor... | keras-core/keras_core/trainers/compile_utils_test.py/0 | {
"file_path": "keras-core/keras_core/trainers/compile_utils_test.py",
"repo_id": "keras-core",
"token_count": 6665
} | 53 |
import platform
import warnings
from keras_core import backend
from keras_core import metrics as metrics_module
from keras_core import ops
from keras_core import optimizers
from keras_core.optimizers.loss_scale_optimizer import LossScaleOptimizer
from keras_core.saving import serialization_lib
from keras_core.trainers... | keras-core/keras_core/trainers/trainer.py/0 | {
"file_path": "keras-core/keras_core/trainers/trainer.py",
"repo_id": "keras-core",
"token_count": 18212
} | 54 |
import os
import numpy as np
from keras_core import testing
from keras_core.utils import image_dataset_utils
from keras_core.utils import image_utils
from keras_core.utils.module_utils import tensorflow as tf
class ImageDatasetFromDirectoryTest(testing.TestCase):
def _get_images(self, count=16, color_mode="rgb"... | keras-core/keras_core/utils/image_dataset_utils_test.py/0 | {
"file_path": "keras-core/keras_core/utils/image_dataset_utils_test.py",
"repo_id": "keras-core",
"token_count": 7204
} | 55 |
import numpy as np
import pytest
import tensorflow as tf
import keras_core
from keras_core import backend
from keras_core.testing import test_case
from keras_core.utils import rng_utils
class TestRandomSeedSetting(test_case.TestCase):
@pytest.mark.skipif(
backend.backend() == "numpy",
reason="Num... | keras-core/keras_core/utils/rng_utils_test.py/0 | {
"file_path": "keras-core/keras_core/utils/rng_utils_test.py",
"repo_id": "keras-core",
"token_count": 518
} | 56 |
# Unique source of truth for the version number.
__version__ = "0.1.7"
| keras-core/keras_core/version.py/0 | {
"file_path": "keras-core/keras_core/version.py",
"repo_id": "keras-core",
"token_count": 23
} | 57 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/benchmarks/vectorized_auto_contrast.py/0 | {
"file_path": "keras-cv/benchmarks/vectorized_auto_contrast.py",
"repo_id": "keras-cv",
"token_count": 2237
} | 58 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/benchmarks/vectorized_random_zoom.py/0 | {
"file_path": "keras-cv/benchmarks/vectorized_random_zoom.py",
"repo_id": "keras-cv",
"token_count": 5924
} | 59 |
{
"convmixer_512_16": {
"v0": {
"accelerators": 8,
"args": {
"batch_size": "64",
"initial_learning_rate": "0.0125",
"use_ema": "True",
"weight_decay": "0.0001"
},
"contributor": "ianstenbit",
... | keras-cv/examples/training/classification/imagenet/training_history.json/0 | {
"file_path": "keras-cv/examples/training/classification/imagenet/training_history.json",
"repo_id": "keras-cv",
"token_count": 5582
} | 60 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/backend/ops.py/0 | {
"file_path": "keras-cv/keras_cv/backend/ops.py",
"repo_id": "keras-cv",
"token_count": 517
} | 61 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/core/factor_sampler/constant_factor_sampler.py/0 | {
"file_path": "keras-cv/keras_cv/core/factor_sampler/constant_factor_sampler.py",
"repo_id": "keras-cv",
"token_count": 553
} | 62 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/hierarchical_transformer_encoder.py/0 | {
"file_path": "keras-cv/keras_cv/layers/hierarchical_transformer_encoder.py",
"repo_id": "keras-cv",
"token_count": 2444
} | 63 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/object_detection/roi_pool_test.py/0 | {
"file_path": "keras-cv/keras_cv/layers/object_detection/roi_pool_test.py",
"repo_id": "keras-cv",
"token_count": 5018
} | 64 |
# Preprocessing Layers
KerasCV offers many preprocessing and data augmentation layers which support classification, object detection, and segmentation masks. When you use KerasCV augmentation layers to augment your training data, class labels, bounding boxes, and mask labels automatically get augmented alongside the i... | keras-cv/keras_cv/layers/preprocessing/README.md/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/README.md",
"repo_id": "keras-cv",
"token_count": 882
} | 65 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/preprocessing/grayscale.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/grayscale.py",
"repo_id": "keras-cv",
"token_count": 1536
} | 66 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/preprocessing/random_apply_test.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/random_apply_test.py",
"repo_id": "keras-cv",
"token_count": 1773
} | 67 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/preprocessing/random_rotation_test.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/random_rotation_test.py",
"repo_id": "keras-cv",
"token_count": 3663
} | 68 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/preprocessing/resizing_test.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/resizing_test.py",
"repo_id": "keras-cv",
"token_count": 6645
} | 69 |
# Copyright 2022 Waymo LLC.
#
# Licensed under the terms in https://github.com/keras-team/keras-cv/blob/master/keras_cv/layers/preprocessing_3d/waymo/LICENSE # noqa: E501
import tensorflow as tf
from keras_cv import point_cloud
from keras_cv.api_export import keras_cv_export
from keras_cv.layers.preprocessing_3d imp... | keras-cv/keras_cv/layers/preprocessing_3d/waymo/frustum_random_point_feature_noise.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing_3d/waymo/frustum_random_point_feature_noise.py",
"repo_id": "keras-cv",
"token_count": 2909
} | 70 |
# Copyright 2022 Waymo LLC.
#
# Licensed under the terms in https://github.com/keras-team/keras-cv/blob/master/keras_cv/layers/preprocessing_3d/waymo/LICENSE # noqa: E501
import tensorflow as tf
from keras_cv.api_export import keras_cv_export
from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
fr... | keras-cv/keras_cv/layers/preprocessing_3d/waymo/random_drop_box.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing_3d/waymo/random_drop_box.py",
"repo_id": "keras-cv",
"token_count": 2157
} | 71 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/losses/iou_loss.py/0 | {
"file_path": "keras-cv/keras_cv/losses/iou_loss.py",
"repo_id": "keras-cv",
"token_count": 2165
} | 72 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/backbones/densenet/densenet_backbone_presets_test.py/0 | {
"file_path": "keras-cv/keras_cv/models/backbones/densenet/densenet_backbone_presets_test.py",
"repo_id": "keras-cv",
"token_count": 1419
} | 73 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/backbones/efficientnet_v2/efficientnet_v2_backbone.py/0 | {
"file_path": "keras-cv/keras_cv/models/backbones/efficientnet_v2/efficientnet_v2_backbone.py",
"repo_id": "keras-cv",
"token_count": 6095
} | 74 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/backbones/vit_det/vit_det_backbone.py/0 | {
"file_path": "keras-cv/keras_cv/models/backbones/vit_det/vit_det_backbone.py",
"repo_id": "keras-cv",
"token_count": 4128
} | 75 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/feature_extractor/clip/clip_text_model.py/0 | {
"file_path": "keras-cv/keras_cv/models/feature_extractor/clip/clip_text_model.py",
"repo_id": "keras-cv",
"token_count": 1964
} | 76 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/legacy/object_detection/faster_rcnn/faster_rcnn_test.py/0 | {
"file_path": "keras-cv/keras_cv/models/legacy/object_detection/faster_rcnn/faster_rcnn_test.py",
"repo_id": "keras-cv",
"token_count": 1681
} | 77 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/object_detection/predict_utils.py/0 | {
"file_path": "keras-cv/keras_cv/models/object_detection/predict_utils.py",
"repo_id": "keras-cv",
"token_count": 1702
} | 78 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/object_detection/yolo_v8/yolo_v8_layers.py/0 | {
"file_path": "keras-cv/keras_cv/models/object_detection/yolo_v8/yolo_v8_layers.py",
"repo_id": "keras-cv",
"token_count": 1278
} | 79 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/object_detection_3d/center_pillar_test.py/0 | {
"file_path": "keras-cv/keras_cv/models/object_detection_3d/center_pillar_test.py",
"repo_id": "keras-cv",
"token_count": 2425
} | 80 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/stable_diffusion/image_encoder.py/0 | {
"file_path": "keras-cv/keras_cv/models/stable_diffusion/image_encoder.py",
"repo_id": "keras-cv",
"token_count": 1309
} | 81 |
# Copyright 2022 The KerasCV Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | keras-cv/keras_cv/point_cloud/within_box_3d_test.py/0 | {
"file_path": "keras-cv/keras_cv/point_cloud/within_box_3d_test.py",
"repo_id": "keras-cv",
"token_count": 4191
} | 82 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/utils/fill_utils_test.py/0 | {
"file_path": "keras-cv/keras_cv/utils/fill_utils_test.py",
"repo_id": "keras-cv",
"token_count": 7182
} | 83 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/visualization/draw_bounding_boxes.py/0 | {
"file_path": "keras-cv/keras_cv/visualization/draw_bounding_boxes.py",
"repo_id": "keras-cv",
"token_count": 2568
} | 84 |
#!/bin/bash
isort .
black .
find . -iname *.h -o -iname *.c -o -iname *.cpp -o -iname *.hpp -o -iname *.cc \
| xargs clang-format --style=google -i -fallback-style=none
| keras-cv/shell/format.sh/0 | {
"file_path": "keras-cv/shell/format.sh",
"repo_id": "keras-cv",
"token_count": 80
} | 85 |
# Japanese translation of the Keras documentation
This is the repository for the translated `.md` sources files of [keras.io](http://keras.io/). The translation project.
---
# Keras documentationの日本語訳化
## 翻訳ガイドライン
- 翻訳対象は本文とコード中のコメント
- 本文は敬体（です・ます調）
- 句読点は「，．」を用いる
- 引用符（'，"）は基本的にそのまま
- 記号`「，．（）？！：；」`は全角
- 文中のシンタッ... | keras-docs-ja/README.md/0 | {
"file_path": "keras-docs-ja/README.md",
"repo_id": "keras-docs-ja",
"token_count": 1620
} | 86 |
<span style="float:right;">[[source]](https://github.com/keras-team/keras/blob/master/keras/layers/convolutional.py#L237)</span>
### Conv1D
```python
keras.layers.Conv1D(filters, kernel_size, strides=1, padding='valid', dilation_rate=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initiali... | keras-docs-ja/sources/layers/convolutional.md/0 | {
"file_path": "keras-docs-ja/sources/layers/convolutional.md",
"repo_id": "keras-docs-ja",
"token_count": 17230
} | 87 |
## オプティマイザ（最適化アルゴリズム）の利用方法
オプティマイザ（最適化アルゴリズム）はモデルをコンパイルする際に必要となるパラメータの1つです:
```python
from keras import optimizers
model = Sequential()
model.add(Dense(64, kernel_initializer='uniform', input_shape=(10,)))
model.add(Activation('tanh'))
model.add(Activation('softmax'))
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momen... | keras-docs-ja/sources/optimizers.md/0 | {
"file_path": "keras-docs-ja/sources/optimizers.md",
"repo_id": "keras-docs-ja",
"token_count": 3825
} | 88 |
# 케라스: 파이썬 딥러닝 라이브러리
<img src='https://s3.amazonaws.com/keras.io/img/keras-logo-2018-large-1200.png' style='max-width: 600px; width: 90%;' />
## 케라스에 오신걸 환영합니다.
케라스는 파이썬으로 작성된 고수준 신경망 API로 [TensorFlow](https://github.com/tensorflow/tensorflow), [CNTK](https://github.com/Microsoft/cntk), 혹은 [Theano](https://github.... | keras-docs-ko/sources/index.md/0 | {
"file_path": "keras-docs-ko/sources/index.md",
"repo_id": "keras-docs-ko",
"token_count": 7712
} | 89 |
## 측정항목의 사용법
측정항목은 모델의 성능을 평가하는데 사용되는 함수입니다. 측정항목 함수는 모델이 컴파일 될 때 `metrics` 매개변수를 통해 공급됩니다.
```python
model.compile(loss='mean_squared_error',
optimizer='sgd',
metrics=['mae', 'acc'])
```
```python
from keras import metrics
model.compile(loss='mean_squared_error',
optimize... | keras-docs-ko/sources/metrics.md/0 | {
"file_path": "keras-docs-ko/sources/metrics.md",
"repo_id": "keras-docs-ko",
"token_count": 1533
} | 90 |
# 数据集
## CIFAR10 小图像分类数据集
50,000 张 32x32 彩色训练图像数据，以及 10,000 张测试图像数据，总共分为 10 个类别。
### 用法：
```python
from keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
```
- __返回：__
- 2 个元组：
- __x_train, x_test__: uint8 数组表示的 RGB 图像数据，尺寸为 (num_samples, 3, 32, 32) 或 (num_samples, 32,... | keras-docs-zh/sources/datasets.md/0 | {
"file_path": "keras-docs-zh/sources/datasets.md",
"repo_id": "keras-docs-zh",
"token_count": 5051
} | 91 |
# 本示例演示了使用 fasttext 进行文本分类
根据Joulin等人的论文：
[Bags of Tricks for Efficient Text Classification](https://arxiv.org/abs/1607.01759)
在具有 uni-gram 和 bi-gram 嵌入的 IMDB 数据集上的结果：
Embedding|Accuracy, 5 epochs|Speed (s/epoch)|Hardware
:--------|-----------------:|----:|:-------
Uni-gram | 0.8813| 8|i7 CPU
Bi-gram ... | keras-docs-zh/sources/examples/imdb_fasttext.md/0 | {
"file_path": "keras-docs-zh/sources/examples/imdb_fasttext.md",
"repo_id": "keras-docs-zh",
"token_count": 2383
} | 92 |
# 转移学习玩具示例。
1 - 在 MNIST 数据集的前 5 位 [0..4] 上训练简单的 convnet。
2 - 冻结卷积层并微调密集层以进行数字分类 [5..9]。
迁移+微调后的前五个数字分类器经过 5 个轮次后，测试准确率达到 99.8％，最后 5 个数字达到 99.2％。
```python
from __future__ import print_function
import datetime
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import... | keras-docs-zh/sources/examples/mnist_transfer_cnn.md/0 | {
"file_path": "keras-docs-zh/sources/examples/mnist_transfer_cnn.md",
"repo_id": "keras-docs-zh",
"token_count": 1850
} | 93 |
<span style="float:right;">[[source]](https://github.com/keras-team/keras/blob/master/keras/layers/wrappers.py#L116)</span>
### TimeDistributed
```python
keras.layers.TimeDistributed(layer)
```
这个封装器将一个层应用于输入的每个时间片。
输入至少为 3D，且第一个维度应该是时间所表示的维度。
考虑 32 个样本的一个 batch，
其中每个样本是 10 个 16 维向量的序列。
那么这个 batch 的输入尺寸为 `(32, 10, ... | keras-docs-zh/sources/layers/wrappers.md/0 | {
"file_path": "keras-docs-zh/sources/layers/wrappers.md",
"repo_id": "keras-docs-zh",
"token_count": 1238
} | 94 |
.git
.gitignore
tmp/*
sources/*
site/*
scripts/upload.py
*.pyc
templates/examples/generative/*
templates/examples/nlp/*
templates/examples/vision/*
templates/examples/structured_data/*
templates/examples/keras_recipes/*
| keras-io/.dockerignore/0 | {
"file_path": "keras-io/.dockerignore",
"repo_id": "keras-io",
"token_count": 81
} | 95 |
"""
Title: GPT2 Text Generation with KerasNLP
Author: Chen Qian
Date created: 04/17/2023
Last modified: 04/17/2023
Description: Use KerasNLP GPT2 model and `samplers` to do text generation.
Accelerator: GPU
"""
"""
In this tutorial, you will learn to use [KerasNLP](https://keras.io/keras_nlp/) to load a
pre-trained La... | keras-io/examples/generative/gpt2_text_generation_with_kerasnlp.py/0 | {
"file_path": "keras-io/examples/generative/gpt2_text_generation_with_kerasnlp.py",
"repo_id": "keras-io",
"token_count": 3983
} | 96 |
<jupyter_start><jupyter_text>GauGAN for conditional image generation**Author:** [Soumik Rakshit](https://github.com/soumik12345), [Sayak Paul](https://twitter.com/RisingSayak)**Date created:** 2021/12/26**Last modified:** 2022/01/03**Description:** Implementing a GauGAN for conditional image generation. IntroductionIn... | keras-io/examples/generative/ipynb/gaugan.ipynb/0 | {
"file_path": "keras-io/examples/generative/ipynb/gaugan.ipynb",
"repo_id": "keras-io",
"token_count": 12513
} | 97 |
"""
Title: Character-level text generation with LSTM
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2015/06/15
Last modified: 2020/04/30
Description: Generate text from Nietzsche's writings with a character-level LSTM.
Accelerator: GPU
"""
"""
## Introduction
This example demonstrates how to use a LST... | keras-io/examples/generative/lstm_character_level_text_generation.py/0 | {
"file_path": "keras-io/examples/generative/lstm_character_level_text_generation.py",
"repo_id": "keras-io",
"token_count": 1392
} | 98 |
"""
Title: Density estimation using Real NVP
Authors: [Mandolini Giorgio Maria](https://www.linkedin.com/in/giorgio-maria-mandolini-a2a1b71b4/), [Sanna Daniele](https://www.linkedin.com/in/daniele-sanna-338629bb/), [Zannini Quirini Giorgio](https://www.linkedin.com/in/giorgio-zannini-quirini-16ab181a0/)
Date created: 2... | keras-io/examples/generative/real_nvp.py/0 | {
"file_path": "keras-io/examples/generative/real_nvp.py",
"repo_id": "keras-io",
"token_count": 2930
} | 99 |
"""
Title: Approximating non-Function Mappings with Mixture Density Networks
Author: [lukewood](https://twitter.com/luke_wood_ml)
Date created: 2023/07/15
Last modified: 2023/07/15
Description: Approximate non one to one mapping using mixture density networks.
Accelerator: None
"""
"""
## Approximating NonFunctions
N... | keras-io/examples/keras_recipes/approximating_non_function_mappings.py/0 | {
"file_path": "keras-io/examples/keras_recipes/approximating_non_function_mappings.py",
"repo_id": "keras-io",
"token_count": 4810
} | 100 |
<jupyter_start><jupyter_text>Memory-efficient embeddings for recommendation systems**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)**Date created:** 2021/02/15**Last modified:** 2023/11/15**Description:** Using compositional & mixed-dimension embeddings for memory-efficient recommendatio... | keras-io/examples/keras_recipes/ipynb/memory_efficient_embeddings.ipynb/0 | {
"file_path": "keras-io/examples/keras_recipes/ipynb/memory_efficient_embeddings.ipynb",
"repo_id": "keras-io",
"token_count": 5816
} | 101 |
# Endpoint layer pattern
**Author:** [fchollet](https://twitter.com/fchollet)<br>
**Date created:** 2019/05/10<br>
**Last modified:** 2023/11/22<br>
**Description:** Demonstration of the "endpoint layer" pattern (layer that handles loss management).
<img class="k-inline-icon" src="https://colab.research.google.com/i... | keras-io/examples/keras_recipes/md/endpoint_layer_pattern.md/0 | {
"file_path": "keras-io/examples/keras_recipes/md/endpoint_layer_pattern.md",
"repo_id": "keras-io",
"token_count": 2012
} | 102 |
"""
Title: Customizing the convolution operation of a Conv2D layer
Author: [lukewood](https://lukewood.xyz)
Date created: 11/03/2021
Last modified: 11/03/2021
Description: This example shows how to implement custom convolution layers using the `Conv.convolution_op()` API.
Accelerator: GPU
"""
"""
## Introduction
You ... | keras-io/examples/keras_recipes/subclassing_conv_layers.py/0 | {
"file_path": "keras-io/examples/keras_recipes/subclassing_conv_layers.py",
"repo_id": "keras-io",
"token_count": 1477
} | 103 |
<jupyter_start><jupyter_text>Character-level recurrent sequence-to-sequence model**Author:** [fchollet](https://twitter.com/fchollet)**Date created:** 2017/09/29**Last modified:** 2023/11/22**Description:** Character-level recurrent sequence-to-sequence model. IntroductionThis example demonstrates how to implement a b... | keras-io/examples/nlp/ipynb/lstm_seq2seq.ipynb/0 | {
"file_path": "keras-io/examples/nlp/ipynb/lstm_seq2seq.ipynb",
"repo_id": "keras-io",
"token_count": 3665
} | 104 |
<jupyter_start><jupyter_text>Abstractive Summarization with Hugging Face Transformers**Author:** Sreyan Ghosh**Date created:** 2022/07/04**Last modified:** 2022/08/28**Description:** Training T5 using Hugging Face Transformers for Abstractive Summarization. IntroductionAutomatic summarization is one of the central pro... | keras-io/examples/nlp/ipynb/t5_hf_summarization.ipynb/0 | {
"file_path": "keras-io/examples/nlp/ipynb/t5_hf_summarization.ipynb",
"repo_id": "keras-io",
"token_count": 3716
} | 105 |
# Training a language model from scratch with 🤗 Transformers and TPUs
**Authors:** [Matthew Carrigan](https://twitter.com/carrigmat), [Sayak Paul](https://twitter.com/RisingSayak)<br>
**Date created:** 2023/05/21<br>
**Last modified:** 2023/05/21<br>
**Description:** Train a masked language model on TPUs using 🤗 Tra... | keras-io/examples/nlp/md/mlm_training_tpus.md/0 | {
"file_path": "keras-io/examples/nlp/md/mlm_training_tpus.md",
"repo_id": "keras-io",
"token_count": 8307
} | 106 |
# Text classification with Switch Transformer
**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)<br>
**Date created:** 2020/05/10<br>
**Last modified:** 2021/02/15<br>
**Description:** Implement a Switch Transformer for text classification.
<img class="k-inline-icon" src="https://colab.... | keras-io/examples/nlp/md/text_classification_with_switch_transformer.md/0 | {
"file_path": "keras-io/examples/nlp/md/text_classification_with_switch_transformer.md",
"repo_id": "keras-io",
"token_count": 5189
} | 107 |
"""
Title: Semantic Similarity with KerasNLP
Author: [Anshuman Mishra](https://github.com/shivance/)
Date created: 2023/02/25
Last modified: 2023/02/25
Description: Use pretrained models from KerasNLP for the Semantic Similarity Task.
Accelerator: GPU
"""
"""
## Introduction
Semantic similarity refers to the task of ... | keras-io/examples/nlp/semantic_similarity_with_keras_nlp.py/0 | {
"file_path": "keras-io/examples/nlp/semantic_similarity_with_keras_nlp.py",
"repo_id": "keras-io",
"token_count": 3440
} | 108 |
# Actor Critic Method
**Author:** [Apoorv Nandan](https://twitter.com/NandanApoorv)<br>
**Date created:** 2020/05/13<br>
**Last modified:** 2020/05/13<br>
**Description:** Implement Actor Critic Method in CartPole environment.
<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/>... | keras-io/examples/rl/md/actor_critic_cartpole.md/0 | {
"file_path": "keras-io/examples/rl/md/actor_critic_cartpole.md",
"repo_id": "keras-io",
"token_count": 2860
} | 109 |
<jupyter_start><jupyter_text>Timeseries classification with a Transformer model**Author:** [Theodoros Ntakouris](https://github.com/ntakouris)**Date created:** 2021/06/25**Last modified:** 2021/08/05**Description:** This notebook demonstrates how to do timeseries classification using a Transformer model. IntroductionT... | keras-io/examples/timeseries/ipynb/timeseries_classification_transformer.ipynb/0 | {
"file_path": "keras-io/examples/timeseries/ipynb/timeseries_classification_transformer.ipynb",
"repo_id": "keras-io",
"token_count": 1659
} | 110 |
"""
Title: Semi-supervision and domain adaptation with AdaMatch
Author: [Sayak Paul](https://twitter.com/RisingSayak)
Date created: 2021/06/19
Last modified: 2021/06/19
Description: Unifying semi-supervised learning and unsupervised domain adaptation with AdaMatch.
Accelerator: GPU
"""
"""
## Introduction
In this exa... | keras-io/examples/vision/adamatch.py/0 | {
"file_path": "keras-io/examples/vision/adamatch.py",
"repo_id": "keras-io",
"token_count": 8380
} | 111 |
"""
Title: Image classification with EANet (External Attention Transformer)
Author: [ZhiYong Chang](https://github.com/czy00000)
Date created: 2021/10/19
Last modified: 2023/07/18
Description: Image classification with a Transformer that leverages external attention.
Accelerator: GPU
Converted to Keras 3: [Muhammad Ana... | keras-io/examples/vision/eanet.py/0 | {
"file_path": "keras-io/examples/vision/eanet.py",
"repo_id": "keras-io",
"token_count": 3597
} | 112 |
<jupyter_start><jupyter_text>Image classification with ConvMixer**Author:** [Sayak Paul](https://twitter.com/RisingSayak)**Date created:** 2021/10/12**Last modified:** 2021/10/12**Description:** An all-convolutional network applied to patches of images. IntroductionVision Transformers (ViT; [Dosovitskiy et al.](https:... | keras-io/examples/vision/ipynb/convmixer.ipynb/0 | {
"file_path": "keras-io/examples/vision/ipynb/convmixer.ipynb",
"repo_id": "keras-io",
"token_count": 3362
} | 113 |
<jupyter_start><jupyter_text>Image classification from scratch**Author:** [fchollet](https://twitter.com/fchollet)**Date created:** 2020/04/27**Last modified:** 2023/11/09**Description:** Training an image classifier from scratch on the Kaggle Cats vs Dogs dataset. IntroductionThis example shows how to do image classi... | keras-io/examples/vision/ipynb/image_classification_from_scratch.ipynb/0 | {
"file_path": "keras-io/examples/vision/ipynb/image_classification_from_scratch.ipynb",
"repo_id": "keras-io",
"token_count": 3308
} | 114 |
<jupyter_start><jupyter_text>Near-duplicate image search**Author:** [Sayak Paul](https://twitter.com/RisingSayak)**Date created:** 2021/09/10**Last modified:** 2023/08/30**Description:** Building a near-duplicate image search utility using deep learning and locality-sensitive hashing. IntroductionFetching similar imag... | keras-io/examples/vision/ipynb/near_dup_search.ipynb/0 | {
"file_path": "keras-io/examples/vision/ipynb/near_dup_search.ipynb",
"repo_id": "keras-io",
"token_count": 6009
} | 115 |
<jupyter_start><jupyter_text>Semantic Image Clustering**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)**Date created:** 2021/02/28**Last modified:** 2021/02/28**Description:** Semantic Clustering by Adopting Nearest neighbors (SCAN) algorithm. IntroductionThis example demonstrates how t... | keras-io/examples/vision/ipynb/semantic_image_clustering.ipynb/0 | {
"file_path": "keras-io/examples/vision/ipynb/semantic_image_clustering.ipynb",
"repo_id": "keras-io",
"token_count": 7503
} | 116 |
<jupyter_start><jupyter_text>Pneumonia Classification on TPU**Author:** Amy MiHyun Jang**Date created:** 2020/07/28**Last modified:** 2024/02/12**Description:** Medical image classification on TPU. Introduction + Set-upThis tutorial will explain how to build an X-ray image classification modelto predict whether an X-r... | keras-io/examples/vision/ipynb/xray_classification_with_tpus.ipynb/0 | {
"file_path": "keras-io/examples/vision/ipynb/xray_classification_with_tpus.ipynb",
"repo_id": "keras-io",
"token_count": 4692
} | 117 |
# Compact Convolutional Transformers
**Author:** [Sayak Paul](https://twitter.com/RisingSayak)<br>
**Date created:** 2021/06/30<br>
**Last modified:** 2023/08/07<br>
**Description:** Compact Convolutional Transformers for efficient image classification.
<img class="k-inline-icon" src="https://colab.research.google.c... | keras-io/examples/vision/md/cct.md/0 | {
"file_path": "keras-io/examples/vision/md/cct.md",
"repo_id": "keras-io",
"token_count": 8939
} | 118 |
# Handwriting recognition
**Authors:** [A_K_Nain](https://twitter.com/A_K_Nain), [Sayak Paul](https://twitter.com/RisingSayak)<br>
**Date created:** 2021/08/16<br>
**Last modified:** 2023/07/06<br>
<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://... | keras-io/examples/vision/md/handwriting_recognition.md/0 | {
"file_path": "keras-io/examples/vision/md/handwriting_recognition.md",
"repo_id": "keras-io",
"token_count": 9127
} | 119 |
# Image classification with modern MLP models
**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)<br>
**Date created:** 2021/05/30<br>
**Last modified:** 2023/08/03<br>
**Description:** Implementing the MLP-Mixer, FNet, and gMLP models for CIFAR-100 image classification.
<img class="k-in... | keras-io/examples/vision/md/mlp_image_classification.md/0 | {
"file_path": "keras-io/examples/vision/md/mlp_image_classification.md",
"repo_id": "keras-io",
"token_count": 6733
} | 120 |
# Object Detection with RetinaNet
**Author:** [Srihari Humbarwadi](https://twitter.com/srihari_rh)<br>
**Date created:** 2020/05/17<br>
**Last modified:** 2023/07/10<br>
**Description:** Implementing RetinaNet: Focal Loss for Dense Object Detection.
<img class="k-inline-icon" src="https://colab.research.google.com/i... | keras-io/examples/vision/md/retinanet.md/0 | {
"file_path": "keras-io/examples/vision/md/retinanet.md",
"repo_id": "keras-io",
"token_count": 15216
} | 121 |
"""
Title: Self-supervised contrastive learning with NNCLR
Author: [Rishit Dagli](https://twitter.com/rishit_dagli)
Date created: 2021/09/13
Last modified: 2024/01/22
Description: Implementation of NNCLR, a self-supervised learning method for computer vision.
Accelerator: GPU
"""
"""
## Introduction
### Self-supervis... | keras-io/examples/vision/nnclr.py/0 | {
"file_path": "keras-io/examples/vision/nnclr.py",
"repo_id": "keras-io",
"token_count": 7932
} | 122 |
"""
Title: Image similarity estimation using a Siamese Network with a contrastive loss
Author: Mehdi
Date created: 2021/05/06
Last modified: 2022/09/10
Description: Similarity learning using a siamese network trained with a contrastive loss.
Accelerator: GPU
"""
"""
## Introduction
[Siamese Networks](https://en.wikip... | keras-io/examples/vision/siamese_contrastive.py/0 | {
"file_path": "keras-io/examples/vision/siamese_contrastive.py",
"repo_id": "keras-io",
"token_count": 4553
} | 123 |
<jupyter_start><jupyter_text>Multi-GPU distributed training with TensorFlow**Author:** [fchollet](https://twitter.com/fchollet)**Date created:** 2020/04/28**Last modified:** 2023/06/29**Description:** Guide to multi-GPU training for Keras models with TensorFlow. IntroductionThere are generally two ways to distribute c... | keras-io/guides/ipynb/keras_core/distributed_training_with_tensorflow.ipynb/0 | {
"file_path": "keras-io/guides/ipynb/keras_core/distributed_training_with_tensorflow.ipynb",
"repo_id": "keras-io",
"token_count": 2697
} | 124 |
<jupyter_start><jupyter_text>Custom Image Augmentations with BaseImageAugmentationLayer**Author:** [lukewood](https://twitter.com/luke_wood_ml)**Date created:** 2022/04/26**Last modified:** 2023/11/29**Description:** Use BaseImageAugmentationLayer to implement custom data augmentations. OverviewData augmentation is an... | keras-io/guides/ipynb/keras_cv/custom_image_augmentations.ipynb/0 | {
"file_path": "keras-io/guides/ipynb/keras_cv/custom_image_augmentations.ipynb",
"repo_id": "keras-io",
"token_count": 5522
} | 125 |
<jupyter_start><jupyter_text>Migrating Keras 2 code to multi-backend Keras 3**Author:** [Divyashree Sreepathihalli](https://github.com/divyashreepathihalli)**Date created:** 2023/10/23**Last modified:** 2023/10/30**Description:** Instructions & troubleshooting for migrating your Keras 2 code to multi-backend Keras 3. T... | keras-io/guides/ipynb/migrating_to_keras_3.ipynb/0 | {
"file_path": "keras-io/guides/ipynb/migrating_to_keras_3.ipynb",
"repo_id": "keras-io",
"token_count": 12439
} | 126 |
# Customizing what happens in `fit()` with JAX
**Author:** [fchollet](https://twitter.com/fchollet)<br>
**Date created:** 2023/06/27<br>
**Last modified:** 2023/06/27<br>
**Description:** Overriding the training step of the Model class with JAX.
<img class="k-inline-icon" src="https://colab.research.google.com/img/c... | keras-io/guides/md/custom_train_step_in_jax.md/0 | {
"file_path": "keras-io/guides/md/custom_train_step_in_jax.md",
"repo_id": "keras-io",
"token_count": 5313
} | 127 |
# CutMix, MixUp, and RandAugment image augmentation with KerasCV
**Author:** [lukewood](https://twitter.com/luke_wood_ml)<br>
**Date created:** 2022/04/08<br>
**Last modified:** 2022/04/08<br>
**Description:** Use KerasCV to augment images with CutMix, MixUp, RandAugment, and more.
<img class="k-inline-icon" src="ht... | keras-io/guides/md/keras_cv/cut_mix_mix_up_and_rand_augment.md/0 | {
"file_path": "keras-io/guides/md/keras_cv/cut_mix_mix_up_and_rand_augment.md",
"repo_id": "keras-io",
"token_count": 4745
} | 128 |
# Working with preprocessing layers
**Authors:** Francois Chollet, Mark Omernick<br>
**Date created:** 2020/07/25<br>
**Last modified:** 2021/04/23<br>
**Description:** Overview of how to leverage preprocessing layers to create end-to-end models.
<img class="k-inline-icon" src="https://colab.research.google.com/img/... | keras-io/guides/md/preprocessing_layers.md/0 | {
"file_path": "keras-io/guides/md/preprocessing_layers.md",
"repo_id": "keras-io",
"token_count": 8972
} | 129 |
<meta http-equiv="refresh" content="0; URL='https://keras.io/api/keras_nlp/modeling_layers/transformer_encoder/'" />
| keras-io/redirects/api/keras_nlp/layers/transformer_encoder/index.html/0 | {
"file_path": "keras-io/redirects/api/keras_nlp/layers/transformer_encoder/index.html",
"repo_id": "keras-io",
"token_count": 47
} | 130 |
<meta http-equiv="refresh" content="0; URL='https://keras.io/guides/functional_api/'" />
| keras-io/redirects/getting-started/functional-api-guide/index.html/0 | {
"file_path": "keras-io/redirects/getting-started/functional-api-guide/index.html",
"repo_id": "keras-io",
"token_count": 34
} | 131 |
<meta http-equiv="refresh" content="0; URL='https://keras.io/api/layers/recurrent_layers/'" />
| keras-io/redirects/layers/wrappers/index.html/0 | {
"file_path": "keras-io/redirects/layers/wrappers/index.html",
"repo_id": "keras-io",
"token_count": 38
} | 132 |
<meta http-equiv="refresh" content="0; URL='https://keras.io/why_keras/'" />
| keras-io/redirects/why-use-keras/index.html/0 | {
"file_path": "keras-io/redirects/why-use-keras/index.html",
"repo_id": "keras-io",
"token_count": 32
} | 133 |
"""Keras tutobooks implementation.
A tutobook is a tutorial available simultaneously as a notebook,
as a Python script, and as a nicely rendered webpage.
Its source-of-truth (for manual edition and version control) is
its Python script form, but you can also create one by starting
from a notebook and converting it wi... | keras-io/scripts/tutobooks.py/0 | {
"file_path": "keras-io/scripts/tutobooks.py",
"repo_id": "keras-io",
"token_count": 9254
} | 134 |
# KerasNLP Metrics
KerasNLP metrics are `keras.Metric` subclasses for NLP-specific use cases.
{{toc}}
| keras-io/templates/api/keras_nlp/metrics/index.md/0 | {
"file_path": "keras-io/templates/api/keras_nlp/metrics/index.md",
"repo_id": "keras-io",
"token_count": 39
} | 135 |
# Preprocessing layers
{{toc}} | keras-io/templates/api/layers/preprocessing_layers/index.md/0 | {
"file_path": "keras-io/templates/api/layers/preprocessing_layers/index.md",
"repo_id": "keras-io",
"token_count": 9
} | 136 |
# Code examples
Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows.
All of our examples are written as Jupyter notebooks and can be run in one click in [Google Colab](https://colab.research.google.com/notebooks/welcome.ipynb),
a hosted notebook enviro... | keras-io/templates/examples/index.md/0 | {
"file_path": "keras-io/templates/examples/index.md",
"repo_id": "keras-io",
"token_count": 343
} | 137 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="description" content="Keras documentation">
<meta name="author" content="Keras Team">
<title>Keras: Deep Learning for humans</title>
<!-- Boot... | keras-io/theme/landing.html/0 | {
"file_path": "keras-io/theme/landing.html",
"repo_id": "keras-io",
"token_count": 5212
} | 138 |
# KerasNLP Examples
The `examples/` directly contains scripts built on top of the library that do not fit well into
the colab format used on [keras.io](https://keras.io/examples/). This includes recipes for
pre-training models and evaluating models on benchmarks such as GLUE.
| keras-nlp/examples/README.md/0 | {
"file_path": "keras-nlp/examples/README.md",
"repo_id": "keras-nlp",
"token_count": 73
} | 139 |
# Copyright 2023 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/examples/machine_translation/train.py/0 | {
"file_path": "keras-nlp/examples/machine_translation/train.py",
"repo_id": "keras-nlp",
"token_count": 1395
} | 140 |
# Copyright 2023 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/backend/ops.py/0 | {
"file_path": "keras-nlp/keras_nlp/backend/ops.py",
"repo_id": "keras-nlp",
"token_count": 649
} | 141 |
# Copyright 2023 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/layers/modeling/reversible_embedding_test.py/0 | {
"file_path": "keras-nlp/keras_nlp/layers/modeling/reversible_embedding_test.py",
"repo_id": "keras-nlp",
"token_count": 1672
} | 142 |
# Copyright 2023 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/layers/preprocessing/multi_segment_packer.py/0 | {
"file_path": "keras-nlp/keras_nlp/layers/preprocessing/multi_segment_packer.py",
"repo_id": "keras-nlp",
"token_count": 5344
} | 143 |
# Copyright 2023 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/metrics/rouge_base.py/0 | {
"file_path": "keras-nlp/keras_nlp/metrics/rouge_base.py",
"repo_id": "keras-nlp",
"token_count": 3350
} | 144 |
# Copyright 2023 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/models/bart/bart_tokenizer_test.py/0 | {
"file_path": "keras-nlp/keras_nlp/models/bart/bart_tokenizer_test.py",
"repo_id": "keras-nlp",
"token_count": 1157
} | 145 |
# Copyright 2023 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/models/bloom/bloom_attention.py/0 | {
"file_path": "keras-nlp/keras_nlp/models/bloom/bloom_attention.py",
"repo_id": "keras-nlp",
"token_count": 3189
} | 146 |
# Copyright 2022 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/models/deberta_v3/deberta_v3_masked_lm.py/0 | {
"file_path": "keras-nlp/keras_nlp/models/deberta_v3/deberta_v3_masked_lm.py",
"repo_id": "keras-nlp",
"token_count": 2189
} | 147 |
# Copyright 2023 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/models/f_net/f_net_backbone.py/0 | {
"file_path": "keras-nlp/keras_nlp/models/f_net/f_net_backbone.py",
"repo_id": "keras-nlp",
"token_count": 4020
} | 148 |
# Copyright 2024 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/models/gemma/gemma_backbone_test.py/0 | {
"file_path": "keras-nlp/keras_nlp/models/gemma/gemma_backbone_test.py",
"repo_id": "keras-nlp",
"token_count": 2360
} | 149 |
# Copyright 2023 The KerasNLP Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | keras-nlp/keras_nlp/models/gpt_neo_x/gpt_neo_x_causal_lm_preprocessor_test.py/0 | {
"file_path": "keras-nlp/keras_nlp/models/gpt_neo_x/gpt_neo_x_causal_lm_preprocessor_test.py",
"repo_id": "keras-nlp",
"token_count": 1658
} | 150 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.