<commit_before>.. _example:

============
Examples
============

We list some examples here, but more tutorials and applications can be found in `Github examples <https://github.com/tensorlayer/tensorlayer/tree/master/examples>`__ and `Awesome-TensorLayer <https://github.com/tensorlayer/awesome-tensorlayer>`_.

Basics
============

 - Multi-layer perceptron (MNIST), simple usage. Classification task, see `tutorial_mnist_simple.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/tutorial_mnist_simple.py>`__.
 - Multi-layer perceptron (MNIST), dynamic model. Classification with dropout using iterator, see `tutorial_mnist_mlp_dynamic.py method2 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/tutorial_mnist_mlp_dynamic.py>`__.
 - Multi-layer perceptron (MNIST), static model. Classification with dropout using iterator, see `tutorial_mnist_mlp_static.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/tutorial_mnist_mlp_static.py>`__.
 - Convolutional Network (CIFAR-10). Classification task, see `tutorial_cifar10_cnn_static.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/tutorial_cifar10_cnn_static.py>`_.
 - TensorFlow dataset API for object detection see `here <https://github.com/tensorlayer/tensorlayer/blob/master/examples/data_process/tutorial_tf_dataset_voc.py>`__.
 - Merge Keras into TensorLayer. `tutorial_keras.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/keras_tfslim/tutorial_keras.py>`__.
 - Data augmentation with TFRecord. Effective way to load and pre-process data, see `tutorial_tfrecord*.py <https://github.com/tensorlayer/tensorlayer/tree/master/examples/data_process>`__ and `tutorial_cifar10_tfrecord.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/data_process/tutorial_tfrecord.py>`__.
 - Data augmentation with TensorLayer. See `tutorial_fast_affine_transform.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/data_process/tutorial_fast_affine_transform.py>`__ (for quick test only).

Pretrained Models
==================

 - VGG 16 (ImageNet). Classification task, see `tutorial_models_vgg16 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_vgg16.py>`__.
 - VGG 19 (ImageNet). Classification task, see `tutorial_models_vgg19.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_vgg19.py>`__.
 - SqueezeNet (ImageNet). Model compression, see `tutorial_models_squeezenetv1.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_squeezenetv1.py>`__.
 - MobileNet (ImageNet). Model compression, see `tutorial_models_mobilenetv1.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_mobilenetv1.py>`__.
 - All pretrained models in `pretrained-models <https://github.com/tensorlayer/pretrained-models>`__.

Vision
==================

 - Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization, see `examples <https://github.com/tensorlayer/adaptive-style-transfer>`__.
 - ArcFace: Additive Angular Margin Loss for Deep Face Recognition, see `InsignFace <https://github.com/auroua/InsightFace_TF>`__.
 - BinaryNet. Model compression, see `mnist <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_binarynet_mnist_cnn.py>`__ `cifar10 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_binarynet_cifar10_tfrecord.py>`__.
 - Ternary Weight Network. Model compression, see `mnist <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_ternaryweight_mnist_cnn.py>`__ `cifar10 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_ternaryweight_cifar10_tfrecord.py>`__.
 - DoReFa-Net. Model compression, see `mnist <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_dorefanet_mnist_cnn.py>`__ `cifar10 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_dorefanet_cifar10_tfrecord.py>`__.<commit_msg>Update examples.rst<commit_after>.. _example:

============
Examples
============

We list some examples here, but more tutorials and applications can be found in `Github examples <https://github.com/tensorlayer/tensorlayer/tree/master/examples>`__ and `Awesome-TensorLayer <https://github.com/tensorlayer/awesome-tensorlayer>`_.

Basics
============

 - Multi-layer perceptron (MNIST), simple usage. Classification task, see `tutorial_mnist_simple.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/tutorial_mnist_simple.py>`__.
 - Multi-layer perceptron (MNIST), dynamic model. Classification with dropout using iterator, see `tutorial_mnist_mlp_dynamic.py method2 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/tutorial_mnist_mlp_dynamic.py>`__.
 - Multi-layer perceptron (MNIST), static model. Classification with dropout using iterator, see `tutorial_mnist_mlp_static.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/tutorial_mnist_mlp_static.py>`__.
 - Convolutional Network (CIFAR-10). Classification task, see `tutorial_cifar10_cnn_static.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/tutorial_cifar10_cnn_static.py>`_.
 - TensorFlow dataset API for object detection see `here <https://github.com/tensorlayer/tensorlayer/blob/master/examples/data_process/tutorial_tf_dataset_voc.py>`__.
 - Data augmentation with TFRecord. Effective way to load and pre-process data, see `tutorial_tfrecord*.py <https://github.com/tensorlayer/tensorlayer/tree/master/examples/data_process>`__ and `tutorial_cifar10_tfrecord.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/basic_tutorials/data_process/tutorial_tfrecord.py>`__.
 - Data augmentation with TensorLayer. See `tutorial_fast_affine_transform.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/data_process/tutorial_fast_affine_transform.py>`__ (for quick test only).

Pretrained Models
==================

 - VGG 16 (ImageNet). Classification task, see `tutorial_models_vgg16 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_vgg16.py>`__.
 - VGG 19 (ImageNet). Classification task, see `tutorial_models_vgg19.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_vgg19.py>`__.
 - SqueezeNet (ImageNet). Model compression, see `tutorial_models_squeezenetv1.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_squeezenetv1.py>`__.
 - MobileNet (ImageNet). Model compression, see `tutorial_models_mobilenetv1.py <https://github.com/tensorlayer/tensorlayer/blob/master/examples/pretrained_cnn/tutorial_models_mobilenetv1.py>`__.
 - All pretrained models in `pretrained-models <https://github.com/tensorlayer/pretrained-models>`__.

Vision
==================

 - Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization, see `examples <https://github.com/tensorlayer/adaptive-style-transfer>`__.
 - ArcFace: Additive Angular Margin Loss for Deep Face Recognition, see `InsignFace <https://github.com/auroua/InsightFace_TF>`__.
 - BinaryNet. Model compression, see `mnist <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_binarynet_mnist_cnn.py>`__ `cifar10 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_binarynet_cifar10_tfrecord.py>`__.
 - Ternary Weight Network. Model compression, see `mnist <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_ternaryweight_mnist_cnn.py>`__ `cifar10 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_ternaryweight_cifar10_tfrecord.py>`__.
 - DoReFa-Net. Model compression, see `mnist <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_dorefanet_mnist_cnn.py>`__ `cifar10 <https://github.com/tensorlayer/tensorlayer/blob/master/examples/quantized_net/tutorial_dorefanet_cifar10_tfrecord.py>`__.
{'fraction_non_alphanumeric': 0.1289742051589682, 'fraction_numerical': 0.008878224355128974, 'mean_word_length': 6.550724637681159, 'pattern_counts': {'":': 0, '<': 50, '<?xml version=': 0, '>': 50, 'https://': 47, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '4189442', 'n_tokens_mistral': 2854, 'n_tokens_neox': 2572, 'n_words': 396}