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- Denoising Autoencoder (MNIST). A multi-layer perceptron implementation for MNIST classification task, see `tutorial_mnist.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py>`_.
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- Stacked Denoising Autoencoder and Fine-Tuning (MNIST). A multi-layer perceptron implementation for MNIST classification task, see `tutorial_mnist.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py>`_.
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- Convolutional Network (MNIST). A Convolutional neural network implementation for classifying MNIST dataset, see `tutorial_mnist.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py>`_.
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- Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset, see `tutorial_cifar10.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10.py>`_ and `tutorial_cifar10_tfrecord.py <tutorial\_cifar10_tfrecord.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10_tfrecord.py>`_.
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- Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset, see `tutorial_cifar10.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10.py>`_ and `tutorial_cifar10_tfrecord.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10_tfrecord.py>`_.
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- VGG 16 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see `tutorial_vgg16.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg16.py>`_.
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- VGG 19 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see `tutorial_vgg19.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py>`_.
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- InceptionV3 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see `tutorial_inceptionV3_tfslim.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py>`_.
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