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We run this script under TensorFlow 1.4 and the TensorLayer 1.8.0+. π This repo will be moved to here (please star) for life-cycle management soon. More cool Computer Vision applications such as pose estimation and style transfer can be found in this organization.

https://github.com/tensorlayer/tensorlayerhttps://github.com/tensorlayer/srgan

Tags | tensorlayer tensorflow super-resolution gan cnn srgan vgg16 vgg19 vgg |

Implementation | Python |

License | Public |

Platform | Windows Linux |

This is an implementation of the SRGAN model proposed in the paper Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network in Keras. Note that this project is a work in progress. The SRGAN model is built in stages within models.py. Initially, only the SR-ResNet model is created, to which the VGG network is appended to create the pre-training model. The VGG weights are freezed as we will not update these weights.

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.

tensorlayer deep-learning tensorflow machine-learning data-science neural-network reinforcement-learning artificial-intelligence gan a3c tensorflow-tutorials dqn object-detection chatbot tensorflow-tutorial imagenet googleWhile research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

tensorlayer tensorflow deep-learning machine-learning data-science neural-network reinforcement-learning neural-networks tensorflow-tutorials tensorflow-models computer-vision tensorflow-framework tensorflow-library tflearn keras tensorboard nlp natural-language-processing lasagne tensorflow-experimentsThis repository contains convolutional neural network (CNN) models trained on ImageNet by Marcel Simon at the Computer Vision Group Jena (CVGJ) using the Caffe framework as published in the accompanying technical report. Each model is in a separate subfolder and contains everything needed to reproduce the results. This repository focuses currently contains the batch-normalization-variants of AlexNet and VGG19 as well as the training code for Residual Networks (Resnet). No mean subtraction is required for the pre-trained models! We have a batch-normalization layer which basically does the same.

cnn-model resnet imagenet alexnet batch-normalization caffe-framework vgg16 vgg19 vggnet vgg resnet-10 resnet-50 resnet-preact ilsvrc pretrained-models pre-trained fine-tune fine-tuning-cnns very-deep-cnn caffeThis is an experimental tensorflow implementation of synthesizing images. The images are synthesized using the GAN-CLS Algorithm from the paper Generative Adversarial Text-to-Image Synthesis. This implementation is built on top of the excellent DCGAN in Tensorflow. N.B You can downloads all data files needed manually or simply run the downloads.py and put the correct files to the right directories.

tensorflow tensorlayer gan text-to-imageTensorLayer δΈζζζ‘£

tensorlayer tensorflow deep-learning neural-networkSome examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnnChatbot in 200 lines of code using TensorLayer

tensorlayer tensorflow chatbot rnn lstm bot nlp chat corpusThis book was downloaded in HTML form and conviniently joined as a single PDF file for your enjoyment. PLEASE SUPPORT IAN GOODFELLOW and the authors if you can purchase the paper book at Amazon. It is not expensive ($72) and probably contains content that is newer and without typographic mistakes.

deep-learning tensorlayer tensorflow gan machine-learningVGG-16 is my favorite image classification model to run because of its simplicity and accuracy. The creators of this model published a pre-trained binary that can be used in Caffe. This is to convert that specific file to a TensorFlow model and check its correctness.

Implementation of Image Super Resolution CNN in Keras from the paper Image Super-Resolution Using Deep Convolutional Networks. Also contains models that outperforms the above mentioned model, termed Expanded Super Resolution, Denoiseing Auto Encoder SRCNN which outperforms both of the above models and Deep Denoise SR, which with certain limitations, outperforms all of the above.

[demos] Demo_test_DnCNN-.m. [models] including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking.

image-denoising residual-learning super-resolution jpeg-deblocking matconvnet pytorch keras-tensorflowIn these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit θ«η¦ Python for more.

tensorflow tensorflow-tutorials gan generative-adversarial-network rnn cnn classification regression autoencoder deep-q-network dqn machine-learning tutorial dropout neural-networkν μνλ‘μ°λ₯Ό κΈ°μ΄λΆν° μμ©κΉμ§ λ¨κ³λ³λ‘ μ°μ΅ν μ μλ μμ€ μ½λλ₯Ό μ κ³΅ν©λλ€. ν μνλ‘μ° κ³΅μ μ¬μ΄νΈμμ μ κ³΅νλ μλ΄μμ λλΆλΆμ λ΄μ©μ λ€λ£¨κ³ μμΌλ©°, κ³΅μ μ¬μ΄νΈμμ μ κ³΅νλ μμ€ μ½λλ³΄λ€λ ν¨μ¬ κ°λ΅νκ² μμ±νμμΌλ―λ‘ μ½κ² κ°λ μ μ΅ν μ μμ κ² μ λλ€. λν, λͺ¨λ μ£Όμμ νκΈλ‘(!) λμ΄ μμ΅λλ€.

neural-network tensorflow mnist autoencoder rnn deep-learning tutorial chatbot seq2seq dqn word2vec cnn gan inceptionCollection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

vae gan pytorch tensorflow generative-model machine-learning rbm restricted-boltzmann-machineThis code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.

text-classification convolutional-neural-networks tensorflow cnn deep-learning chinese nlpTrain Compositional Pattern Producing Network as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images. Run python train.py from the command line to train from scratch and experiment with different settings.

The accuracy of TorchCV SSD is ~1% lower than ChainerCV. This is because the VGG base model I use performs slightly worse. I did the experiment by replacing pytorch/vision VGG16 model with the model used in ChainerCV, the SSD512 model got 79.85% accuracy. FPNSSD512 is created by replacing SSD VGG16 network with FPN50, the rest is the same. It beats all SSD models. You can download the trained params here.

A tensorflow implementation for Perceptual Losses for Real-Time Style Transfer and Super-Resolution. This code is based on Tensorflow-Slim and OlavHN/fast-neural-style.

This package is part of the Kadenze Academy program Creative Applications of Deep Learning w/ TensorFlow. from cadl import and then pressing tab to see the list of available modules.

deep-learning neural-network tutorial mooc gan vae vae-gan pixelcnn wavenet magenta nsynth tensorflow celeba cyclegan dcgan word2vec glove autoregressive conditional course
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