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Some 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 deion of that dataset, you can check this notebook. https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb Official Website: More Examples The following examples are coming from TFLearn(https://github.com/tflearn/tflearn) a library that provides a simplified interface for TensorFlow. You can have a look, there are many examples(https://github.com/tflearn/tflearn/tree/master/examples)and pre-built operations and layers(#api). Tutorials TFLearn Quickstart. Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md Basics Linear Regression. Implement a linear regression using TFLearn. https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py Logical Operators. Implement logical operators with TFLearn (also includes a usage of 'merge'). https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py Weights Persistence. Save and Restore a model. https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py Fine-Tuning. Fine-Tune a pre-trained model on a new task. https://github.com/tflearn/tflearn/blob/master/examples/basics/finetuning.py Using HDF5. Use HDF5 to handle large datasets. https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py Using DASK. Use DASK to handle large datasets. https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py Computer Vision Multi-layer perceptron. A multi-layer perceptron implementation for MNIST classification task. https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py Convolutional Network (MNIST). A Convolutional neural network implementation for classifying MNIST dataset. https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset. https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py Network in Network. 'Network in Network' implementation for classifying CIFAR-10 dataset. https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py Alexnet. Apply Alexnet to Oxford Flowers 17 classification task. https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py VGGNet. Apply VGG Network to Oxford Flowers 17 classification task. https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py VGGNet Finetuning (Fast Training). Use a pre-trained VGG Network and retrain it on your own data, for fast training. https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network_finetuning.py RNN Pixels. Use RNN (over sequence of pixels) to classify images. https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py Highway Network. Highway Network implementation for classifying MNIST dataset. https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py Highway Convolutional Network. Highway Convolutional Network implementation for classifying MNIST dataset. https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py Residual Network (MNIST). A bottleneck residual network applied to MNIST classification task. https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py Residual Network (CIFAR-10). A residual network applied to CIFAR-10 classification task. https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py Google Inception (v3). Google's Inception v3 network applied to Oxford Flowers 17 classification task. https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py Auto Encoder. An auto encoder applied to MNIST handwritten digits. https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py Natural Language Processing Recurrent Neural Network (LSTM). Apply an LSTM to IMDB sentiment dataset classification task. https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py Bi-Directional RNN (LSTM). Apply a bi-directional LSTM to IMDB sentiment dataset classification task. https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py Dynamic RNN (LSTM). Apply a dynamic LSTM to classify variable length text from IMDB dataset. https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py City Name Generation. Generates new US-cities name, using LSTM network. https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py Shakespeare s Generation. Generates new Shakespeare s, using LSTM network. https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py Seq2seq. Pedagogical example of seq2seq reccurent network. See this repo for full instructions. https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py https://github.com/ichuang/tflearn_seq2seq CNN Seq. Apply a 1-D convolutional network to classify sequence of words from IMDB sentiment dataset. https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sentence_classification.py Reinforcement Learning Atari Pacman 1-step Q-Learning. Teach a machine to play Atari games (Pacman by default) using 1-step Q-learning. https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py Others Recommender - Wide & Deep Network. Pedagogical example of wide & deep networks for recommender systems. https://github.com/tflearn/tflearn/blob/master/examples/others/recommender_wide_and_deep.py Notebooks Spiral Classification Problem. TFLearn implementation of spiral classification problem from Stanford CS231n. https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb Extending TensorFlow Layers. Use TFLearn layers along with TensorFlow. https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py Trainer. Use TFLearn trainer class to train any TensorFlow graph. https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py Built-in Ops. Use TFLearn built-in operations along with TensorFlow. https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py Summaries. Use TFLearn summarizers along with TensorFlow. https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py (责任编辑：本港台直播) |