- Simple MNIST classification with logistic regression. It's a practice to learn the MNIST dataset and use a simple regressor to classify the dataset.
- MNIST Training, saving and loading model. It's a practice to learn how to save and load models trained by tensorflow. Reference: 10_save_restore_net.py.
- Visualize training process. A practice to learn how to visualize the training process with tensorboard. Reference: mnist_with_summaries.py.
- Train MNIST dataset via CNN. A simple convolutional neural networks example to deal with a classification tasks. Reference: 05_convolutional_net.py.
- Train MNIST dataset via RNN. A simple recurrent neural networks example to train MNIST dataset. Reference: recurrent_network.py, Supervised Sequence Labelling with Recurrent Neural Networks.
- Train MNIST with autoencoder. An example of unsupervised learning. Reference: Autoencoders and Sparsity.
- Train MNIST to explore the effect of training epochs for losses in training dataset and in validation dataset (Maxout Net). (Deep Learning Book P152 Figure)
- Example to convert MNIST dataset to TFRecords. link
- Maxout Networks in Tensorflow to train MNIST dataset. Reference: philipperemy/tensorflow-maxout, Maxout Networks.
Reference book: Tensorflow 技术解析与实战, 深度学习 (Deep Learning).