Deep Learning

Link: http://tflearn.org

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it.

TFLearn features include:

Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples.
Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics…
Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn.
Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs and optimizers.
Easy and beautiful graph visualization, with details about weights, gradients, activations and more…
Effortless device placement for using multiple CPU/GPU.

The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks… In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques.

Quick overview

Code Example

# Classification
tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)

net = tflearn.input_data(shape=[None, 784])
net = tflearn.fully_connected(net, 64)
net = tflearn.dropout(net, 0.5)
net = tflearn.fully_connected(net, 10, activation=’softmax’)
net = tflearn.regression(net, optimizer=’adam’, loss=’categorical_crossentropy’)

model = tflearn.DNN(net)
model.fit(X, Y)

# Sequence Generation
net = tflearn.input_data(shape=[None, 100, 5000])
net = tflearn.lstm(net, 64)
net = tflearn.dropout(net, 0.5)
net = tflearn.fully_connected(net, 5000, activation=’softmax’)
net = tflearn.regression(net, optimizer=’adam’, loss=’categorical_crossentropy’)

model = tflearn.SequenceGenerator(net, dictionary=idx, seq_maxlen=100)
model.fit(X, Y)
model.generate(50, temperature=1.0)

There are many more examples available here.

Where to Start?

To install TFLearn, see: Installation Guide.

If your version of Tensorflow is under 0.7: Upgrade Tensorflow.

For a tutorial: Getting Started with TFLearn.

For more examples: Examples List.

To browse the API, check the API Documentation.

Model Visualization

Graph

Loss & Accuracy (multiple runs)

Layers

Sources

GitHub: https://github.com/tflearn/tflearn

Contributions

This is the first release of TFLearn, if you find any bug, please report it in the GitHub issues section.

Improvements and requests for new features are more than welcome! Do not hesitate to twist and tweak TFLearn, and send pull-requests.

License

MIT License

Here are some simple tutorial to discover TensorFlow.

Link: https://github.com/aymericdamien/TensorFlow-Examples

This tutorial is designed to easily dive into TensorFlow through examples. Basic calculation examples and famous machine learning algorithms (such as Regression, Nearest Neighbor, Neural Network, Convolutional Neural Network) are implemented, with code explanation.

Tutorial Index:

1 – Introduction

– Hello World
– Basic Operations

2 – Basic Classifiers

– Nearest Neighbor
– Linear Regression
– Logistic Regression

3 – Neural Networks

– Multilayer Perceptron
– Convolutional Neural Network
– AlexNet
– Reccurent Network

4 – Multi GPU

– Basic Operations on multi-GPU

5 – User Interface (Tensorboard)

– Graph Visualization
– Loss Visualization

If you have any question, you can leave a comment or open an issue on github 🙂

TFLearn: A Simplified Interface for TensorFlow