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https://github.com/tflearn/tflearn

Deep learning library featuring a higher-level API for TensorFlow.
https://github.com/tflearn/tflearn

data-science deep-learning machine-learning neural-network tensorflow tflearn

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Deep learning library featuring a higher-level API for TensorFlow.

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# TFLearn: Deep learning library featuring a higher-level API for TensorFlow.



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.



Note: Latest TFLearn (v0.5) is only compatible with TensorFlow v2.0 and over.



## Overview

```python

# 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)

```



```python

# 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](http://tflearn.org/examples)*.



## Compatibility

TFLearn is based on the original tensorflow v1 graph API. When using TFLearn, make sure to import tensorflow that way:

```

import tflearn

import tensorflow.compat.v1 as tf

```



## Installation



**TensorFlow Installation**



TFLearn requires Tensorflow (version 2.0+) to be installed.



To install TensorFlow, simply run:

```

pip install tensorflow

```

or, with GPU-support:

```

pip install tensorflow-gpu

```



For more details see *[TensorFlow installation instructions](https://www.tensorflow.org/install)*



**TFLearn Installation**



To install TFLearn, the easiest way is to run



For the bleeding edge version (recommended):

```python

pip install git+https://github.com/tflearn/tflearn.git

```

For the latest stable version:

```python

pip install tflearn

```

Otherwise, you can also install from source by running (from source folder):

```python

python setup.py install

```



- For more details, please see the *[Installation Guide](http://tflearn.org/installation)*.



## Getting Started



See *[Getting Started with TFLearn](http://tflearn.org/getting_started)* to learn about TFLearn basic functionalities or start browsing *[TFLearn Tutorials](http://tflearn.org/tutorials)*.



## Examples



There are many neural network implementation available, see *[Examples](http://tflearn.org/examples)*.



## Documentation



[http://tflearn.org/doc_index](http://tflearn.org/doc_index)



## Model Visualization



**Graph**



![Graph Visualization](docs/templates/img/graph.png)



**Loss & Accuracy (multiple runs)**



![Loss Visualization](docs/templates/img/loss_acc.png)



**Layers**



![Layers Visualization](docs/templates/img/layer_visualization.png)



## 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.



For more info: *[Contribute to TFLearn](http://tflearn.org/contributions)*.



## License



MIT License