|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Tensorflow||177,836||327||77||2 hours ago||46||October 23, 2019||2,048||apache-2.0||C++|
|An Open Source Machine Learning Framework for Everyone|
|Transformers||112,535||64||1,869||2 hours ago||114||July 18, 2023||844||apache-2.0||Python|
|🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.|
|Keras||59,445||578||6 hours ago||80||June 27, 2023||98||apache-2.0||Python|
|Deep Learning for humans|
|Real Time Voice Cloning||47,152||4 days ago||168||other||Python|
|Clone a voice in 5 seconds to generate arbitrary speech in real-time|
|Tensorflow Examples||42,312||a year ago||218||other||Jupyter Notebook|
|TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)|
|Ray||27,922||80||298||2 hours ago||87||July 24, 2023||3,428||apache-2.0||Python|
|Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.|
|Data Science Ipython Notebooks||25,242||3 months ago||34||other||Python|
|Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.|
|Handson Ml||25,030||3 months ago||139||apache-2.0||Jupyter Notebook|
|⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.|
|Visualizer for neural network, deep learning, and machine learning models|
|Spleeter||23,335||6||a month ago||37||June 10, 2022||213||mit||Python|
|Deezer source separation library including pretrained models.|
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:
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.
# 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.
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
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
To install TFLearn, the easiest way is to run
For the bleeding edge version (recommended):
pip install git+https://github.com/tflearn/tflearn.git
For the latest stable version:
pip install tflearn
Otherwise, you can also install from source by running (from source folder):
python setup.py install
There are many neural network implementation available, see Examples.
Loss & Accuracy (multiple runs)
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.