TensorLayer is a Deep Learning (DL) and Reinforcement Learning (RL) library extended from Google TensorFlow. It provides popular DL and RL modules that can be easily customized and assembled for tackling real-world machine learning problems.

Why TensorLayer

TensorLayer grow out from a need to combine the power of TensorFlow with the right building modules for deep neural networks. According to our years of research and practical experiences of tackling real-world machine learning problems, we come up with three design goals for TensorLayer:

• Simplicity: We make TensorLayer easy to work with by providing mass tutorials that can be deployed and run through in minutes. A TensorFlow user may find it easier to bootstrap with the simple, high-level APIs provided by TensorLayer, and then deep dive into their implementation details if need.
• Flexibility: Developing an effective DL algorithm for a specific domain typically requires careful tunings from many aspects. Without the loss of simplicity, TensorLayer allows users to customize their modules by manipulating the native APIs of TensorFlow (e.g., training parameters, iteration control and tensor components).
• Performance: TensorLayer aims to provide zero-cost abstraction for TensorFlow. With its first-class support for TensorFlow, it can easily run on either heterogeneous platforms or multiple computation nodes without compromise in performance.

A frequent question regarding TensorLayer is that why do we develop this library instead of leveraging existing ones like Keras and Tflearn. TensorLayer differentiates with those with its pursuits for flexibility and performance. A DL user may find it comfortable to bootstrap with Keras and Tflearn. These libraries provide high-level abstractions that completely mask the underlying engine from users. Though good for using, it becomes hard to tune and modify from the bottom, which is necessary when addressing domain-specific problems (i.e., one model does not fit all).

In contrast, TensorLayer advocates a flexible programming paradigm where third-party neural network libraries can be used interchangeably with the native TensorFlow functions. This allows users to enjoy the ease of using powerful pre-built modules without sacrificing the control of the underlying training process. TensorLayer's noninvasive nature also makes it easy to consolidate with other wrapper libraries such as TF-Slim. However, flexibility does not come with the loss of performance and heterogeneity support. TensorLayer allows seamless distributed and heterogeneous deployment with its first-class support for the TensorFlow roadmap.

Installation

TensorLayer has install prerequisites including TensorFlow, numpy and matplotlib. For GPU support, CUDA and cuDNN are required. Please check here for detailed instructions.

If you already had the pre-requisites ready (numpy, scipy, scikit-image, matplotlib and nltk(optional)), the simplest way to install TensorLayer in your python program is:

[for stable version] pip install tensorlayer
[for master version] pip install git+https://github.com/zsdonghao/tensorlayer.git (recommended)

Documentation

The documentation [Online] [PDF] [Epub] [HTML] describes the usages of TensorLayer APIs. It is also a self-contained document that walks through different types of deep neural networks, reinforcement learning and their applications in Natural Language Processing (NLP) problems.

We have included the corresponding modularized implementations of Google TensorFlow Deep Learning tutorial, so you can read the TensorFlow tutorial [en] [cn] along with our document.

Chinese documentation is also available.

Your First Program

The first program trains a multi-layer perception network to solve the MNIST problem. We use the well-known scikit-style functions such as fit() and test(). The program is self-explained.

import tensorflow as tf
import tensorlayer as tl

sess = tf.InteractiveSession()

# Prepare data
X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1,784))

# Define placeholder
x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')

# Define the neural network structure
network = tl.layers.InputLayer(x, name='input_layer')
network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
network = tl.layers.DenseLayer(network, n_units=800, act = tf.nn.relu, name='relu1')
network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
network = tl.layers.DenseLayer(network, n_units=800, act = tf.nn.relu, name='relu2')
network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')

# The softmax is implemented internally in tl.cost.cross_entropy(y, y_) to
# speed up computation, so we use identity here.
# see tf.nn.sparse_softmax_cross_entropy_with_logits()
network = tl.layers.DenseLayer(network, n_units=10, act = tf.identity, name='output_layer')
                                
# Define cost function and metric.
y = network.outputs
cost = tl.cost.cross_entropy(y, y_)
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
y_op = tf.argmax(tf.nn.softmax(y), 1)

# Define the optimizer
train_params = network.all_params
train_op = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9, beta2=0.999,
                            epsilon=1e-08, use_locking=False).minimize(cost, var_list=train_params)

# Initialize all variables
sess.run(tf.initialize_all_variables())

# Print network information
network.print_params()
network.print_layers()

# Train the network, we recommend to use tl.iterate.minibatches()
tl.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_,
            acc=acc, batch_size=500, n_epoch=500, print_freq=5,
            X_val=X_val, y_val=y_val, eval_train=False)

# Evaluation
tl.utils.test(sess, network, acc, X_test, y_test, x, y_, batch_size=None, cost=cost)

# Save the network to .npz file
tl.files.save_npz(network.all_params , name='model.npz')

sess.close()

We provide many helper functions (like fit() , test()) that is similar to Keras to facilitate your development; however, if you want to obtain a fine-grain control over the model or its training process, you can use TensorFlow’s methods like sess.run() in your program directly (tutorial_mnist.py provides more details about this). Many more DL and RL examples can be found here.

Contribution Guideline

TensorLayer is a major ongoing research project in Data Science Institute, Imperial College London. TensorLayer contributors are from Imperial College, Tsinghua University, Carnegie Mellon University, Google, Microsoft, Bloomberg and etc. The goal of the project is to develop a compositional language while complex learning systems can be build through composition of neural network modules. The whole development is now participated by numerous contributors here.

• 🇬🇧 If you are in London, we can discuss in person. Drop us an email to organize a meetup: tensorlayer@gmail.com.
• 🇨🇳 我们有官方的 中文文档。另外, 我们建立了多种交流渠道，如QQ 群和微信群*（申请入群时请star该项目，并告知github用户名）*. 需加入微信群，请将个人介绍和微信号发送到 tensorlayer@gmail.com.
• 🇹🇭 เราขอเรียนเชิญนักพัฒนาคนไทยทุกคนที่สนใจจะเข้าร่วมทีมพัฒนา TensorLayer ติดต่อสอบถามรายละเอียดเพิ่มเติมได้ที่ tensorlayer@gmail.com.

Examples

Note

• TensorLayer provides two set of Convolutional layer APIs, see (Professional)and (Simplified) on readthedocs website.
• If you get into trouble, you can start a discussion on Gitter, Help Wanted Issues, QQ group and Wechat group.

Basics

• Multi-layer perceptron (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist_simple.py.

Computer Vision

• Denoising Autoencoder (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist.py.
• Stacked Denoising Autoencoder and Fine-Tuning (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist.py.
• Convolutional Network (MNIST). A Convolutional neural network implementation for classifying MNIST dataset, see tutorial_mnist.py.
• Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset, see tutorial_cifar10.py and tutorial_cifar10_tfrecord.py.
• VGG 16 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_vgg16.py.
• VGG 19 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_vgg19.py.
• InceptionV3 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_inceptionV3_tfslim.py.
• Wild ResNet (CIFAR) by ritchieng.
• More CNN implementations of TF-Slim can be connected to TensorLayer via SlimNetsLayer.

Natural Language Processing

• Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset for language modeling, see tutorial_ptb_lstm_state_is_tuple.py.
• Word Embedding - Word2vec. Train a word embedding matrix, see tutorial_word2vec_basic.py.
• Restore Embedding matrix. Restore a pre-train embedding matrix, see tutorial_generate_text.py.
• Text Generation. Generates new text scripts, using LSTM network, see tutorial_generate_text.py.
• Machine Translation (WMT). Translate English to French. Apply Attention mechanism and Seq2seq to WMT English-to-French translation data, see tutorial_translate.py.

Reinforcement Learning

• Deep Reinforcement Learning - Pong Game. Teach a machine to play Pong games, see tutorial_atari_pong.py.

Applications

• Image Captioning - Reimplementation of Google's im2txt by zsdonghao.
• DCGAN - Generating images by Deep Convolutional Generative Adversarial Networks by zsdonghao.

Special Examples

• Merge TF-Slim into TensorLayer. tutorial_inceptionV3_tfslim.py.
• MultiplexerLayer. tutorial_mnist_multiplexer.py.
• Data augmentation with TFRecord. Effective way to load and pre-process data, see tutorial_tfrecord*.py and tutorial_cifar10_tfrecord.py.
• Data augmentation with TensorLayer, see tutorial_image_preprocess.py.

License

TensorLayer is releazed under the Apache 2.0 license.