Metadata-Version: 2.1 Name: thinc Version: 6.12.1 Summary: Practical Machine Learning for NLP Home-page: https://github.com/explosion/thinc Author: Matthew Honnibal Author-email: matt@explosion.ai License: MIT Platform: UNKNOWN Classifier: Development Status :: 5 - Production/Stable Classifier: Environment :: Console Classifier: Intended Audience :: Developers Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: POSIX :: Linux Classifier: Operating System :: MacOS :: MacOS X Classifier: Operating System :: Microsoft :: Windows Classifier: Programming Language :: Cython Classifier: Programming Language :: Python :: 2.6 Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3.3 Classifier: Programming Language :: Python :: 3.4 Classifier: Programming Language :: Python :: 3.5 Classifier: Programming Language :: Python :: 3.6 Classifier: Programming Language :: Python :: 3.7 Classifier: Topic :: Scientific/Engineering Requires-Dist: numpy (>=1.7.0) Requires-Dist: msgpack (<0.6.0,>=0.5.6) Requires-Dist: msgpack-numpy (<0.4.4) Requires-Dist: murmurhash (<1.1.0,>=0.28.0) Requires-Dist: cymem (<3.0.0,>=2.0.2) Requires-Dist: preshed (<3.0.0,>=2.0.1) Requires-Dist: cytoolz (<0.10,>=0.9.0) Requires-Dist: wrapt (<1.11.0,>=1.10.0) Requires-Dist: plac (<1.0.0,>=0.9.6) Requires-Dist: tqdm (<5.0.0,>=4.10.0) Requires-Dist: six (<2.0.0,>=1.10.0) Requires-Dist: dill (<0.3.0,>=0.2.7) Requires-Dist: pathlib (==1.0.1) ; python_version < "3.4" Provides-Extra: cuda Requires-Dist: thinc-gpu-ops (<0.1.0,>=0.0.3) ; extra == 'cuda' Requires-Dist: cupy (>=5.0.0b4) ; extra == 'cuda' Provides-Extra: cuda100 Requires-Dist: thinc-gpu-ops (<0.1.0,>=0.0.3) ; extra == 'cuda100' Requires-Dist: cupy-cuda100 (>=5.0.0b4) ; extra == 'cuda100' Provides-Extra: cuda80 Requires-Dist: thinc-gpu-ops (<0.1.0,>=0.0.3) ; extra == 'cuda80' Requires-Dist: cupy-cuda80 (>=5.0.0b4) ; extra == 'cuda80' Provides-Extra: cuda90 Requires-Dist: thinc-gpu-ops (<0.1.0,>=0.0.3) ; extra == 'cuda90' Requires-Dist: cupy-cuda90 (>=5.0.0b4) ; extra == 'cuda90' Provides-Extra: cuda91 Requires-Dist: thinc-gpu-ops (<0.1.0,>=0.0.3) ; extra == 'cuda91' Requires-Dist: cupy-cuda91 (>=5.0.0b4) ; extra == 'cuda91' Provides-Extra: cuda92 Requires-Dist: thinc-gpu-ops (<0.1.0,>=0.0.3) ; extra == 'cuda92' Requires-Dist: cupy-cuda92 (>=5.0.0b4) ; extra == 'cuda92' Thinc: Practical Machine Learning for NLP in Python *************************************************** **Thinc** is the machine learning library powering `spaCy `_. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for `spaCy v2.0 `_. Thinc is a practical toolkit for implementing models that follow the `"Embed, encode, attend, predict" `_ architecture. It's designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences. 🔮 **Version 6.10 out now!** `Read the release notes here. `_ .. image:: https://img.shields.io/travis/explosion/thinc/master.svg?style=flat-square :target: https://travis-ci.org/explosion/thinc :alt: Build Status .. image:: https://img.shields.io/appveyor/ci/explosion/thinc/master.svg?style=flat-square :target: https://ci.appveyor.com/project/explosion/thinc :alt: Appveyor Build Status .. image:: https://img.shields.io/coveralls/explosion/thinc.svg?style=flat-square :target: https://coveralls.io/github/explosion/thinc :alt: Test Coverage .. image:: https://img.shields.io/github/release/explosion/thinc.svg?style=flat-square :target: https://github.com/explosion/thinc/releases :alt: Current Release Version .. image:: https://img.shields.io/pypi/v/thinc.svg?style=flat-square :target: https://pypi.python.org/pypi/thinc :alt: pypi Version .. image:: https://anaconda.org/conda-forge/thinc/badges/version.svg :target: https://anaconda.org/conda-forge/thinc :alt: conda Version .. image:: https://img.shields.io/badge/gitter-join%20chat%20%E2%86%92-7676d1.svg?style=flat-square :target: https://gitter.im/explosion/thinc :alt: Thinc on Gitter .. image:: https://img.shields.io/twitter/follow/explosion_ai.svg?style=social&label=Follow :target: https://twitter.com/explosion_ai :alt: Follow us on Twitter What's where (as of v6.9.0) =========================== ======================== === ``thinc.v2v.Model`` Base class. ``thinc.v2v`` Layers transforming vectors to vectors. ``thinc.i2v`` Layers embedding IDs to vectors. ``thinc.t2v`` Layers pooling tensors to vectors. ``thinc.t2t`` Layers transforming tensors to tensors (e.g. CNN, LSTM). ``thinc.api`` Higher-order functions, for building networks. Will be renamed. ``thinc.extra`` Datasets and utilities. ``thinc.neural.ops`` Container classes for mathematical operations. Will be reorganized. ``thinc.linear.avgtron`` Legacy efficient Averaged Perceptron implementation. ======================== === Development status ================== Thinc's deep learning functionality is still under active development: APIs are unstable, and we're not yet ready to provide usage support. However, if you're already quite familiar with neural networks, there's a lot here you might find interesting. Thinc's conceptual model is quite different from TensorFlow's. Thinc also implements some novel features, such as a small DSL for concisely wiring up models, embedding tables that support pre-computation and the hashing trick, dynamic batch sizes, a concatenation-based approach to variable-length sequences, and support for model averaging for the Adam solver (which performs very well). No computational graph – just higher order functions ====================================================== The central problem for a neural network implementation is this: during the forward pass, you compute results that will later be useful during the backward pass. How do you keep track of this arbitrary state, while making sure that layers can be cleanly composed? Most libraries solve this problem by having you declare the forward computations, which are then compiled into a graph somewhere behind the scenes. Thinc doesn't have a "computational graph". Instead, we just use the stack, because we put the state from the forward pass into callbacks. All nodes in the network have a simple signature: .. code:: none f(inputs) -> {outputs, f(d_outputs)->d_inputs} To make this less abstract, here's a ReLu activation, following this signature: .. code:: python def relu(inputs): mask = inputs > 0 def backprop_relu(d_outputs, optimizer): return d_outputs * mask return inputs * mask, backprop_relu When you call the ``relu`` function, you get back an output variable, and a callback. This lets you calculate a gradient using the output, and then pass it into the callback to perform the backward pass. This signature makes it easy to build a complex network out of smaller pieces, using arbitrary higher-order functions you can write yourself. To make this clearer, we need a function for a weights layer. Usually this will be implemented as a class — but let's continue using closures, to keep things concise, and to keep the simplicity of the interface explicit: .. code:: python import numpy def create_linear_layer(n_out, n_in): W = numpy.zeros((n_out, n_in)) b = numpy.zeros((n_out, 1)) def forward(X): Y = W @ X + b def backward(dY, optimizer): dX = W.T @ dY dW = numpy.einsum('ik,jk->ij', dY, X) db = dY.sum(axis=0) optimizer(W, dW) optimizer(b, db) return dX return Y, backward return forward If we call ``Wb = create_linear_layer(5, 4)``, the variable ``Wb`` will be the ``forward()`` function, implemented inside the body of ``create_linear_layer()``. The `Wb` instance will have access to the ``W`` and ``b`` variable defined in its outer scope. If we invoke ``create_linear_layer()`` again, we get a new instance, with its own internal state. The ``Wb`` instance and the ``relu`` function have exactly the same signature. This makes it easy to write higher order functions to compose them. The most obvious thing to do is chain them together: .. code:: python def chain(*layers): def forward(X): backprops = [] Y = X for layer in layers: Y, backprop = layer(Y) backprops.append(backprop) def backward(dY, optimizer): for backprop in reversed(backprops): dY = backprop(dY, optimizer) return dY return Y, backward return forward We could now chain our linear layer together with the ``relu`` activation, to create a simple feed-forward network: .. code:: python Wb1 = create_linear_layer(10, 5) Wb2 = create_linear_layer(3, 10) model = chain(Wb1, relu, Wb2) X = numpy.random.uniform(size=(5, 4)) y, bp_y = model(X) dY = y - truth dX = bp_y(dY, optimizer) This conceptual model makes Thinc very flexible. The trade-off is that Thinc is less convenient and efficient at workloads that fit exactly into what `Tensorflow `_ etc. are designed for. If your graph really is static, and your inputs are homogenous in size and shape, `Keras `_ will likely be faster and simpler. But if you want to pass normal Python objects through your network, or handle sequences and recursions of arbitrary length or complexity, you might find Thinc's design a better fit for your problem. Quickstart ========== Thinc should install cleanly with both `pip `_ and `conda `_, for **Pythons 2.7+ and 3.5+**, on **Linux**, **macOS / OSX** and **Windows**. Its only system dependency is a compiler tool-chain (e.g. ``build-essential``) and the Python development headers (e.g. ``python-dev``). .. code:: bash pip install thinc For GPU support, we're grateful to use the work of Chainer's cupy module, which provides a numpy-compatible interface for GPU arrays. However, installing Chainer when no GPU is available currently causes an error. We therefore do not list Chainer as an explicit dependency --- so building ``Thinc`` for GPU requires some extra steps: .. code:: bash export CUDA_HOME=/usr/local/cuda-8.0 # Or wherever your CUDA is export PATH=$PATH:$CUDA_HOME/bin pip install chainer python -c "import cupy; assert cupy" # Check it installed pip install thinc python -c "import thinc.neural.gpu_ops" # Check the GPU ops were built The rest of this section describes how to build Thinc from source. If you have `Fabric `_ installed, you can use the shortcut: .. code:: bash git clone https://github.com/explosion/thinc cd thinc fab clean env make test You can then run the examples as follows: .. code:: bash fab eg.mnist fab eg.basic_tagger fab eg.cnn_tagger Otherwise, you can build and test explicitly with: .. code:: bash git clone https://github.com/explosion/thinc cd thinc virtualenv .env source .env/bin/activate pip install -r requirements.txt python setup.py build_ext --inplace py.test thinc/ And then run the examples as follows: .. code:: bash python examples/mnist.py python examples/basic_tagger.py python examples/cnn_tagger.py Usage ===== The Neural Network API is still subject to change, even within minor versions. You can get a feel for the current API by checking out the examples. Here are a few quick highlights. 1. Shape inference ------------------ Models can be created with some dimensions unspecified. Missing dimensions are inferred when pre-trained weights are loaded or when training begins. This eliminates a common source of programmer error: .. code:: python # Invalid network — shape mismatch model = chain(ReLu(512, 748), ReLu(512, 784), Softmax(10)) # Leave the dimensions unspecified, and you can't be wrong. model = chain(ReLu(512), ReLu(512), Softmax()) 2. Operator overloading ----------------------- The ``Model.define_operators()`` classmethod allows you to bind arbitrary binary functions to Python operators, for use in any ``Model`` instance. The method can (and should) be used as a context-manager, so that the overloading is limited to the immediate block. This allows concise and expressive model definition: .. code:: python with Model.define_operators({'>>': chain}): model = ReLu(512) >> ReLu(512) >> Softmax() The overloading is cleaned up at the end of the block. A fairly arbitrary zoo of functions are currently implemented. Some of the most useful: * ``chain(model1, model2)``: Compose two models ``f(x)`` and ``g(x)`` into a single model computing ``g(f(x))``. * ``clone(model1, int)``: Create ``n`` copies of a model, each with distinct weights, and chain them together. * ``concatenate(model1, model2)``: Given two models with output dimensions ``(n,)`` and ``(m,)``, construct a model with output dimensions ``(m+n,)``. * ``add(model1, model2)``: ``add(f(x), g(x)) = f(x)+g(x)`` * ``make_tuple(model1, model2)``: Construct tuples of the outputs of two models, at the batch level. The backward pass expects to receive a tuple of gradients, which are routed through the appropriate model, and summed. Putting these things together, here's the sort of tagging model that Thinc is designed to make easy. .. code:: python with Model.define_operators({'>>': chain, '**': clone, '|': concatenate}): model = ( add_eol_markers('EOL') >> flatten >> memoize( CharLSTM(char_width) | (normalize >> str2int >> Embed(word_width))) >> ExtractWindow(nW=2) >> BatchNorm(ReLu(hidden_width)) ** 3 >> Softmax() ) Not all of these pieces are implemented yet, but hopefully this shows where we're going. The ``memoize`` function will be particularly important: in any batch of text, the common words will be very common. It's therefore important to evaluate models such as the ``CharLSTM`` once per word type per minibatch, rather than once per token. 3. Callback-based backpropagation --------------------------------- Most neural network libraries use a computational graph abstraction. This takes the execution away from you, so that gradients can be computed automatically. Thinc follows a style more like the ``autograd`` library, but with larger operations. Usage is as follows: .. code:: python def explicit_sgd_update(X, y): sgd = lambda weights, gradient: weights - gradient * 0.001 yh, finish_update = model.begin_update(X, drop=0.2) finish_update(y-yh, sgd) Separating the backpropagation into three parts like this has many advantages. The interface to all models is completely uniform — there is no distinction between the top-level model you use as a predictor and the internal models for the layers. We also make concurrency simple, by making the ``begin_update()`` step a pure function, and separating the accumulation of the gradient from the action of the optimizer. 4. Class annotations -------------------- To keep the class hierarchy shallow, Thinc uses class decorators to reuse code for layer definitions. Specifically, the following decorators are available: * ``describe.attributes()``: Allows attributes to be specified by keyword argument. Used especially for dimensions and parameters. * ``describe.on_init()``: Allows callbacks to be specified, which will be called at the end of the ``__init__.py``. * ``describe.on_data()``: Allows callbacks to be specified, which will be called on ``Model.begin_training()``. 🛠 Changelog ============ =========== ============== =========== Version Date Description =========== ============== =========== `v6.10.1`_ ``2017-11-15`` Fix GPU install and minor memory leak `v6.10.0`_ ``2017-10-28`` CPU efficiency improvements, refactoring `v6.9.0`_ ``2017-10-03`` Reorganize layers, bug fix to Layer Normalization `v6.8.2`_   ``2017-09-26`` Fix packaging of `gpu_ops` `v6.8.1`_   ``2017-08-23`` Fix Windows support `v6.8.0`_ ``2017-07-25`` SELU layer, attention, improved GPU/CPU compatibility `v6.7.3`_ ``2017-06-05`` Fix convolution on GPU `v6.7.2`_ ``2017-06-02`` Bug fixes to serialization `v6.7.1`_ ``2017-06-02`` Improve serialization `v6.7.0`_ ``2017-06-01`` Fixes to serialization, hash embeddings and flatten ops `v6.6.0`_ ``2017-05-14`` Improved GPU usage and examples v6.5.2 ``2017-03-20`` *n/a* `v6.5.1`_ ``2017-03-20`` Improved linear class and Windows fix `v6.5.0`_ ``2017-03-11`` Supervised similarity, fancier embedding and improvements to linear model v6.4.0 ``2017-02-15`` *n/a* `v6.3.0`_ ``2017-01-25`` Efficiency improvements, argument checking and error messaging `v6.2.0`_ ``2017-01-15`` Improve API and introduce overloaded operators `v6.1.3`_ ``2017-01-10`` More neural network functions and training continuation v6.1.3 ``2017-01-09`` *n/a* v6.1.2 ``2017-01-09`` *n/a* v6.1.1 ``2017-01-09`` *n/a* v6.1.0 ``2017-01-09`` *n/a* `v6.0.0`_ ``2016-12-31`` Add ``thinc.neural`` for NLP-oriented deep learning =========== ============== =========== .. _v6.10.1: https://github.com/explosion/thinc/releases/tag/v6.10.1 .. _v6.10.0: https://github.com/explosion/thinc/releases/tag/v6.10.0 .. _v6.9.0: https://github.com/explosion/thinc/releases/tag/v6.9.0 .. _v6.8.2: https://github.com/explosion/thinc/releases/tag/v6.8.2 .. _v6.8.1: https://github.com/explosion/thinc/releases/tag/v6.8.1 .. _v6.8.0: https://github.com/explosion/thinc/releases/tag/v6.8.0 .. _v6.7.3: https://github.com/explosion/thinc/releases/tag/v6.7.3 .. _v6.7.2: https://github.com/explosion/thinc/releases/tag/v6.7.2 .. _v6.7.1: https://github.com/explosion/thinc/releases/tag/v6.7.1 .. _v6.7.0: https://github.com/explosion/thinc/releases/tag/v6.7.0 .. _v6.6.0: https://github.com/explosion/thinc/releases/tag/v6.6.0 .. _v6.5.1: https://github.com/explosion/thinc/releases/tag/v6.5.1 .. _v6.5.0: https://github.com/explosion/thinc/releases/tag/v6.5.0 .. _v6.3.0: https://github.com/explosion/thinc/releases/tag/v6.3.0 .. _v6.2.0: https://github.com/explosion/thinc/releases/tag/v6.2.0 .. _v6.1.3: https://github.com/explosion/thinc/releases/tag/v6.1.3 .. _v6.0.0: https://github.com/explosion/thinc/releases/tag/v6.0.0