Welcome to Blocks’ documentation!¶
Blocks is a framework that helps you build and manage neural network models on using Theano.
Blocks is developed in parallel with Fuel, a dataset processing framework.
Blocks is a new project which is still under development. As such, certain
(all) parts of the framework are subject to change. The last stable (and
thus likely an outdated) version can be found in the
That said, if you are interested in using Blocks and run into any problems, feel free to ask your question on the mailing list. Also, don’t hesitate to file bug reports and feature requests by making a GitHub issue.
Construct your model.
>>> mlp = MLP(activations=[Tanh(), Softmax()], dims=[784, 100, 10], ... weights_init=IsotropicGaussian(0.01), biases_init=Constant(0)) >>> mlp.initialize()
Calculate your loss function.
>>> x = tensor.matrix('features') >>> y = tensor.lmatrix('targets') >>> y_hat = mlp.apply(x) >>> cost = CategoricalCrossEntropy().apply(y.flatten(), y_hat) >>> error_rate = MisclassificationRate().apply(y.flatten(), y_hat)
Load your training data using Fuel.
>>> mnist_train = MNIST(("train",)) >>> train_stream = Flatten( ... DataStream.default_stream( ... dataset=mnist_train, ... iteration_scheme=SequentialScheme(mnist_train.num_examples, 128)), ... which_sources=('features',)) >>> mnist_test = MNIST(("test",)) >>> test_stream = Flatten( ... DataStream.default_stream( ... dataset=mnist_test, ... iteration_scheme=SequentialScheme(mnist_test.num_examples, 1024)), ... which_sources=('features',))
>>> from blocks.model import Model >>> main_loop = MainLoop( ... model=Model(cost), data_stream=train_stream, ... algorithm=GradientDescent( ... cost=cost, parameters=ComputationGraph(cost).parameters, ... step_rule=Scale(learning_rate=0.1)), ... extensions=[FinishAfter(after_n_epochs=5), ... DataStreamMonitoring( ... variables=[cost, error_rate], ... data_stream=test_stream, ... prefix="test"), ... Printing()]) >>> main_loop.run() ...
For a runnable version of this code, please see the MNIST demo in our repository with examples.
Currently Blocks supports and provides:
- Constructing parametrized Theano operations, called “bricks”
- Pattern matching to select variables and bricks in large models
- Algorithms to optimize your model
- Saving and resuming of training
- Monitoring and analyzing values during training progress (on the training set as well as on test sets)
- Application of graph transformations, such as dropout (limited support)
In the future we also hope to support:
- Dimension, type and axes-checking