API Documentation

This documentation covers Theano module-wise. This is suited to finding theTypes and Ops that you can use to build and compile expression graphs.

• compile – Transforming Expression Graphs to Functions
• config – Theano Configuration
• d3viz – d3viz: Interactive visualization of Theano compute graphs
• gof – Theano Internals [doc TODO]
• gpuarray – The (new) GPU backend
• gradient – Symbolic Differentiation
• misc.pkl_utils - Tools for serialization.
• printing – Graph Printing and Symbolic Print Statement
• sandbox – Experimental Code
• scalar – Symbolic Scalar Types, Ops [doc TODO]
• scan – Looping in Theano
• sparse – Symbolic Sparse Matrices
• sparse – Sparse Op
• sparse.sandbox – Sparse Op Sandbox
• tensor – Types and Ops for Symbolic numpy
• typed_list – Typed List
• tests – Tests

There are also some top-level imports that you might find more convenient:

• theano.function(…)[source]
• Alias for theano.compile.function.function()

• theano.functiondump(…_)[source]
• Alias for theano.compile.function.function_dump()

• theano.shared(…)[source]
• Alias for theano.compile.sharedvalue.shared()

• class theano.In[source]
• Alias for function.In

• theano.dot(x, y)[source]
• Works like tensor.dot() for both sparse and dense matrix products

• theano.clone(output, replace=None, strict=True, share_inputs=True, copy_inputs=
  • theano.sparsegrad(_var)[source]
  • This function return a new variable whose gradient will bestored in a sparse format instead of dense.

  Currently only variable created by AdvancedSubtensor1 is supported.i.e. a_tensor_var[an_int_vector].

  New in version 0.6rc4.