Library Documentation

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

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

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

theano.function(...)

Alias for function.function()

theano.function_dump(...)

Alias for theano.compile.function.function_dump()

theano.shared(...)

Alias for theano.compile.sharedvalue.shared()

class theano.Param

Alias for function.Param

theano.dot(x, y)

Works like tensor.dot() for both sparse and dense matrix products

theano.clone(output, replace=None, strict=True, share_inputs=True, copy_inputs=<object object at 0x3372bc0>)

Function that allows replacing subgraphs of a computational graph.

It returns a copy of the initial subgraph with the corresponding substitutions.

Parameters:

• output (Theano Variables (or Theano expressions)) – Theano expression that represents the computational graph.
• replace (dict) – Dictionary describing which subgraphs should be replaced by what.
• share_inputs (bool) – If True, use the same inputs (and shared variables) as the original graph. If False, clone them. Note that cloned shared variables still use the same underlying storage, so they will always have the same value.
• copy_inputs – Deprecated, use share_inputs.

theano.sparse_grad(var)

This function return a new variable whose gradient will be stored 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.