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.
compile– Transforming Expression Graphs to Functions
config– Theano Configuration
d3viz– d3viz: Interactive visualization of Theano compute graphs
graph– 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
There are also some top-level imports that you might find more convenient:
clone_replace(output: Collection[theano.graph.basic.Variable], replace: Optional[Dict[theano.graph.basic.Variable, theano.graph.basic.Variable]] = None, strict: bool = True, share_inputs: bool = True) → Collection[theano.graph.basic.Variable]¶
Clone a graph and replace subgraphs within it.
It returns a copy of the initial subgraph with the corresponding substitutions.
- 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.