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tensor – Types and Ops for Symbolic numpy

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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.

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

theano.function(...)

Alias for function.function()

theano.shared(...)

Alias for shared.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, copy_inputs=True)

Function that allows replacing subgraphs of a computational graph. It returns a copy of the initial subgraph with the corresponding substitutions.

Parameters:
  • outputs – Theano expression that represents the computational graph
  • replace (dict) – dictionary describing which subgraphs should be replaced by what
  • copy_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.
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.