Mercurial > pylearn
view stat_ops.py @ 399:8796b91a9f09
RBM minibatch works
author | Joseph Turian <turian@gmail.com> |
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date | Tue, 08 Jul 2008 21:42:21 -0400 |
parents | fe57b96f33d4 |
children |
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import theano from theano import gof from theano import tensor import numpy class ExampleWiseMean(gof.Op): def __init__(self): self.destroy_map = {0: [1, 2]} def make_node(self, x): return gof.Apply(self, [x, tensor.value(float('nan')), tensor.value(0)], [tensor.Tensor(dtype = 'float64', broadcastable = x.type.broadcastable)()]) def perform(self, node, (x, sum, n), (out,)): if numpy.isnan(sum).any(): sum.resize(x.shape, refcheck=0) sum[:] = x else: sum += x n += 1 out[0] = sum / n def c_code(self, name, node, (x, sum, n), (out, ), sub): return """ PyObject* multi; int nelems; if (isnan(((double*)(%(sum)s->data))[0])) { PyArray_Dims dims; dims.len = %(x)s->nd; dims.ptr = %(x)s->dimensions; PyArray_Resize(%(sum)s, &dims, 0, PyArray_CORDER); multi = PyArray_MultiIterNew(2, %(sum)s, %(x)s); nelems = PyArray_SIZE(%(sum)s); while (nelems--) { // Copy %(x)s in %(sum)s *(double*)PyArray_MultiIter_DATA(multi, 0) = *(double*)PyArray_MultiIter_DATA(multi, 1); PyArray_MultiIter_NEXT(multi); } } else { // Add some error checking on the size of x multi = PyArray_MultiIterNew(2, %(sum)s, %(x)s); nelems = PyArray_SIZE(%(sum)s); while (nelems--) { // Add %(x)s to %(sum)s *(double*)PyArray_MultiIter_DATA(multi, 0) += *(double*)PyArray_MultiIter_DATA(multi, 1); PyArray_MultiIter_NEXT(multi); } } ((npy_int64*)(%(n)s->data))[0]++; int n = ((npy_int64*)(%(n)s->data))[0]; if (%(out)s == NULL) { %(out)s = (PyArrayObject*)PyArray_EMPTY(%(sum)s->nd, %(sum)s->dimensions, NPY_FLOAT64, 0); } multi = PyArray_MultiIterNew(2, %(sum)s, %(out)s); nelems = PyArray_SIZE(%(sum)s); while (nelems--) { // %(out)s <- %(sum)s / %(n)s *(double*)PyArray_MultiIter_DATA(multi, 1) = *(double*)PyArray_MultiIter_DATA(multi, 0) / n; PyArray_MultiIter_NEXT(multi); } """ % dict(locals(), **sub) if __name__ == '__main__': vectors = numpy.random.RandomState(666).rand(10, 2) x = tensor.dvector() e = ExampleWiseMean()(x) # f = theano.function([x], [e], linker = 'py') # for i, v in enumerate(vectors): # print v, "->", f(v), numpy.mean(vectors[:i+1], axis=0) # print f = theano.function([x], [e], linker = 'c|py') for i, v in enumerate(vectors): print v, "->", f(v), numpy.mean(vectors[:i+1], axis=0)