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pytensor
提交 2b7ee2ec authored 作者: Frédéric Bastien's avatar Frédéric Bastien 提交者： GitHub
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Merge pull request #5193 from kvmanohar22/numpy_imports

Numpy imports
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theano/sparse/basic.py
浏览文件 @ 2b7ee2ec
......@@ -12,7 +12,7 @@ from __future__ import absolute_import, print_function, division
import sys
import numpy
import numpy as np
from numpy.lib.stride_tricks import as_strided
from six import integer_types
from six.moves import xrange
......@@ -86,11 +86,11 @@ def _is_dense(x):
L{numpy.ndarray}).
"""
if not isinstance(x, (scipy.sparse.spmatrix, numpy.ndarray)):
if not isinstance(x, (scipy.sparse.spmatrix, np.ndarray)):
raise NotImplementedError("this function should only be called on "
"sparse.scipy.sparse.spmatrix or "
"numpy.ndarray, not,", x)
return isinstance(x, numpy.ndarray)
return isinstance(x, np.ndarray)
# Wrapper type
......@@ -205,8 +205,8 @@ def sp_zeros_like(x):
# TODO: don't restrict to CSM formats
_, _, indptr, shape = csm_properties(x)
return CSM(format=x.format)(data=numpy.array([], dtype=x.type.dtype),
indices=numpy.array([], dtype='int32'),
return CSM(format=x.format)(data=np.array([], dtype=x.type.dtype),
indices=np.array([], dtype='int32'),
indptr=tensor.zeros_like(indptr),
shape=shape)
......@@ -293,9 +293,9 @@ class _sparse_py_operators:
args = args,
if len(args) == 2:
scalar_arg_1 = (numpy.isscalar(args[0]) or
scalar_arg_1 = (np.isscalar(args[0]) or
getattr(args[0], 'type', None) == tensor.iscalar)
scalar_arg_2 = (numpy.isscalar(args[1]) or
scalar_arg_2 = (np.isscalar(args[1]) or
getattr(args[1], 'type', None) == tensor.iscalar)
if scalar_arg_1 and scalar_arg_2:
ret = get_item_scalar(self, args)
......@@ -554,17 +554,17 @@ class CSM(gof.Op):
data = tensor.as_tensor_variable(data)
if not isinstance(indices, gof.Variable):
indices_ = numpy.asarray(indices)
indices_ = np.asarray(indices)
indices_32 = theano._asarray(indices, dtype='int32')
assert (indices_ == indices_32).all()
indices = indices_32
if not isinstance(indptr, gof.Variable):
indptr_ = numpy.asarray(indptr)
indptr_ = np.asarray(indptr)
indptr_32 = theano._asarray(indptr, dtype='int32')
assert (indptr_ == indptr_32).all()
indptr = indptr_32
if not isinstance(shape, gof.Variable):
shape_ = numpy.asarray(shape)
shape_ = np.asarray(shape)
shape_32 = theano._asarray(shape, dtype='int32')
assert (shape_ == shape_32).all()
shape = shape_32
......@@ -606,7 +606,7 @@ class CSM(gof.Op):
if self.format == 'csc':
out[0] = scipy.sparse.csc_matrix((data, indices.copy(),
indptr.copy()),
numpy.asarray(shape), copy=False)
np.asarray(shape), copy=False)
else:
assert self.format == 'csr'
out[0] = scipy.sparse.csr_matrix((data, indices.copy(),
......@@ -729,8 +729,8 @@ class CSMGrad(gof.op.Op):
else:
sp_dim = x_shape[0]
g_row = numpy.zeros(sp_dim, dtype=g_data.dtype)
gout_data = numpy.zeros(x_data.shape, dtype=node.outputs[0].dtype)
g_row = np.zeros(sp_dim, dtype=g_data.dtype)
gout_data = np.zeros(x_data.shape, dtype=node.outputs[0].dtype)
for i in range(len(x_indptr) - 1):
for j_ptr in range(g_indptr[i], g_indptr[i + 1]):
......@@ -1100,7 +1100,7 @@ class GetItem2Lists(gof.op.Op):
x = inp[0]
ind1 = inp[1]
ind2 = inp[2]
out[0] = numpy.asarray(x[ind1, ind2]).flatten()
out[0] = np.asarray(x[ind1, ind2]).flatten()
"""
Here scipy returns the corresponding elements in a matrix which isn't
what we are aiming for. Using asarray and flatten, out[0] becomes an
......@@ -1244,7 +1244,7 @@ class GetItem2d(gof.op.Op):
elif ((isinstance(ind, gof.Variable) and
getattr(ind, 'ndim', -1) == 0) or
numpy.isscalar(ind)):
np.isscalar(ind)):
raise NotImplementedError(
'Theano has no sparse vector' +
'Use X[a:b, c:d], X[a:b, c:c+1] or X[a:b] instead.')
......@@ -1653,9 +1653,9 @@ class SpSum(gof.op.Op):
(x,) = inputs
(z,) = outputs
if self.axis is None:
z[0] = numpy.asarray(x.sum())
z[0] = np.asarray(x.sum())
else:
z[0] = numpy.asarray(x.sum(self.axis)).ravel()
z[0] = np.asarray(x.sum(self.axis)).ravel()
def grad(self, inputs, gout):
(x,) = inputs
......@@ -2540,7 +2540,7 @@ class __ComparisonOpSD(gof.op.Op):
assert x.shape == y.shape
assert _is_dense(y)
o = self.comparison(x, y).astype('uint8')
o = numpy.asarray(o)
o = np.asarray(o)
out[0] = o
def infer_shape(self, node, ins_shapes):
......@@ -3382,7 +3382,7 @@ class TrueDot(gof.op.Op):
# 'ushort', 'intc', 'uintc', 'longlong', 'ulonglong', 'single',
# 'double', 'longdouble', 'csingle', 'cdouble', 'clongdouble']
# But ulonglong is uint64 on x86-64, but with a different typenum!
if rval.dtype.num != numpy.dtype(str(rval.dtype)).num:
if rval.dtype.num != np.dtype(str(rval.dtype)).num:
assert str(rval.dtype) == node.outputs[0].dtype
# Create a view with the expected typenum.
format = node.outputs[0].type.format
......@@ -3509,7 +3509,7 @@ class StructuredDot(gof.Op):
# dot of an NxM sparse matrix, with a Mx1 dense matrix, returns vector
# not matrix
if variable.ndim == 1:
variable = numpy.expand_dims(variable, 1)
variable = np.expand_dims(variable, 1)
elif variable.ndim != 2:
raise Exception('Output of structured dot should be a matrix '
'(ndim=2)')
......@@ -3622,7 +3622,7 @@ class StructuredDotGradCSC(gof.Op):
def perform(self, node, inputs, outputs):
(a_indices, a_indptr, b, g_ab) = inputs
(out,) = outputs
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
g_a_data = np.zeros(a_indices.shape, dtype=g_ab.dtype)
for j in xrange(len(a_indptr) - 1):
ind0 = a_indptr[j]
ind1 = a_indptr[j + 1]
......@@ -3631,7 +3631,7 @@ class StructuredDotGradCSC(gof.Op):
# Depending on the type of g_ab and b (sparse or dense),
# the following dot product can result in a scalar or
# a (1, 1) sparse matrix.
dot_val = numpy.dot(g_ab[i], b[j].T)
dot_val = np.dot(g_ab[i], b[j].T)
if isinstance(dot_val, scipy.sparse.spmatrix):
dot_val = dot_val[0, 0]
g_a_data[i_idx] = dot_val
......@@ -3752,7 +3752,7 @@ class StructuredDotGradCSR(gof.Op):
def perform(self, node, inputs, outputs):
(a_indices, a_indptr, b, g_ab) = inputs
(out,) = outputs
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
g_a_data = np.zeros(a_indices.shape, dtype=g_ab.dtype)
for i in xrange(len(a_indptr) - 1): # loop over rows
ind0 = a_indptr[i]
ind1 = a_indptr[i + 1]
......@@ -3763,7 +3763,7 @@ class StructuredDotGradCSR(gof.Op):
# Depending on the type of g_ab and b (sparse or dense),
# the following dot product can result in a scalar or
# a (1, 1) sparse matrix.
dot_val = numpy.dot(g_ab[i], b[j].T)
dot_val = np.dot(g_ab[i], b[j].T)
if isinstance(dot_val, scipy.sparse.spmatrix):
dot_val = dot_val[0, 0]
g_a_data[j_idx] = dot_val
......@@ -3910,7 +3910,7 @@ class SamplingDot(gof.op.Op):
if not _is_sparse(p):
raise TypeError(p)
out[0] = p.__class__(p.multiply(numpy.dot(x, y.T)))
out[0] = p.__class__(p.multiply(np.dot(x, y.T)))
def grad(self, inputs, gout):
(x, y, p) = inputs
......@@ -4243,7 +4243,7 @@ class ConstructSparseFromList(gof.Op):
out, = out_
rows, cols = values.shape
assert rows == len(ilist)
indptr = numpy.arange(cols + 1) * rows
indptr = np.arange(cols + 1) * rows
indices = as_strided(ilist,
strides=(0, ilist.strides[0]),
shape=(cols, ilist.shape[0])).flatten()
......
theano/sparse/opt.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import scipy
import theano
......@@ -879,7 +879,7 @@ local_usmm = gof.opt.PatternSub(
(theano.tensor.sub, 'z',
(theano.tensor.mul,
{'pattern': 'alpha',
'constraint': lambda expr: (numpy.all(expr.type.broadcastable) and
'constraint': lambda expr: (np.all(expr.type.broadcastable) and
theano.config.blas.ldflags)},
(sparse._dot, 'x', 'y'))),
(usmm, (theano.tensor.neg, 'alpha'), 'x', 'y', 'z'))
......
theano/sparse/sandbox/sp.py
浏览文件 @ 2b7ee2ec
......@@ -8,7 +8,7 @@ U{http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps}.
"""
# COPIED FROM hpu/icml09/sp.py
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import scipy
from scipy import sparse as scipy_sparse
from six.moves import xrange
......@@ -81,18 +81,17 @@ class ConvolutionIndices(Op):
raise Exception("ws is obsolete and it must be always True")
(dx, dy) = strides
N = numpy
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if N.size(inshp) == 2:
if np.size(inshp) == 2:
inshp = (1,) + inshp
inshp = N.array(inshp)
kshp = N.array(kshp)
ksize = N.prod(kshp)
inshp = np.array(inshp)
kshp = np.array(kshp)
ksize = np.prod(kshp)
kern = ksize - 1 - N.arange(ksize)
kern = ksize - 1 - np.arange(ksize)
# size of output image if doing proper convolution
# (mode='full',dx=dy=0) outshp is the actual output shape
......@@ -102,32 +101,32 @@ class ConvolutionIndices(Op):
s = -1
else:
s = 1
outshp = N.int64(N.ceil((inshp[1:] + s * kshp - s * 1) \
/ N.array([dy, dx], dtype='float')))
outshp = np.int64(np.ceil((inshp[1:] + s * kshp - s * 1) \
/ np.array([dy, dx], dtype='float')))
if any(outshp <= 0):
err = 'Invalid kernel', kshp, 'and/or step size', (dx, dy),\
'for given input shape', inshp
raise ValueError(err)
outsize = N.prod(outshp)
insize = N.prod(inshp)
outsize = np.prod(outshp)
insize = np.prod(inshp)
# range of output units over which to iterate
if mode == 'valid':
lbound = N.array([kshp[0] - 1, kshp[1] - 1])
lbound = np.array([kshp[0] - 1, kshp[1] - 1])
ubound = lbound + (inshp[1:] - kshp + 1)
else:
lbound = N.zeros(2)
lbound = np.zeros(2)
ubound = fulloutshp
# coordinates of image in "fulloutshp" coordinates
topleft = N.array([kshp[0] - 1, kshp[1] - 1])
topleft = np.array([kshp[0] - 1, kshp[1] - 1])
# bound when counting the receptive field
botright = topleft + inshp[1:]
# sparse matrix specifics...
if ws:
spmatshp = (outsize * N.prod(kshp) * inshp[0], insize)
spmatshp = (outsize * np.prod(kshp) * inshp[0], insize)
else:
spmatshp = (nkern * outsize, insize)
spmat = scipy_sparse.lil_matrix(spmatshp)
......@@ -152,17 +151,17 @@ class ConvolutionIndices(Op):
# FOR EACH OUTPUT PIXEL...
# loop over output image height
for oy in N.arange(lbound[0], ubound[0], dy):
for oy in np.arange(lbound[0], ubound[0], dy):
# loop over output image width
for ox in N.arange(lbound[1], ubound[1], dx):
for ox in np.arange(lbound[1], ubound[1], dx):
# kern[l] is filter value to apply at (oj,oi)
# for (iy,ix)
l = 0
# ... ITERATE OVER INPUT UNITS IN RECEPTIVE FIELD
for ky in oy + N.arange(kshp[0]):
for kx in ox + N.arange(kshp[1]):
for ky in oy + np.arange(kshp[0]):
for kx in ox + np.arange(kshp[1]):
# verify if we are still within image
# boundaries. Equivalent to
......@@ -173,13 +172,13 @@ class ConvolutionIndices(Op):
# convert to "valid" input space
# coords used to determine column
# index to write to in sparse mat
iy, ix = N.array((ky, kx)) - topleft
iy, ix = np.array((ky, kx)) - topleft
# determine raster-index of input pixel...
# taking into account multiple
# input features
col = iy * inshp[2] + ix + \
fmapi * N.prod(inshp[1:])
fmapi * np.prod(inshp[1:])
# convert oy,ox values to output
# space coordinates
......@@ -188,7 +187,7 @@ class ConvolutionIndices(Op):
else:
(y, x) = (oy, ox) - topleft
# taking into account step size
(y, x) = N.array([y, x]) / (dy, dx)
(y, x) = np.array([y, x]) / (dy, dx)
# convert to row index of sparse matrix
if ws:
......@@ -228,7 +227,7 @@ class ConvolutionIndices(Op):
if ws:
kmap = None
else:
kmap = N.zeros(ntaps, dtype='int')
kmap = np.zeros(ntaps, dtype='int')
k = 0
# print 'TEMPORARY BUGFIX: REMOVE !!!'
for j in xrange(spmat.shape[1]):
......@@ -259,7 +258,7 @@ class ConvolutionIndices(Op):
indices, indptr, spmatshp, outshp = self.evaluate(inshp, kshp)
out_indices[0] = indices
out_indptr[0] = indptr
spmat_shape[0] = numpy.asarray(spmatshp)
spmat_shape[0] = np.asarray(spmatshp)
convolution_indices = ConvolutionIndices()
......@@ -318,13 +317,12 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None,
:TODO: test for 1D and think of how to do n-d convolutions
"""
N = numpy
# start by computing output dimensions, size, etc
kern_size = N.int64(N.prod(kshp))
kern_size = np.int64(np.prod(kshp))
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if N.size(imgshp) == 2:
if np.size(imgshp) == 2:
imgshp = (1,) + imgshp
# construct indices and index pointers for sparse matrix, which,
......@@ -334,12 +332,12 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None,
convolution_indices.conv_eval(imgshp, kshp, step, mode)
# build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(N.ones(indices.size), indices,
csc = theano.sparse.CSM(sptype)(np.ones(indices.size), indices,
indptr, spmat_shape)
patches = (sparse.structured_dot(csc, images.T)).T
# compute output of linear classifier
pshape = tensor.stack([images.shape[0] * tensor.as_tensor(N.prod(outshp)),\
pshape = tensor.stack([images.shape[0] * tensor.as_tensor(np.prod(outshp)),\
tensor.as_tensor(imgshp[0] * kern_size)])
patch_stack = tensor.reshape(patches, pshape, ndim=2)
......@@ -354,14 +352,14 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None,
# now to have feature maps in raster order ...
# go from bsize*outshp x nkern to bsize x nkern*outshp
newshp = tensor.stack([images.shape[0],\
tensor.as_tensor(N.prod(outshp)),\
tensor.as_tensor(np.prod(outshp)),\
tensor.as_tensor(nkern)])
tensout = tensor.reshape(output, newshp, ndim=3)
output = tensor.DimShuffle((False,) * tensout.ndim, (0, 2, 1))(tensout)
if flatten:
output = tensor.flatten(output, 2)
return output, N.hstack((nkern, outshp))
return output, np.hstack((nkern, outshp))
def max_pool(images, imgshp, maxpoolshp):
......@@ -380,12 +378,11 @@ def max_pool(images, imgshp, maxpoolshp):
:return: out1, symbolic result (2D tensor)
:return: out2, logical shape of the output
"""
N = numpy
poolsize = N.int64(N.prod(maxpoolshp))
poolsize = np.int64(np.prod(maxpoolshp))
# imgshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if N.size(imgshp) == 2:
if np.size(imgshp) == 2:
imgshp = (1,) + imgshp
# construct indices and index pointers for sparse matrix, which,
......@@ -401,12 +398,12 @@ def max_pool(images, imgshp, maxpoolshp):
# print 'outshp = ', outshp
# build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(N.ones(indices.size), indices,
csc = theano.sparse.CSM(sptype)(np.ones(indices.size), indices,
indptr, spmat_shape)
patches = sparse.structured_dot(csc, images.T).T
pshape = tensor.stack([images.shape[0] *\
tensor.as_tensor(N.prod(outshp)),
tensor.as_tensor(np.prod(outshp)),
tensor.as_tensor(imgshp[0]),
tensor.as_tensor(poolsize)])
patch_stack = tensor.reshape(patches, pshape, ndim=3)
......@@ -414,7 +411,7 @@ def max_pool(images, imgshp, maxpoolshp):
out1 = tensor.max(patch_stack, axis=2)
pshape = tensor.stack([images.shape[0],
tensor.as_tensor(N.prod(outshp)),
tensor.as_tensor(np.prod(outshp)),
tensor.as_tensor(imgshp[0])])
out2 = tensor.reshape(out1, pshape, ndim=3)
......
theano/sparse/sandbox/sp2.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
from six.moves import xrange
import theano
import scipy.sparse
......@@ -74,7 +74,7 @@ class Poisson(gof.op.Op):
assert _is_sparse(x)
assert x.format in ["csr", "csc"]
out[0] = x.copy()
out[0].data = numpy.asarray(numpy.random.poisson(out[0].data),
out[0].data = np.asarray(np.random.poisson(out[0].data),
dtype=x.dtype)
out[0].eliminate_zeros()
......@@ -123,7 +123,7 @@ class Binomial(gof.op.Op):
def perform(self, node, inputs, outputs):
(n, p, shape) = inputs
(out,) = outputs
binomial = numpy.random.binomial(n, p, size=shape)
binomial = np.random.binomial(n, p, size=shape)
csx_matrix = getattr(scipy.sparse, self.format + '_matrix')
out[0] = csx_matrix(binomial, dtype=self.dtype)
......@@ -195,14 +195,14 @@ class Multinomial(gof.op.Op):
if n.ndim == 0:
for i in xrange(p.shape[0]):
k, l = p.indptr[i], p.indptr[i + 1]
out[0].data[k:l] = numpy.random.multinomial(n, p.data[k:l])
out[0].data[k:l] = np.random.multinomial(n, p.data[k:l])
elif n.ndim == 1:
if n.shape[0] != p.shape[0]:
raise ValueError('The number of element of n must be '
'the same as the number of row of p.')
for i in xrange(p.shape[0]):
k, l = p.indptr[i], p.indptr[i + 1]
out[0].data[k:l] = numpy.random.multinomial(n[i], p.data[k:l])
out[0].data[k:l] = np.random.multinomial(n[i], p.data[k:l])
def grad(self, inputs, outputs_gradients):
comment_n = "No gradient exists for the number of samples in class\
......
theano/sparse/sandbox/test_sp.py
浏览文件 @ 2b7ee2ec
......@@ -11,7 +11,7 @@ if not theano.sparse.enable_sparse:
import scipy.sparse
from scipy.signal import convolve2d
import scipy.sparse as sparse
import numpy
import numpy as np
from six.moves import xrange
from theano import function, tensor
......@@ -43,8 +43,8 @@ class TestSP(unittest.TestCase):
bias = tensor.dvector()
kerns = tensor.dmatrix()
input = tensor.dmatrix()
rng = numpy.random.RandomState(3423489)
filters = rng.randn(nkern, numpy.prod(kshp))
rng = np.random.RandomState(3423489)
filters = rng.randn(nkern, np.prod(kshp))
biasvals = rng.randn(nkern)
for mode in ('FAST_COMPILE', 'FAST_RUN'):
......@@ -57,12 +57,12 @@ class TestSP(unittest.TestCase):
f = function([kerns, bias, input], output, mode=mode)
# now test with real values
img2d = numpy.arange(bsize * numpy.prod(imshp)).reshape(( \
img2d = np.arange(bsize * np.prod(imshp)).reshape(( \
bsize,) + imshp)
img1d = img2d.reshape(bsize, -1)
# create filters (need to be flipped to use convolve2d)
filtersflipped = numpy.zeros((nkern,) + kshp)
filtersflipped = np.zeros((nkern,) + kshp)
for k in range(nkern):
it = reversed(filters[k, :])
for i in range(kshp[0]):
......@@ -71,11 +71,11 @@ class TestSP(unittest.TestCase):
# compute output with convolve2d
if conv_mode == 'valid':
fulloutshp = numpy.array(imshp) - numpy.array(kshp) + 1
fulloutshp = np.array(imshp) - np.array(kshp) + 1
else:
fulloutshp = numpy.array(imshp) + numpy.array(kshp) - 1
fulloutshp = np.array(imshp) + np.array(kshp) - 1
ntime1 = time.time()
refout = numpy.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
refout = np.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
for b in range(bsize):
for n in range(nkern):
refout[b, ..., n] = convolve2d(img2d[b, :, :],
......@@ -88,7 +88,7 @@ class TestSP(unittest.TestCase):
bench1 += biasvals.reshape(1, 1, nkern)
# swap the last two dimensions (output needs to be nkern x outshp)
bench1 = numpy.swapaxes(bench1, 1, 2)
bench1 = np.swapaxes(bench1, 1, 2)
ttime1 = time.time()
out1 = f(filters, biasvals, img1d)
ttot += time.time() - ttime1
......@@ -101,13 +101,13 @@ class TestSP(unittest.TestCase):
#downprop = function([kerns,input], vis, mode=mode)
#visval = downprop(filters,img1d)
# test downward propagation -- reference implementation
#pshape = (img1d.shape[0],numpy.prod(outshp[1:]),numpy.prod(kshp))
#patchstack = numpy.zeros(pshape)
# for bi in numpy.arange(pshape[0]): # batch index
#pshape = (img1d.shape[0],np.prod(outshp[1:]),np.prod(kshp))
#patchstack = np.zeros(pshape)
# for bi in np.arange(pshape[0]): # batch index
#abspos = 0
# for outy in numpy.arange(outshp[1]):
# for outx in numpy.arange(outshp[2]):
# for ni in numpy.arange(nkern):
# for outy in np.arange(outshp[1]):
# for outx in np.arange(outshp[2]):
# for ni in np.arange(nkern):
# print 'filters[n,:].shape = ', filters[n,:].shape
# print 'out1[bi,abspos].shape =',out1[bi,abspos].shape
#patchstack[bi,abspos,:] = filters[n,:]*out1[bi,abspos]
......@@ -115,13 +115,13 @@ class TestSP(unittest.TestCase):
#patchstack = patchstack.reshape(1,-1)
# indices, indptr, spmat_shape, sptype, outshp = \
# sp.convolution_indices.conv_eval(imshp,kshp,ss,conv_mode)
#spmat = sparse.csc_matrix((numpy.ones_like(indices),indices,indptr),spmat_shape)
#visref = numpy.dot(patchstack, spmat.todense())
#spmat = sparse.csc_matrix((np.ones_like(indices),indices,indptr),spmat_shape)
#visref = np.dot(patchstack, spmat.todense())
# print 'visval = ', visval
# print 'visref = ', visref
#assert numpy.all(visref==visval)
#assert np.all(visref==visval)
# print '**** Convolution Profiling Results (',mode,') ****'
......@@ -143,10 +143,10 @@ class TestSP(unittest.TestCase):
# symbolic stuff
kerns = [tensor.dmatrix(), tensor.dmatrix()]
input = tensor.dmatrix()
rng = numpy.random.RandomState(3423489)
rng = np.random.RandomState(3423489)
# build actual input images
img2d = numpy.arange(bsize*numpy.prod(imshp)).reshape((bsize,)+imshp)
img2d = np.arange(bsize*np.prod(imshp)).reshape((bsize,)+imshp)
img1d = img2d.reshape(bsize, -1)
for mode in ('FAST_COMPILE', 'FAST_RUN'):
......@@ -157,8 +157,8 @@ class TestSP(unittest.TestCase):
nkerns[0], input, imshp, ss[0], mode=conv_mode)
l1propup = function([kerns[0], input], l1hid, mode=mode)
#l1kernvals = numpy.random.rand(nkerns[0],numpy.prod(kshp[0]))
l1kernvals = numpy.arange(nkerns[0]*numpy.prod(kshp[0])).reshape(nkerns[0], numpy.prod(kshp[0]))
#l1kernvals = np.random.rand(nkerns[0],np.prod(kshp[0]))
l1kernvals = np.arange(nkerns[0]*np.prod(kshp[0])).reshape(nkerns[0], np.prod(kshp[0]))
l1hidval = l1propup(l1kernvals, img1d)
# actual values
......@@ -166,17 +166,17 @@ class TestSP(unittest.TestCase):
nkerns[1], l1hid, l1shp, ss[1], mode=conv_mode)
l2propup = function([kerns[1], l1hid], l2hid, mode=mode)
#l2kernvals = numpy.random.rand(nkerns[1],numpy.prod(kshp[1])*nkerns[0])
l2kernvals = numpy.arange(nkerns[1]*numpy.prod(kshp[1])*nkerns[0]).reshape(nkerns[1], numpy.prod(kshp[1])*nkerns[0])
#l2kernvals = np.random.rand(nkerns[1],np.prod(kshp[1])*nkerns[0])
l2kernvals = np.arange(nkerns[1]*np.prod(kshp[1])*nkerns[0]).reshape(nkerns[1], np.prod(kshp[1])*nkerns[0])
# for debugging, we bring things back to integers
l1hidval = numpy.arange(numpy.size(l1hidval)).reshape(l1hidval.shape)
l1hidval = np.arange(np.size(l1hidval)).reshape(l1hidval.shape)
l2hidval = l2propup(l2kernvals, l1hidval)
def test_maxpool(self):
# generate flatted images
maxpoolshps = ((2, 2), (3, 3), (4, 4), (5, 5), (6, 6))
imval = numpy.random.rand(4, 5, 10, 10)
imval = np.random.rand(4, 5, 10, 10)
images = tensor.dmatrix()
for maxpoolshp in maxpoolshps:
......@@ -187,10 +187,10 @@ class TestSP(unittest.TestCase):
output_val = f(imval.reshape(imval.shape[0], -1))
# numeric verification
my_output_val = numpy.zeros((imval.shape[0], imval.shape[1],
my_output_val = np.zeros((imval.shape[0], imval.shape[1],
imval.shape[2] // maxpoolshp[0],
imval.shape[3] // maxpoolshp[1]))
assert numpy.prod(my_output_val.shape[1:]) == numpy.prod(numpy.r_[imval.shape[1], outshp])
assert np.prod(my_output_val.shape[1:]) == np.prod(np.r_[imval.shape[1], outshp])
for n in range(imval.shape[0]):
for k in range(imval.shape[1]):
......@@ -198,9 +198,9 @@ class TestSP(unittest.TestCase):
for j in range(imval.shape[3] // maxpoolshp[1]):
ii, jj = i*maxpoolshp[0], j*maxpoolshp[1]
patch = imval[n, k, ii:ii+maxpoolshp[0], jj:jj+maxpoolshp[1]]
my_output_val[n, k, i, j] = numpy.max(patch)
my_output_val[n, k, i, j] = np.max(patch)
my_output_val = my_output_val.reshape(imval.shape[0], -1)
assert numpy.all(output_val == my_output_val)
assert np.all(output_val == my_output_val)
def mp(input):
output, outshp = sp.max_pool(input, imval.shape[1:], maxpoolshp)
......
theano/sparse/sandbox/truedot.py
浏览文件 @ 2b7ee2ec
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import unittest
import theano
import numpy
import numpy as np
import scipy.sparse as sp
from theano import sparse
......
theano/sparse/tests/test_opt.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest
import numpy
import numpy as np
try:
import scipy.sparse as sp
import scipy.sparse
......@@ -157,14 +157,14 @@ def test_local_dense_from_sparse_sparse_from_dense():
def test_sd_csc():
A = sp.rand(4, 5, density=0.60, format='csc', dtype=numpy.float32)
b = numpy.random.rand(5,2).astype(numpy.float32)
A = sp.rand(4, 5, density=0.60, format='csc', dtype=np.float32)
b = np.random.rand(5,2).astype(np.float32)
target = A*b
a_val = theano.tensor.as_tensor_variable(A.data)
a_ind = theano.tensor.as_tensor_variable(A.indices)
a_ptr = theano.tensor.as_tensor_variable(A.indptr)
nrows = theano.tensor.as_tensor_variable(numpy.int32(A.shape[0]))
nrows = theano.tensor.as_tensor_variable(np.int32(A.shape[0]))
b = theano.tensor.as_tensor_variable(b)
res = theano.sparse.opt.sd_csc(a_val, a_ind, a_ptr, nrows, b).eval()
......
theano/sparse/tests/test_sp2.py
浏览文件 @ 2b7ee2ec
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import unittest
from nose.plugins.skip import SkipTest
import numpy
import numpy as np
try:
import scipy.sparse as sp
except ImportError:
......@@ -30,7 +30,7 @@ class PoissonTester(utt.InferShapeTester):
for format in sparse.sparse_formats:
variable = getattr(theano.sparse, format + '_matrix')
rand = numpy.array(numpy.random.randint(1, 4, size=(3, 4)) - 1,
rand = np.array(np.random.randint(1, 4, size=(3, 4)) - 1,
dtype=theano.config.floatX)
x[format] = variable()
......@@ -50,7 +50,7 @@ class PoissonTester(utt.InferShapeTester):
assert tested.format == format
assert tested.dtype == self.a[format].dtype
assert numpy.allclose(numpy.floor(tested.data), tested.data)
assert np.allclose(np.floor(tested.data), tested.data)
assert tested.shape == self.a[format].shape
def test_infer_shape(self):
......@@ -67,7 +67,7 @@ class BinomialTester(utt.InferShapeTester):
shape = tensor.lvector()
_n = 5
_p = .25
_shape = numpy.asarray([3, 5], dtype='int64')
_shape = np.asarray([3, 5], dtype='int64')
inputs = [n, p, shape]
_inputs = [_n, _p, _shape]
......@@ -88,7 +88,7 @@ class BinomialTester(utt.InferShapeTester):
assert tested.shape == tuple(self._shape)
assert tested.format == sp_format
assert tested.dtype == o_type
assert numpy.allclose(numpy.floor(tested.todense()),
assert np.allclose(np.floor(tested.todense()),
tested.todense())
def test_infer_shape(self):
......@@ -103,7 +103,7 @@ class BinomialTester(utt.InferShapeTester):
class MultinomialTester(utt.InferShapeTester):
p = sparse.csr_matrix()
_p = sp.csr_matrix(numpy.asarray([[0.0, 0.5, 0.0, 0.5],
_p = sp.csr_matrix(np.asarray([[0.0, 0.5, 0.0, 0.5],
[0.1, 0.2, 0.3, 0.4],
[0.0, 1.0, 0.0, 0.0],
[0.3, 0.3, 0.0, 0.4]],
......@@ -120,16 +120,16 @@ class MultinomialTester(utt.InferShapeTester):
_n = 5
tested = f(self._p, _n)
assert tested.shape == self._p.shape
assert numpy.allclose(numpy.floor(tested.todense()), tested.todense())
assert np.allclose(np.floor(tested.todense()), tested.todense())
assert tested[2, 1] == _n
n = tensor.lvector()
f = theano.function([self.p, n], multinomial(n, self.p))
_n = numpy.asarray([1, 2, 3, 4], dtype='int64')
_n = np.asarray([1, 2, 3, 4], dtype='int64')
tested = f(self._p, _n)
assert tested.shape == self._p.shape
assert numpy.allclose(numpy.floor(tested.todense()), tested.todense())
assert np.allclose(np.floor(tested.todense()), tested.todense())
assert tested[2, 1] == _n[2]
def test_infer_shape(self):
......
theano/sparse/tests/test_utils.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest
import numpy
import numpy as np
import theano.sparse
if not theano.sparse.enable_sparse:
raise SkipTest('Optional package sparse disabled')
......@@ -11,21 +11,21 @@ from theano.sparse.tests.test_basic import as_sparse_format
def test_hash_from_sparse():
hashs = []
rng = numpy.random.rand(5, 5)
rng = np.random.rand(5, 5)
for format in ['csc', 'csr']:
rng = as_sparse_format(rng, format)
for data in [[[-2]], [[-1]], [[0]], [[1]], [[2]],
numpy.zeros((1, 5)), numpy.zeros((1, 6)),
np.zeros((1, 5)), np.zeros((1, 6)),
# Data buffer empty but different shapes
# numpy.zeros((1, 0)), numpy.zeros((2, 0)),
# np.zeros((1, 0)), np.zeros((2, 0)),
# Same data buffer and shapes but different strides
numpy.arange(25).reshape(5, 5),
numpy.arange(25).reshape(5, 5).T,
np.arange(25).reshape(5, 5),
np.arange(25).reshape(5, 5).T,
# Same data buffer, shapes and strides
# but different dtypes
numpy.zeros((5, 5), dtype="uint32"),
numpy.zeros((5, 5), dtype="int32"),
np.zeros((5, 5), dtype="uint32"),
np.zeros((5, 5), dtype="int32"),
# Test slice
rng, rng[1:], rng[:4], rng[1:3],
# Don't test step as they are not supported by sparse
......
theano/sparse/type.py
浏览文件 @ 2b7ee2ec
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
try:
import scipy.sparse
imported_scipy = True
......@@ -20,7 +20,7 @@ def _is_sparse(x):
True iff x is a L{scipy.sparse.spmatrix} (and not a L{numpy.ndarray}).
"""
if not isinstance(x, (scipy.sparse.spmatrix, numpy.ndarray, tuple, list)):
if not isinstance(x, (scipy.sparse.spmatrix, np.ndarray, tuple, list)):
raise NotImplementedError("this function should only be called on "
"sparse.scipy.sparse.spmatrix or "
"numpy.ndarray, not,", x)
......@@ -107,12 +107,12 @@ class SparseType(gof.Type):
return (SparseType.may_share_memory(a, b.data) or
SparseType.may_share_memory(a, b.indices) or
SparseType.may_share_memory(a, b.indptr))
if _is_sparse(b) and isinstance(a, numpy.ndarray):
if _is_sparse(b) and isinstance(a, np.ndarray):
a, b = b, a
if _is_sparse(a) and isinstance(b, numpy.ndarray):
if (numpy.may_share_memory(a.data, b) or
numpy.may_share_memory(a.indices, b) or
numpy.may_share_memory(a.indptr, b)):
if _is_sparse(a) and isinstance(b, np.ndarray):
if (np.may_share_memory(a.data, b) or
np.may_share_memory(a.indices, b) or
np.may_share_memory(a.indptr, b)):
# currently we can't share memory with a.shape as it is a tuple
return True
return False
......@@ -168,8 +168,8 @@ class SparseType(gof.Type):
obj.indices.size, obj.indptr.size, obj.nnz)
def get_size(self, shape_info):
return (shape_info[1] * numpy.dtype(self.dtype).itemsize +
(shape_info[2] + shape_info[3]) * numpy.dtype('int32').itemsize)
return (shape_info[1] * np.dtype(self.dtype).itemsize +
(shape_info[2] + shape_info[3]) * np.dtype('int32').itemsize)
# Register SparseType's C code for ViewOp.
theano.compile.register_view_op_c_code(
......
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