def _compile_message_factor_(self, _factor, _node):
'''
Pseudocode:
(treat _node as X and _factor as L)
if L is a unary factor:
emit v_L,_X = v_L
elif X is the output node of L
v_i = compilemessage_node(X_o, L)
elif X is the output node of L
v_i = compilemessage_node(X_i, L)
return this
'''
if _factor.o.null:
#If the factor is unary.
return _factor.M #@TODO: See if we need another variable like self.v to represent the value of unary predicates.
elif _factor.o == _node:
#If the node is the output node for this factor
v_i = self._comiple_message_node_(_factor.i, _factor)
return sparse.structured_dot(v_i, _factor.M)
# return v_i.dot(_factor.M)
elif _factor.i == _node:
#If the node is the input node for this factor
v_i = self._comiple_message_node_(_factor.o, _factor)
return sparse.structured_dot(v_i, _factor.M)
def get_output_for(self, input, **kwargs):
if not isinstance(input, (S.SparseVariable, S.SparseConstant,
S.sharedvar.SparseTensorSharedVariable)):
raise ValueError("Input for this layer must be sparse")
activation = S.structured_dot(input, self.W)
#do the convolution
activation = S.structured_dot(self.A, activation)
if self.b is not None:
activation = activation + self.b.dimshuffle('x', 0)
return self.nonlinearity(activation)
def get_output_for(self, input, target_indices=None, **kwargs):
#do the convolution
activation = S.structured_dot(self.A, input)
if self.use_target_indices and target_indices:
activation = activation[target_indices, :]
return self.nonlinearity(activation)
def buildgraphCSC(spdata, sym_mat):
csc = CSC(spdata, spmat.indices[:spmat.size],
spmat.indptr, spmat.shape)
assert csc.type.dtype == 'float32'
rval = structured_dot(csc, sym_mat)
assert rval.type.dtype == 'float32'
return rval
def buildgraph(spdata, sym_mat):
csr = CSR(spdata, spmat.indices[:spmat.size], spmat.indptr,
spmat.shape)
assert csr.type.dtype == 'float64'
rval = structured_dot(csr, sym_mat)
assert rval.type.dtype == 'float64'
return rval
def test_upcast(self):
typenames = ('float32', 'int64', 'int8', 'int32', 'int16', 'float64',
'complex64', 'complex128')
for dense_dtype in typenames:
for sparse_dtype in typenames:
correct_dtype = theano.scalar.upcast(sparse_dtype, dense_dtype)
a = SparseType('csc', dtype=sparse_dtype)()
b = tensor.matrix(dtype=dense_dtype)
d = structured_dot(a, b)
assert d.type.dtype == correct_dtype
# compile and run a function
f = theano.function([a, b], d)
M, N, K, nnz = (4, 3, 5, 3)
spmat = sp.csc_matrix(random_lil((M, N), sparse_dtype, nnz))
# the following madness is necessary to workaround
# an intc vs. int32 bug.
# The lil makes an intc on my computer when sparse_dtype
# is int32.
spmat.dtype = numpy.dtype(sparse_dtype)
mat = numpy.asarray(numpy.random.randn(N, K) * 9,
dtype=dense_dtype)
print 'DTYPES', sparse_dtype, dense_dtype
print 'sym types', a.type, b.type
print 'dtype strings', spmat.dtype, mat.dtype
print 'numpy dtype num', mat.dtype.num
print 'scipy dtype num', spmat.data.dtype.num
theano_result = f(spmat, mat)
scipy_result = spmat * mat
assert theano_result.shape == scipy_result.shape
assert theano_result.dtype == scipy_result.dtype
assert _allclose(theano_result, scipy_result)
def buildgraphCSC(spdata, sym_mat):
csc = CSC(spdata, spmat.indices[:spmat.size], spmat.indptr,
spmat.shape)
assert csc.type.dtype == 'float32'
rval = structured_dot(csc, sym_mat)
assert rval.type.dtype == 'float32'
return rval
def __call__(self, inputs):
"""
Compute and return the PCA transformation of sparse data.
Precondition: `self.mean` has been subtracted from inputs. The reason
for this is that, as far as I can tell, there is no way to subtract a
vector from a sparse matrix without constructing an intermediary dense
matrix, in theano; even the hack used in `train()` won't do, because
there is no way to symbolically construct a sparse matrix by repeating
a vector (again, as far as I can tell).
Parameters
----------
inputs : scipy.sparse matrix object
Sparse matrix of shape (n, d) on which to compute PCA
Returns
-------
WRITEME
"""
# Update component cutoff, in case min_variance or num_components has
# changed (or both).
self._update_cutoff()
Y = structured_dot(inputs, self.W[:, :self.component_cutoff])
if self.whiten:
Y /= tensor.sqrt(self.v[:self.component_cutoff])
return Y
def buildgraph(spdata, sym_mat):
csr = CSR(spdata, spmat.indices[:spmat.size],
spmat.indptr, spmat.shape)
assert csr.type.dtype == 'float64'
rval = structured_dot(csr, sym_mat)
assert rval.type.dtype == 'float64'
return rval
def applySparseFilter(kerns, kshp, nkern, images, imgshp, step=(1,1), bias=None, mode='valid'):
"""
"images" is assumed to be a matrix of shape batch_size x img_size, where the second
dimension represents each image in raster order
Output feature map will have shape:
.. code-block:: python
batch_size x number of kernels * output_size
.. note::
IMPORTANT: note that this means that each feature map is contiguous in memory.
The memory layout will therefore be:
[ <feature_map_0> <feature_map_1> ... <feature_map_n>],
where <feature_map> represents a "feature map" in raster order
Note that the concept of feature map doesn't really apply to sparse filters without
weight sharing. Basically, nkern=1 will generate one output img/feature map,
nkern=2 a second feature map, etc.
kerns is a 1D tensor, and assume to be of shape:
.. code-block:: python
nkern * N.prod(outshp) x N.prod(kshp)
Each filter is applied seperately to consecutive output pixels.
:param kerns: nkern*outsize*ksize vector containing kernels
:param kshp: tuple containing actual dimensions of kernel (not symbolic)
:param nkern: number of kernels to apply at each pixel in the input image.
nkern=1 will apply a single unique filter for each input pixel.
:param images: bsize x imgsize matrix containing images on which to apply filters
:param imgshp: tuple containing actual image dimensions (not symbolic)
:param step: determines number of pixels between adjacent receptive fields
(tuple containing dx,dy values)
:param mode: 'full', 'valid' see CSM.evaluate function for details
:return: out1, symbolic result
:return: out2, logical shape of the output img (nkern,height,width)
(after dot product, not of the sparse matrix!)
"""
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if numpy.size(imgshp)==2:
imgshp = (1,)+imgshp
# construct indices and index pointers for sparse matrix
indices, indptr, spmat_shape, sptype, outshp, kmap = \
convolution_indices.sparse_eval(imgshp, kshp, nkern, step, mode)
# build a sparse weight matrix
sparsew = theano.sparse.CSM(sptype, kmap)(kerns, indices, indptr, spmat_shape)
output = sparse.structured_dot(sparsew, images.T).T
if bias is not None:
output += bias
return output, numpy.hstack((nkern,outshp))
def conv2d(images, kerns, ishp4, kshp4, subsample=(1, 1), border_mode='valid'):
# start by computing output dimensions, size, etc
B, C, IR, IC = ishp4
K, CH, KR, KC = kshp4
assert C == CH # number of channels must match
OR, OC = conv_out_shp(IR, IC, KR, KC, border_mode, subsample)
oshp = (B, OR, OC, K)
# construct indices and index pointers for sparse matrix, which, when multiplied
# with input images will generate a stack of image patches
patch_extractor = sp_extract_patches(IR,
IC,
KR,
KC,
CH,
RasterOrders.channel_row_col,
RasterOrders.channel_row_col,
subsample,
border_mode,
flip_patches=True).tocsc()
#print IR, IC, KR, KC, CH, patch_extractor.shape, patch_extractor.nnz
patches = sparse.structured_dot(images.flatten(2), patch_extractor)
# compute output of linear classifier
patch_stack = patches.reshape((B * OR * OC, KR * KC * CH))
# kern is of shape: nkern x ksize*number_of_input_features
# output is thus of shape: bsize*outshp x nkern
output = tensor.dot(patch_stack,
kerns.flatten(2).T).reshape((B, OR, OC, K))
return output, oshp
def applySparseFilter(kerns, kshp, nkern, images, imgshp, step=(1,1), bias=None, mode='valid'):
"""
"images" is assumed to be a matrix of shape batch_size x img_size, where the second
dimension represents each image in raster order
Output feature map will have shape:
.. code-block:: python
batch_size x number of kernels * output_size
.. note::
IMPORTANT: note that this means that each feature map is contiguous in memory.
The memory layout will therefore be:
[ <feature_map_0> <feature_map_1> ... <feature_map_n>],
where <feature_map> represents a "feature map" in raster order
Note that the concept of feature map doesn't really apply to sparse filters without
weight sharing. Basically, nkern=1 will generate one output img/feature map,
nkern=2 a second feature map, etc.
kerns is a 1D tensor, and assume to be of shape:
.. code-block:: python
nkern * N.prod(outshp) x N.prod(kshp)
Each filter is applied seperately to consecutive output pixels.
:param kerns: nkern*outsize*ksize vector containing kernels
:param kshp: tuple containing actual dimensions of kernel (not symbolic)
:param nkern: number of kernels to apply at each pixel in the input image.
nkern=1 will apply a single unique filter for each input pixel.
:param images: bsize x imgsize matrix containing images on which to apply filters
:param imgshp: tuple containing actual image dimensions (not symbolic)
:param step: determines number of pixels between adjacent receptive fields
(tuple containing dx,dy values)
:param mode: 'full', 'valid' see CSM.evaluate function for details
:return: out1, symbolic result
:return: out2, logical shape of the output img (nkern,height,width)
(after dot product, not of the sparse matrix!)
"""
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if numpy.size(imgshp)==2:
imgshp = (1,)+imgshp
# construct indices and index pointers for sparse matrix
indices, indptr, spmat_shape, sptype, outshp, kmap = \
convolution_indices.sparse_eval(imgshp, kshp, nkern, step, mode)
# build a sparse weight matrix
sparsew = theano.sparse.CSM(sptype, kmap)(kerns, indices, indptr, spmat_shape)
output = sparse.structured_dot(sparsew, images.T).T
if bias is not None:
output += bias
return output, numpy.hstack((nkern,outshp))
def __call__(self, inputs):
"""
Compute and return the PCA transformation of sparse data.
Precondition: self.mean has been subtracted from inputs. The reason
for this is that, as far as I can tell, there is no way to subtract a
vector from a sparse matrix without constructing an intermediary dense
matrix, in theano; even the hack used in train() won't do, because
there is no way to symbolically construct a sparse matrix by repeating
a vector (again, as far as I can tell).
:type inputs: scipy.sparse matrix object, shape (n, d)
:param inputs: sparse matrix on which to compute PCA
TODO: docstring upgrade. Make it consistent with the numpy/pylearn
standard.
"""
# Update component cutoff, in case min_variance or num_components has
# changed (or both).
self._update_cutoff()
Y = structured_dot(inputs, self.W[:, :self.component_cutoff])
if self.whiten:
Y /= tensor.sqrt(self.v[:self.component_cutoff])
return Y
def sample_v_given_h(self, h0_sample):
vs_stat = []
vs_sample = []
for i in xrange(len(self.v_layers)):
v1_stat, v1_sample = self.v_layers[i].sample_v_given_h(h0_sample)
vs_stat.append(v1_stat)
vs_sample.append(v1_sample)
v_stat = sparse.structured_dot(T.concatenate(vs_stat, axis=1),
self.big_mask)
v_sample = sparse.structured_dot(T.concatenate(vs_sample, axis=1),
self.big_mask)
return [v_stat, v_sample]
def __call__(self, inputs):
"""
Compute and return the PCA transformation of sparse data.
Precondition: self.mean has been subtracted from inputs. The reason
for this is that, as far as I can tell, there is no way to subtract a
vector from a sparse matrix without constructing an intermediary dense
matrix, in theano; even the hack used in train() won't do, because
there is no way to symbolically construct a sparse matrix by repeating
a vector (again, as far as I can tell).
:type inputs: scipy.sparse matrix object, shape (n, d)
:param inputs: sparse matrix on which to compute PCA
TODO: docstring upgrade. Make it consistent with the numpy/pylearn
standard.
"""
# Update component cutoff, in case min_variance or num_components has
# changed (or both).
self._update_cutoff()
Y = structured_dot(inputs, self.W[:, :self.component_cutoff])
if self.whiten:
Y /= tensor.sqrt(self.v[:self.component_cutoff])
return Y
def test_structured_dot_grad(self):
# We also need the grad of CSM to be implemetned.
raise SkipTest("infer_shape not implemented for the grad" " of structured_dot")
for format, op in [("csc", StructuredDotGradCSC), ("csr", StructuredDotGradCSR)]:
x = SparseType(format, dtype=config.floatX)()
y = SparseType(format, dtype=config.floatX)()
grads = tensor.grad(dense_from_sparse(structured_dot(x, y)).sum(), [x, y])
self._compile_and_check(
[x, y],
[grads[0]],
[
as_sparse_format(random_lil((4, 5), config.floatX, 3), format),
as_sparse_format(random_lil((5, 3), config.floatX, 3), format),
],
op,
)
self._compile_and_check(
[x, y],
[grads[1]],
[
as_sparse_format(random_lil((4, 5), config.floatX, 3), format),
as_sparse_format(random_lil((5, 3), config.floatX, 3), format),
],
op,
)
def __call__(self, inputs):
"""
Compute and return the PCA transformation of sparse data.
Precondition: `self.mean` has been subtracted from inputs. The reason
for this is that, as far as I can tell, there is no way to subtract a
vector from a sparse matrix without constructing an intermediary dense
matrix, in theano; even the hack used in `train()` won't do, because
there is no way to symbolically construct a sparse matrix by repeating
a vector (again, as far as I can tell).
Parameters
----------
inputs : scipy.sparse matrix object
Sparse matrix of shape (n, d) on which to compute PCA
Returns
-------
WRITEME
"""
# Update component cutoff, in case min_variance or num_components has
# changed (or both).
self._update_cutoff()
Y = structured_dot(inputs, self.W[:, :self.component_cutoff])
if self.whiten:
Y /= tensor.sqrt(self.v[:self.component_cutoff])
return Y
def test_upcast(self):
typenames = ("float32", "int64", "int8", "int32", "int16", "float64", "complex64", "complex128")
for dense_dtype in typenames:
for sparse_dtype in typenames:
correct_dtype = theano.scalar.upcast(sparse_dtype, dense_dtype)
a = SparseType("csc", dtype=sparse_dtype)()
b = tensor.matrix(dtype=dense_dtype)
d = structured_dot(a, b)
assert d.type.dtype == correct_dtype
# compile and run a function
f = theano.function([a, b], d)
M, N, K, nnz = (4, 3, 5, 3)
spmat = sp.csc_matrix(random_lil((M, N), sparse_dtype, nnz))
# the following madness is necessary to workaround
# an intc vs. int32 bug.
# The lil makes an intc on my computer when sparse_dtype
# is int32.
spmat.dtype = numpy.dtype(sparse_dtype)
mat = numpy.asarray(numpy.random.randn(N, K) * 9, dtype=dense_dtype)
print "DTYPES", sparse_dtype, dense_dtype
print "sym types", a.type, b.type
print "dtype strings", spmat.dtype, mat.dtype
print "numpy dtype num", mat.dtype.num
print "scipy dtype num", spmat.data.dtype.num
theano_result = f(spmat, mat)
scipy_result = spmat * mat
assert theano_result.shape == scipy_result.shape
assert theano_result.dtype == scipy_result.dtype
assert _allclose(theano_result, scipy_result)
def conv2d_channel_minor(images, kerns, ishp4, kshp4, subsample=(1,1),
border_mode='valid'):
# start by computing output dimensions, size, etc
B, IR, IC, C = ishp4
K, KR, KC, CH = kshp4
assert C == CH # number of channels must match
OR, OC = conv_out_shp(IR, IC, KR, KC, border_mode, subsample)
oshp = (B, OR, OC, K)
# construct indices and index pointers for sparse matrix, which, when multiplied
# with input images will generate a stack of image patches
patch_extractor = sp_extract_patches(IR, IC, KR, KC, CH,
RasterOrders.row_col_channel,
RasterOrders.row_col_channel,
subsample,
border_mode,
flip_patches=True).tocsc()
#print IR, IC, KR, KC, CH, patch_extractor.shape, patch_extractor.nnz
patches = sparse.structured_dot(
images.flatten(2),
patch_extractor)
# compute output of linear classifier
patch_stack = patches.reshape((B*OR*OC, KR*KC*CH))
# kern is of shape: nkern x ksize*number_of_input_features
# output is thus of shape: bsize*outshp x nkern
output = tensor.dot(patch_stack, kerns.flatten(2).T).reshape((B, OR, OC, K))
return output, oshp
def max_pool(images, imgshp, maxpoolshp):
"""Implements a max pooling layer
Takes as input a 2D tensor of shape batch_size x img_size and
performs max pooling. Max pooling downsamples by taking the max
value in a given area, here defined by maxpoolshp. Outputs a 2D
tensor of shape batch_size x output_size.
:param images: 2D tensor containing images on which to apply convolution.
Assumed to be of shape batch_size x img_size
:param imgshp: tuple containing image dimensions
:param maxpoolshp: tuple containing shape of area to max pool over
:return: out1, symbolic result (2D tensor)
:return: out2, logical shape of the output
"""
N = numpy
poolsize = N.int64(N.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:
imgshp = (1, ) + imgshp
# construct indices and index pointers for sparse matrix, which,
# when multiplied with input images will generate a stack of image
# patches
indices, indptr, spmat_shape, sptype, outshp = \
convolution_indices.conv_eval(imgshp, maxpoolshp,
maxpoolshp, mode='valid')
# print 'XXXXXXXXXXXXXXXX MAX POOLING LAYER XXXXXXXXXXXXXXXXXXXX'
# print 'imgshp = ', imgshp
# print 'maxpoolshp = ', maxpoolshp
# print 'outshp = ', outshp
# build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(N.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(imgshp[0]),
tensor.as_tensor(poolsize)])
patch_stack = tensor.reshape(patches, pshape, ndim=3)
out1 = tensor.max(patch_stack, axis=2)
pshape = tensor.stack([
images.shape[0],
tensor.as_tensor(N.prod(outshp)),
tensor.as_tensor(imgshp[0])
])
out2 = tensor.reshape(out1, pshape, ndim=3)
out3 = tensor.DimShuffle(out2.broadcastable, (0, 2, 1))(out2)
return tensor.flatten(out3, 2), outshp
def max_pool(images, imgshp, maxpoolshp):
"""Implements a max pooling layer
Takes as input a 2D tensor of shape batch_size x img_size and
performs max pooling. Max pooling downsamples by taking the max
value in a given area, here defined by maxpoolshp. Outputs a 2D
tensor of shape batch_size x output_size.
:param images: 2D tensor containing images on which to apply convolution.
Assumed to be of shape batch_size x img_size
:param imgshp: tuple containing image dimensions
:param maxpoolshp: tuple containing shape of area to max pool over
:return: out1, symbolic result (2D tensor)
:return: out2, logical shape of the output
"""
N = numpy
poolsize = N.int64(N.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:
imgshp = (1,) + imgshp
# construct indices and index pointers for sparse matrix, which,
# when multiplied with input images will generate a stack of image
# patches
indices, indptr, spmat_shape, sptype, outshp = \
convolution_indices.conv_eval(imgshp, maxpoolshp,
maxpoolshp, mode='valid')
# print 'XXXXXXXXXXXXXXXX MAX POOLING LAYER XXXXXXXXXXXXXXXXXXXX'
# print 'imgshp = ', imgshp
# print 'maxpoolshp = ', maxpoolshp
# print 'outshp = ', outshp
# build sparse matrix, then generate stack of image patches
csc = theano.sparse.CSM(sptype)(N.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(imgshp[0]),
tensor.as_tensor(poolsize)])
patch_stack = tensor.reshape(patches, pshape, ndim=3)
out1 = tensor.max(patch_stack, axis=2)
pshape = tensor.stack([images.shape[0],
tensor.as_tensor(N.prod(outshp)),
tensor.as_tensor(imgshp[0])])
out2 = tensor.reshape(out1, pshape, ndim=3)
out3 = tensor.DimShuffle(out2.broadcastable, (0, 2, 1))(out2)
return tensor.flatten(out3, 2), outshp
def test_structured_dot(self):
x = SparseType("csc", dtype=config.floatX)()
y = SparseType("csc", dtype=config.floatX)()
self._compile_and_check(
[x, y],
[structured_dot(x, y)],
[sp.csc_matrix(random_lil((4, 5), config.floatX, 3)), sp.csc_matrix(random_lil((5, 3), config.floatX, 3))],
StructuredDot,
)
def test_infer_shape(self):
a = SparseType('csc', dtype=config.floatX)()
b = SparseType('csc', dtype=config.floatX)()
f = theano.function([a, b], structured_dot(a, b).shape)
topo = f.maker.env.toposort()
assert not any(isinstance(t, self.__class__) for t in topo)
x = sp.csc_matrix((4, 5), dtype=config.floatX)
y = sp.csc_matrix((5, 3), dtype=config.floatX)
assert numpy.all(f(x, y) == numpy.array((4, 3)))
def get_output_for(self, input, **kwargs):
if input.ndim > 2:
# if the input has more than two dimensions, flatten it into a
# batch of feature vectors.
input = input.flatten(2)
activation = sp.structured_dot(input, self.W)
if self.b is not None:
activation = activation + self.b.dimshuffle('x', 0)
return self.nonlinearity(activation)
def get_output_for(self, input, **kwargs):
if input.ndim > 2:
# if the input has more than two dimensions, flatten it into a
# batch of feature vectors.
input = input.flatten(2)
activation = sp.structured_dot(input, self.W)
if self.b is not None:
activation = activation + self.b.dimshuffle('x', 0)
return self.nonlinearity(activation)
def get_output_for(self, input, **kwargs):
activation = T.dot(input, self.W)
#do the convolution
activation = S.structured_dot(self.A, activation)
if self.b is not None:
activation = activation + self.b.dimshuffle('x', 0)
return self.nonlinearity(activation)
def __call__(self, inputs):
self._update_cutoff()
Y = structured_dot(inputs, self.W[:, :self.component_cutoff])
Z = Y - tensor.dot(self.mean, self.W[:, :self.component_cutoff])
if self.whiten:
Z /= tensor.sqrt(self.v[:self.component_cutoff])
return Z
def __call__(self, inputs):
self._update_cutoff()
Y = structured_dot(inputs, self.W[:, :self.component_cutoff])
Z = Y - tensor.dot(self.mean, self.W[:, :self.component_cutoff])
#TODO-- this is inefficient, should work by modifying W not Z
if self.whiten:
Z /= tensor.sqrt(self.v[:self.component_cutoff])
return Z
def __call__(self, inputs):
self._update_cutoff()
Y = structured_dot(inputs, self.W[:, :self.component_cutoff])
Z = Y - tensor.dot(self.mean, self.W[:, :self.component_cutoff])
#TODO-- this is inefficient, should work by modifying W not Z
if self.whiten:
Z /= tensor.sqrt(self.v[:self.component_cutoff])
return Z
def get_output_for(self, input, A=None, target_indices=None, **kwargs):
activation = T.dot(input, self.W)
#do the convolution
if A:
activation = S.structured_dot(A, activation)
if self.b is not None:
activation = activation + self.b.dimshuffle('x', 0)
if self.use_target_indices and target_indices:
activation = activation[target_indices, :]
return self.nonlinearity(activation)
def __init__(self, input, n_in, n_out, inputIsSparse=True):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# start-snippet-1
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(value=np.zeros((n_in, n_out),
dtype=theano.config.floatX),
name='W',
borrow=True)
# initialize the biases b as a vector of n_out 0s
self.b = theano.shared(value=np.zeros((n_out, ),
dtype=theano.config.floatX),
name='b',
borrow=True)
# symbolic expression for computing the matrix of class-membership
# probabilities
# Where:
# W is a matrix where column-k represent the separation hyperplane for
# class-k
# x is a matrix where row-j represents input training sample-j
# b is a vector where element-k represent the free parameter of
# hyperplane-k
if inputIsSparse:
self.p_y_given_x = T.nnet.softmax(
sparse.structured_dot(input, self.W) + self.b)
else:
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
# symbolic description of how to compute prediction as class whose
# probability is maximal
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
# end-snippet-1
# parameters of the model
self.params = [self.W, self.b]
# keep track of model input
self.input = input
def test_infer_shape_csr_csc_grad(self):
for sparsetype in ('csr', 'csc'):
a = SparseType(sparsetype, dtype=config.floatX)()
b = SparseType(sparsetype, dtype=config.floatX)()
grads = tensor.grad(dense_from_sparse(structured_dot(a, b)).sum(),
[a, b])
f = theano.function([a, b], [g.shape for g in grads])
topo = f.maker.env.toposort()
assert not any(isinstance(t, self.__class__) for t in topo)
call = getattr(sp, sparsetype + '_matrix')
x = call(random_lil((500, 300), config.floatX, 10))
y = call(random_lil((300, 400), config.floatX, 5))
out1, out2 = f(x, y)
assert numpy.all(out1 == x.shape)
assert numpy.all(out2 == y.shape)
def test_opt_unpack(self):
#
# Test that a graph involving
# structured_dot(assembled_csc_matrix) is optimized to be just
# a structured_dot_csc Op and no assembly of a csc_matrix.
#
# The optimization from structured_dot -> structured_dot_csc
# is currently disabled, So this test is not expected to pass
return
#
kerns = tensor.Tensor(dtype='int64', broadcastable=[False])('kerns')
spmat = sp.lil_matrix((4, 6), dtype='int64')
for i in range(5):
# set non-zeros in random locations (row x, col y)
x = numpy.floor(numpy.random.rand() * spmat.shape[0])
y = numpy.floor(numpy.random.rand() * spmat.shape[1])
spmat[x, y] = numpy.random.rand() * 10
spmat = sp.csc_matrix(spmat)
images = tensor.Tensor(dtype='float32',
broadcastable=[False, False])(
'images')
cscmat = CSC(kerns, spmat.indices[:spmat.size],
spmat.indptr, spmat.shape)
f = theano.function([kerns, images], structured_dot(cscmat, images.T))
sdcscpresent = False
for node in f.maker.env.toposort():
print node.op
assert not isinstance(node.op, CSM)
assert not isinstance(node.op, CSMProperties)
if isinstance(f.maker.env.toposort()[1].op, StructuredDotCSC):
sdcscpresent = True
assert sdcscpresent
kernvals = numpy.array(spmat.data[:spmat.size])
#print 'kdtype', kernvals.dtype, kernvals.shape,
#print kernvals.ndim, kernvals.dtype.num
#print 'type of kernvals = ', kernvals.dtype
bsize = 3
imvals = 1.0 * numpy.array(numpy.arange(bsize * spmat.shape[1]).\
reshape(bsize, spmat.shape[1]), dtype='float32')
outvals = f(kernvals, imvals)
print outvals
def test_opt_unpack(self):
#
# Test that a graph involving
# structured_dot(assembled_csc_matrix) is optimized to be just
# a structured_dot_csc Op and no assembly of a csc_matrix.
#
# The optimization from structured_dot -> structured_dot_csc
# is currently disabled, So this test is not expected to pass
return
#
kerns = tensor.Tensor(dtype='int64', broadcastable=[False])('kerns')
spmat = sp.lil_matrix((4, 6), dtype='int64')
for i in range(5):
# set non-zeros in random locations (row x, col y)
x = numpy.floor(numpy.random.rand() * spmat.shape[0])
y = numpy.floor(numpy.random.rand() * spmat.shape[1])
spmat[x, y] = numpy.random.rand() * 10
spmat = sp.csc_matrix(spmat)
images = tensor.Tensor(dtype='float32',
broadcastable=[False, False])('images')
cscmat = CSC(kerns, spmat.indices[:spmat.size], spmat.indptr,
spmat.shape)
f = theano.function([kerns, images], structured_dot(cscmat, images.T))
sdcscpresent = False
for node in f.maker.env.toposort():
print node.op
assert not isinstance(node.op, CSM)
assert not isinstance(node.op, CSMProperties)
if isinstance(f.maker.env.toposort()[1].op, StructuredDotCSC):
sdcscpresent = True
assert sdcscpresent
kernvals = numpy.array(spmat.data[:spmat.size])
#print 'kdtype', kernvals.dtype, kernvals.shape,
#print kernvals.ndim, kernvals.dtype.num
#print 'type of kernvals = ', kernvals.dtype
bsize = 3
imvals = 1.0 * numpy.array(numpy.arange(bsize * spmat.shape[1]).\
reshape(bsize, spmat.shape[1]), dtype='float32')
outvals = f(kernvals, imvals)
print outvals
def __init__(self, input, n_in, n_out, inputIsSparse=True):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
self.W = theano.shared(value=np.zeros((n_in, n_out),
dtype=theano.config.floatX),
name='W',
borrow=True)
# initialize the baises b as a vector of n_out 0s
self.b = theano.shared(value=np.zeros((n_out, ),
dtype=theano.config.floatX),
name='b',
borrow=True)
# parameters of the model
self.params = [self.W, self.b]
if inputIsSparse:
self.output = sparse.structured_dot(input, self.W) + self.b
else:
self.output = T.dot(input, self.W) + self.b
self.y_pred = T.argmax(self.output, axis=1)
def getUpdateParams(self): update = [] aux = [] # Update state update.append( (self.params[0], input_layer.output) ) # Update output print 'Length: ' + str(len(self.connections)) for i, c in enumerate(self.connections): aux.append(sparse.structured_dot( sparse.transpose(c.input), self.params[2][i] * c.inhibition )) aux2 = aux.pop() for a in range(len(aux)): aux2 = sparse.add(aux2,aux.pop()) print aux2 from theano import pp print 'out: ' print pp(aux2) update.append((self.params[1],sparse.transpose(sparse.structured_sigmoid(aux2)))) # Hardcoded!! '''update.append((self.params[1], sparse.transpose( sparse.structured_sigmoid(sparse.structured_dot( sparse.transpose(self.connections[0].input), self.params[2][0]))))) ''' ''' update.append((self.params[1], sparse.transpose( sparse.structured_sigmoid( sparse.structured_dot( sparse.transpose(self.connections[0].input), # Input self.params[2][0]))))) # Weights ''' # Update weights ''' #Old ones (OJA) for i, w in enumerate(self.params[2]): update.append( (w, #layer.params[0])) sparse.add( w, self.LR[i]*sparse.transpose( sparse.structured_dot(self.params[1], self.x_yw[i]) ) ) )) ''' for i, w in enumerate(self.params[2]): update.append( (w, #w)) #layer.params[0])) sparse.structured_maximum( sparse.add( w, sparse.add(self.xy[i], self.AWW[i])), 0) ) ) return update
def addConnections(self, connections): global delta, Wmin, Wmax, awe self.connections = self.connections + connections i=0 for i, c in enumerate(connections): j = self.i + i # Weights self.weights.append( theano.shared( sp.csc_matrix( np.asarray( c.generateConnectionMatrix(self.o_shape, generate), dtype=self.input.dtype) ), name ='Wi_' + str(j))) self.Wmax.append( theano.shared( sp.csc_matrix( np.asarray( np.ones((sizeFromShape(c.i_shape),sizeFromShape(self.o_shape)))*Wmax, dtype=self.input.dtype) ), name ='WM_' + str(i))) self.Wmin.append( theano.shared( sp.csc_matrix( np.asarray( np.ones((sizeFromShape(c.i_shape),sizeFromShape(self.o_shape)))*Wmin, dtype=self.input.dtype) ), name ='WM_' + str(i))) # yw # out: nx1 # Wi: mxn # outT x WiT : 1xm self.yw.append( sparse.structured_dot( sparse.transpose(self.output), sparse.transpose(self.weights[j]))) # x_yw # in: nx1 self.x_yw.append( sparse.sub( sparse.transpose(c.input), self.yw[j])) print len(self.weights) print self.weights[i].type print self.weights[i].type.ndim print if self.weights: auxX=sparse.sub(self.Wmax[j], self.weights[i]) auxY=sparse.sub(self.weights[i], self.Wmin[j]) self.LR.append(delta*( sparse.sub( sparse.structured_pow( sparse.sub(self.Wmax[j], self.weights[i]), 1), sparse.structured_pow( sparse.sub(self.Wmin[j], self.weights[i]), 1)))) self.xy.append( self.LR[i]*sparse.structured_dot( c.input, sparse.transpose(self.output))) self.AWW.append( awe*delta*sparse.structured_pow( sparse.sub(self.Wmax[j], self.weights[i]), 1)*self.weights[i]) self.i +=i self.params[2] = self.weights
def run2(i, xt, L, W, labels, tsh):
Wtemp = sparse.structured_dot(L[i], W)
ft = T.log1p(T.exp(-labels[i] * T.dot(Wtemp, xt))).sum()
gt = T.grad(ft, W)
#return ft, {tgWh:tgWh+sparse.true_dot(L[i].T, gt)}
return ft, {tgWh: tgWh + W}
def _dot(x, y):
if isinstance(x, SS.SparseVariable):
return SS.structured_dot(x, y)
else:
return TT.dot(x, y)
def call(self, x, mask=None):
#output = K.dot(x, self.W)
output = sparse.structured_dot(x, self.W)
if self.bias:
output += self.b
return self.activation(output)
def buildgraph_T(spmat, mat):
return structured_dot(mat.T, spmat.T)
new_W = old_W #hebbianL = theano.function([old_W], new_W) print layer0.input print layer0.output print layer0.Wi print old_W #wi=hebbianL([layer.params[0]]) ''' for layer in layers: Wis.append( (layer.params[0], #layer.params[0])) sparse.add( layer.params[0] , LR*sparse.transpose( sparse.structured_dot(layer.output, layer.x_yw) ) ) )) # (layer.params[0], # sparse.add( # layer.params[0] , # LR*sparse.sub( # sparse.structured_dot(sparse.transpose(layer.output), layer.input) , # sparse.structured_dot( # sparse.structured_dot( # sparse.transpose(layer.output), # layer.output), # layer.params[0])
def __init__(self, input, filter_shape, sigma,i_shape,o_shape, Wi = False, Wr = False):
global generate
# Mean neuron density ~80k/mm^3 in V2 (skoglund 1996)
# Synapse length follow a power law ()
# Synapse length for feedback interareal ~10-40mm, feedforward same, but less connections
# Synapse lengths is found by Sholl analysis.
# Ahould compare RF data with Van den Bergh 2010
# Initialize weights as a shared variable
#n_col=input.shape[1]
try:
if generate:
np.load('asd')
else:
Wi=np.load(i_file)
print '[info] Weights loaded from file!'
print 'Shape = ' + str(Wi.shape)
except IOError:
print "[info] Weights file wasn't found. Generating new connections"
kern1 = gkern2(filter_shape,sigma)
Wi = kernel2connection(kern1, i_shape, o_shape)
#Wi /= np.sum(Wi,1).reshape((Wi.shape[0],1))*15
print 'Shape = ' + str(Wi.shape)
np.save(i_file,Wi)
try:
if generate:
np.load('asd')
else:
Wr=np.load(r_file)
print 'Weights loaded from file!'
except IOError:
print "Weights file wasn't found. Generating new connections"
kern2 = gkern2(filter_shape,sigma)
Wr = kernel2connection(kern2, o_shape,o_shape)
#Wr /= np.sum(Wi,1)
np.save(r_file,Wr)
if np.sum(Wi,1)[0] != 1:
Wi /= np.sum(Wi,1).reshape((Wi.shape[0],1))*5
if np.sum(Wr,1)[0] != 1:
Wr /= np.sum(Wr,1).reshape((Wr.shape[0],1))
print np.sum(Wi,0)
print np.sum(Wi,1)
plt.plot(Wi[1,:])
plt.show()
self.Wi= theano.shared(
sp.csc_matrix(
np.asarray(
Wi,
dtype=input.dtype) ), name ='Wi')
self.Wr = theano.shared(
sp.csc_matrix(
np.asarray(
Wr,
dtype=input.dtype) ), name ='Wr')
# Output of the layer is the sigmoid of the convolved network
self.state = theano.shared(
sp.csc_matrix(
np.asarray(
np.zeros((o_shape[0]*o_shape[1],1)),
dtype=input.dtype) ), name ='St')
self.input = input
# I could do the same with biases if needed
#print self.input.get_value().shape
#print self.Wi.get_value().shape
self.output = theano.shared(
sp.csc_matrix(
np.asarray(
np.zeros((o_shape[0]*o_shape[1],1)),
dtype=input.dtype) ), name ='Out')
#sparse.structured_sigmoid(sparse.structured_dot(self.input, self.Wi)) #T.dot(self.input, self.Wi))
# input = external + recursive (from layer)
# self.input = T.dot(input, self.Wi) #+ T.sum(T.dot(self.state,self.Wr),1)
# out: nx1
# Wi: mxn
# outT x WiT : 1xm
self.yw = sparse.structured_dot(
sparse.transpose(self.output),
sparse.transpose(self.Wi))
# in: nx1
self.x_yw = sparse.sub(
sparse.transpose(self.input),
self.yw)
# optional: self.output = T.nnet.sigmoid(conv_out+self.output)
self.params = [self.Wi, self.Wr, self.state, self.output]
def model(radial_index, white, affine=None, surf_mask=None, min_dist=-2., smooth_weight=0.7,
watson=False, idx_vertex=None, dist=None):
"""
Creates a probabilistic model of the transition across the white/gray matter bounary
:param radial_index: (Nx, Ny, Nz) array with the radial index
:param white: white/gray matter boundary
:param affine: (4, 4) array with the transformation from voxel to mm space
:param surf_mask: (Nvertex, ) boolean array, which is True on vertices to be included in the fit
:param min_dist: only include voxels within this distance from WM/GM boundary
:param smooth_weight: weighting for the smoothing parameter
:param watson: assume a Watson distribution for the radial index rather than a normal distribution
:param idx_vertex: (Nx, Ny, Nz) array with index of closest vertex
:param dist: distance from the WM/GM boundary
:return: pymc3 model which fits the sigmoidal transition across the surface
"""
if affine is None:
affine = np.eye(4)
if surf_mask is None:
surf_mask = np.ones(white.nvertex, dtype='bool')
vol_mask = np.isfinite(radial_index) & (radial_index != 0.)
if dist is None:
dist = grid.signed_distance(white, radial_index.shape, affine)
if idx_vertex is None:
idx_vertex_raw = grid.closest_surface(white, vol_mask, affine)
else:
idx_vertex_raw = idx_vertex
nvertex = surf_mask.sum()
idx_real_vertex = -np.ones(surf_mask.size, dtype='i4')
idx_real_vertex[surf_mask] = np.arange(nvertex)
voxel_vertex = idx_real_vertex[idx_vertex_raw]
voxel_vertex[idx_vertex_raw == -1] = -1
voxel_use = (voxel_vertex != -1) & (dist > min_dist) & vol_mask & (dist != 0)
gpp = white.graph_point_point()[surf_mask, :][:, surf_mask]
smooth = sp_sparse.diags(np.array(1 / gpp.sum(-1)).ravel()).dot(gpp)
assert abs(smooth.dot(np.ones(smooth.shape[0])) - 1).max() < 1e-13
# radial index model
with pymc3.Model() as model:
d0 = pymc3.Flat('d0', testval=0.5, shape=nvertex)
log_sigma = pymc3.Flat('log_sigma', testval=-0.5, shape=nvertex)
sigma = tensor.exp(log_sigma)
# radial index is zero in WM, 1 in GM
model_ri = 1 / (1 + tensor.exp(-(dist[voxel_use] - d0[voxel_vertex[voxel_use]]) / sigma[voxel_vertex[voxel_use]]))
if watson:
pymc3.Potential('alignment', model_ri * abs(radial_index[voxel_use]) +
tensor.sqrt(1 - model_ri ** 2) * np.sqrt(1 - radial_index[voxel_use] ** 2))
else:
pymc3.Potential('alignment', -(model_ri - abs(radial_index[voxel_use])) ** 2)
d_neigh = sparse.structured_dot(smooth, tensor.stack([d0], -1))[:, 0]
pymc3.Potential('d0_smooth', -smooth_weight * (d_neigh - d0) ** 2)
ls_neigh = sparse.structured_dot(smooth, tensor.stack([log_sigma], -1))[:, 0]
pymc3.Potential('ls_smooth', -smooth_weight * (ls_neigh - log_sigma) ** 2)
# additional output to check
pymc3.Deterministic('model_ri_1d', model_ri)
for name, arr_1d in [('model_ri', model_ri), ('observed_ri', abs(radial_index[voxel_use])),
('dist', dist[voxel_use]), ('d0', d0[voxel_vertex[voxel_use]]),
('sigma', sigma[voxel_vertex[voxel_use]])]:
vol_nan = tensor.fill(radial_index, np.nan)
vol_filled = tensor.set_subtensor(vol_nan[voxel_use.nonzero()], arr_1d)
pymc3.Deterministic('%s_3d' % name, vol_filled)
return model
def convolve(kerns, kshp, nkern, images, imgshp, step=(1,1), bias=None,\
mode='valid', flatten=True):
"""Convolution implementation by sparse matrix multiplication.
@note: For best speed, put the matrix which you expect to be smaller as the 'kernel'
argument
=== Input / Output conventions===
"images" is assumed to be a matrix of shape batch_size x img_size, where the second
dimension represents each image in raster order
If flatten is "False", the output feature map will have shape:
batch_size x number of kernels x output_size
If flatten is "True", the output feature map will have shape:
batch_size x number of kernels * output_size
IMPORTANT: note that this means that each feature map (image generate by each
kernel) is contiguous in memory. The memory layout will therefore be:
[ <feature_map_0> <feature_map_1> ... <feature_map_n>],
where <feature_map> represents a "feature map" in raster order
kerns is a 2D tensor of shape nkern x N.prod(kshp)
@param kerns: 2D tensor containing kernels which are applied at every pixel
@param kshp: tuple containing actual dimensions of kernel (not symbolic)
@param nkern: number of kernels/filters to apply.
nkern=1 will apply one common filter to all input pixels
@param images: tensor containing images on which to apply convolution
@param imgshp: tuple containing image dimensions
@param step: determines number of pixels between adjacent receptive fields
(tuple containing dx,dy values)
@param mode: 'full', 'valid' see CSM.evaluate function for details
@param sumdims: dimensions over which to sum for the tensordot operation. By default
((2,),(1,)) assumes kerns is a nkern x kernsize matrix and images is a batchsize x
imgsize matrix containing flattened images in raster order
@param flatten: flatten the last 2 dimensions of the output. By default, instead of
generating a batchsize x outsize x nkern tensor, will flatten to
batchsize x outsize*nkern
@output out1: symbolic result
@output out2: logical shape of the output img (nkern,heigt,width)
@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))
# 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:
imgshp = (1,)+imgshp
# construct indices and index pointers for sparse matrix, which, when multiplied
# with input images will generate a stack of image patches
indices, indptr, spmat_shape, sptype, outshp = \
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, 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)),\
tensor.as_tensor(imgshp[0]*kern_size))
patch_stack = tensor.reshape(patches, pshape, ndim=2);
# kern is of shape: nkern x ksize*number_of_input_features
# output is thus of shape: bsize*outshp x nkern
output = tensor.dot(patch_stack,kerns.T)
# add bias across each feature map (more efficient to do it now)
if bias is not None:
output += bias
# 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(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))
def applySparseFilter(kerns, kshp, nkern, images, imgshp, step=(1,1), bias=None, mode='valid'):
"""
WRITEME
Output feature map will have shape
`batch_size x number of kernels * output_size`.
Each filter is applied seperately to consecutive output pixels.
Parameters
----------
kerns : 1D tensor_like
`nkern * outsize * ksize` vector containing kernels
kshp : tuple
Tuple containing actual dimensions of kernel (not symbolic)
nkern : int
Number of kernels to apply at each pixel in the input image. \
`nkern = 1` will apply a single unique filter for each input pixel.
images : WRITEME
`bsize x imgsize` matrix containing images on which to apply filters. \
Second dimension represents each image in raster order.
imgshp : tuple
Tuple containing actual image dimensions (not symbolic)
step : WRITEME
Determines number of pixels between adjacent receptive fields \
(tuple containing dx,dy values)
mode : str
'full', 'valid' see `CSM.evaluate` function for details
Returns
-------
out1 : WRITEME
Symbolic result
out2 : WRITEME
Logical shape of the output img (nkern,height,width) \
(after dot product, not of the sparse matrix!)
Notes
-----
Note that this means that each feature map is contiguous in memory.
The memory layout will therefore be
`[ <feature_map_0> <feature_map_1> ... <feature_map_n>]`,
where `<feature_map>` represents a "feature map" in raster order.
Also, the concept of feature map doesn't really apply to sparse filters
without weight sharing. Basically, nkern=1 will generate one output
img/feature map, nkern=2 a second feature map, etc.
"""
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if numpy.size(imgshp)==2:
imgshp = (1,)+imgshp
# construct indices and index pointers for sparse matrix
indices, indptr, spmat_shape, sptype, outshp, kmap = \
convolution_indices.sparse_eval(imgshp, kshp, nkern, step, mode)
# build a sparse weight matrix
sparsew = theano.sparse.CSM(sptype, kmap)(kerns, indices, indptr, spmat_shape)
output = sparse.structured_dot(sparsew, images.T).T
if bias is not None:
output += bias
return output, numpy.hstack((nkern,outshp))
def _dot(x, y):
if isinstance(x, SS.SparseVariable):
return SS.structured_dot(x, y)
else:
return TT.dot(x, y)
new_W = old_W #hebbianL = theano.function([old_W], new_W) print layer0.input print layer0.output print layer0.Wi print old_W #wi=hebbianL([layer.params[0]]) ''' for layer in layers: Wis.append( (layer.params[0], #layer.params[0])) sparse.add( layer.params[0] , LR*sparse.transpose( sparse.structured_dot(layer.output, layer.x_yw) ) ) )) # (layer.params[0], # sparse.add( # layer.params[0] , # LR*sparse.sub( # sparse.structured_dot(sparse.transpose(layer.output), layer.input) , # sparse.structured_dot( # sparse.structured_dot( # sparse.transpose(layer.output), # layer.output), # layer.params[0])
targety = TT.lvector()
#print x, targ
#random_weights
w1 = TT.dmatrix()
b1 = TT.dvector()
if HLAYERS == 2:
wh = TT.dmatrix()
bh = TT.dvector()
w2 = TT.dmatrix()
b2 = TT.dvector()
from theano.tensor.nnet import crossentropy_softmax_argmax_1hot_with_bias
from theano.compile.function_module import function
xw1 = TS.structured_dot(w1.T, x.T).T
h = ACTIVATION_FUNCTION(xw1 + b1)
if HLAYERS == 2:
xwh = theano.dot(wh.T, h.T).T
h = ACTIVATION_FUNCTION(xwh + bh)
#zero = tensor.zeros_like(x[0,:])
(kl, softmax,
argmax) = crossentropy_softmax_argmax_1hot_with_bias(theano.dot(h, w2), b2,
targety)
if HLAYERS == 2:
validatefn = function([x, targety, w1, b1, wh, bh, w2, b2],
[kl, softmax, argmax, xw1, xwh],
mode=COMPILE_MODE)
targety = TT.lvector()
#print x, targ
#random_weights
w1 = TT.dmatrix()
b1 = TT.dvector()
if HLAYERS == 2:
wh = TT.dmatrix()
bh = TT.dvector()
w2 = TT.dmatrix()
b2 = TT.dvector()
from theano.tensor.nnet import crossentropy_softmax_argmax_1hot_with_bias
from theano.compile.function_module import function
xw1 = TS.structured_dot(w1.T, x.T).T
h = ACTIVATION_FUNCTION(xw1 + b1)
if HLAYERS == 2:
xwh = theano.dot(wh.T, h.T).T
h = ACTIVATION_FUNCTION(xwh + bh)
#zero = tensor.zeros_like(x[0,:])
(kl, softmax, argmax) = crossentropy_softmax_argmax_1hot_with_bias(theano.dot(h, w2), b2, targety)
if HLAYERS == 2:
validatefn = function([x, targety, w1, b1, wh, bh, w2, b2], [kl, softmax, argmax, xw1, xwh], mode=COMPILE_MODE)
(gw1, gb1, gwh, gbh, gw2, gb2) = TT.grad(kl, [w1, b1, wh, bh, w2, b2])
trainfn = function([x, targety, w1, b1, wh, bh, w2, b2], [kl, softmax, argmax, xw1, xwh, theano.compile.io.Out(gw1, borrow = True), gb1, gwh, gbh, gw2, gb2], mode=COMPILE_MODE)
else:
validatefn = function([x, targety, w1, b1, w2, b2], [kl, softmax, argmax, xw1], mode=COMPILE_MODE)
def run2(i,xt, L, W, labels, tsh):
Wtemp = sparse.structured_dot(L[i], W)
ft = T.log1p(T.exp(-labels[i] * T.dot(Wtemp, xt))).sum()
gt = T.grad(ft, W)
#return ft, {tgWh:tgWh+sparse.true_dot(L[i].T, gt)}
return ft, {tgWh:tgWh+W}
w2 = random_weights(HID, 1)
print "w2", w2, w2.shape, w2.dtype
b2 = N.zeros(1)
print "b2", b2, b2.shape, b2.dtype
#random_weights
w1R = TT.dmatrix('w1')
b1R = TT.dvector('b1')
w2R = TT.dmatrix('w2')
b2R = TT.dvector('b2')
import pylearn.algorithms.cost as cost
from theano.compile.function_module import function
#xw1R = theano.dot(w1R.T, xR.T).T
xw1R = TS.structured_dot(w1R.T, xR.T).T
#print w1R.type
#print xR.type
hR = ACTIVATION_FUNCTION(xw1R + b1R)
yR = nnet.sigmoid(theano.dot(hR, w2R).T + b2R)
loss = cost.KL_divergence(targR, yR)
fn = function([xR, targR, w1R, b1R, w2R, b2R], [yR, loss], mode=COMPILE_MODE)
(gw1, gb1, gw2, gb2) = TT.grad(loss, [w1R, b1R, w2R, b2R])
trainfn = function([xR, targR, w1R, b1R, w2R, b2R], [yR, loss, theano.compile.io.Out(gw1, borrow = True), gb1, gw2, gb2, hR], mode=COMPILE_MODE)
#print type(hR), type(yR)
print "TRAINING"
nex = xinstances.shape[0]
for epoch in range(EPOCHS):
print "Epoch #", epoch
def __init__(self,
rng,
input,
n_in,
n_out,
W=None,
b=None,
activation=T.tanh,
inputIsSparse=True):
"""
Typical hidden layer of a MLP: units are fully-connected and have
sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
and the bias vector b is of shape (n_out,).
NOTE : The nonlinearity used here is tanh
Hidden unit activation is given by: tanh(dot(input,W) + b)
:type rng: np.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.dmatrix
:param input: a symbolic tensor of shape (n_examples, n_in)
:type n_in: int
:param n_in: dimensionality of input
:type n_out: int
:param n_out: number of hidden units
:type activation: theano.Op or function
:param activation: Non linearity to be applied in the hidden
layer
"""
self.input = input
# if inputIsSparse:
# activation = sparse.tanh
# else:
# activation = T.tanh
# `W` is initialized with `W_values` which is uniformely sampled
# from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))
# for tanh activation function
# the output of uniform if converted using asarray to dtype
# theano.config.floatX so that the code is runable on GPU
# Note : optimal initialization of weights is dependent on the
# activation function used (among other things).
# For example, results presented in [Xavier10] suggest that you
# should use 4 times larger initial weights for sigmoid
# compared to tanh
# We have no info for other function, so we use the same as
# tanh.
if W is None:
W_values = np.asarray(rng.uniform(
low=-np.sqrt(6. / (n_in + n_out)),
high=np.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)),
dtype=theano.config.floatX)
if activation == theano.tensor.nnet.sigmoid:
W_values *= 4
W = theano.shared(value=W_values, name='W', borrow=True)
if b is None:
b_values = np.zeros((n_out, ), dtype=theano.config.floatX)
b = theano.shared(value=b_values, name='b', borrow=True)
self.W = W
self.b = b
if inputIsSparse:
lin_output = sparse.structured_dot(input, self.W) + self.b
else:
lin_output = T.dot(input, self.W) + self.b
# parameters of the model
self.output = (lin_output
if activation is None else activation(lin_output))
self.params = [self.W, self.b]
def __init__(self, d, V, r, #nf,
embeddings=None,
nc=2,
nf=0,
pairwise_constraint=False,
lambda_w=0.01,
lambda_e=0.01,
lambda_f=0.01,
learning_rate='optimal',
rnn=True,
l1_ratio=0.15,
beta=None,
fixed_beta=True):
assert(0 <= l1_ratio <= 1)
if not rnn:
print('skipping rnn...')
#d = dimensionality of embeddings
#V = size of vocabulary
#r = number of dependency relations
#nc = number of classes for classification
self.learning_rate = learning_rate
#|V| x d embedding matrix
if embeddings is None:
self.We = theano.shared(name='embeddings',
value=0.2 * np.random.uniform(-1.0, 1.0, (V, d))
).astype(theano.config.floatX)
else:
self.We = theano.shared(name='embeddings',
value=embeddings,
borrow=True
).astype(theano.config.floatX)
#r x d x d tensor (matrix for each dependency relation)
self.Wr = theano.shared(name='dependencies',
value=0.2 * np.random.uniform(-1.0, 1.0, (r, d, d))
).astype(theano.config.floatX)
#d x d map from embedding to hidden vector
self.Wv = theano.shared(name='Wv',
value=0.2 * np.random.uniform(-1.0, 1.0, (d, d))
).astype(theano.config.floatX)
#d long bias vector
self.b = theano.shared(name='b',
value=np.zeros(d, dtype=theano.config.floatX))
if nc > 2:
self.gamma = theano.shared(name='gamma',
value=0.2 * np.random.uniform(-1.0, 1.0, (d, nc))
).astype(theano.config.floatX)
if nf > 0:
#weights for fine grained features plus bias
self.beta = theano.shared(name='beta',
value=0.2 * np.random.uniform(-1.0, 1.0, (nf, nc))
).astype(theano.config.floatX)
else:
self.gamma = theano.shared(name='gamma',
value=0.2 * np.random.uniform(-1.0, 1.0, (d))
).astype(theano.config.floatX)
if nf > 0:
#weights for fine grained features plus bias
self.beta = theano.shared(name='beta',
value=0.2 * np.random.uniform(-1.0, 1.0, (nf))
).astype(theano.config.floatX)
if nf > 0 and beta is not None:
self.beta = theano.shared(name='beta',
value=beta
).astype(theano.config.floatX)
self.params = []
if rnn:
self.params += [self.We, self.Wr, self.Wv, self.b, self.gamma]
if nf > 0 and (beta is None or not fixed_beta):
self.params += [self.beta]
if learning_rate == 'adagrad':
self.descender = Adagrad(self.params)
#self.f = T.tanh
self.f = normalized_tanh
def recurrence(k, hidden_states, hidden_sums, x, r, p, mask):
#at each node n in the tree, calculate Wr(p,n) \dot f(W_v \dot We_word(n) + b + sum_n) and add to sum_p
h_k = self.f((T.dot(self.Wv, x[k].T) + hidden_sums[k].T).T + self.b).T*mask[k] #D x N
sum_k = T.batched_dot(r[k], h_k.T) #N x D
return T.set_subtensor(hidden_states[k],
h_k.T), T.inc_subtensor(hidden_sums[p[k],
T.arange(sum_k.shape[0])],
sum_k)
y = T.ivector('y')
#all N x K matrices, where N is batch size and K is max sentence length (padded)
x_idxs = T.imatrix('x')
x_parents = T.imatrix('x_parents')
x_rel_idxs = T.imatrix('x_rel')
x_mask = T.imatrix('x_mask')
#now these are K x N x D tensors
X = self.We[x_idxs.T]
#these are K x N x D x D tensors
X_rel = self.Wr[x_rel_idxs.T]
X_hidden_states = T.zeros((x_idxs.shape[1],
x_idxs.shape[0],
d),
dtype=theano.config.floatX)
X_hidden_sums = T.zeros((x_idxs.shape[1]+1,
x_idxs.shape[0],
d),
dtype=theano.config.floatX)
#these are K(+1) x K x N x D
[X_h, X_s], updates = theano.scan(fn=recurrence,
sequences=T.arange(x_idxs.shape[1]),
outputs_info=[X_hidden_states, X_hidden_sums],
non_sequences=[X, X_rel, x_parents.T, x_mask.T])
phi = sp.csc_fmatrix('phi')
#X_h[-1, -1] is N x D
base = 0
if rnn:
base = base + T.dot(X_h[-1, -1], self.gamma)
if nf > 0:
if nc > 2:
base = base + sp.structured_dot(phi, self.beta)
else:
base = base + sp.structured_dot(phi, self.beta.dimshuffle(0, 'x')).flatten()
if nc > 2:
p_y_given_x = T.nnet.softmax(base)
y_pred = T.argmax(p_y_given_x, axis=1)
costs = -T.log(p_y_given_x)[T.arange(y.shape[0]), y]
else:
p_y_given_x = T.nnet.sigmoid(base)
y_pred = p_y_given_x > 0.5
costs = -y * T.log(p_y_given_x) - (1-y) * T.log(1-p_y_given_x)
cost = costs.mean()
if rnn:
cost = cost + lambda_w * (self.We ** 2).sum() + lambda_w * (self.Wr ** 2).sum() + lambda_w * (self.Wv ** 2).sum() + lambda_w * (self.b ** 2).sum() + lambda_w * (self.gamma ** 2).sum()
if pairwise_constraint:
cost = cost - lambda_e * T.batched_dot(X_h[-1, -1][::2], X_h[-1, -1][1::2]).mean()
if nf > 0 and (beta is None or not fixed_beta):
cost = cost + lambda_f*(l1_ratio*T.abs_(self.beta).sum() + (1-l1_ratio) * (self.beta ** 2).sum())
grad = T.grad(cost, self.params)
if learning_rate == 'optimal':
def dloss(p, y):
z = p * y
if z > 18:
return np.exp(-z) * -y
if z < -18:
return -y
return -y / (np.exp(z) + 1)
typw = np.sqrt(1.0 / np.sqrt(lambda_w))
initial_eta0 = typw / max(1.0, dloss(-typw, 1.0))
optimal_init = 1.0 / (initial_eta0 * lambda_w)
print(typw, initial_eta0, optimal_init)
self.t = theano.shared(name='t',
value=0.).astype(theano.config.floatX)
eta = 1.0 / (lambda_w * (optimal_init + self.t))
updates = [(p, p - eta*g) for p,g in zip(self.params, grad)]
else:
updates = []
inputs = []
if rnn:
inputs += [x_idxs, x_parents, x_rel_idxs, x_mask]
if nf > 0:
inputs += [phi]
inputs += [y]
self.cost_and_grad = theano.function(inputs=inputs,
outputs=[cost] + grad,
updates=updates,
allow_input_downcast=True)
self.sums = theano.function(inputs=[x_idxs, x_parents, x_rel_idxs, x_mask],
outputs=X_s,
allow_input_downcast=True)
self.states = theano.function(inputs=[x_idxs, x_parents, x_rel_idxs, x_mask],
outputs=X_h,
allow_input_downcast=True)
self.classify = theano.function(inputs=inputs[:-1],
outputs=y_pred,
allow_input_downcast=True)
def convolve(kerns,
kshp,
nkern,
images,
imgshp,
step=(1, 1),
bias=None,
mode='valid',
flatten=True):
"""Convolution implementation by sparse matrix multiplication.
:note: For best speed, put the matrix which you expect to be
smaller as the 'kernel' argument
"images" is assumed to be a matrix of shape batch_size x img_size,
where the second dimension represents each image in raster order
If flatten is "False", the output feature map will have shape:
.. code-block:: python
batch_size x number of kernels x output_size
If flatten is "True", the output feature map will have shape:
.. code-block:: python
batch_size x number of kernels * output_size
.. note::
IMPORTANT: note that this means that each feature map (image
generate by each kernel) is contiguous in memory. The memory
layout will therefore be: [ <feature_map_0> <feature_map_1>
... <feature_map_n>], where <feature_map> represents a
"feature map" in raster order
kerns is a 2D tensor of shape nkern x N.prod(kshp)
:param kerns: 2D tensor containing kernels which are applied at every pixel
:param kshp: tuple containing actual dimensions of kernel (not symbolic)
:param nkern: number of kernels/filters to apply.
nkern=1 will apply one common filter to all input pixels
:param images: tensor containing images on which to apply convolution
:param imgshp: tuple containing image dimensions
:param step: determines number of pixels between adjacent receptive fields
(tuple containing dx,dy values)
:param mode: 'full', 'valid' see CSM.evaluate function for details
:param sumdims: dimensions over which to sum for the tensordot operation.
By default ((2,),(1,)) assumes kerns is a nkern x kernsize
matrix and images is a batchsize x imgsize matrix
containing flattened images in raster order
:param flatten: flatten the last 2 dimensions of the output. By default,
instead of generating a batchsize x outsize x nkern tensor,
will flatten to batchsize x outsize*nkern
:return: out1, symbolic result
:return: out2, logical shape of the output img (nkern,heigt,width)
: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))
# 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:
imgshp = (1, ) + imgshp
# construct indices and index pointers for sparse matrix, which,
# when multiplied with input images will generate a stack of image
# patches
indices, indptr, spmat_shape, sptype, outshp = \
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, 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)),\
tensor.as_tensor(imgshp[0] * kern_size))
patch_stack = tensor.reshape(patches, pshape, ndim=2)
# kern is of shape: nkern x ksize*number_of_input_features
# output is thus of shape: bsize*outshp x nkern
output = tensor.dot(patch_stack, kerns.T)
# add bias across each feature map (more efficient to do it now)
if bias is not None:
output += bias
# 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(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))
def applySparseFilter(kerns, kshp, nkern, images, imgshp, step=(1,1), bias=None, mode='valid'):
"""
WRITEME
Output feature map will have shape
`batch_size x number of kernels * output_size`.
Each filter is applied seperately to consecutive output pixels.
Parameters
----------
kerns : 1D tensor_like
`nkern * outsize * ksize` vector containing kernels
kshp : tuple
Tuple containing actual dimensions of kernel (not symbolic)
nkern : int
Number of kernels to apply at each pixel in the input image. \
`nkern = 1` will apply a single unique filter for each input pixel.
images : WRITEME
`bsize x imgsize` matrix containing images on which to apply filters. \
Second dimension represents each image in raster order.
imgshp : tuple
Tuple containing actual image dimensions (not symbolic)
step : WRITEME
Determines number of pixels between adjacent receptive fields \
(tuple containing dx,dy values)
mode : str
'full', 'valid' see `CSM.evaluate` function for details
Returns
-------
out1 : WRITEME
Symbolic result
out2 : WRITEME
Logical shape of the output img (nkern,height,width) \
(after dot product, not of the sparse matrix!)
Notes
-----
Note that this means that each feature map is contiguous in memory.
The memory layout will therefore be
`[ <feature_map_0> <feature_map_1> ... <feature_map_n>]`,
where `<feature_map>` represents a "feature map" in raster order.
Also, the concept of feature map doesn't really apply to sparse filters
without weight sharing. Basically, nkern=1 will generate one output
img/feature map, nkern=2 a second feature map, etc.
"""
# inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
# in the first case, default nfeatures to 1
if numpy.size(imgshp)==2:
imgshp = (1,)+imgshp
# construct indices and index pointers for sparse matrix
indices, indptr, spmat_shape, sptype, outshp, kmap = \
convolution_indices.sparse_eval(imgshp, kshp, nkern, step, mode)
# build a sparse weight matrix
sparsew = theano.sparse.CSM(sptype, kmap)(kerns, indices, indptr, spmat_shape)
output = sparse.structured_dot(sparsew, images.T).T
if bias is not None:
output += bias
return output, numpy.hstack((nkern,outshp))
def v_given_h(self, h):
vs_stat = []
for i in xrange(len(self.v_layers)):
vs_stat.append(self.v_layers[i].v_given_h(h))
return sparse.structured_dot(T.concatenate(vs_stat, axis=1),
self.big_mask)