def test_elemwise2():
""" Several kinds of elemwise expressions with dimension permutations """
rng = numpy.random.RandomState(int(time.time()))
print 'random?', rng.rand(3)
shape = (3,5)
for pattern in [(0,1), (1,0)]:
a = tcn.shared_constructor(theano._asarray(rng.rand(*shape),dtype='float32'), name=None)
b = tensor.Tensor(dtype='float32', broadcastable=[0]*len(shape))()
f = pfunc([b], [], updates=[(a, (a+b).dimshuffle(pattern))], mode=mode_with_gpu)
has_elemwise = False
for i, node in enumerate(f.maker.env.toposort()):
print >> sys.stdout, i, node
has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
assert not has_elemwise
#let debugmode catch errors
print >> sys.stdout, 'pattern', pattern
f(theano._asarray(rng.rand(*shape),dtype='float32')*.3)
shape = (3,4,5,6)
a = tcn.shared_constructor(theano._asarray(rng.rand(*shape),dtype='float32'), 'a')
b = tensor.Tensor(dtype='float32', broadcastable=[0]*len(shape))()
f = pfunc([b], [], updates=[(a, (a+b).dimshuffle([2,0,3,1]) *
tensor.exp(b**a).dimshuffle([2,0,3,1]))], mode=mode_with_gpu)
has_elemwise = False
for i, node in enumerate(f.maker.env.toposort()):
print i, node
has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
assert not has_elemwise
#let debugmode catch errors
f(theano._asarray(rng.rand(*shape),dtype='float32'))
def make_node(self, y, y_starts, y_lengths, g_costs, **kwargs):
y = T.as_tensor_variable(y)
y_lengths = T.as_tensor_variable(y_lengths)
y_starts = T.as_tensor_variable(y_starts)
g_costs = T.as_tensor_variable(g_costs)
if (y.type.ndim != 3 or y.type.dtype not in T.float_dtypes):
raise ValueError('y must be 3-d tensor of floats', y.type)
if (y_lengths.type.ndim != 1
or y_lengths.type.dtype not in T.discrete_dtypes):
raise ValueError('y_lengths must be 1-d tensor of integers',
y_lengths.type)
if (y_starts.type.ndim != 1
or y_starts.type.dtype not in T.discrete_dtypes):
raise ValueError('y_starts must be 1-d tensor of integers',
y_starts.type)
if (g_costs.type.ndim != 1
or g_costs.type.dtype not in T.float_dtypes):
raise ValueError('g_costs must be 1-d tensor of floats',
g_costs.type)
return Apply(
self, [y, y_starts, y_lengths, g_costs],
[T.Tensor(dtype=y.dtype, broadcastable=y.type.broadcastable)()])
def make_node(self, softmaxes, y_idxes, y_lengths, y_startidxes, **kwargs):
softmaxes = T.as_tensor_variable(softmaxes)
y_idxes = T.as_tensor_variable(y_idxes)
y_lengths = T.as_tensor_variable(y_lengths)
y_startidxes = T.as_tensor_variable(y_startidxes)
if (softmaxes.type.ndim != 3
or softmaxes.type.dtype not in T.float_dtypes):
raise ValueError('dy must be 3-d tensor of floats', softmaxes.type)
if (y_idxes.type.ndim != 2
or y_idxes.type.dtype not in T.discrete_dtypes):
raise ValueError('y_idxes must be 2-d tensor of integers',
y_idxes.type)
if (y_lengths.type.ndim != 1
or y_lengths.type.dtype not in T.discrete_dtypes):
raise ValueError('y_lengths must be 1-d tensor of integers',
y_lengths.type)
if (y_startidxes.type.ndim != 1
or y_startidxes.type.dtype not in T.discrete_dtypes):
raise ValueError('y_startidxes must be 1-d tensor of integers',
y_startidxes.type)
return Apply(
self, [softmaxes, y_idxes, y_lengths, y_startidxes],
[T.Tensor(dtype=softmaxes.dtype, broadcastable=[False])()])
def run_conv_nnet1(use_gpu):
if use_gpu:
shared_fn = tcn.shared_constructor
else:
shared_fn = shared
n_batch = 16
n_kern = 20
shape_img = (n_batch, 1, 32, 32)
shape_kern = (n_kern, 1, 5, 5)
n_train = 10
if config.mode == 'DEBUG_MODE':
n_train = 1
logical_hid_shape = tcn.blas.GpuConv.logical_output_shape_2d(
shape_img[2:], shape_kern[2:], 'valid')
n_hid = n_kern * logical_hid_shape[0] * logical_hid_shape[1]
n_out = 10
w = shared_fn(0.01 * (my_rand(*shape_kern) - 0.5), 'w')
b = shared_fn(my_zeros((n_kern,)), 'b')
v = shared_fn(my_zeros((n_hid, n_out)), 'c')
c = shared_fn(my_zeros(n_out), 'c')
x = tensor.Tensor(dtype='float32', broadcastable=(0, 1, 0, 0))('x')
y = tensor.fmatrix('y')
lr = tensor.fscalar('lr')
conv_op = conv.ConvOp(shape_img[2:], shape_kern[2:], n_kern, n_batch, 1, 1)
hid = tensor.tanh(conv_op(x, w) + b.dimshuffle((0, 'x', 'x')))
hid_flat = hid.reshape((n_batch, n_hid))
out = tensor.tanh(tensor.dot(hid_flat, v) + c)
loss = tensor.sum(0.5 * (out - y) ** 2 * lr)
# print 'loss type', loss.type
params = [w, b, v, c]
gparams = tensor.grad(loss, params)
mode = get_mode(use_gpu)
# print 'building pfunc ...'
train = pfunc(
[x, y, lr],
[loss],
mode=mode,
updates=[(p, p - g) for p, g in zip(params, gparams)])
# for i, n in enumerate(train.maker.fgraph.toposort()):
# print i, n
xval = my_rand(*shape_img)
yval = my_rand(n_batch, n_out)
lr = theano._asarray(0.01, dtype='float32')
for i in xrange(n_train):
rval = train(xval, yval, lr)
# print 'training done'
print_mode(mode)
return rval
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 cmp_sigmoids(shape):
def numpy_sigmoid(input):
rval = 1.0 / (1.0 + numpy.exp(-input))
sinput = tensor.Tensor(dtype='float32', broadcastable=(0,)*len(shape))()
shared_input = tcn.shared_constructor(numpy.random.rand(*shape), 'shared_input')
times = compare_fns(
dict( numpy=numpy_sigmoid
, theano_cpu=pfunc([sinput], 1.0 / (1.0 + tensor.exp(-sinput)))
, theano_gpu_onboard=pfunc([sinput], [], updates=[(shared_input, 1.0 / (1.0 + tensor.exp(-shared_input)))])
),
input=shared_input.value)
showtimes(times)
def make_node(self, coding_dist, true_one_of_n):
_coding_dist = T.as_tensor_variable(coding_dist)
_true_one_of_n = T.as_tensor_variable(true_one_of_n)
if _coding_dist.type.ndim != 2:
raise TypeError('matrix required for argument: coding_dist')
if _true_one_of_n.type not in (T.lvector, T.ivector):
raise TypeError(
'integer vector required for argument: true_one_of_n'
'(got type: %s instead of: %s)' % (_true_one_of_n.type,
T.lvector))
return gof.Apply(self, [_coding_dist, _true_one_of_n],
[T.Tensor(dtype=_coding_dist.dtype,
broadcastable=[False])()])
def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
downsample_ops=True, verbose=0, version=-1,
check_isfinite=True):
if use_gpu:
shared_fn = tcn.shared_constructor
else:
shared_fn = shared
isize1 = isize
isize2 = isize
if isinstance(isize, (tuple, )):
isize1 = isize[0]
isize2 = isize[1]
shape_img = (n_batch, 1, isize1, isize2)
n_kern = 20 # 6 were used in LeNet5
shape_kern = (n_kern, 1, ksize, ksize)
n_kern1 = 30 # 16 were used in LeNet5
shape_kern1 = (n_kern1, n_kern, ksize, ksize)
logical_hid_shape = tcn.blas.GpuConv.logical_output_shape_2d(
(isize1, isize2), (ksize, ksize), 'valid')
logical_hid_shape1 = tcn.blas.GpuConv.logical_output_shape_2d(
(logical_hid_shape[0] // 2, logical_hid_shape[1] // 2),
(ksize, ksize), 'valid')
n_hid = n_kern1 * logical_hid_shape1[0] * logical_hid_shape1[1]
n_out = 10
w0 = shared_fn(0.01 * (my_rand(*shape_kern) - 0.5), 'w0')
b0 = shared_fn(my_zeros((n_kern,)), 'b0')
w1 = shared_fn(0.01 * (my_rand(*shape_kern1) - 0.5), 'w1')
b1 = shared_fn(my_zeros((n_kern1,)), 'b1')
v = shared_fn(0.01 * my_randn(n_hid, n_out), 'v')
c = shared_fn(my_zeros(n_out), 'c')
# print 'ALLOCATING ARCH: w0 shape', w0.get_value(borrow=True).shape
# print 'ALLOCATING ARCH: w1 shape', w1.get_value(borrow=True).shape
# print 'ALLOCATING ARCH: v shape', v.get_value(borrow=True).shape
x = tensor.Tensor(dtype='float32', broadcastable=(0, 1, 0, 0))('x')
y = tensor.fmatrix('y')
lr = tensor.fscalar('lr')
conv_op = conv.ConvOp(shape_img[2:], shape_kern[2:], n_kern,
n_batch, 1, 1, verbose=verbose, version=version)
conv_op1 = conv.ConvOp(
(n_kern, logical_hid_shape[0] // 2, logical_hid_shape[1] // 2),
shape_kern1[2:], n_kern1, n_batch, 1, 1, verbose=verbose, version=version)
ds_op = pool.Pool((2, 2), ignore_border=False)
if downsample_ops:
hid = tensor.tanh(ds_op(conv_op(x, w0) + b0.dimshuffle((0, 'x', 'x'))))
else:
hid = tensor.tanh(
(conv_op(x, w0) + b0.dimshuffle(
(0, 'x', 'x')))[:, :, ::2, ::2])
hid1 = tensor.tanh(conv_op1(hid, w1) + b1.dimshuffle((0, 'x', 'x')))
hid_flat = hid1.reshape((n_batch, n_hid))
out = tensor.nnet.softmax(tensor.dot(hid_flat, v) + c)
loss = tensor.sum(tensor.nnet.crossentropy_categorical_1hot(
out, tensor.argmax(y, axis=1)) * lr)
# print 'loss type', loss.type
params = [w0, b0, w1, b1, v, c]
gparams = tensor.grad(loss, params)
mode = get_mode(use_gpu, check_isfinite)
# print 'building pfunc ...'
train = pfunc(
[x, y, lr],
[loss],
mode=mode,
updates=[(p, p - g) for p, g in zip(params, gparams)])
if verbose:
theano.printing.debugprint(train)
if use_gpu:
# Check that GpuConv is used
topo = train.maker.fgraph.toposort()
conv_ops = (tcn.blas.GpuConv,
tcn.dnn.GpuDnnConv,
tcn.dnn.GpuDnnConvGradI,
tcn.dnn.GpuDnnConvGradW,
tcn.blas.BaseGpuCorrMM)
assert len([n for n in topo if isinstance(n.op, conv_ops)]) > 0
shape_target = (n_batch, n_out)
return train, params, shape_img, shape_target, mode
def run_conv_nnet2(use_gpu): # pretend we are training LeNet for MNIST
if use_gpu:
shared_fn = tcn.shared_constructor
else:
shared_fn = shared
# cumulativ rounding error affect this comparaison of result. So we lower the tolerance.
# TODO: why the last two example see the error lower? We are converging?
# n_train=10, n_batch=3, n_kern=1, n_kern1=1, error see of 1e-9
# n_train=10, n_batch=3, n_kern=10, n_kern1=1, error see of -1.27777e-06
# n_train=10, n_batch=3, n_kern=10, n_kern1=10, error see of -6.91377e-05
# n_train=10, n_batch=30, n_kern=10, n_kern1=10, error see of -0.00185963
# n_train=10, n_batch=60, n_kern=10, n_kern1=10, error see of -5.26905e-05
# n_train=30, n_batch=60, n_kern=10, n_kern1=10, error see of -3.8147e-06
# n_train=30, n_batch=60, n_kern=20, n_kern1=10, error see of 6.82771e-05
# n_train=30, n_batch=60, n_kern=20, n_kern1=30, error see of 0.000231534
n_batch = 60
shape_img = (n_batch, 1, 32, 32)
n_kern = 20
shape_kern = (n_kern, 1, 5, 5)
n_kern1 = 10
shape_kern1 = (n_kern1, n_kern, 5, 5)
n_train = 30
if config.mode == 'DEBUG_MODE':
n_train = 1
logical_hid_shape = tcn.blas.GpuConv.logical_output_shape_2d(tuple(
shape_img[2:]), tuple(shape_kern[2:]), 'valid')
logical_hid_shape1 = tcn.blas.GpuConv.logical_output_shape_2d(
(logical_hid_shape[0] // 2, logical_hid_shape[1] // 2),
tuple(shape_kern1[2:]), 'valid')
n_hid = n_kern1 * logical_hid_shape1[0] * logical_hid_shape1[1]
n_out = 10
w0 = shared_fn(0.01 * (my_rand(*shape_kern) - 0.5), 'w0')
b0 = shared_fn(my_zeros((n_kern,)), 'b0')
w1 = shared_fn(0.01 * (my_rand(*shape_kern1) - 0.5), 'w1')
b1 = shared_fn(my_zeros((n_kern1,)), 'b1')
v = shared_fn(my_zeros((n_hid, n_out)), 'c')
c = shared_fn(my_zeros(n_out), 'c')
x = tensor.Tensor(dtype='float32', broadcastable=(0, 1, 0, 0))('x')
y = tensor.fmatrix('y')
lr = tensor.fscalar('lr')
conv_op = conv.ConvOp(shape_img[2:], shape_kern[2:], n_kern, n_batch, 1, 1)
conv_op1 = conv.ConvOp((n_kern, logical_hid_shape[0] // 2,
logical_hid_shape[1] // 2),
shape_kern1[2:],
n_kern1, n_batch, 1, 1)
hid = tensor.tanh(conv_op(x, w0) + b0.dimshuffle((0, 'x', 'x')))
hid1 = tensor.tanh(conv_op1(hid[:, :, ::2, ::2], w1) + b1.dimshuffle((
0, 'x', 'x')))
hid_flat = hid1.reshape((n_batch, n_hid))
out = tensor.tanh(tensor.dot(hid_flat, v) + c)
loss = tensor.sum(0.5 * (out - y) ** 2 * lr)
# print 'loss type', loss.type
params = [w0, b0, w1, b1, v, c]
gparams = tensor.grad(loss, params)
mode = get_mode(use_gpu)
# print 'building pfunc ...'
train = pfunc(
[x, y, lr],
[loss],
mode=mode,
updates=[(p, p - g) for p, g in zip(params, gparams)])
# for i, n in enumerate(train.maker.fgraph.toposort()):
# print i, n
xval = my_rand(*shape_img)
yval = my_rand(n_batch, n_out) # int32 make all 0...
lr = theano._asarray(0.01, dtype='float32')
for i in xrange(n_train):
rval = train(xval, yval, lr)
print_mode(mode)
return rval
