本文整理汇总了Python中theano.tensor.tensor方法的典型用法代码示例。如果您正苦于以下问题:Python tensor.tensor方法的具体用法?Python tensor.tensor怎么用?Python tensor.tensor使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类theano.tensor
的用法示例。
在下文中一共展示了tensor.tensor方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: local_csm_properties_csm
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def local_csm_properties_csm(node):
"""
If we find csm_properties(CSM(*args)), then we can replace that with the
*args directly.
"""
if node.op == csm_properties:
csm, = node.inputs
if csm.owner and (csm.owner.op == CSC or csm.owner.op == CSR):
# csm.owner.inputs could be broadcastable. In that case, we have
# to adjust the broadcasting flag here.
ret_var = [theano.tensor.patternbroadcast(i, o.broadcastable)
for i, o in izip(csm.owner.inputs, node.outputs)]
return ret_var
return False
示例2: make_node
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def make_node(self, x, y):
x, y = sparse.as_sparse_variable(x), tensor.as_tensor_variable(y)
out_dtype = scalar.upcast(x.type.dtype, y.type.dtype)
if self.inplace:
assert out_dtype == y.dtype
indices, indptr, data = csm_indices(x), csm_indptr(x), csm_data(x)
# We either use CSC or CSR depending on the format of input
assert self.format == x.type.format
# The magic number two here arises because L{scipy.sparse}
# objects must be matrices (have dimension 2)
assert y.type.ndim == 2
out = tensor.TensorType(dtype=out_dtype,
broadcastable=y.type.broadcastable)()
return gof.Apply(self,
[data, indices, indptr, y],
[out])
示例3: __generalized_sd_test
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def __generalized_sd_test(self, theanop, symbolicType, testOp, scipyType):
scipy_ver = [int(n) for n in scipy.__version__.split('.')[:2]]
if (bool(scipy_ver < [0, 13])):
raise SkipTest("comparison operators need newer release of scipy")
x = symbolicType()
y = theano.tensor.matrix()
op = theanop(x, y)
f = theano.function([x, y], op)
m1 = scipyType(random_lil((10, 40), config.floatX, 3))
m2 = self._rand_ranged(1000, -1000, [10, 40])
self.assertTrue(numpy.array_equal(f(m1, m2).data, testOp(m1, m2).data))
示例4: test_equality_case
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def test_equality_case(self):
"""
Test assuring normal behaviour when values
in the matrices are equal
"""
scipy_ver = [int(n) for n in scipy.__version__.split('.')[:2]]
if (bool(scipy_ver < [0, 13])):
raise SkipTest("comparison operators need newer release of scipy")
x = sparse.csc_matrix()
y = theano.tensor.matrix()
m1 = sp.csc_matrix((2, 2), dtype=theano.config.floatX)
m2 = numpy.asarray([[0, 0], [0, 0]], dtype=theano.config.floatX)
for func in self.testsDic:
op = func(y, x)
f = theano.function([y, x], op)
self.assertTrue(numpy.array_equal(f(m2, m1),
self.testsDic[func](m2, m1)))
示例5: test_csm
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def test_csm(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
for format in ['csc', 'csr']:
for dtype in ['float32', 'float64']:
x = tensor.tensor(dtype=dtype, broadcastable=(False,))
y = tensor.ivector()
z = tensor.ivector()
s = tensor.ivector()
f = theano.function([x, y, z, s], CSM(format)(x, y, z, s))
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
res = f(spmat.data, spmat.indices, spmat.indptr,
numpy.asarray(spmat.shape, 'int32'))
assert numpy.all(res.data == spmat.data)
assert numpy.all(res.indices == spmat.indices)
assert numpy.all(res.indptr == spmat.indptr)
assert numpy.all(res.shape == spmat.shape)
示例6: test_csc_dense
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def test_csc_dense(self):
x = theano.sparse.csc_matrix('x')
y = theano.tensor.matrix('y')
v = theano.tensor.vector('v')
for (x, y, x_v, y_v) in [(x, y, self.x_csc, self.y),
(x, v, self.x_csc, self.v_100),
(v, x, self.v_10, self.x_csc)]:
f_a = theano.function([x, y], theano.sparse.dot(x, y))
f_b = lambda x, y: x * y
utt.assert_allclose(f_a(x_v, y_v), f_b(x_v, y_v))
# Test infer_shape
self._compile_and_check([x, y], [theano.sparse.dot(x, y)],
[x_v, y_v],
(Dot, Usmm, UsmmCscDense))
示例7: test_int32_dtype
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def test_int32_dtype(self):
# Reported on the theano-user mailing-list:
# https://groups.google.com/d/msg/theano-users/MT9ui8LtTsY/rwatwEF9zWAJ
size = 9
intX = 'int32'
C = tensor.matrix('C', dtype=intX)
I = tensor.matrix('I', dtype=intX)
fI = I.flatten()
data = tensor.ones_like(fI)
indptr = tensor.arange(data.shape[0] + 1, dtype='int32')
m1 = sparse.CSR(data, fI, indptr, (8, size))
m2 = sparse.dot(m1, C)
y = m2.reshape(shape=(2, 4, 9), ndim=3)
f = theano.function(inputs=[I, C], outputs=y)
i = numpy.asarray([[4, 3, 7, 7], [2, 8, 4, 5]], dtype=intX)
a = numpy.asarray(numpy.random.randint(0, 100, (size, size)),
dtype=intX)
f(i, a)
示例8: test_grad
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def test_grad(self):
for format in sparse.sparse_formats:
for i_dtype in sparse.float_dtypes:
for o_dtype in tensor.float_dtypes:
if o_dtype == 'float16':
# Don't test float16 output.
continue
_, data = sparse_random_inputs(
format,
shape=(4, 7),
out_dtype=i_dtype)
eps = None
if o_dtype == 'float32':
eps = 1e-2
verify_grad_sparse(Cast(o_dtype), data, eps=eps)
示例9: test_mul_s_v
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def test_mul_s_v(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
for format in ['csr', 'csc']:
for dtype in ['float32', 'float64']:
x = theano.sparse.SparseType(format, dtype=dtype)()
y = tensor.vector(dtype=dtype)
f = theano.function([x, y], mul_s_v(x, y))
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)
out = f(spmat, mat)
utt.assert_allclose(spmat.toarray() * mat, out.toarray())
示例10: test_structured_add_s_v
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def test_structured_add_s_v(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
for format in ['csr', 'csc']:
for dtype in ['float32', 'float64']:
x = theano.sparse.SparseType(format, dtype=dtype)()
y = tensor.vector(dtype=dtype)
f = theano.function([x, y], structured_add_s_v(x, y))
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
spones = spmat.copy()
spones.data = numpy.ones_like(spones.data)
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)
out = f(spmat, mat)
utt.assert_allclose(as_ndarray(spones.multiply(spmat + mat)),
out.toarray())
示例11: _is_sparse_variable
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def _is_sparse_variable(x):
"""
Returns
-------
boolean
True iff x is a L{SparseVariable} (and not a L{tensor.TensorType},
for instance).
"""
if not isinstance(x, gof.Variable):
raise NotImplementedError("this function should only be called on "
"*variables* (of type sparse.SparseType "
"or tensor.TensorType, for instance), not ",
x)
return isinstance(x.type, SparseType)
示例12: as_sparse_or_tensor_variable
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def as_sparse_or_tensor_variable(x, name=None):
"""
Same as `as_sparse_variable` but if we can't make a
sparse variable, we try to make a tensor variable.
Parameters
----------
x
A sparse matrix.
Returns
-------
SparseVariable or TensorVariable version of `x`
"""
try:
return as_sparse_variable(x, name)
except (ValueError, TypeError):
return theano.tensor.as_tensor_variable(x, name)
示例13: sp_ones_like
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def sp_ones_like(x):
"""
Construct a sparse matrix of ones with the same sparsity pattern.
Parameters
----------
x
Sparse matrix to take the sparsity pattern.
Returns
-------
A sparse matrix
The same as `x` with data changed for ones.
"""
# TODO: don't restrict to CSM formats
data, indices, indptr, shape = csm_properties(x)
return CSM(format=x.format)(tensor.ones_like(data), indices, indptr, shape)
示例14: sp_zeros_like
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def sp_zeros_like(x):
"""
Construct a sparse matrix of zeros.
Parameters
----------
x
Sparse matrix to take the shape.
Returns
-------
A sparse matrix
The same as `x` with zero entries for all element.
"""
# 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'),
indptr=tensor.zeros_like(indptr),
shape=shape)
示例15: make_node
# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import tensor [as 别名]
def make_node(self, x):
x = tensor.as_tensor_variable(x)
if x.ndim > 2:
raise TypeError(
"Theano does not have sparse tensor types with more "
"than 2 dimensions, but %s.ndim = %i" % (x, x.ndim))
elif x.ndim == 1:
x = x.dimshuffle('x', 0)
elif x.ndim == 0:
x = x.dimshuffle('x', 'x')
else:
assert x.ndim == 2
return gof.Apply(self,
[x],
[SparseType(dtype=x.type.dtype,
format=self.format)()])