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pytensor
提交 ef9f6efc authored 作者: Frédéric Bastien's avatar Frédéric Bastien 提交者： GitHub
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Merge pull request #5763 from Amrithasuresh/master

Updated numpy as np #4218
上级 fd685902 77db5733
隐藏空白字符变更
内嵌 并排
正在显示 包含 572 行增加 和 573 行删除
theano/tensor/basic.py
浏览文件 @ ef9f6efc
...@@ -5,7 +5,7 @@ from six.moves import builtins ...@@ -5,7 +5,7 @@ from six.moves import builtins
import sys import sys
import warnings import warnings
import numpy import numpy as np
from six import integer_types from six import integer_types
from six.moves import xrange from six.moves import xrange
import numbers import numbers
...@@ -72,12 +72,12 @@ def check_equal_numpy(x, y): ...@@ -72,12 +72,12 @@ def check_equal_numpy(x, y):
Checks the dtype and shape if x and y are numpy.ndarray instances. Checks the dtype and shape if x and y are numpy.ndarray instances.
""" """
if isinstance(x, numpy.ndarray) and isinstance(y, numpy.ndarray): if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
return (x.dtype == y.dtype and x.shape == y.shape and return (x.dtype == y.dtype and x.shape == y.shape and
numpy.all(abs(x - y) < 1e-10)) np.all(abs(x - y) < 1e-10))
elif (isinstance(x, numpy.random.RandomState) and elif (isinstance(x, np.random.RandomState) and
isinstance(y, numpy.random.RandomState)): isinstance(y, np.random.RandomState)):
return python_all(numpy.all(a == b) for a, b in return python_all(np.all(a == b) for a, b in
izip(x.__getstate__(), y.__getstate__())) izip(x.__getstate__(), y.__getstate__()))
else: else:
return x == y return x == y
...@@ -348,15 +348,15 @@ def _get_atol_rtol(a, b): ...@@ -348,15 +348,15 @@ def _get_atol_rtol(a, b):
def _allclose(a, b, rtol=None, atol=None): def _allclose(a, b, rtol=None, atol=None):
a = numpy.asarray(a) a = np.asarray(a)
b = numpy.asarray(b) b = np.asarray(b)
atol_, rtol_ = _get_atol_rtol(a, b) atol_, rtol_ = _get_atol_rtol(a, b)
if rtol is not None: if rtol is not None:
rtol_ = rtol rtol_ = rtol
if atol is not None: if atol is not None:
atol_ = atol atol_ = atol
return numpy.allclose(a, b, atol=atol_, rtol=rtol_) return np.allclose(a, b, atol=atol_, rtol=rtol_)
class NotScalarConstantError(Exception): class NotScalarConstantError(Exception):
...@@ -387,10 +387,10 @@ def numpy_scalar(data): ...@@ -387,10 +387,10 @@ def numpy_scalar(data):
if (data.ndim > 0 and if (data.ndim > 0 and
(len(data.shape) == 0 or (len(data.shape) == 0 or
builtins.max(data.shape) == 0)): builtins.max(data.shape) == 0)):
assert numpy.all(numpy.array([]) == data) assert np.all(np.array([]) == data)
raise EmptyConstantError() raise EmptyConstantError()
try: try:
numpy.complex(data) # works for all numeric scalars np.complex(data) # works for all numeric scalars
return data return data
except Exception: except Exception:
raise NotScalarConstantError( raise NotScalarConstantError(
...@@ -444,10 +444,10 @@ def get_scalar_constant_value(orig_v, elemwise=True, ...@@ -444,10 +444,10 @@ def get_scalar_constant_value(orig_v, elemwise=True,
# to depend on passing it None) # to depend on passing it None)
raise NotScalarConstantError() raise NotScalarConstantError()
if isinstance(v, (numpy.integer, integer_types, float)): if isinstance(v, (np.integer, integer_types, float)):
return numpy.asarray(v) return np.asarray(v)
if isinstance(v, numpy.ndarray): if isinstance(v, np.ndarray):
return numpy_scalar(v).copy() return numpy_scalar(v).copy()
if isinstance(v, Constant): if isinstance(v, Constant):
...@@ -470,11 +470,11 @@ def get_scalar_constant_value(orig_v, elemwise=True, ...@@ -470,11 +470,11 @@ def get_scalar_constant_value(orig_v, elemwise=True,
i = v.owner.op.i i = v.owner.op.i
inp = v.owner.inputs[0] inp = v.owner.inputs[0]
if isinstance(inp, Constant): if isinstance(inp, Constant):
return numpy.asarray(inp.data.shape[i]) return np.asarray(inp.data.shape[i])
# The shape of a broadcastable dimension is 1 # The shape of a broadcastable dimension is 1
if (hasattr(inp.type, 'broadcastable') and if (hasattr(inp.type, 'broadcastable') and
inp.type.broadcastable[i]): inp.type.broadcastable[i]):
return numpy.asarray(1) return np.asarray(1)
# Don't act as the constant_folding optimization here as this # Don't act as the constant_folding optimization here as this
# fct is used too early in the optimization phase. This would # fct is used too early in the optimization phase. This would
...@@ -639,7 +639,7 @@ def get_scalar_constant_value(orig_v, elemwise=True, ...@@ -639,7 +639,7 @@ def get_scalar_constant_value(orig_v, elemwise=True,
raise ValueError(msg) raise ValueError(msg)
if gp_broadcastable[idx]: if gp_broadcastable[idx]:
return numpy.asarray(1) return np.asarray(1)
raise NotScalarConstantError(v) raise NotScalarConstantError(v)
...@@ -1002,7 +1002,7 @@ class TensorFromScalar(Op): ...@@ -1002,7 +1002,7 @@ class TensorFromScalar(Op):
def perform(self, node, inp, out_): def perform(self, node, inp, out_):
s, = inp s, = inp
out, = out_ out, = out_
out[0] = numpy.asarray(s) out[0] = np.asarray(s)
def infer_shape(self, node, in_shapes): def infer_shape(self, node, in_shapes):
return [()] return [()]
...@@ -1216,23 +1216,23 @@ class MaxAndArgmax(Op): ...@@ -1216,23 +1216,23 @@ class MaxAndArgmax(Op):
axes = tuple(range(x.ndim)) axes = tuple(range(x.ndim))
else: else:
axes = tuple(int(ax) for ax in axes) axes = tuple(int(ax) for ax in axes)
max[0] = theano._asarray(numpy.max(x, axes), max[0] = theano._asarray(np.max(x, axes),
dtype=node.outputs[0].dtype) dtype=node.outputs[0].dtype)
# Numpy does not support multiple axes for argmax # Numpy does not support multiple axes for argmax
# Work around # Work around
keep_axes = numpy.array([i for i in range(x.ndim) if i not in axes], keep_axes = np.array([i for i in range(x.ndim) if i not in axes],
dtype='int64') dtype='int64')
# Not-reduced axes in front # Not-reduced axes in front
transposed_x = numpy.transpose(x, numpy.concatenate((keep_axes, axes))) transposed_x = np.transpose(x, np.concatenate((keep_axes, axes)))
kept_shape = transposed_x.shape[:len(keep_axes)] kept_shape = transposed_x.shape[:len(keep_axes)]
reduced_shape = transposed_x.shape[len(keep_axes):] reduced_shape = transposed_x.shape[len(keep_axes):]
# Numpy.prod returns 1.0 when arg is empty, so we cast it to int64 # Numpy.prod returns 1.0 when arg is empty, so we cast it to int64
# Otherwise reshape would complain citing float arg # Otherwise reshape would complain citing float arg
new_shape = kept_shape + (numpy.prod(reduced_shape, dtype='int64'),) new_shape = kept_shape + (np.prod(reduced_shape, dtype='int64'),)
reshaped_x = transposed_x.reshape(new_shape) reshaped_x = transposed_x.reshape(new_shape)
max_idx[0] = theano._asarray(numpy.argmax(reshaped_x, axis=-1), max_idx[0] = theano._asarray(np.argmax(reshaped_x, axis=-1),
dtype='int64') dtype='int64')
def c_code(self, node, name, inp, out, sub): def c_code(self, node, name, inp, out, sub):
...@@ -1399,11 +1399,11 @@ class Argmax(Op): ...@@ -1399,11 +1399,11 @@ class Argmax(Op):
def make_node(self, x, axis=None): def make_node(self, x, axis=None):
x = _as_tensor_variable(x) x = _as_tensor_variable(x)
if isinstance(axis, (integer_types, numpy.integer)): if isinstance(axis, (integer_types, np.integer)):
axis = [int(axis)] axis = [int(axis)]
elif isinstance(axis, numpy.ndarray) and axis.ndim == 0: elif isinstance(axis, np.ndarray) and axis.ndim == 0:
axis = [int(axis)] axis = [int(axis)]
elif isinstance(axis, (tuple, list, numpy.ndarray)): elif isinstance(axis, (tuple, list, np.ndarray)):
axis = [int(a) for a in axis] axis = [int(a) for a in axis]
if axis == list(range(x.type.ndim)): if axis == list(range(x.type.ndim)):
axis = None axis = None
...@@ -1415,11 +1415,11 @@ class Argmax(Op): ...@@ -1415,11 +1415,11 @@ class Argmax(Op):
"Argmax needs a constant axis. Got %s" % axis) "Argmax needs a constant axis. Got %s" % axis)
else: else:
assert axis.dtype in integer_dtypes assert axis.dtype in integer_dtypes
if isinstance(axis.data, (integer_types, numpy.integer)) or \ if isinstance(axis.data, (integer_types, np.integer)) or \
(isinstance(axis.data, numpy.ndarray) and (isinstance(axis.data, np.ndarray) and
axis.data.ndim == 0): axis.data.ndim == 0):
axis = [int(axis.data)] axis = [int(axis.data)]
elif isinstance(axis.data, (list, numpy.ndarray)): elif isinstance(axis.data, (list, np.ndarray)):
axis = [int(i) for i in axis.data] axis = [int(i) for i in axis.data]
# Make axis entries non-negative, and sort them # Make axis entries non-negative, and sort them
...@@ -1466,17 +1466,17 @@ class Argmax(Op): ...@@ -1466,17 +1466,17 @@ class Argmax(Op):
# Numpy does not support multiple axes for argmax # Numpy does not support multiple axes for argmax
# Work around # Work around
keep_axes = numpy.array([i for i in range(x.ndim) if i not in axes], keep_axes = np.array([i for i in range(x.ndim) if i not in axes],
dtype='int64') dtype='int64')
# Not-reduced axes in front # Not-reduced axes in front
transposed_x = numpy.transpose(x, numpy.concatenate((keep_axes, transposed_x = np.transpose(x, np.concatenate((keep_axes,
axes))) axes)))
kept_shape = transposed_x.shape[:len(keep_axes)] kept_shape = transposed_x.shape[:len(keep_axes)]
reduced_shape = transposed_x.shape[len(keep_axes):] reduced_shape = transposed_x.shape[len(keep_axes):]
new_shape = kept_shape + (numpy.prod(reduced_shape),) new_shape = kept_shape + (np.prod(reduced_shape),)
reshaped_x = transposed_x.reshape(new_shape) reshaped_x = transposed_x.reshape(new_shape)
max_idx[0] = theano._asarray(numpy.argmax(reshaped_x, axis=-1), max_idx[0] = theano._asarray(np.argmax(reshaped_x, axis=-1),
dtype='int64') dtype='int64')
def c_code(self, node, name, inp, out, sub): def c_code(self, node, name, inp, out, sub):
...@@ -1562,9 +1562,9 @@ def makeKeepDims(x, y, axis): ...@@ -1562,9 +1562,9 @@ def makeKeepDims(x, y, axis):
if axis is None: if axis is None:
axis = list(range(x.type.ndim)) axis = list(range(x.type.ndim))
elif isinstance(axis, (integer_types, numpy.integer)): elif isinstance(axis, (integer_types, np.integer)):
axis = [axis] axis = [axis]
elif isinstance(axis, numpy.ndarray) and axis.ndim == 0: elif isinstance(axis, np.ndarray) and axis.ndim == 0:
axis = [int(axis)] axis = [int(axis)]
else: else:
axis = [int(a) for a in axis] axis = [int(a) for a in axis]
...@@ -1609,10 +1609,10 @@ def max_and_argmax(a, axis=None, keepdims=False): ...@@ -1609,10 +1609,10 @@ def max_and_argmax(a, axis=None, keepdims=False):
a = as_tensor_variable(a) a = as_tensor_variable(a)
if axis is None: if axis is None:
axis = list(range(a.type.ndim)) axis = list(range(a.type.ndim))
elif (isinstance(axis, (integer_types, numpy.integer)) or elif (isinstance(axis, (integer_types, np.integer)) or
(isinstance(axis, numpy.ndarray) and axis.ndim == 0)): (isinstance(axis, np.ndarray) and axis.ndim == 0)):
axis = [int(axis)] axis = [int(axis)]
elif isinstance(axis, (tuple, list, numpy.ndarray)): elif isinstance(axis, (tuple, list, np.ndarray)):
axis = [int(i) for i in axis] axis = [int(i) for i in axis]
elif isinstance(axis, Variable): elif isinstance(axis, Variable):
if NoneConst.equals(axis): if NoneConst.equals(axis):
...@@ -1621,10 +1621,10 @@ def max_and_argmax(a, axis=None, keepdims=False): ...@@ -1621,10 +1621,10 @@ def max_and_argmax(a, axis=None, keepdims=False):
raise TypeError("max and argmax computation needs a constant axis. Got %s" % axis) raise TypeError("max and argmax computation needs a constant axis. Got %s" % axis)
else: else:
assert axis.dtype in integer_dtypes assert axis.dtype in integer_dtypes
if (isinstance(axis.data, (integer_types, numpy.integer)) or if (isinstance(axis.data, (integer_types, np.integer)) or
(isinstance(axis.data, numpy.ndarray) and axis.data.ndim == 0)): (isinstance(axis.data, np.ndarray) and axis.data.ndim == 0)):
axis = [int(axis.data)] axis = [int(axis.data)]
elif isinstance(axis.data, (list, numpy.ndarray)): elif isinstance(axis.data, (list, np.ndarray)):
axis = [int(i) for i in axis.data] axis = [int(i) for i in axis.data]
if len(axis) == 0: if len(axis) == 0:
axis = list(range(a.type.ndim)) axis = list(range(a.type.ndim))
...@@ -1838,7 +1838,7 @@ def isnan(a): ...@@ -1838,7 +1838,7 @@ def isnan(a):
"""isnan(a)""" """isnan(a)"""
a = as_tensor_variable(a) a = as_tensor_variable(a)
if a.dtype in discrete_dtypes: if a.dtype in discrete_dtypes:
return alloc(numpy.asarray(False, dtype="bool"), return alloc(np.asarray(False, dtype="bool"),
*[a.shape[i] for i in range(a.ndim)]) *[a.shape[i] for i in range(a.ndim)])
return isnan_(a) return isnan_(a)
...@@ -1857,7 +1857,7 @@ def isinf(a): ...@@ -1857,7 +1857,7 @@ def isinf(a):
"""isinf(a)""" """isinf(a)"""
a = as_tensor_variable(a) a = as_tensor_variable(a)
if a.dtype in discrete_dtypes: if a.dtype in discrete_dtypes:
return alloc(numpy.asarray(False, dtype="bool"), return alloc(np.asarray(False, dtype="bool"),
*[a.shape[i] for i in range(a.ndim)]) *[a.shape[i] for i in range(a.ndim)])
return isinf_(a) return isinf_(a)
...@@ -2426,7 +2426,7 @@ def zeros(shape, dtype=None): ...@@ -2426,7 +2426,7 @@ def zeros(shape, dtype=None):
shape = [shape] shape = [shape]
if dtype is None: if dtype is None:
dtype = config.floatX dtype = config.floatX
return alloc(numpy.array(0, dtype=dtype), *shape) return alloc(np.array(0, dtype=dtype), *shape)
def ones(shape, dtype=None): def ones(shape, dtype=None):
...@@ -2437,7 +2437,7 @@ def ones(shape, dtype=None): ...@@ -2437,7 +2437,7 @@ def ones(shape, dtype=None):
shape = [shape] shape = [shape]
if dtype is None: if dtype is None:
dtype = config.floatX dtype = config.floatX
return alloc(numpy.array(1, dtype=dtype), *shape) return alloc(np.array(1, dtype=dtype), *shape)
class Nonzero(gof.Op): class Nonzero(gof.Op):
...@@ -2481,11 +2481,11 @@ class Nonzero(gof.Op): ...@@ -2481,11 +2481,11 @@ class Nonzero(gof.Op):
a = inp[0] a = inp[0]
out, = out_ out, = out_
result_tuple = numpy.nonzero(a) result_tuple = np.nonzero(a)
if len(result_tuple[0]) > 0: if len(result_tuple[0]) > 0:
result = numpy.vstack(result_tuple) result = np.vstack(result_tuple)
else: else:
result = numpy.zeros((len(result_tuple), 0)) result = np.zeros((len(result_tuple), 0))
out[0] = result.astype('int64') out[0] = result.astype('int64')
...@@ -2627,7 +2627,7 @@ class Tri(gof.Op): ...@@ -2627,7 +2627,7 @@ class Tri(gof.Op):
def perform(self, node, inp, out_): def perform(self, node, inp, out_):
N, M, k = inp N, M, k = inp
out, = out_ out, = out_
out[0] = numpy.tri(N, M, k, dtype=self.dtype) out[0] = np.tri(N, M, k, dtype=self.dtype)
def infer_shape(self, node, in_shapes): def infer_shape(self, node, in_shapes):
out_shape = [node.inputs[0], node.inputs[1]] out_shape = [node.inputs[0], node.inputs[1]]
...@@ -2738,7 +2738,7 @@ class Eye(gof.Op): ...@@ -2738,7 +2738,7 @@ class Eye(gof.Op):
def perform(self, node, inp, out_): def perform(self, node, inp, out_):
n, m, k = inp n, m, k = inp
out, = out_ out, = out_
out[0] = numpy.eye(n, m, k, dtype=self.dtype) out[0] = np.eye(n, m, k, dtype=self.dtype)
def infer_shape(self, node, in_shapes): def infer_shape(self, node, in_shapes):
out_shape = [node.inputs[0], node.inputs[1]] out_shape = [node.inputs[0], node.inputs[1]]
...@@ -2853,9 +2853,9 @@ class Alloc(gof.Op): ...@@ -2853,9 +2853,9 @@ class Alloc(gof.Op):
sh = tuple([int(i) for i in inputs[1:]]) sh = tuple([int(i) for i in inputs[1:]])
if out[0] is None or out[0].shape != sh: if out[0] is None or out[0].shape != sh:
if v.size == 1 and v.item() == 0: if v.size == 1 and v.item() == 0:
out[0] = numpy.zeros(sh, dtype=v.dtype) out[0] = np.zeros(sh, dtype=v.dtype)
else: else:
out[0] = numpy.empty(sh, dtype=v.dtype) out[0] = np.empty(sh, dtype=v.dtype)
out[0][...] = v # broadcast v to fill us up out[0][...] = v # broadcast v to fill us up
else: else:
# reuse the allocated memory. # reuse the allocated memory.
...@@ -3139,8 +3139,8 @@ class Mean(elemwise.CAReduce): ...@@ -3139,8 +3139,8 @@ class Mean(elemwise.CAReduce):
axis = self.axis[0] axis = self.axis[0]
# numpy.asarray is needed as otherwise we can end up with a # numpy.asarray is needed as otherwise we can end up with a
# numpy scalar. # numpy scalar.
output[0] = numpy.asarray(numpy.mean(input, dtype='float64', output[0] = np.asarray(np.mean(input, dtype='float64',
axis=axis)) axis=axis))
def c_code(self, node, name, inames, onames, sub): def c_code(self, node, name, inames, onames, sub):
if self.axis is not None: if self.axis is not None:
...@@ -3232,9 +3232,9 @@ def mean(input, axis=None, dtype=None, op=False, keepdims=False, ...@@ -3232,9 +3232,9 @@ def mean(input, axis=None, dtype=None, op=False, keepdims=False,
if axis is None: if axis is None:
axis = list(range(input.ndim)) axis = list(range(input.ndim))
elif isinstance(axis, (integer_types, numpy.integer)): elif isinstance(axis, (integer_types, np.integer)):
axis = [axis] axis = [axis]
elif isinstance(axis, numpy.ndarray) and axis.ndim == 0: elif isinstance(axis, np.ndarray) and axis.ndim == 0:
axis = [int(axis)] axis = [int(axis)]
else: else:
axis = [int(a) for a in axis] axis = [int(a) for a in axis]
...@@ -3291,9 +3291,9 @@ def var(input, axis=None, ddof=0, keepdims=False, corrected=False): ...@@ -3291,9 +3291,9 @@ def var(input, axis=None, ddof=0, keepdims=False, corrected=False):
input_ndim = input.type.ndim input_ndim = input.type.ndim
if axis is None: if axis is None:
axis = list(range(input_ndim)) axis = list(range(input_ndim))
elif isinstance(axis, (integer_types, numpy.integer)): elif isinstance(axis, (integer_types, np.integer)):
axis = [axis] axis = [axis]
elif isinstance(axis, numpy.ndarray) and axis.ndim == 0: elif isinstance(axis, np.ndarray) and axis.ndim == 0:
axis = [int(axis)] axis = [int(axis)]
else: else:
axis = [int(a) for a in axis] axis = [int(a) for a in axis]
...@@ -3617,7 +3617,7 @@ def batched_dot(a, b): ...@@ -3617,7 +3617,7 @@ def batched_dot(a, b):
return a * b.dimshuffle(*([0] + ["x"] * (a.ndim - 1))) return a * b.dimshuffle(*([0] + ["x"] * (a.ndim - 1)))
elif a.ndim > 3 or b.ndim > 3: elif a.ndim > 3 or b.ndim > 3:
return batched_tensordot( return batched_tensordot(
a, b, [[a.ndim - 1], [numpy.maximum(1, b.ndim - 2)]]) a, b, [[a.ndim - 1], [np.maximum(1, b.ndim - 2)]])
else: else:
# avoid circular import # avoid circular import
return theano.tensor.blas.BatchedDot()(a, b) return theano.tensor.blas.BatchedDot()(a, b)
...@@ -3736,9 +3736,9 @@ class Split(Op): ...@@ -3736,9 +3736,9 @@ class Split(Op):
raise ValueError('In Split.perform(), len(splits) != len_splits.', raise ValueError('In Split.perform(), len(splits) != len_splits.',
(len(splits), self.len_splits)) (len(splits), self.len_splits))
if numpy.sum(splits) != len_along_axis: if np.sum(splits) != len_along_axis:
raise ValueError('The splits sum to %s, expected %s' % raise ValueError('The splits sum to %s, expected %s' %
(numpy.sum(splits), len_along_axis)) (np.sum(splits), len_along_axis))
if python_any([nb < 0 for nb in splits]): if python_any([nb < 0 for nb in splits]):
raise ValueError('Split: you tried to make an ndarray with a ' raise ValueError('Split: you tried to make an ndarray with a '
'negative number of elements.') 'negative number of elements.')
...@@ -3828,8 +3828,8 @@ class Split(Op): ...@@ -3828,8 +3828,8 @@ class Split(Op):
outputs_pointers = '&' + (', &'.join(outputs)) outputs_pointers = '&' + (', &'.join(outputs))
x, axis, splits = inputs x, axis, splits = inputs
fail = sub['fail'] fail = sub['fail']
x_typenum = numpy.dtype(node.inputs[0].dtype).num x_typenum = np.dtype(node.inputs[0].dtype).num
x_itemsize = numpy.dtype(node.inputs[0].dtype).itemsize x_itemsize = np.dtype(node.inputs[0].dtype).itemsize
axis_dtype = node.inputs[1].type.dtype_specs()[1] axis_dtype = node.inputs[1].type.dtype_specs()[1]
splits_dtype = node.inputs[2].type.dtype_specs()[1] splits_dtype = node.inputs[2].type.dtype_specs()[1]
expected_splits_count = self.len_splits expected_splits_count = self.len_splits
...@@ -4187,7 +4187,7 @@ class Join(Op): ...@@ -4187,7 +4187,7 @@ class Join(Op):
view = self.view view = self.view
axis, tensors = axis_and_tensors[0], axis_and_tensors[1:] axis, tensors = axis_and_tensors[0], axis_and_tensors[1:]
# we check these tensors for being empty. # we check these tensors for being empty.
if (view != -1) and numpy.all( if (view != -1) and np.all(
[tensor.shape[axis] == 0 for tensor in [tensor.shape[axis] == 0 for tensor in
tensors[0:view] + tensors[view + 1:]]): tensors[0:view] + tensors[view + 1:]]):
out[0] = tensors[view] out[0] = tensors[view]
...@@ -4198,7 +4198,7 @@ class Join(Op): ...@@ -4198,7 +4198,7 @@ class Join(Op):
raise IndexError("Join axis %d out of bounds [0, %d)" % raise IndexError("Join axis %d out of bounds [0, %d)" %
(axis, ndim)) (axis, ndim))
out[0] = theano._asarray(numpy.concatenate(tensors, axis=axis), out[0] = theano._asarray(np.concatenate(tensors, axis=axis),
dtype=node.outputs[0].type.dtype) dtype=node.outputs[0].type.dtype)
def c_code_cache_version(self): def c_code_cache_version(self):
...@@ -4584,9 +4584,9 @@ def stack(*tensors, **kwargs): ...@@ -4584,9 +4584,9 @@ def stack(*tensors, **kwargs):
# And DebugMode can't detect error in this code as it is not in an # And DebugMode can't detect error in this code as it is not in an
# optimization. # optimization.
# See ticket #660 # See ticket #660
if numpy.all( if np.all(
[ # in case there is direct int in tensors. [ # in case there is direct int in tensors.
isinstance(t, (numpy.number, float, integer_types, isinstance(t, (np.number, float, integer_types,
python_complex)) or python_complex)) or
(isinstance(t, Variable) and (isinstance(t, Variable) and
isinstance(t.type, TensorType) and isinstance(t.type, TensorType) and
...@@ -4669,7 +4669,7 @@ def get_vector_length(v): ...@@ -4669,7 +4669,7 @@ def get_vector_length(v):
v.owner.inputs, v.owner.op.idx_list)[0].step) v.owner.inputs, v.owner.op.idx_list)[0].step)
ndim = v.owner.inputs[0].owner.inputs[0].ndim ndim = v.owner.inputs[0].owner.inputs[0].ndim
types = (numbers.Integral, numpy.integer) types = (numbers.Integral, np.integer)
if start is None: if start is None:
start = 0 start = 0
elif isinstance(start, types) and start < 0: elif isinstance(start, types) and start < 0:
...@@ -4790,7 +4790,7 @@ class Reshape(Op): ...@@ -4790,7 +4790,7 @@ class Reshape(Op):
' length %i' ' length %i'
', should be %i' % (len(shp), self.ndim), shp) ', should be %i' % (len(shp), self.ndim), shp)
try: try:
out[0] = numpy.reshape(x, shp) out[0] = np.reshape(x, shp)
except Exception: except Exception:
raise ValueError('Cannot reshape input of shape %s to shape %s' % raise ValueError('Cannot reshape input of shape %s to shape %s' %
(x.shape, shp)) (x.shape, shp))
...@@ -4976,12 +4976,12 @@ class Flatten(Op): ...@@ -4976,12 +4976,12 @@ class Flatten(Op):
try: try:
out[0] = x.reshape(x.size) out[0] = x.reshape(x.size)
except AttributeError: except AttributeError:
out[0] = x.reshape((numpy.prod(x.shape),)) out[0] = x.reshape((np.prod(x.shape),))
elif outdim == len(x.shape): elif outdim == len(x.shape):
out[0] = x out[0] = x
else: else:
newshape = (x.shape[:outdim - 1] + newshape = (x.shape[:outdim - 1] +
(numpy.prod(x.shape[outdim - 1:]),)) (np.prod(x.shape[outdim - 1:]),))
out[0] = x.reshape(newshape) out[0] = x.reshape(newshape)
def infer_shape(self, node, in_shapes): def infer_shape(self, node, in_shapes):
...@@ -5196,16 +5196,16 @@ class Tile(Op): ...@@ -5196,16 +5196,16 @@ class Tile(Op):
def perform(self, node, inp, out_): def perform(self, node, inp, out_):
x, reps = inp x, reps = inp
out, = out_ out, = out_
res = numpy.tile(x, reps) res = np.tile(x, reps)
if res.ndim != self.ndim: if res.ndim != self.ndim:
raise ValueError( raise ValueError(
'Tile.perform produced incorrect number of dimensions') 'Tile.perform produced incorrect number of dimensions')
if (numpy.asarray(reps) == 1).all(): if (np.asarray(reps) == 1).all():
# In that case, some NumPy version return a view! As this # In that case, some NumPy version return a view! As this
# op isn't declared as inplace, we need to check that and # op isn't declared as inplace, we need to check that and
# copy the data. # copy the data.
if numpy.may_share_memory(res, x): if np.may_share_memory(res, x):
res = res.copy() res = res.copy()
out[0] = res out[0] = res
...@@ -5289,9 +5289,9 @@ def tile(x, reps, ndim=None): ...@@ -5289,9 +5289,9 @@ def tile(x, reps, ndim=None):
else: else:
if ndim is not None and len(reps) > ndim: if ndim is not None and len(reps) > ndim:
raise ValueError("len(reps) should be equal or less than ndim") raise ValueError("len(reps) should be equal or less than ndim")
if not numpy.all([isinstance(r, integer_types) or if not np.all([isinstance(r, integer_types) or
(isinstance(r, TensorVariable) and (isinstance(r, TensorVariable) and
r.dtype in theano.tensor.discrete_dtypes) for r in reps]): r.dtype in theano.tensor.discrete_dtypes) for r in reps]):
raise ValueError("elements of reps must be scalars of integer dtype") raise ValueError("elements of reps must be scalars of integer dtype")
# if reps.ndim is less than x.ndim, we pad the reps with # if reps.ndim is less than x.ndim, we pad the reps with
...@@ -5305,7 +5305,7 @@ def tile(x, reps, ndim=None): ...@@ -5305,7 +5305,7 @@ def tile(x, reps, ndim=None):
shape = [1] * (ndim - x.ndim) + [x.shape[i] for i in xrange(x.ndim)] shape = [1] * (ndim - x.ndim) + [x.shape[i] for i in xrange(x.ndim)]
alloc_shape = reps + shape alloc_shape = reps + shape
y = alloc(x, *alloc_shape) y = alloc(x, *alloc_shape)
shuffle_ind = numpy.arange(ndim * 2).reshape(2, ndim) shuffle_ind = np.arange(ndim * 2).reshape(2, ndim)
shuffle_ind = shuffle_ind.transpose().flatten() shuffle_ind = shuffle_ind.transpose().flatten()
y = y.dimshuffle(*shuffle_ind) y = y.dimshuffle(*shuffle_ind)
new_shapes = [sh * reps[i] for i, sh in enumerate(shape)] new_shapes = [sh * reps[i] for i, sh in enumerate(shape)]
...@@ -5343,7 +5343,7 @@ class ARange(Op): ...@@ -5343,7 +5343,7 @@ class ARange(Op):
def is_constant_value(var, value): def is_constant_value(var, value):
try: try:
v = get_scalar_constant_value(var) v = get_scalar_constant_value(var)
return numpy.all(v == value) return np.all(v == value)
except NotScalarConstantError: except NotScalarConstantError:
pass pass
return False return False
...@@ -5378,7 +5378,7 @@ class ARange(Op): ...@@ -5378,7 +5378,7 @@ class ARange(Op):
start = start.item() start = start.item()
stop = stop.item() stop = stop.item()
step = step.item() step = step.item()
out[0] = numpy.arange(start, stop, step, dtype=self.dtype) out[0] = np.arange(start, stop, step, dtype=self.dtype)
def connection_pattern(self, node): def connection_pattern(self, node):
...@@ -5424,10 +5424,10 @@ def arange(start, stop=None, step=1, dtype=None): ...@@ -5424,10 +5424,10 @@ def arange(start, stop=None, step=1, dtype=None):
# As an example, if `start`, `stop` and `step` are all int32, # As an example, if `start`, `stop` and `step` are all int32,
# `numpy.arange` returns an int64 array (on 64-bit platforms), # `numpy.arange` returns an int64 array (on 64-bit platforms),
# while the upcast above returns int32. # while the upcast above returns int32.
numpy_dtype = numpy.arange( numpy_dtype = np.arange(
start=numpy.array(0, dtype=start.dtype), start=np.array(0, dtype=start.dtype),
stop=numpy.array(1, dtype=stop.dtype), stop=np.array(1, dtype=stop.dtype),
step=numpy.array(1, dtype=step.dtype)).dtype step=np.array(1, dtype=step.dtype)).dtype
if numpy_dtype != dtype: if numpy_dtype != dtype:
if (config.cast_policy == 'numpy+floatX' and if (config.cast_policy == 'numpy+floatX' and
config.floatX == 'float32' and config.floatX == 'float32' and
...@@ -5653,7 +5653,7 @@ class PermuteRowElements(Op): ...@@ -5653,7 +5653,7 @@ class PermuteRowElements(Op):
out_s.append(outdim) out_s.append(outdim)
if outs[0] is None or outs[0].shape != out_s: if outs[0] is None or outs[0].shape != out_s:
outs[0] = numpy.empty(out_s, dtype=x.dtype) outs[0] = np.empty(out_s, dtype=x.dtype)
self._rec_perform(node, x, y, inverse, outs[0], curdim=0) self._rec_perform(node, x, y, inverse, outs[0], curdim=0)
...@@ -5796,7 +5796,7 @@ class Dot(Op): ...@@ -5796,7 +5796,7 @@ class Dot(Op):
# the asarray is here because dot between two vectors # the asarray is here because dot between two vectors
# gives a numpy float object but we need to return a 0d # gives a numpy float object but we need to return a 0d
# ndarray # ndarray
z[0] = numpy.asarray(numpy.dot(x, y)) z[0] = np.asarray(np.dot(x, y))
def grad(self, inp, grads): def grad(self, inp, grads):
...@@ -5976,7 +5976,7 @@ def dot(a, b): ...@@ -5976,7 +5976,7 @@ def dot(a, b):
if a.ndim == 0 or b.ndim == 0: if a.ndim == 0 or b.ndim == 0:
return a * b return a * b
elif a.ndim > 2 or b.ndim > 2: elif a.ndim > 2 or b.ndim > 2:
return tensordot(a, b, [[a.ndim - 1], [numpy.maximum(0, b.ndim - 2)]]) return tensordot(a, b, [[a.ndim - 1], [np.maximum(0, b.ndim - 2)]])
else: else:
return _dot(a, b) return _dot(a, b)
...@@ -6012,14 +6012,14 @@ def _tensordot_as_dot(a, b, axes, dot, batched): ...@@ -6012,14 +6012,14 @@ def _tensordot_as_dot(a, b, axes, dot, batched):
""" """
a, b = as_tensor_variable(a), as_tensor_variable(b) a, b = as_tensor_variable(a), as_tensor_variable(b)
if not numpy.isscalar(axes) and len(axes) != 2: if not np.isscalar(axes) and len(axes) != 2:
raise ValueError('Axes should be an integer or a ' raise ValueError('Axes should be an integer or a '
'list/tuple of len 2 (%s was provided)' 'list/tuple of len 2 (%s was provided)'
% str(axes)) % str(axes))
# if 'axes' is a number of axes to multiply and sum over (trailing axes # if 'axes' is a number of axes to multiply and sum over (trailing axes
# of a, leading axes of b), we can just reshape and use dot. # of a, leading axes of b), we can just reshape and use dot.
elif numpy.isscalar(axes): elif np.isscalar(axes):
axes = int(axes) axes = int(axes)
for operand_name, operand in (("a", a), ("b", b)): for operand_name, operand in (("a", a), ("b", b)):
...@@ -6083,12 +6083,12 @@ def _tensordot_as_dot(a, b, axes, dot, batched): ...@@ -6083,12 +6083,12 @@ def _tensordot_as_dot(a, b, axes, dot, batched):
'the dimensions of %s (%s.ndim=%i, len(axes[0])=%i).' % 'the dimensions of %s (%s.ndim=%i, len(axes[0])=%i).' %
(i, operand_name, operand_name, operand.ndim, (i, operand_name, operand_name, operand.ndim,
len(axes[i]))) len(axes[i])))
if len(axes[i]) > 0 and numpy.max(axes[i]) >= operand.ndim: if len(axes[i]) > 0 and np.max(axes[i]) >= operand.ndim:
raise ValueError( raise ValueError(
'axes[%i] contains dimensions greater than or equal ' 'axes[%i] contains dimensions greater than or equal '
'to %s.ndim (%s.ndim=%i, max(axes[0])=%i).' % 'to %s.ndim (%s.ndim=%i, max(axes[0])=%i).' %
(i, operand_name, operand_name, operand.ndim, (i, operand_name, operand_name, operand.ndim,
numpy.max(numpy.array(axes[i])))) np.max(np.array(axes[i]))))
if batched and 0 in axes[i]: if batched and 0 in axes[i]:
raise ValueError( raise ValueError(
'axes to sum over must not contain the batch axis ' 'axes to sum over must not contain the batch axis '
...@@ -6243,8 +6243,8 @@ def all(x, axis=None, keepdims=False): ...@@ -6243,8 +6243,8 @@ def all(x, axis=None, keepdims=False):
# Some NumPy version like 1.9.2 return a view for numpy.diagonal # Some NumPy version like 1.9.2 return a view for numpy.diagonal
x = numpy.zeros((4, 4)) x = np.zeros((4, 4))
numpy_diagonal_return_view = numpy.may_share_memory(numpy.diagonal(x), x) numpy_diagonal_return_view = np.may_share_memory(np.diagonal(x), x)
del x del x
...@@ -6271,7 +6271,7 @@ class ExtractDiag(Op): ...@@ -6271,7 +6271,7 @@ class ExtractDiag(Op):
"set to True but numpy version %s and prior versions of " "set to True but numpy version %s and prior versions of "
"numpy.diagonal() do not return a view. Update " "numpy.diagonal() do not return a view. Update "
"numpy to use ExtractDiag(view=True)" % "numpy to use ExtractDiag(view=True)" %
numpy.version.version) np.version.version)
self.view = False self.view = False
if self.view: if self.view:
self.view_map = {0: [0]} self.view_map = {0: [0]}
...@@ -6395,7 +6395,7 @@ class AllocDiag(Op): ...@@ -6395,7 +6395,7 @@ class AllocDiag(Op):
def perform(self, node, inputs, outputs): def perform(self, node, inputs, outputs):
(z,) = outputs (z,) = outputs
z[0] = numpy.diag(inputs[0], self.offset) z[0] = np.diag(inputs[0], self.offset)
def grad(self, inputs, gout): def grad(self, inputs, gout):
(gz,) = gout (gz,) = gout
...@@ -6629,7 +6629,7 @@ class Choose(Op): ...@@ -6629,7 +6629,7 @@ class Choose(Op):
choice = as_tensor_variable(choices) choice = as_tensor_variable(choices)
choice_ndim = choice.ndim - 1 choice_ndim = choice.ndim - 1
choice_bcast = choice.broadcastable[1:] choice_bcast = choice.broadcastable[1:]
out_ndim = numpy.max([a.ndim, choice_ndim]) out_ndim = np.max([a.ndim, choice_ndim])
# Make explicit all added broadcastable dimensions. # Make explicit all added broadcastable dimensions.
a = shape_padleft(a, out_ndim - a.ndim) a = shape_padleft(a, out_ndim - a.ndim)
...@@ -6660,7 +6660,7 @@ class Choose(Op): ...@@ -6660,7 +6660,7 @@ class Choose(Op):
a = inputs[0] a = inputs[0]
choice = inputs[1] choice = inputs[1]
# TODO reuse out? # TODO reuse out?
z[0] = numpy.choose(a, choice, mode=self.mode) z[0] = np.choose(a, choice, mode=self.mode)
class AllocEmpty(gof.Op): class AllocEmpty(gof.Op):
...@@ -6699,7 +6699,7 @@ class AllocEmpty(gof.Op): ...@@ -6699,7 +6699,7 @@ class AllocEmpty(gof.Op):
out, = out_ out, = out_
sh = tuple([int(i) for i in inputs]) sh = tuple([int(i) for i in inputs])
if out[0] is None or out[0].shape != sh: if out[0] is None or out[0].shape != sh:
out[0] = numpy.empty(sh, dtype=self.dtype) out[0] = np.empty(sh, dtype=self.dtype)
def c_code(self, node, name, inputs, out_, sub): def c_code(self, node, name, inputs, out_, sub):
dtype = "NPY_" + self.dtype.upper() dtype = "NPY_" + self.dtype.upper()
......
theano/tensor/blas_scipy.py
浏览文件 @ ef9f6efc
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
Implementations of BLAS Ops based on scipy's BLAS bindings. Implementations of BLAS Ops based on scipy's BLAS bindings.
""" """
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
from theano.tensor.blas import Ger, ger, ger_destructive, have_fblas from theano.tensor.blas import Ger, ger, ger_destructive, have_fblas
from theano.tensor.blas import blas_optdb, optdb, local_optimizer from theano.tensor.blas import blas_optdb, optdb, local_optimizer
...@@ -13,10 +13,10 @@ from theano.tensor.opt import in2out ...@@ -13,10 +13,10 @@ from theano.tensor.opt import in2out
if have_fblas: if have_fblas:
from theano.tensor.blas import fblas from theano.tensor.blas import fblas
_blas_ger_fns = { _blas_ger_fns = {
numpy.dtype('float32'): fblas.sger, np.dtype('float32'): fblas.sger,
numpy.dtype('float64'): fblas.dger, np.dtype('float64'): fblas.dger,
numpy.dtype('complex64'): fblas.cgeru, np.dtype('complex64'): fblas.cgeru,
numpy.dtype('complex128'): fblas.zgeru, np.dtype('complex128'): fblas.zgeru,
} }
...@@ -24,7 +24,7 @@ class ScipyGer(Ger): ...@@ -24,7 +24,7 @@ class ScipyGer(Ger):
def prepare_node(self, node, storage_map, compute_map, impl): def prepare_node(self, node, storage_map, compute_map, impl):
if impl == 'py': if impl == 'py':
node.tag.local_ger = _blas_ger_fns[numpy.dtype( node.tag.local_ger = _blas_ger_fns[np.dtype(
node.inputs[0].type.dtype)] node.inputs[0].type.dtype)]
def perform(self, node, inputs, output_storage): def perform(self, node, inputs, output_storage):
......
theano/tensor/elemwise.py
浏览文件 @ ef9f6efc
...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division ...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import sys import sys
from copy import copy from copy import copy
import numpy import numpy as np
from six import iteritems, integer_types from six import iteritems, integer_types
from six.moves import xrange from six.moves import xrange
...@@ -21,7 +21,7 @@ from theano.misc.frozendict import frozendict ...@@ -21,7 +21,7 @@ from theano.misc.frozendict import frozendict
config = theano.config config = theano.config
_numpy_ver = [int(n) for n in numpy.__version__.split('.')[:2]] _numpy_ver = [int(n) for n in np.__version__.split('.')[:2]]
# tensor depends on elemwise to provide definitions for several ops # tensor depends on elemwise to provide definitions for several ops
...@@ -148,7 +148,7 @@ class DimShuffle(Op): ...@@ -148,7 +148,7 @@ class DimShuffle(Op):
# isinstance(x, integer_types) returning False for # isinstance(x, integer_types) returning False for
# numpy integers. See # numpy integers. See
# <http://projects.scipy.org/numpy/ticket/2235>. # <http://projects.scipy.org/numpy/ticket/2235>.
if not isinstance(j, (integer_types, numpy.integer)): if not isinstance(j, (integer_types, np.integer)):
raise TypeError( raise TypeError(
"DimShuffle indices must be python ints. " "DimShuffle indices must be python ints. "
"Got: '%s' of type '%s'.", "Got: '%s' of type '%s'.",
...@@ -228,7 +228,7 @@ class DimShuffle(Op): ...@@ -228,7 +228,7 @@ class DimShuffle(Op):
storage, = out storage, = out
# drop # drop
res = input res = input
if type(res) != numpy.ndarray and type(res) != numpy.memmap: if type(res) != np.ndarray and type(res) != np.memmap:
raise TypeError(res) raise TypeError(res)
# transpose # transpose
...@@ -242,9 +242,9 @@ class DimShuffle(Op): ...@@ -242,9 +242,9 @@ class DimShuffle(Op):
# copy (if not inplace) # copy (if not inplace)
if not self.inplace: if not self.inplace:
res = numpy.copy(res) res = np.copy(res)
storage[0] = numpy.asarray(res) # asarray puts scalars back into array storage[0] = np.asarray(res) # asarray puts scalars back into array
def infer_shape(self, node, shapes): def infer_shape(self, node, shapes):
ishp, = shapes ishp, = shapes
...@@ -487,7 +487,7 @@ second dimension ...@@ -487,7 +487,7 @@ second dimension
nfunc_spec = getattr(scalar_op, 'nfunc_spec', None) nfunc_spec = getattr(scalar_op, 'nfunc_spec', None)
self.nfunc_spec = nfunc_spec self.nfunc_spec = nfunc_spec
if nfunc_spec: if nfunc_spec:
self.nfunc = getattr(numpy, nfunc_spec[0]) self.nfunc = getattr(np, nfunc_spec[0])
super(Elemwise, self).__init__(openmp=openmp) super(Elemwise, self).__init__(openmp=openmp)
...@@ -504,11 +504,11 @@ second dimension ...@@ -504,11 +504,11 @@ second dimension
self.nfunc = None self.nfunc = None
self.inplace_pattern = frozendict(self.inplace_pattern) self.inplace_pattern = frozendict(self.inplace_pattern)
if getattr(self, 'nfunc_spec', None): if getattr(self, 'nfunc_spec', None):
self.nfunc = getattr(numpy, self.nfunc_spec[0]) self.nfunc = getattr(np, self.nfunc_spec[0])
elif 0 < self.scalar_op.nin < 32: elif 0 < self.scalar_op.nin < 32:
self.ufunc = numpy.frompyfunc(self.scalar_op.impl, self.ufunc = np.frompyfunc(self.scalar_op.impl,
self.scalar_op.nin, self.scalar_op.nin,
self.scalar_op.nout) self.scalar_op.nout)
def get_output_info(self, dim_shuffle, *inputs): def get_output_info(self, dim_shuffle, *inputs):
"""Return the outputs dtype and broadcastable pattern and the """Return the outputs dtype and broadcastable pattern and the
...@@ -723,7 +723,7 @@ second dimension ...@@ -723,7 +723,7 @@ second dimension
# the gradient contains a constant, translate it as # the gradient contains a constant, translate it as
# an equivalent TensorType of size 1 and proper number of # an equivalent TensorType of size 1 and proper number of
# dimensions # dimensions
res = theano.tensor.constant(numpy.asarray(r.data), res = theano.tensor.constant(np.asarray(r.data),
dtype=r.type.dtype) dtype=r.type.dtype)
return DimShuffle((), ['x'] * nd)(res) return DimShuffle((), ['x'] * nd)(res)
...@@ -750,9 +750,9 @@ second dimension ...@@ -750,9 +750,9 @@ second dimension
self.ufunc is None and self.ufunc is None and
impl == 'py'): impl == 'py'):
ufunc = numpy.frompyfunc(self.scalar_op.impl, ufunc = np.frompyfunc(self.scalar_op.impl,
len(node.inputs), len(node.inputs),
self.scalar_op.nout) self.scalar_op.nout)
if self.scalar_op.nin > 0: if self.scalar_op.nin > 0:
# We can reuse it for many nodes # We can reuse it for many nodes
self.ufunc = ufunc self.ufunc = ufunc
...@@ -772,9 +772,9 @@ second dimension ...@@ -772,9 +772,9 @@ second dimension
# when the input is complex. So add it only when inputs is int. # when the input is complex. So add it only when inputs is int.
out_dtype = node.outputs[0].dtype out_dtype = node.outputs[0].dtype
if (out_dtype in theano.tensor.float_dtypes and if (out_dtype in theano.tensor.float_dtypes and
isinstance(self.nfunc, numpy.ufunc) and isinstance(self.nfunc, np.ufunc) and
node.inputs[0].dtype in theano.tensor.discrete_dtypes): node.inputs[0].dtype in theano.tensor.discrete_dtypes):
char = numpy.sctype2char(out_dtype) char = np.sctype2char(out_dtype)
sig = char * node.nin + '->' + char * node.nout sig = char * node.nin + '->' + char * node.nout
node.tag.sig = sig node.tag.sig = sig
node.tag.fake_node = Apply( node.tag.fake_node = Apply(
...@@ -870,7 +870,7 @@ second dimension ...@@ -870,7 +870,7 @@ second dimension
if getattr(variable, "dtype", "") == 'object': if getattr(variable, "dtype", "") == 'object':
# Since numpy 1.6, function created with numpy.frompyfunc # Since numpy 1.6, function created with numpy.frompyfunc
# always return an ndarray with dtype object # always return an ndarray with dtype object
variable = numpy.asarray(variable, dtype=nout.dtype) variable = np.asarray(variable, dtype=nout.dtype)
if i in self.inplace_pattern: if i in self.inplace_pattern:
odat = inputs[self.inplace_pattern[i]] odat = inputs[self.inplace_pattern[i]]
...@@ -879,15 +879,15 @@ second dimension ...@@ -879,15 +879,15 @@ second dimension
# Sometimes NumPy return a Python type. # Sometimes NumPy return a Python type.
# Some Theano op return a different dtype like floor, ceil, # Some Theano op return a different dtype like floor, ceil,
# trunc, eq, ... # trunc, eq, ...
elif (not isinstance(variable, numpy.ndarray) or elif (not isinstance(variable, np.ndarray) or
variable.dtype != nout.dtype): variable.dtype != nout.dtype):
variable = numpy.asarray(variable, nout.dtype) variable = np.asarray(variable, nout.dtype)
# The next line is needed for numpy 1.9. Otherwise # The next line is needed for numpy 1.9. Otherwise
# there are tests that fail in DebugMode. # there are tests that fail in DebugMode.
# Normally we would call theano.misc._asarray, but it # Normally we would call theano.misc._asarray, but it
# is faster to inline the code. We know that the dtype # is faster to inline the code. We know that the dtype
# are the same string, just different typenum. # are the same string, just different typenum.
if numpy.dtype(nout.dtype).num != variable.dtype.num: if np.dtype(nout.dtype).num != variable.dtype.num:
variable = variable.view(dtype=nout.dtype) variable = variable.view(dtype=nout.dtype)
storage[0] = variable storage[0] = variable
# numpy.real return a view! # numpy.real return a view!
...@@ -1302,9 +1302,9 @@ class CAReduce(Op): ...@@ -1302,9 +1302,9 @@ class CAReduce(Op):
# There is a bug in numpy that results in isinstance(x, # There is a bug in numpy that results in isinstance(x,
# integer_types) returning False for numpy integers. See # integer_types) returning False for numpy integers. See
# <http://projects.scipy.org/numpy/ticket/2235>. # <http://projects.scipy.org/numpy/ticket/2235>.
elif isinstance(axis, (integer_types, numpy.integer)): elif isinstance(axis, (integer_types, np.integer)):
self.axis = (axis,) self.axis = (axis,)
elif isinstance(axis, numpy.ndarray) and axis.ndim == 0: elif isinstance(axis, np.ndarray) and axis.ndim == 0:
self.axis = (int(axis),) self.axis = (int(axis),)
else: else:
self.axis = list(set(int(a) for a in axis)) self.axis = list(set(int(a) for a in axis))
...@@ -1316,26 +1316,26 @@ class CAReduce(Op): ...@@ -1316,26 +1316,26 @@ class CAReduce(Op):
def set_ufunc(self, scalar_op): def set_ufunc(self, scalar_op):
# This is probably a speed up of the implementation # This is probably a speed up of the implementation
if isinstance(scalar_op, theano.scalar.basic.Add): if isinstance(scalar_op, theano.scalar.basic.Add):
self.ufunc = numpy.add self.ufunc = np.add
elif isinstance(scalar_op, theano.scalar.basic.Mul): elif isinstance(scalar_op, theano.scalar.basic.Mul):
self.ufunc = numpy.multiply self.ufunc = np.multiply
elif isinstance(scalar_op, theano.scalar.basic.Maximum): elif isinstance(scalar_op, theano.scalar.basic.Maximum):
self.ufunc = numpy.maximum self.ufunc = np.maximum
elif isinstance(scalar_op, theano.scalar.basic.Minimum): elif isinstance(scalar_op, theano.scalar.basic.Minimum):
self.ufunc = numpy.minimum self.ufunc = np.minimum
elif (isinstance(scalar_op, theano.scalar.basic.AND) and elif (isinstance(scalar_op, theano.scalar.basic.AND) and
_numpy_ver >= [1, 12]): _numpy_ver >= [1, 12]):
# numpy.bitwise_and.identity was incorrect for versions before # numpy.bitwise_and.identity was incorrect for versions before
# 1.12 (it was 1 instead of -1), so we skip it in that case. # 1.12 (it was 1 instead of -1), so we skip it in that case.
# We will fall back to the "else:" case, which defines a # We will fall back to the "else:" case, which defines a
# ufunc without identity. # ufunc without identity.
self.ufunc = numpy.bitwise_and self.ufunc = np.bitwise_and
elif isinstance(scalar_op, theano.scalar.basic.OR): elif isinstance(scalar_op, theano.scalar.basic.OR):
self.ufunc = numpy.bitwise_or self.ufunc = np.bitwise_or
elif isinstance(scalar_op, theano.scalar.basic.XOR): elif isinstance(scalar_op, theano.scalar.basic.XOR):
self.ufunc = numpy.bitwise_xor self.ufunc = np.bitwise_xor
else: else:
self.ufunc = numpy.frompyfunc(scalar_op.impl, 2, 1) self.ufunc = np.frompyfunc(scalar_op.impl, 2, 1)
def _output_dtype(self, input_dtype): def _output_dtype(self, input_dtype):
return input_dtype return input_dtype
...@@ -1415,8 +1415,8 @@ class CAReduce(Op): ...@@ -1415,8 +1415,8 @@ class CAReduce(Op):
# Compute the shape of the output # Compute the shape of the output
v_shape = list(variable.shape) v_shape = list(variable.shape)
del v_shape[dimension] del v_shape[dimension]
variable = numpy.empty(tuple(v_shape), variable = np.empty(tuple(v_shape),
dtype=acc_dtype) dtype=acc_dtype)
variable.fill(self.scalar_op.identity) variable.fill(self.scalar_op.identity)
else: else:
raise ValueError(( raise ValueError((
...@@ -1427,8 +1427,8 @@ class CAReduce(Op): ...@@ -1427,8 +1427,8 @@ class CAReduce(Op):
variable = self.ufunc.reduce(variable, dimension, variable = self.ufunc.reduce(variable, dimension,
dtype=acc_dtype) dtype=acc_dtype)
variable = numpy.asarray(variable) variable = np.asarray(variable)
if numpy.may_share_memory(variable, input): if np.may_share_memory(variable, input):
# perhaps numpy is clever for reductions of size 1? # perhaps numpy is clever for reductions of size 1?
# We don't want this. # We don't want this.
variable = variable.copy() variable = variable.copy()
...@@ -1436,8 +1436,8 @@ class CAReduce(Op): ...@@ -1436,8 +1436,8 @@ class CAReduce(Op):
dtype=node.outputs[0].type.dtype) dtype=node.outputs[0].type.dtype)
else: else:
# Force a copy # Force a copy
output[0] = numpy.array(variable, copy=True, output[0] = np.array(variable, copy=True,
dtype=node.outputs[0].type.dtype) dtype=node.outputs[0].type.dtype)
def infer_shape(self, node, shapes): def infer_shape(self, node, shapes):
ishape, = shapes ishape, = shapes
......
theano/tensor/extra_ops.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy as np import numpy as np
import numpy
from six.moves import xrange from six.moves import xrange
import theano import theano
...@@ -778,7 +777,7 @@ def repeat(x, repeats, axis=None): ...@@ -778,7 +777,7 @@ def repeat(x, repeats, axis=None):
shape[axis] = shape[axis] * repeats shape[axis] = shape[axis] * repeats
# dims_ is the dimension of that intermediate tensor. # dims_ is the dimension of that intermediate tensor.
dims_ = list(numpy.arange(x.ndim)) dims_ = list(np.arange(x.ndim))
dims_.insert(axis + 1, 'x') dims_.insert(axis + 1, 'x')
# After the original tensor is duplicated along the additional # After the original tensor is duplicated along the additional
...@@ -806,7 +805,7 @@ class Bartlett(gof.Op): ...@@ -806,7 +805,7 @@ class Bartlett(gof.Op):
def perform(self, node, inputs, out_): def perform(self, node, inputs, out_):
M = inputs[0] M = inputs[0]
out, = out_ out, = out_
out[0] = numpy.bartlett(M) out[0] = np.bartlett(M)
def infer_shape(self, node, in_shapes): def infer_shape(self, node, in_shapes):
temp = node.inputs[0] temp = node.inputs[0]
...@@ -882,7 +881,7 @@ class FillDiagonal(gof.Op): ...@@ -882,7 +881,7 @@ class FillDiagonal(gof.Op):
# Write the value out into the diagonal. # Write the value out into the diagonal.
a.flat[:end:step] = val a.flat[:end:step] = val
else: else:
numpy.fill_diagonal(a, val) np.fill_diagonal(a, val)
output_storage[0][0] = a output_storage[0][0] = a
...@@ -1132,7 +1131,7 @@ class Unique(theano.Op): ...@@ -1132,7 +1131,7 @@ class Unique(theano.Op):
self.return_index = return_index self.return_index = return_index
self.return_inverse = return_inverse self.return_inverse = return_inverse
self.return_counts = return_counts self.return_counts = return_counts
numpy_ver = [int(n) for n in numpy.__version__.split('.')[:2]] numpy_ver = [int(n) for n in np.__version__.split('.')[:2]]
if self.return_counts and bool(numpy_ver < [1, 9]): if self.return_counts and bool(numpy_ver < [1, 9]):
raise RuntimeError( raise RuntimeError(
"Numpy version = " + np.__version__ + "Numpy version = " + np.__version__ +
......
theano/tensor/fourier.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
import math import math
from theano import gof, tensor from theano import gof, tensor
...@@ -98,7 +98,7 @@ class Fourier(gof.Op): ...@@ -98,7 +98,7 @@ class Fourier(gof.Op):
a = inputs[0] a = inputs[0]
n = inputs[1] n = inputs[1]
axis = inputs[2] axis = inputs[2]
output_storage[0][0] = numpy.fft.fft(a, n=int(n), axis=axis.item()) output_storage[0][0] = np.fft.fft(a, n=int(n), axis=axis.item())
def grad(self, inputs, cost_grad): def grad(self, inputs, cost_grad):
""" """
...@@ -128,7 +128,7 @@ class Fourier(gof.Op): ...@@ -128,7 +128,7 @@ class Fourier(gof.Op):
# tensor.set_subtensor(res[...,n::], 0, False, False), res) # tensor.set_subtensor(res[...,n::], 0, False, False), res)
# Instead we resort to that to account for truncation: # Instead we resort to that to account for truncation:
flip_shape = list(numpy.arange(0, a.ndim)[::-1]) flip_shape = list(np.arange(0, a.ndim)[::-1])
res = res.dimshuffle(flip_shape) res = res.dimshuffle(flip_shape)
res = tensor.switch(tensor.lt(n, tensor.shape(a)[axis]), res = tensor.switch(tensor.lt(n, tensor.shape(a)[axis]),
tensor.set_subtensor(res[n::, ], 0, False, False), tensor.set_subtensor(res[n::, ], 0, False, False),
...@@ -136,8 +136,8 @@ class Fourier(gof.Op): ...@@ -136,8 +136,8 @@ class Fourier(gof.Op):
res = res.dimshuffle(flip_shape) res = res.dimshuffle(flip_shape)
# insures that gradient shape conforms to input shape: # insures that gradient shape conforms to input shape:
out_shape = list(numpy.arange(0, axis)) + [a.ndim - 1] +\ out_shape = list(np.arange(0, axis)) + [a.ndim - 1] +\
list(numpy.arange(axis, a.ndim - 1)) list(np.arange(axis, a.ndim - 1))
res = res.dimshuffle(*out_shape) res = res.dimshuffle(*out_shape)
return [res, None, None] return [res, None, None]
......
theano/tensor/io.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
from theano import gof from theano import gof
from theano.gof import Constant, Generic, Op from theano.gof import Constant, Generic, Op
from theano.gof.sched import key_to_cmp from theano.gof.sched import key_to_cmp
...@@ -27,7 +27,7 @@ class LoadFromDisk(Op): ...@@ -27,7 +27,7 @@ class LoadFromDisk(Op):
__props__ = ("dtype", "broadcastable", "mmap_mode") __props__ = ("dtype", "broadcastable", "mmap_mode")
def __init__(self, dtype, broadcastable, mmap_mode=None): def __init__(self, dtype, broadcastable, mmap_mode=None):
self.dtype = numpy.dtype(dtype) # turn "float64" into numpy.float64 self.dtype = np.dtype(dtype) # turn "float64" into np.float64
self.broadcastable = broadcastable self.broadcastable = broadcastable
if mmap_mode not in (None, 'c'): if mmap_mode not in (None, 'c'):
raise ValueError("The only supported values for mmap_mode " raise ValueError("The only supported values for mmap_mode "
...@@ -44,7 +44,7 @@ class LoadFromDisk(Op): ...@@ -44,7 +44,7 @@ class LoadFromDisk(Op):
path = inp[0] path = inp[0]
if (path.split('.')[-1] == 'npz'): if (path.split('.')[-1] == 'npz'):
raise ValueError("Expected a .npy file, got %s instead" % path) raise ValueError("Expected a .npy file, got %s instead" % path)
result = numpy.load(path, mmap_mode=self.mmap_mode) result = np.load(path, mmap_mode=self.mmap_mode)
if result.dtype != self.dtype: if result.dtype != self.dtype:
raise TypeError("Expected an array of type %s, got %s instead" % raise TypeError("Expected an array of type %s, got %s instead" %
(self.dtype, result.dtype)) (self.dtype, result.dtype))
...@@ -125,7 +125,7 @@ class MPIRecv(Op): ...@@ -125,7 +125,7 @@ class MPIRecv(Op):
self.source = source self.source = source
self.tag = tag self.tag = tag
self.shape = shape self.shape = shape
self.dtype = numpy.dtype(dtype) # turn "float64" into numpy.float64 self.dtype = np.dtype(dtype) # turn "float64" into numpy.float64
self.broadcastable = (False,) * len(shape) self.broadcastable = (False,) * len(shape)
def make_node(self): def make_node(self):
...@@ -135,7 +135,7 @@ class MPIRecv(Op): ...@@ -135,7 +135,7 @@ class MPIRecv(Op):
def perform(self, node, inp, out): def perform(self, node, inp, out):
data = numpy.zeros(self.shape, dtype=self.dtype) data = np.zeros(self.shape, dtype=self.dtype)
request = comm.Irecv(data, self.source, self.tag) request = comm.Irecv(data, self.source, self.tag)
out[0][0] = request out[0][0] = request
......
theano/tensor/nlinalg.py
浏览文件 @ ef9f6efc
...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division ...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import logging import logging
import warnings import warnings
import numpy import numpy as np
from six.moves import xrange from six.moves import xrange
import theano import theano
...@@ -44,7 +44,7 @@ class MatrixPinv(Op): ...@@ -44,7 +44,7 @@ class MatrixPinv(Op):
def perform(self, node, inputs, outputs): def perform(self, node, inputs, outputs):
(x,) = inputs (x,) = inputs
(z,) = outputs (z,) = outputs
z[0] = numpy.linalg.pinv(x).astype(x.dtype) z[0] = np.linalg.pinv(x).astype(x.dtype)
pinv = MatrixPinv() pinv = MatrixPinv()
...@@ -76,7 +76,7 @@ class MatrixInverse(Op): ...@@ -76,7 +76,7 @@ class MatrixInverse(Op):
def perform(self, node, inputs, outputs): def perform(self, node, inputs, outputs):
(x,) = inputs (x,) = inputs
(z,) = outputs (z,) = outputs
z[0] = numpy.linalg.inv(x).astype(x.dtype) z[0] = np.linalg.inv(x).astype(x.dtype)
def grad(self, inputs, g_outputs): def grad(self, inputs, g_outputs):
r"""The gradient function should return r"""The gradient function should return
...@@ -162,7 +162,7 @@ class AllocDiag(Op): ...@@ -162,7 +162,7 @@ class AllocDiag(Op):
(z,) = outputs (z,) = outputs
if x.ndim != 1: if x.ndim != 1:
raise TypeError(x) raise TypeError(x)
z[0] = numpy.diag(x) z[0] = np.diag(x)
def infer_shape(self, node, shapes): def infer_shape(self, node, shapes):
x_s, = shapes x_s, = shapes
...@@ -289,7 +289,7 @@ class Det(Op): ...@@ -289,7 +289,7 @@ class Det(Op):
(x,) = inputs (x,) = inputs
(z,) = outputs (z,) = outputs
try: try:
z[0] = numpy.asarray(numpy.linalg.det(x), dtype=x.dtype) z[0] = np.asarray(np.linalg.det(x), dtype=x.dtype)
except Exception: except Exception:
print('Failed to compute determinant', x) print('Failed to compute determinant', x)
raise raise
...@@ -313,7 +313,7 @@ class Eig(Op): ...@@ -313,7 +313,7 @@ class Eig(Op):
""" """
_numop = staticmethod(numpy.linalg.eig) _numop = staticmethod(np.linalg.eig)
__props__ = () __props__ = ()
def make_node(self, x): def make_node(self, x):
...@@ -341,7 +341,7 @@ class Eigh(Eig): ...@@ -341,7 +341,7 @@ class Eigh(Eig):
""" """
_numop = staticmethod(numpy.linalg.eigh) _numop = staticmethod(np.linalg.eigh)
__props__ = ('UPLO',) __props__ = ('UPLO',)
def __init__(self, UPLO='L'): def __init__(self, UPLO='L'):
...@@ -356,7 +356,7 @@ class Eigh(Eig): ...@@ -356,7 +356,7 @@ class Eigh(Eig):
# LAPACK. Rather than trying to reproduce the (rather # LAPACK. Rather than trying to reproduce the (rather
# involved) logic, we just probe linalg.eigh with a trivial # involved) logic, we just probe linalg.eigh with a trivial
# input. # input.
w_dtype = self._numop([[numpy.dtype(x.dtype).type()]])[0].dtype.name w_dtype = self._numop([[np.dtype(x.dtype).type()]])[0].dtype.name
w = theano.tensor.vector(dtype=w_dtype) w = theano.tensor.vector(dtype=w_dtype)
v = theano.tensor.matrix(dtype=x.dtype) v = theano.tensor.matrix(dtype=x.dtype)
return Apply(self, [x], [w, v]) return Apply(self, [x], [w, v])
...@@ -419,11 +419,11 @@ class EighGrad(Op): ...@@ -419,11 +419,11 @@ class EighGrad(Op):
assert UPLO in ['L', 'U'] assert UPLO in ['L', 'U']
self.UPLO = UPLO self.UPLO = UPLO
if UPLO == 'L': if UPLO == 'L':
self.tri0 = numpy.tril self.tri0 = np.tril
self.tri1 = lambda a: numpy.triu(a, 1) self.tri1 = lambda a: np.triu(a, 1)
else: else:
self.tri0 = numpy.triu self.tri0 = np.triu
self.tri1 = lambda a: numpy.tril(a, -1) self.tri1 = lambda a: np.tril(a, -1)
def make_node(self, x, w, v, gw, gv): def make_node(self, x, w, v, gw, gv):
x, w, v, gw, gv = map(as_tensor_variable, (x, w, v, gw, gv)) x, w, v, gw, gv = map(as_tensor_variable, (x, w, v, gw, gv))
...@@ -445,7 +445,7 @@ class EighGrad(Op): ...@@ -445,7 +445,7 @@ class EighGrad(Op):
""" """
x, w, v, W, V = inputs x, w, v, W, V = inputs
N = x.shape[0] N = x.shape[0]
outer = numpy.outer outer = np.outer
def G(n): def G(n):
return sum(v[:, m] * V.T[n].dot(v[:, m]) / (w[n] - w[m]) return sum(v[:, m] * V.T[n].dot(v[:, m]) / (w[n] - w[m])
...@@ -466,7 +466,7 @@ class EighGrad(Op): ...@@ -466,7 +466,7 @@ class EighGrad(Op):
# Make sure we return the right dtype even if NumPy performed # Make sure we return the right dtype even if NumPy performed
# upcasting in self.tri0. # upcasting in self.tri0.
outputs[0][0] = numpy.asarray(out, dtype=node.outputs[0].dtype) outputs[0][0] = np.asarray(out, dtype=node.outputs[0].dtype)
def infer_shape(self, node, shapes): def infer_shape(self, node, shapes):
return [shapes[0]] return [shapes[0]]
...@@ -486,7 +486,7 @@ class QRFull(Op): ...@@ -486,7 +486,7 @@ class QRFull(Op):
""" """
_numop = staticmethod(numpy.linalg.qr) _numop = staticmethod(np.linalg.qr)
__props__ = ('mode',) __props__ = ('mode',)
def __init__(self, mode): def __init__(self, mode):
...@@ -519,7 +519,7 @@ class QRIncomplete(Op): ...@@ -519,7 +519,7 @@ class QRIncomplete(Op):
""" """
_numop = staticmethod(numpy.linalg.qr) _numop = staticmethod(np.linalg.qr)
__props__ = ('mode',) __props__ = ('mode',)
def __init__(self, mode): def __init__(self, mode):
...@@ -583,7 +583,7 @@ def qr(a, mode="reduced"): ...@@ -583,7 +583,7 @@ def qr(a, mode="reduced"):
""" """
x = [[2, 1], [3, 4]] x = [[2, 1], [3, 4]]
if isinstance(numpy.linalg.qr(x, mode), tuple): if isinstance(np.linalg.qr(x, mode), tuple):
return QRFull(mode)(a) return QRFull(mode)(a)
else: else:
return QRIncomplete(mode)(a) return QRIncomplete(mode)(a)
...@@ -606,7 +606,7 @@ class SVD(Op): ...@@ -606,7 +606,7 @@ class SVD(Op):
""" """
# See doc in the docstring of the function just after this class. # See doc in the docstring of the function just after this class.
_numop = staticmethod(numpy.linalg.svd) _numop = staticmethod(np.linalg.svd)
__props__ = ('full_matrices', 'compute_uv') __props__ = ('full_matrices', 'compute_uv')
def __init__(self, full_matrices=True, compute_uv=True): def __init__(self, full_matrices=True, compute_uv=True):
...@@ -666,10 +666,10 @@ class lstsq(Op): ...@@ -666,10 +666,10 @@ class lstsq(Op):
theano.tensor.lscalar(), theano.tensor.dvector()]) theano.tensor.lscalar(), theano.tensor.dvector()])
def perform(self, node, inputs, outputs): def perform(self, node, inputs, outputs):
zz = numpy.linalg.lstsq(inputs[0], inputs[1], inputs[2]) zz = np.linalg.lstsq(inputs[0], inputs[1], inputs[2])
outputs[0][0] = zz[0] outputs[0][0] = zz[0]
outputs[1][0] = zz[1] outputs[1][0] = zz[1]
outputs[2][0] = numpy.array(zz[2]) outputs[2][0] = np.array(zz[2])
outputs[3][0] = zz[3] outputs[3][0] = zz[3]
...@@ -730,7 +730,7 @@ class TensorInv(Op): ...@@ -730,7 +730,7 @@ class TensorInv(Op):
Class wrapper for tensorinv() function; Class wrapper for tensorinv() function;
Theano utilization of numpy.linalg.tensorinv; Theano utilization of numpy.linalg.tensorinv;
""" """
_numop = staticmethod(numpy.linalg.tensorinv) _numop = staticmethod(np.linalg.tensorinv)
__props__ = ('ind',) __props__ = ('ind',)
def __init__(self, ind=2): def __init__(self, ind=2):
...@@ -790,7 +790,7 @@ class TensorSolve(Op): ...@@ -790,7 +790,7 @@ class TensorSolve(Op):
Class wrapper for tensorsolve function. Class wrapper for tensorsolve function.
""" """
_numop = staticmethod(numpy.linalg.tensorsolve) _numop = staticmethod(np.linalg.tensorsolve)
__props__ = ('axes', ) __props__ = ('axes', )
def __init__(self, axes=None): def __init__(self, axes=None):
......
theano/tensor/opt.py
浏览文件 @ ef9f6efc
...@@ -14,7 +14,7 @@ import time ...@@ -14,7 +14,7 @@ import time
import traceback import traceback
import warnings import warnings
import numpy import numpy as np
from six import integer_types, iteritems from six import integer_types, iteritems
from six.moves import reduce, xrange from six.moves import reduce, xrange
...@@ -786,7 +786,7 @@ class MakeVector(T.Op): ...@@ -786,7 +786,7 @@ class MakeVector(T.Op):
# So there will be (1 * nb_dtype) + ((nb len(inp) - 1 )) # So there will be (1 * nb_dtype) + ((nb len(inp) - 1 ))
# different c code with the following algo # different c code with the following algo
out_shape = len(inp) out_shape = len(inp)
out_num = numpy.dtype(node.outputs[0].dtype).num out_num = np.dtype(node.outputs[0].dtype).num
# don't use dtype_%(out)s as when check_input=False, it isn't defined. # don't use dtype_%(out)s as when check_input=False, it isn't defined.
out_dtype = node.outputs[0].type.dtype_specs()[1] out_dtype = node.outputs[0].type.dtype_specs()[1]
if len(inp) > 0: if len(inp) > 0:
...@@ -1064,8 +1064,8 @@ class ShapeFeature(object): ...@@ -1064,8 +1064,8 @@ class ShapeFeature(object):
if type(s_i) is float and int(s_i) == s_i: if type(s_i) is float and int(s_i) == s_i:
s_i = int(s_i) s_i = int(s_i)
if (type(s_i) in integer_types or if (type(s_i) in integer_types or
isinstance(s_i, numpy.integer) or isinstance(s_i, np.integer) or
(isinstance(s_i, numpy.ndarray) and s_i.ndim == 0)): (isinstance(s_i, np.ndarray) and s_i.ndim == 0)):
# this shape is a constant # this shape is a constant
if s_i < 0: if s_i < 0:
msg = "There is a negative shape in the graph!" msg = "There is a negative shape in the graph!"
...@@ -1975,7 +1975,7 @@ def local_subtensor_remove_broadcastable_index(node): ...@@ -1975,7 +1975,7 @@ def local_subtensor_remove_broadcastable_index(node):
elif isinstance(elem, slice): elif isinstance(elem, slice):
if elem != slice(None): if elem != slice(None):
return return
elif isinstance(elem, (integer_types, numpy.integer)): elif isinstance(elem, (integer_types, np.integer)):
if elem in [0, -1] and node.inputs[0].broadcastable[dim]: if elem in [0, -1] and node.inputs[0].broadcastable[dim]:
remove_dim.append(dim) remove_dim.append(dim)
else: else:
...@@ -2027,7 +2027,7 @@ def local_subtensor_make_vector(node): ...@@ -2027,7 +2027,7 @@ def local_subtensor_make_vector(node):
else: else:
return return
if isinstance(idx, (integer_types, numpy.integer)): if isinstance(idx, (integer_types, np.integer)):
# We don't need to copy over any stack traces here # We don't need to copy over any stack traces here
return [x.owner.inputs[idx]] return [x.owner.inputs[idx]]
elif isinstance(idx, Variable): elif isinstance(idx, Variable):
...@@ -2035,7 +2035,7 @@ def local_subtensor_make_vector(node): ...@@ -2035,7 +2035,7 @@ def local_subtensor_make_vector(node):
# if it is a constant we can do something with it # if it is a constant we can do something with it
try: try:
v = get_scalar_constant_value(idx, only_process_constants=True) v = get_scalar_constant_value(idx, only_process_constants=True)
if isinstance(v, numpy.integer): if isinstance(v, np.integer):
# Python 2.4 wants to index only with Python integers # Python 2.4 wants to index only with Python integers
v = int(v) v = int(v)
# We don't need to copy over any stack traces here # We don't need to copy over any stack traces here
...@@ -2359,14 +2359,14 @@ class Assert(T.Op): ...@@ -2359,14 +2359,14 @@ class Assert(T.Op):
if not isinstance(value, Variable): if not isinstance(value, Variable):
value = T.as_tensor_variable(value) value = T.as_tensor_variable(value)
cond = [T.as_tensor_variable(c) for c in conds] cond = [T.as_tensor_variable(c) for c in conds]
assert numpy.all([c.type.ndim == 0 for c in cond]) assert np.all([c.type.ndim == 0 for c in cond])
return gof.Apply(self, [value] + cond, [value.type()]) return gof.Apply(self, [value] + cond, [value.type()])
def perform(self, node, inputs, out_): def perform(self, node, inputs, out_):
out, = out_ out, = out_
v = inputs[0] v = inputs[0]
out[0] = v out[0] = v
assert numpy.all(inputs[1:]), self.msg assert np.all(inputs[1:]), self.msg
def grad(self, input, output_gradients): def grad(self, input, output_gradients):
return output_gradients + [DisconnectedType()()] * (len(input) - 1) return output_gradients + [DisconnectedType()()] * (len(input) - 1)
...@@ -2708,7 +2708,7 @@ def local_useless_subtensor(node): ...@@ -2708,7 +2708,7 @@ def local_useless_subtensor(node):
length_pos = shape_of[node.inputs[0]][pos] length_pos = shape_of[node.inputs[0]][pos]
if isinstance(idx.stop, (integer_types, numpy.integer)): if isinstance(idx.stop, (integer_types, np.integer)):
length_pos_data = sys.maxsize length_pos_data = sys.maxsize
try: try:
length_pos_data = get_scalar_constant_value(length_pos, length_pos_data = get_scalar_constant_value(length_pos,
...@@ -2766,7 +2766,7 @@ def local_useless_subtensor(node): ...@@ -2766,7 +2766,7 @@ def local_useless_subtensor(node):
idx = idx.value idx = idx.value
if len(idx) != length: if len(idx) != length:
return False return False
if numpy.any(idx != numpy.arange(length)): if np.any(idx != np.arange(length)):
return False return False
elif idx.owner is not None and isinstance(idx.owner.op, T.ARange): elif idx.owner is not None and isinstance(idx.owner.op, T.ARange):
try: try:
...@@ -3625,7 +3625,7 @@ def local_useless_rebroadcast(node): ...@@ -3625,7 +3625,7 @@ def local_useless_rebroadcast(node):
""" """
if isinstance(node.op, T.Rebroadcast): if isinstance(node.op, T.Rebroadcast):
x = node.inputs[0] x = node.inputs[0]
if numpy.all(x.broadcastable == node.outputs[0].broadcastable): if np.all(x.broadcastable == node.outputs[0].broadcastable):
# No broadcastable flag was modified # No broadcastable flag was modified
# No need to copy over stack trace, # No need to copy over stack trace,
# because x should already have a stack trace. # because x should already have a stack trace.
...@@ -3938,8 +3938,8 @@ def local_useless_switch(node): ...@@ -3938,8 +3938,8 @@ def local_useless_switch(node):
isinstance(node.op.scalar_op, scalar.basic.Switch)): isinstance(node.op.scalar_op, scalar.basic.Switch)):
cond = T.extract_constant(node.inputs[0], cond = T.extract_constant(node.inputs[0],
only_process_constants=True) only_process_constants=True)
if ((type(cond) is numpy.ndarray and cond.ndim == 0) or if ((type(cond) is np.ndarray and cond.ndim == 0) or
isinstance(cond, numpy.number)): isinstance(cond, np.number)):
if cond == 0: if cond == 0:
correct_out = node.inputs[2] correct_out = node.inputs[2]
else: else:
...@@ -4985,7 +4985,7 @@ class Canonizer(gof.LocalOptimizer): ...@@ -4985,7 +4985,7 @@ class Canonizer(gof.LocalOptimizer):
num, denum = self.simplify(list(orig_num), list(orig_denum), out.type) num, denum = self.simplify(list(orig_num), list(orig_denum), out.type)
def same(x, y): def same(x, y):
return len(x) == len(y) and all(numpy.all(xe == ye) for xe, ye in return len(x) == len(y) and all(np.all(xe == ye) for xe, ye in
zip(x, y)) zip(x, y))
if same(orig_num, num) and same(orig_denum, denum): if same(orig_num, num) and same(orig_denum, denum):
...@@ -5029,7 +5029,7 @@ def mul_calculate(num, denum, aslist=False, out_type=None): ...@@ -5029,7 +5029,7 @@ def mul_calculate(num, denum, aslist=False, out_type=None):
if aslist: if aslist:
return [] return []
else: else:
return numpy.int8(1) return np.int8(1)
# Make sure we do not accidently upcast data types. # Make sure we do not accidently upcast data types.
if out_type is None: if out_type is None:
...@@ -5038,9 +5038,9 @@ def mul_calculate(num, denum, aslist=False, out_type=None): ...@@ -5038,9 +5038,9 @@ def mul_calculate(num, denum, aslist=False, out_type=None):
out_dtype = out_type.dtype out_dtype = out_type.dtype
one = theano._asarray(1, dtype=out_dtype) one = theano._asarray(1, dtype=out_dtype)
v = reduce(numpy.multiply, num, one) / reduce(numpy.multiply, denum, one) v = reduce(np.multiply, num, one) / reduce(np.multiply, denum, one)
if aslist: if aslist:
if numpy.all(v == 1): if np.all(v == 1):
return [] return []
else: else:
return [v] return [v]
...@@ -5053,7 +5053,7 @@ register_canonicalize(local_mul_canonizer, name='local_mul_canonizer') ...@@ -5053,7 +5053,7 @@ register_canonicalize(local_mul_canonizer, name='local_mul_canonizer')
@gof.local_optimizer([T.neg]) @gof.local_optimizer([T.neg])
def local_neg_to_mul(node): def local_neg_to_mul(node):
if node.op == T.neg: if node.op == T.neg:
return [T.mul(numpy.array(-1, dtype=node.inputs[0].dtype), return [T.mul(np.array(-1, dtype=node.inputs[0].dtype),
node.inputs[0])] node.inputs[0])]
register_canonicalize(local_neg_to_mul) register_canonicalize(local_neg_to_mul)
...@@ -5078,13 +5078,13 @@ def local_sum_prod_mul_by_scalar(node): ...@@ -5078,13 +5078,13 @@ def local_sum_prod_mul_by_scalar(node):
if node_inps.owner and node_inps.owner.op == T.mul: if node_inps.owner and node_inps.owner.op == T.mul:
terms = node_inps.owner.inputs terms = node_inps.owner.inputs
scalars = [t.dimshuffle() for t in terms if scalars = [t.dimshuffle() for t in terms if
numpy.all(t.type.broadcastable)] np.all(t.type.broadcastable)]
if len(scalars) == 0: if len(scalars) == 0:
# Nothing to optimize here # Nothing to optimize here
return return
non_scalars = [t for t in terms if not numpy.all(t.broadcastable)] non_scalars = [t for t in terms if not np.all(t.broadcastable)]
# Perform the op only on the non-scalar inputs, if applicable # Perform the op only on the non-scalar inputs, if applicable
if len(non_scalars) == 0: if len(non_scalars) == 0:
...@@ -5780,7 +5780,7 @@ def local_neg_div_neg(node): ...@@ -5780,7 +5780,7 @@ def local_neg_div_neg(node):
# No other clients of the original division # No other clients of the original division
new_num = num.owner.inputs[0] new_num = num.owner.inputs[0]
return [T.true_div(new_num, denom)] return [T.true_div(new_num, denom)]
elif numpy.all(num.broadcastable) and isinstance(num, Constant): elif np.all(num.broadcastable) and isinstance(num, Constant):
if len(frac.clients) == 1: if len(frac.clients) == 1:
new_num = -num.data new_num = -num.data
return [T.true_div(new_num, denom)] return [T.true_div(new_num, denom)]
...@@ -5811,7 +5811,7 @@ register_canonicalize(local_mul_zero) ...@@ -5811,7 +5811,7 @@ register_canonicalize(local_mul_zero)
@gof.local_optimizer([T.true_div]) @gof.local_optimizer([T.true_div])
def local_div_to_inv(node): def local_div_to_inv(node):
if node.op == T.true_div and numpy.all( if node.op == T.true_div and np.all(
local_mul_canonizer.get_constant(node.inputs[0]) == 1.0): local_mul_canonizer.get_constant(node.inputs[0]) == 1.0):
out = node.outputs[0] out = node.outputs[0]
new_out = T.inv(local_mul_canonizer.merge_num_denum(node.inputs[1:], new_out = T.inv(local_mul_canonizer.merge_num_denum(node.inputs[1:],
...@@ -5873,7 +5873,7 @@ def local_intdiv_by_one(node): ...@@ -5873,7 +5873,7 @@ def local_intdiv_by_one(node):
""" """
if node.op in [T.int_div]: if node.op in [T.int_div]:
if isinstance(node.inputs[1], T.TensorConstant) and \ if isinstance(node.inputs[1], T.TensorConstant) and \
numpy.all(node.inputs[1].value == 1): np.all(node.inputs[1].value == 1):
return [node.inputs[0].astype(node.outputs[0].dtype)] return [node.inputs[0].astype(node.outputs[0].dtype)]
...@@ -5906,19 +5906,19 @@ def local_pow_specialize(node): ...@@ -5906,19 +5906,19 @@ def local_pow_specialize(node):
ysym.type.broadcastable): ysym.type.broadcastable):
rval = None rval = None
if numpy.all(y == 2): if np.all(y == 2):
rval = [T.sqr(xsym)] rval = [T.sqr(xsym)]
if numpy.all(y == 1): if np.all(y == 1):
rval = [xsym] rval = [xsym]
if numpy.all(y == 0): if np.all(y == 0):
rval = [T.fill(xsym, numpy.asarray(1, dtype=odtype))] rval = [T.fill(xsym, np.asarray(1, dtype=odtype))]
if numpy.all(y == 0.5): if np.all(y == 0.5):
rval = [T.sqrt(xsym)] rval = [T.sqrt(xsym)]
if numpy.all(y == -0.5): if np.all(y == -0.5):
rval = [T.inv(T.sqrt(xsym))] rval = [T.inv(T.sqrt(xsym))]
if numpy.all(y == -1): if np.all(y == -1):
rval = [T.inv(xsym)] rval = [T.inv(xsym)]
if numpy.all(y == -2): if np.all(y == -2):
rval = [T.inv(T.sqr(xsym))] rval = [T.inv(T.sqr(xsym))]
if rval: if rval:
rval[0] = T.cast(rval[0], odtype) rval[0] = T.cast(rval[0], odtype)
...@@ -5951,7 +5951,7 @@ def local_pow_specialize_device(node): ...@@ -5951,7 +5951,7 @@ def local_pow_specialize_device(node):
# taking the value outside ndarray solve the problem. # taking the value outside ndarray solve the problem.
# it could be that in that case, numpy make the comparaison # it could be that in that case, numpy make the comparaison
# into the wrong type(do in int8 that overflow.) # into the wrong type(do in int8 that overflow.)
if isinstance(y, numpy.ndarray): if isinstance(y, np.ndarray):
assert y.size == 1 assert y.size == 1
try: try:
y = y[0] y = y[0]
...@@ -5966,13 +5966,13 @@ def local_pow_specialize_device(node): ...@@ -5966,13 +5966,13 @@ def local_pow_specialize_device(node):
pow2 = [xsym] pow2 = [xsym]
pow2_scal = [theano.scalar.get_scalar_type(xsym.dtype)()] pow2_scal = [theano.scalar.get_scalar_type(xsym.dtype)()]
y_to_do = abs(y) y_to_do = abs(y)
for i in xrange(int(numpy.log2(y_to_do))): for i in xrange(int(np.log2(y_to_do))):
pow2.append(T.sqr(pow2[i])) pow2.append(T.sqr(pow2[i]))
pow2_scal.append(theano.scalar.sqr(pow2_scal[i])) pow2_scal.append(theano.scalar.sqr(pow2_scal[i]))
rval1 = None rval1 = None
rval1_scal = None rval1_scal = None
while y_to_do > 0: while y_to_do > 0:
log_to_do = int(numpy.log2(y_to_do)) log_to_do = int(np.log2(y_to_do))
if rval1: if rval1:
rval1 *= pow2[log_to_do] rval1 *= pow2[log_to_do]
rval1_scal *= pow2_scal[log_to_do] rval1_scal *= pow2_scal[log_to_do]
...@@ -6061,7 +6061,7 @@ def local_mul_specialize(node): ...@@ -6061,7 +6061,7 @@ def local_mul_specialize(node):
elif neg: elif neg:
# Don't add an extra neg node as we can't # Don't add an extra neg node as we can't
# fully replace this mul by a neg. # fully replace this mul by a neg.
m1 = numpy.asarray(-1, dtype=node.outputs[0].dtype) m1 = np.asarray(-1, dtype=node.outputs[0].dtype)
new_inputs = [m1] + new_inputs new_inputs = [m1] + new_inputs
rval = T.mul(*new_inputs) rval = T.mul(*new_inputs)
...@@ -6092,7 +6092,7 @@ def local_add_specialize(node): ...@@ -6092,7 +6092,7 @@ def local_add_specialize(node):
y = get_scalar_constant_value(input) y = get_scalar_constant_value(input)
except NotScalarConstantError: except NotScalarConstantError:
y = input y = input
if numpy.all(y == 0.0): if np.all(y == 0.0):
continue continue
new_inputs.append(input) new_inputs.append(input)
...@@ -6102,7 +6102,7 @@ def local_add_specialize(node): ...@@ -6102,7 +6102,7 @@ def local_add_specialize(node):
# we got rid of the entire expression! # we got rid of the entire expression!
ndim = node.outputs[0].type.ndim ndim = node.outputs[0].type.ndim
# Reuse call to constant for cache() # Reuse call to constant for cache()
cst = T.constant(numpy.zeros((1,) * ndim, dtype=dtype)) cst = T.constant(np.zeros((1,) * ndim, dtype=dtype))
assert cst.type.broadcastable == (True,) * ndim assert cst.type.broadcastable == (True,) * ndim
return fill_chain(cst) return fill_chain(cst)
...@@ -6209,7 +6209,7 @@ def local_log1p(node): ...@@ -6209,7 +6209,7 @@ def local_log1p(node):
scalars, scalar_inputs, nonconsts = scalarconsts_rest( scalars, scalar_inputs, nonconsts = scalarconsts_rest(
log_arg.owner.inputs, only_process_constants=True) log_arg.owner.inputs, only_process_constants=True)
# scalar_inputs are potentially dimshuffled and fill'd scalars # scalar_inputs are potentially dimshuffled and fill'd scalars
if scalars and numpy.allclose(numpy.sum(scalars), 1): if scalars and np.allclose(np.sum(scalars), 1):
if nonconsts: if nonconsts:
if len(nonconsts) > 1: if len(nonconsts) > 1:
ninp = T.add(*nonconsts) ninp = T.add(*nonconsts)
...@@ -6315,9 +6315,9 @@ def add_calculate(num, denum, aslist=False, out_type=None): ...@@ -6315,9 +6315,9 @@ def add_calculate(num, denum, aslist=False, out_type=None):
zero = theano._asarray(0, dtype=out_type.dtype) zero = theano._asarray(0, dtype=out_type.dtype)
# zero = 0.0 if out_type is None else theano._asarray(0, # zero = 0.0 if out_type is None else theano._asarray(0,
# dtype=out_type.dtype) # dtype=out_type.dtype)
v = reduce(numpy.add, num, zero) - reduce(numpy.add, denum, zero) v = reduce(np.add, num, zero) - reduce(np.add, denum, zero)
if aslist: if aslist:
if numpy.all(v == 0): if np.all(v == 0):
return [] return []
else: else:
return [v] return [v]
...@@ -6708,7 +6708,7 @@ def local_log_erfc(node): ...@@ -6708,7 +6708,7 @@ def local_log_erfc(node):
node.tag.local_log_erfc_applied = True node.tag.local_log_erfc_applied = True
x = node.inputs[0].owner.inputs[0] x = node.inputs[0].owner.inputs[0]
stab_value = (-x ** 2 - T.log(x) - .5 * T.log(numpy.pi) + stab_value = (-x ** 2 - T.log(x) - .5 * T.log(np.pi) +
T.log(1 - 1 / (2 * x ** 2) + 3 / (4 * x ** 4) - T.log(1 - 1 / (2 * x ** 2) + 3 / (4 * x ** 4) -
15 / (8 * x ** 6))) 15 / (8 * x ** 6)))
...@@ -6863,7 +6863,7 @@ def local_grad_log_erfc_neg(node): ...@@ -6863,7 +6863,7 @@ def local_grad_log_erfc_neg(node):
# aaron value # aaron value
stab_value = (x * T.pow(1 - 1 / (2 * (x ** 2)) + stab_value = (x * T.pow(1 - 1 / (2 * (x ** 2)) +
3 / (4 * (x ** 4)) - 15 / (8 * (x ** 6)), -1) * 3 / (4 * (x ** 4)) - 15 / (8 * (x ** 6)), -1) *
T.cast(T.sqrt(numpy.pi), dtype=x.dtype)) T.cast(T.sqrt(np.pi), dtype=x.dtype))
if x.dtype == 'float32' or x.dtype == 'float16': if x.dtype == 'float32' or x.dtype == 'float16':
threshold = 9.3 threshold = 9.3
......
theano/tensor/raw_random.py
浏览文件 @ ef9f6efc
...@@ -4,7 +4,7 @@ from __future__ import absolute_import, print_function, division ...@@ -4,7 +4,7 @@ from __future__ import absolute_import, print_function, division
import sys import sys
from copy import copy from copy import copy
import numpy import numpy as np
from six import string_types from six import string_types
from six.moves import reduce, xrange from six.moves import reduce, xrange
...@@ -38,7 +38,7 @@ class RandomStateType(gof.Type): ...@@ -38,7 +38,7 @@ class RandomStateType(gof.Type):
raise TypeError() raise TypeError()
def is_valid_value(self, a): def is_valid_value(self, a):
return type(a) == numpy.random.RandomState return type(a) == np.random.RandomState
def values_eq(self, a, b): def values_eq(self, a, b):
sa = a.get_state() sa = a.get_state()
...@@ -47,7 +47,7 @@ class RandomStateType(gof.Type): ...@@ -47,7 +47,7 @@ class RandomStateType(gof.Type):
if sa[0] != sb[0]: if sa[0] != sb[0]:
return False return False
# 1-D array of 624 unsigned integer keys # 1-D array of 624 unsigned integer keys
if not numpy.all(sa[1] == sb[1]): if not np.all(sa[1] == sb[1]):
return False return False
# integer "pos" representing the position in the array # integer "pos" representing the position in the array
if sa[2] != sb[2]: if sa[2] != sb[2]:
...@@ -67,17 +67,17 @@ class RandomStateType(gof.Type): ...@@ -67,17 +67,17 @@ class RandomStateType(gof.Type):
def get_size(self, shape_info): def get_size(self, shape_info):
# The size is the data, that have constant size. # The size is the data, that have constant size.
state = numpy.random.RandomState().get_state() state = np.random.RandomState().get_state()
size = 0 size = 0
for elem in state: for elem in state:
if isinstance(elem, str): if isinstance(elem, str):
size += len(elem) size += len(elem)
elif isinstance(elem, numpy.ndarray): elif isinstance(elem, np.ndarray):
size += elem.size * elem.itemsize size += elem.size * elem.itemsize
elif isinstance(elem, int): elif isinstance(elem, int):
size += numpy.dtype("int").itemsize size += np.dtype("int").itemsize
elif isinstance(elem, float): elif isinstance(elem, float):
size += numpy.dtype("float").itemsize size += np.dtype("float").itemsize
else: else:
raise NotImplementedError() raise NotImplementedError()
return size return size
...@@ -151,7 +151,7 @@ class RandomFunction(gof.Op): ...@@ -151,7 +151,7 @@ class RandomFunction(gof.Op):
fn, outtype, inplace, ndim_added = state fn, outtype, inplace, ndim_added = state
self.fn = fn self.fn = fn
if isinstance(fn, string_types): if isinstance(fn, string_types):
self.exec_fn = getattr(numpy.random.RandomState, fn) self.exec_fn = getattr(np.random.RandomState, fn)
else: else:
self.exec_fn = fn self.exec_fn = fn
self.outtype = outtype self.outtype = outtype
...@@ -240,7 +240,7 @@ class RandomFunction(gof.Op): ...@@ -240,7 +240,7 @@ class RandomFunction(gof.Op):
# Numbers are drawn from r if self.inplace is True, and from a # Numbers are drawn from r if self.inplace is True, and from a
# copy of r if self.inplace is False # copy of r if self.inplace is False
r, shape, args = inputs[0], inputs[1], inputs[2:] r, shape, args = inputs[0], inputs[1], inputs[2:]
assert type(r) == numpy.random.RandomState, (type(r), r) assert type(r) == np.random.RandomState, (type(r), r)
# If shape == [], that means no shape is enforced, and numpy is # If shape == [], that means no shape is enforced, and numpy is
# trusted to draw the appropriate number of samples, numpy uses # trusted to draw the appropriate number of samples, numpy uses
...@@ -260,7 +260,7 @@ class RandomFunction(gof.Op): ...@@ -260,7 +260,7 @@ class RandomFunction(gof.Op):
r = copy(r) r = copy(r)
rout[0] = r rout[0] = r
rval = self.exec_fn(r, *(args + [shape])) rval = self.exec_fn(r, *(args + [shape]))
if (not isinstance(rval, numpy.ndarray) or if (not isinstance(rval, np.ndarray) or
str(rval.dtype) != node.outputs[1].type.dtype): str(rval.dtype) != node.outputs[1].type.dtype):
rval = theano._asarray(rval, dtype=node.outputs[1].type.dtype) rval = theano._asarray(rval, dtype=node.outputs[1].type.dtype)
...@@ -527,13 +527,13 @@ def binomial(random_state, size=None, n=1, p=0.5, ndim=None, ...@@ -527,13 +527,13 @@ def binomial(random_state, size=None, n=1, p=0.5, ndim=None,
""" """
if prob is not None: if prob is not None:
p = prob p = prob
print("DEPRECATION WARNING: the parameter prob to the binomal fct have been renamed to p to have the same name as numpy.", file=sys.stderr) print("DEPRECATION WARNING: the parameter prob to the binomal fct have been renamed to p to have the same name as np.", file=sys.stderr)
n = tensor.as_tensor_variable(n) n = tensor.as_tensor_variable(n)
p = tensor.as_tensor_variable(p) p = tensor.as_tensor_variable(p)
ndim, size, bcast = _infer_ndim_bcast(ndim, size, n, p) ndim, size, bcast = _infer_ndim_bcast(ndim, size, n, p)
if n.dtype == 'int64': if n.dtype == 'int64':
try: try:
numpy.random.binomial(n=numpy.asarray([2, 3, 4], dtype='int64'), p=numpy.asarray([.1, .2, .3], dtype='float64')) np.random.binomial(n=np.asarray([2, 3, 4], dtype='int64'), p=np.asarray([.1, .2, .3], dtype='float64'))
except TypeError: except TypeError:
# THIS WORKS AROUND A NUMPY BUG on 32bit machine # THIS WORKS AROUND A NUMPY BUG on 32bit machine
n = tensor.cast(n, 'int32') n = tensor.cast(n, 'int32')
...@@ -583,7 +583,7 @@ def random_integers_helper(random_state, low, high, size): ...@@ -583,7 +583,7 @@ def random_integers_helper(random_state, low, high, size):
out_size = out_size + (dim_len,) out_size = out_size + (dim_len,)
# Build the indices over which to loop # Build the indices over which to loop
out = numpy.ndarray(out_size) out = np.ndarray(out_size)
broadcast_ind = _generate_broadcasting_indices(out_size, low.shape, broadcast_ind = _generate_broadcasting_indices(out_size, low.shape,
high.shape) high.shape)
# Iterate over these indices, drawing one sample at a time from numpy # Iterate over these indices, drawing one sample at a time from numpy
...@@ -716,8 +716,8 @@ def permutation_helper(random_state, n, shape): ...@@ -716,8 +716,8 @@ def permutation_helper(random_state, n, shape):
shape = () shape = ()
out_shape = list(shape) out_shape = list(shape)
out_shape.append(n) out_shape.append(n)
out = numpy.empty(out_shape, int) out = np.empty(out_shape, int)
for i in numpy.ndindex(*shape): for i in np.ndindex(*shape):
out[i] = random_state.permutation(n) out[i] = random_state.permutation(n)
# print 'RETURNING', out.shape # print 'RETURNING', out.shape
...@@ -801,7 +801,7 @@ def multinomial_helper(random_state, n, pvals, size): ...@@ -801,7 +801,7 @@ def multinomial_helper(random_state, n, pvals, size):
# Build the indices over which to loop # Build the indices over which to loop
# Note that here, the rows (inner-most 1D subtensors) of pvals and out # Note that here, the rows (inner-most 1D subtensors) of pvals and out
# are indexed, not their individual elements # are indexed, not their individual elements
out = numpy.ndarray(out_size) out = np.ndarray(out_size)
broadcast_ind = _generate_broadcasting_indices(size, n.shape, broadcast_ind = _generate_broadcasting_indices(size, n.shape,
pvals.shape[:-1]) pvals.shape[:-1])
# Iterate over these indices, drawing from one multinomial at a # Iterate over these indices, drawing from one multinomial at a
...@@ -815,16 +815,16 @@ def multinomial_helper(random_state, n, pvals, size): ...@@ -815,16 +815,16 @@ def multinomial_helper(random_state, n, pvals, size):
# of probabilities meets or exceeds 1.0. # of probabilities meets or exceeds 1.0.
# In perfect arithmetic this would be correct, but in float32 or # In perfect arithmetic this would be correct, but in float32 or
# float64 it is too strict. # float64 it is too strict.
pisum = numpy.sum(pvi) pisum = np.sum(pvi)
if 1.0 < pisum < 1.0 + 1e-5: # correct if we went a little over if 1.0 < pisum < 1.0 + 1e-5: # correct if we went a little over
# because mtrand.pyx has a ValueError that will trigger if # because mtrand.pyx has a ValueError that will trigger if
# sum(pvals[:-1]) > 1.0 # sum(pvals[:-1]) > 1.0
pvi = pvi * (1.0 - 5e-5) pvi = pvi * (1.0 - 5e-5)
# pvi = pvi * .9 # pvi = pvi * .9
pisum = numpy.sum(pvi) pisum = np.sum(pvi)
elif pvi[-1] < 5e-5: # will this even work? elif pvi[-1] < 5e-5: # will this even work?
pvi = pvi * (1.0 - 5e-5) pvi = pvi * (1.0 - 5e-5)
pisum = numpy.sum(pvi) pisum = np.sum(pvi)
assert pisum <= 1.0, pisum assert pisum <= 1.0, pisum
out[mi] = random_state.multinomial(n=n[ni], out[mi] = random_state.multinomial(n=n[ni],
pvals=pvi.astype('float64')) pvals=pvi.astype('float64'))
......
theano/tensor/shared_randomstreams.py
浏览文件 @ ef9f6efc
...@@ -7,7 +7,7 @@ from __future__ import absolute_import, print_function, division ...@@ -7,7 +7,7 @@ from __future__ import absolute_import, print_function, division
import copy import copy
import numpy import numpy as np
from theano.compile.sharedvalue import (SharedVariable, shared_constructor, from theano.compile.sharedvalue import (SharedVariable, shared_constructor,
shared) shared)
...@@ -27,7 +27,7 @@ def randomstate_constructor(value, name=None, strict=False, ...@@ -27,7 +27,7 @@ def randomstate_constructor(value, name=None, strict=False,
SharedVariable Constructor for RandomState. SharedVariable Constructor for RandomState.
""" """
if not isinstance(value, numpy.random.RandomState): if not isinstance(value, np.random.RandomState):
raise TypeError raise TypeError
if not borrow: if not borrow:
value = copy.deepcopy(value) value = copy.deepcopy(value)
...@@ -65,7 +65,7 @@ class RandomStreams(raw_random.RandomStreamsBase): ...@@ -65,7 +65,7 @@ class RandomStreams(raw_random.RandomStreamsBase):
# random number generator that provides seeds for member streams. # random number generator that provides seeds for member streams.
self.default_instance_seed = seed self.default_instance_seed = seed
# numpy.RandomState instance that gen() uses to seed new streams. # numpy.RandomState instance that gen() uses to seed new streams.
self.gen_seedgen = numpy.random.RandomState(seed) self.gen_seedgen = np.random.RandomState(seed)
def seed(self, seed=None): def seed(self, seed=None):
""" """
...@@ -85,10 +85,10 @@ class RandomStreams(raw_random.RandomStreamsBase): ...@@ -85,10 +85,10 @@ class RandomStreams(raw_random.RandomStreamsBase):
if seed is None: if seed is None:
seed = self.default_instance_seed seed = self.default_instance_seed
seedgen = numpy.random.RandomState(seed) seedgen = np.random.RandomState(seed)
for old_r, new_r in self.state_updates: for old_r, new_r in self.state_updates:
old_r_seed = seedgen.randint(2 ** 30) old_r_seed = seedgen.randint(2 ** 30)
old_r.set_value(numpy.random.RandomState(int(old_r_seed)), old_r.set_value(np.random.RandomState(int(old_r_seed)),
borrow=True) borrow=True)
def __getitem__(self, item): def __getitem__(self, item):
...@@ -161,7 +161,7 @@ class RandomStreams(raw_random.RandomStreamsBase): ...@@ -161,7 +161,7 @@ class RandomStreams(raw_random.RandomStreamsBase):
""" """
seed = int(self.gen_seedgen.randint(2 ** 30)) seed = int(self.gen_seedgen.randint(2 ** 30))
random_state_variable = shared(numpy.random.RandomState(seed)) random_state_variable = shared(np.random.RandomState(seed))
# Add a reference to distinguish from other shared variables # Add a reference to distinguish from other shared variables
random_state_variable.tag.is_rng = True random_state_variable.tag.is_rng = True
new_r, out = op(random_state_variable, *args, **kwargs) new_r, out = op(random_state_variable, *args, **kwargs)
......
theano/tensor/sharedvar.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import traceback import traceback
import numpy import numpy as np
from six import integer_types from six import integer_types
import theano.tensor.basic import theano.tensor.basic
...@@ -41,7 +41,7 @@ def tensor_constructor(value, name=None, strict=False, allow_downcast=None, ...@@ -41,7 +41,7 @@ def tensor_constructor(value, name=None, strict=False, allow_downcast=None,
if target != 'cpu': if target != 'cpu':
raise TypeError('not for cpu') raise TypeError('not for cpu')
if not isinstance(value, numpy.ndarray): if not isinstance(value, np.ndarray):
raise TypeError() raise TypeError()
# if no broadcastable is given, then the default is to assume that # if no broadcastable is given, then the default is to assume that
...@@ -51,7 +51,7 @@ def tensor_constructor(value, name=None, strict=False, allow_downcast=None, ...@@ -51,7 +51,7 @@ def tensor_constructor(value, name=None, strict=False, allow_downcast=None,
broadcastable = (False,) * len(value.shape) broadcastable = (False,) * len(value.shape)
type = TensorType(value.dtype, broadcastable=broadcastable) type = TensorType(value.dtype, broadcastable=broadcastable)
return TensorSharedVariable(type=type, return TensorSharedVariable(type=type,
value=numpy.array(value, copy=(not borrow)), value=np.array(value, copy=(not borrow)),
name=name, name=name,
strict=strict, strict=strict,
allow_downcast=allow_downcast) allow_downcast=allow_downcast)
...@@ -86,12 +86,12 @@ def scalar_constructor(value, name=None, strict=False, allow_downcast=None, ...@@ -86,12 +86,12 @@ def scalar_constructor(value, name=None, strict=False, allow_downcast=None,
if target != 'cpu': if target != 'cpu':
raise TypeError('not for cpu') raise TypeError('not for cpu')
if not isinstance(value, (numpy.number, float, integer_types, complex)): if not isinstance(value, (np.number, float, integer_types, complex)):
raise TypeError() raise TypeError()
try: try:
dtype = value.dtype dtype = value.dtype
except Exception: except Exception:
dtype = numpy.asarray(value).dtype dtype = np.asarray(value).dtype
dtype = str(dtype) dtype = str(dtype)
value = theano._asarray(value, dtype=dtype) value = theano._asarray(value, dtype=dtype)
...@@ -101,7 +101,7 @@ def scalar_constructor(value, name=None, strict=False, allow_downcast=None, ...@@ -101,7 +101,7 @@ def scalar_constructor(value, name=None, strict=False, allow_downcast=None,
# Do not pass the dtype to asarray because we want this to fail if # Do not pass the dtype to asarray because we want this to fail if
# strict is True and the types do not match. # strict is True and the types do not match.
rval = ScalarSharedVariable(type=tensor_type, rval = ScalarSharedVariable(type=tensor_type,
value=numpy.array(value, copy=True), value=np.array(value, copy=True),
name=name, name=name,
strict=strict, strict=strict,
allow_downcast=allow_downcast) allow_downcast=allow_downcast)
......
theano/tensor/signal/pool.py
浏览文件 @ ef9f6efc
...@@ -9,7 +9,7 @@ from __future__ import absolute_import, print_function, division ...@@ -9,7 +9,7 @@ from __future__ import absolute_import, print_function, division
import warnings import warnings
import itertools import itertools
import numpy import numpy as np
from six.moves import xrange from six.moves import xrange
import six.moves.builtins as builtins import six.moves.builtins as builtins
import theano import theano
...@@ -412,7 +412,7 @@ class Pool(OpenMPOp): ...@@ -412,7 +412,7 @@ class Pool(OpenMPOp):
if isinstance(out, theano.Variable): if isinstance(out, theano.Variable):
return tensor.maximum(out, 0) return tensor.maximum(out, 0)
else: else:
return numpy.maximum(out, 0) return np.maximum(out, 0)
else: else:
if isinstance(v, theano.Variable): if isinstance(v, theano.Variable):
return tensor.switch(tensor.ge(stride, downsample), return tensor.switch(tensor.ge(stride, downsample),
...@@ -516,7 +516,7 @@ class Pool(OpenMPOp): ...@@ -516,7 +516,7 @@ class Pool(OpenMPOp):
if not self.ignore_border: if not self.ignore_border:
assert all(z > 0 for z in z_shape[-nd:]) assert all(z > 0 for z in z_shape[-nd:])
if (z[0] is None) or (z[0].shape != z_shape): if (z[0] is None) or (z[0].shape != z_shape):
z[0] = numpy.empty(z_shape, dtype=x.dtype) z[0] = np.empty(z_shape, dtype=x.dtype)
zz = z[0] zz = z[0]
# size of pooling output # size of pooling output
pool_out_shp = zz.shape[-nd:] pool_out_shp = zz.shape[-nd:]
...@@ -525,16 +525,16 @@ class Pool(OpenMPOp): ...@@ -525,16 +525,16 @@ class Pool(OpenMPOp):
# pad the image # pad the image
if max(pad) != 0: if max(pad) != 0:
y = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y[(slice(None),) * (len(x.shape) - nd) + y[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x
else: else:
y = x y = x
func = numpy.max func = np.max
if self.mode == 'sum': if self.mode == 'sum':
func = numpy.sum func = np.sum
elif self.mode != 'max': elif self.mode != 'max':
func = numpy.average func = np.average
# precompute the region boundaries for each dimension # precompute the region boundaries for each dimension
region_slices = [[] for i in xrange(nd)] region_slices = [[] for i in xrange(nd)]
...@@ -548,11 +548,11 @@ class Pool(OpenMPOp): ...@@ -548,11 +548,11 @@ class Pool(OpenMPOp):
region_slices[i].append(slice(start, end)) region_slices[i].append(slice(start, end))
# iterate over non-pooling dimensions # iterate over non-pooling dimensions
for k in numpy.ndindex(*x.shape[:-nd]): for k in np.ndindex(*x.shape[:-nd]):
zzk = zz[k] zzk = zz[k]
yk = y[k] yk = y[k]
# iterate over pooling regions # iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp): for r in np.ndindex(*pool_out_shp):
zzk[r] = func( zzk[r] = func(
yk[[region_slices[i][r[i]] for i in xrange(nd)]]) yk[[region_slices[i][r[i]] for i in xrange(nd)]])
...@@ -1020,7 +1020,7 @@ class PoolGrad(OpenMPOp): ...@@ -1020,7 +1020,7 @@ class PoolGrad(OpenMPOp):
if isinstance(out, theano.Variable): if isinstance(out, theano.Variable):
return tensor.maximum(out, 0) return tensor.maximum(out, 0)
else: else:
return numpy.maximum(out, 0) return np.maximum(out, 0)
else: else:
if isinstance(v, theano.Variable): if isinstance(v, theano.Variable):
return tensor.switch(tensor.ge(stride, downsample), return tensor.switch(tensor.ge(stride, downsample),
...@@ -1128,12 +1128,12 @@ class MaxPoolGrad(PoolGrad): ...@@ -1128,12 +1128,12 @@ class MaxPoolGrad(PoolGrad):
# pad the image # pad the image
if max(pad) != 0: if max(pad) != 0:
y = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y[(slice(None),) * (len(x.shape) - nd) + y[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x
else: else:
y = x y = x
gx = numpy.zeros_like(y) gx = np.zeros_like(y)
# precompute the region boundaries for each dimension # precompute the region boundaries for each dimension
region_ranges = [[] for i in xrange(nd)] region_ranges = [[] for i in xrange(nd)]
...@@ -1144,13 +1144,13 @@ class MaxPoolGrad(PoolGrad): ...@@ -1144,13 +1144,13 @@ class MaxPoolGrad(PoolGrad):
region_ranges[i].append(xrange(start, end)) region_ranges[i].append(xrange(start, end))
# iterate over non-pooling dimensions # iterate over non-pooling dimensions
for k in numpy.ndindex(*x.shape[:-nd]): for k in np.ndindex(*x.shape[:-nd]):
gxk = gx[k] gxk = gx[k]
gzk = gz[k] gzk = gz[k]
yk = y[k] yk = y[k]
maxoutk = maxout[k] maxoutk = maxout[k]
# iterate over pooling regions # iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp): for r in np.ndindex(*pool_out_shp):
maxout_value = maxoutk[r] maxout_value = maxoutk[r]
# iterate inside region # iterate inside region
for c in itertools.product(*[region_ranges[i][r[i]] for c in itertools.product(*[region_ranges[i][r[i]]
...@@ -1444,7 +1444,7 @@ class AveragePoolGrad(PoolGrad): ...@@ -1444,7 +1444,7 @@ class AveragePoolGrad(PoolGrad):
raise NotImplementedError() raise NotImplementedError()
z_shape = self.out_shape(x.shape, ws, self.ignore_border, stride, pad, nd) z_shape = self.out_shape(x.shape, ws, self.ignore_border, stride, pad, nd)
if (gx_stg[0] is None) or (gx_stg[0].shape != z_shape): if (gx_stg[0] is None) or (gx_stg[0].shape != z_shape):
gx_stg[0] = numpy.empty(z_shape, dtype=x.dtype) gx_stg[0] = np.empty(z_shape, dtype=x.dtype)
zz = gx_stg[0] zz = gx_stg[0]
# size of pooling output # size of pooling output
pool_out_shp = zz.shape[-nd:] pool_out_shp = zz.shape[-nd:]
...@@ -1453,7 +1453,7 @@ class AveragePoolGrad(PoolGrad): ...@@ -1453,7 +1453,7 @@ class AveragePoolGrad(PoolGrad):
sum_mode = self.mode == 'sum' sum_mode = self.mode == 'sum'
# initialize the padded output # initialize the padded output
gx = numpy.zeros((x.shape[:-nd] + img_shp), dtype=x.dtype) gx = np.zeros((x.shape[:-nd] + img_shp), dtype=x.dtype)
# precompute the region boundaries and sizes for each dimension # precompute the region boundaries and sizes for each dimension
region_slices = [[] for i in xrange(nd)] region_slices = [[] for i in xrange(nd)]
...@@ -1470,11 +1470,11 @@ class AveragePoolGrad(PoolGrad): ...@@ -1470,11 +1470,11 @@ class AveragePoolGrad(PoolGrad):
# iterate over non-pooling dimensions # iterate over non-pooling dimensions
region_slice = [None] * nd region_slice = [None] * nd
for k in numpy.ndindex(*x.shape[:-nd]): for k in np.ndindex(*x.shape[:-nd]):
gzk = gz[k] gzk = gz[k]
gxk = gx[k] gxk = gx[k]
# iterate over pooling regions # iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp): for r in np.ndindex(*pool_out_shp):
region_size = 1 region_size = 1
for i in xrange(nd): for i in xrange(nd):
region_slice[i] = region_slices[i][r[i]] region_slice[i] = region_slices[i][r[i]]
...@@ -1783,7 +1783,7 @@ class DownsampleFactorMaxGradGrad(OpenMPOp): ...@@ -1783,7 +1783,7 @@ class DownsampleFactorMaxGradGrad(OpenMPOp):
'DownsampleFactorMaxGradGrad requires input ' 'DownsampleFactorMaxGradGrad requires input '
'with {} or more dimensions'.format(nd)) 'with {} or more dimensions'.format(nd))
if (z[0] is None) or (z[0].shape != maxout.shape): if (z[0] is None) or (z[0].shape != maxout.shape):
z[0] = numpy.zeros(maxout.shape, dtype=x.dtype) z[0] = np.zeros(maxout.shape, dtype=x.dtype)
ggz = z[0] # grad wrt maxout_grad has the same shape as maxout ggz = z[0] # grad wrt maxout_grad has the same shape as maxout
# size of pooling output # size of pooling output
pool_out_shp = ggz.shape[-nd:] pool_out_shp = ggz.shape[-nd:]
...@@ -1791,10 +1791,10 @@ class DownsampleFactorMaxGradGrad(OpenMPOp): ...@@ -1791,10 +1791,10 @@ class DownsampleFactorMaxGradGrad(OpenMPOp):
# pad the image and its gradients # pad the image and its gradients
if max(pad) > 0: if max(pad) > 0:
y_padded = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y_padded = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y_padded[(slice(None),) * (len(x.shape) - nd) + y_padded[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x
ggx_padded = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) ggx_padded = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
ggx_padded[(slice(None),) * (len(x.shape) - nd) + ggx_padded[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ggx tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ggx
...@@ -1811,13 +1811,13 @@ class DownsampleFactorMaxGradGrad(OpenMPOp): ...@@ -1811,13 +1811,13 @@ class DownsampleFactorMaxGradGrad(OpenMPOp):
region_ranges[i].append(xrange(start, end)) region_ranges[i].append(xrange(start, end))
# iterate over non-pooling dimensions # iterate over non-pooling dimensions
for k in numpy.ndindex(*x.shape[:-nd]): for k in np.ndindex(*x.shape[:-nd]):
ggxk = ggx_padded[k] ggxk = ggx_padded[k]
ggzk = ggz[k] ggzk = ggz[k]
yk = y_padded[k] yk = y_padded[k]
maxoutk = maxout[k] maxoutk = maxout[k]
# iterate over pooling regions # iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp): for r in np.ndindex(*pool_out_shp):
# iterate inside region # iterate inside region
maxout_value = maxoutk[r] maxout_value = maxoutk[r]
for c in itertools.product(*[region_ranges[i][r[i]] for c in itertools.product(*[region_ranges[i][r[i]]
...@@ -2113,7 +2113,7 @@ class MaxPoolRop(OpenMPOp): ...@@ -2113,7 +2113,7 @@ class MaxPoolRop(OpenMPOp):
if not self.ignore_border: if not self.ignore_border:
assert all(z > 0 for z in z_shape[-nd:]) assert all(z > 0 for z in z_shape[-nd:])
if (z[0] is None) or (z[0].shape != z_shape): if (z[0] is None) or (z[0].shape != z_shape):
z[0] = numpy.empty(z_shape, dtype=x.dtype) z[0] = np.empty(z_shape, dtype=x.dtype)
zz = z[0] zz = z[0]
# size of pooling output # size of pooling output
pool_out_shp = zz.shape[-nd:] pool_out_shp = zz.shape[-nd:]
...@@ -2122,10 +2122,10 @@ class MaxPoolRop(OpenMPOp): ...@@ -2122,10 +2122,10 @@ class MaxPoolRop(OpenMPOp):
# pad the image and the eval point # pad the image and the eval point
if max(pad) != 0: if max(pad) != 0:
y = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype) y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y[(slice(None),) * (len(x.shape) - nd) + y[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x
ey = numpy.zeros(ex.shape[:-nd] + img_shp, dtype=ex.dtype) ey = np.zeros(ex.shape[:-nd] + img_shp, dtype=ex.dtype)
ey[(slice(None),) * (len(ex.shape) - nd) + ey[(slice(None),) * (len(ex.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ex tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ex
else: else:
...@@ -2144,18 +2144,18 @@ class MaxPoolRop(OpenMPOp): ...@@ -2144,18 +2144,18 @@ class MaxPoolRop(OpenMPOp):
region_slices[i].append(slice(start, end)) region_slices[i].append(slice(start, end))
# iterate over non-pooling dimensions # iterate over non-pooling dimensions
for k in numpy.ndindex(*x.shape[:-nd]): for k in np.ndindex(*x.shape[:-nd]):
zzk = zz[k] zzk = zz[k]
yk = y[k] yk = y[k]
eyk = ey[k] eyk = ey[k]
# iterate over pooling regions # iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp): for r in np.ndindex(*pool_out_shp):
# current slice in padded input # current slice in padded input
ykslice = yk[[region_slices[i][r[i]] for i in xrange(nd)]] ykslice = yk[[region_slices[i][r[i]] for i in xrange(nd)]]
# current slice in eval points # current slice in eval points
eykslice = eyk[[region_slices[i][r[i]] for i in xrange(nd)]] eykslice = eyk[[region_slices[i][r[i]] for i in xrange(nd)]]
# indices of maximum # indices of maximum
idx = numpy.unravel_index(numpy.argmax(ykslice), ykslice.shape) idx = np.unravel_index(np.argmax(ykslice), ykslice.shape)
zzk[r] = eykslice[idx] zzk[r] = eykslice[idx]
def c_headers(self): def c_headers(self):
......
theano/tensor/signal/tests/test_conv.py
浏览文件 @ ef9f6efc
...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division ...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import unittest import unittest
from nose.plugins.skip import SkipTest from nose.plugins.skip import SkipTest
import numpy import numpy as np
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -41,13 +41,13 @@ class TestSignalConv2D(unittest.TestCase): ...@@ -41,13 +41,13 @@ class TestSignalConv2D(unittest.TestCase):
theano_conv = theano.function([input, filters], output) theano_conv = theano.function([input, filters], output)
# initialize input and compute result # initialize input and compute result
image_data = numpy.random.random(image_shape) image_data = np.random.random(image_shape)
filter_data = numpy.random.random(filter_shape) filter_data = np.random.random(filter_shape)
theano_output = theano_conv(image_data, filter_data) theano_output = theano_conv(image_data, filter_data)
# REFERENCE IMPLEMENTATION ############ # REFERENCE IMPLEMENTATION ############
out_shape2d = numpy.array(image_shape[-2:]) - numpy.array(filter_shape[-2:]) + 1 out_shape2d = np.array(image_shape[-2:]) - np.array(filter_shape[-2:]) + 1
ref_output = numpy.zeros(tuple(out_shape2d)) ref_output = np.zeros(tuple(out_shape2d))
# reshape as 3D input tensors to make life easier # reshape as 3D input tensors to make life easier
image_data3d = image_data.reshape((bsize,) + image_shape[-2:]) image_data3d = image_data.reshape((bsize,) + image_shape[-2:])
...@@ -64,7 +64,7 @@ class TestSignalConv2D(unittest.TestCase): ...@@ -64,7 +64,7 @@ class TestSignalConv2D(unittest.TestCase):
image2d = image_data3d[b, :, :] image2d = image_data3d[b, :, :]
filter2d = filter_data3d[k, :, :] filter2d = filter_data3d[k, :, :]
output2d = numpy.zeros(ref_output.shape) output2d = np.zeros(ref_output.shape)
for row in range(ref_output.shape[0]): for row in range(ref_output.shape[0]):
for col in range(ref_output.shape[1]): for col in range(ref_output.shape[1]):
output2d[row, col] += ( output2d[row, col] += (
......
theano/tensor/signal/tests/test_pool.py
浏览文件 @ ef9f6efc
...@@ -10,7 +10,7 @@ from six.moves import cPickle ...@@ -10,7 +10,7 @@ from six.moves import cPickle
import six.moves.builtins as builtins import six.moves.builtins as builtins
import sys import sys
import numpy import numpy as np
import theano import theano
import theano.tensor as tensor import theano.tensor as tensor
...@@ -46,14 +46,14 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -46,14 +46,14 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp = list(input.shape[:-2]) out_shp = list(input.shape[:-2])
out_shp.append(input.shape[-2] // ws[0] + xi) out_shp.append(input.shape[-2] // ws[0] + xi)
out_shp.append(input.shape[-1] // ws[1] + yi) out_shp.append(input.shape[-1] // ws[1] + yi)
output_val = numpy.zeros(out_shp) output_val = np.zeros(out_shp)
func = numpy.max func = np.max
if mode == 'sum': if mode == 'sum':
func = numpy.sum func = np.sum
elif mode != 'max': elif mode != 'max':
func = numpy.average func = np.average
for k in numpy.ndindex(*input.shape[:-2]): for k in np.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]): for i in range(output_val.shape[-2]):
ii = i * ws[0] ii = i * ws[0]
for j in range(output_val.shape[-1]): for j in range(output_val.shape[-1]):
...@@ -78,15 +78,15 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -78,15 +78,15 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp = list(input.shape[:-nd]) out_shp = list(input.shape[:-nd])
for i in range(nd): for i in range(nd):
out_shp.append(input.shape[-nd + i] // ws[i] + si[i]) out_shp.append(input.shape[-nd + i] // ws[i] + si[i])
output_val = numpy.zeros(out_shp) output_val = np.zeros(out_shp)
func = numpy.max func = np.max
if mode == 'sum': if mode == 'sum':
func = numpy.sum func = np.sum
elif mode != 'max': elif mode != 'max':
func = numpy.average func = np.average
for l in numpy.ndindex(*input.shape[:-nd]): for l in np.ndindex(*input.shape[:-nd]):
for r in numpy.ndindex(*output_val.shape[-nd:]): for r in np.ndindex(*output_val.shape[-nd:]):
patch = input[l][tuple(slice(r[i] * ws[i], (r[i] + 1) * ws[i]) patch = input[l][tuple(slice(r[i] * ws[i], (r[i] + 1) * ws[i])
for i in range(nd))] for i in range(nd))]
output_val[l][r] = func(patch) output_val[l][r] = func(patch)
...@@ -104,7 +104,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -104,7 +104,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
assert ws[1] > pad_w assert ws[1] > pad_w
def pad_img(x): def pad_img(x):
y = numpy.zeros( y = np.zeros(
(x.shape[0], x.shape[1], (x.shape[0], x.shape[1],
x.shape[2] + pad_h * 2, x.shape[3] + pad_w * 2), x.shape[2] + pad_h * 2, x.shape[3] + pad_w * 2),
dtype=x.dtype) dtype=x.dtype)
...@@ -120,16 +120,16 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -120,16 +120,16 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp.append(out_c) out_shp.append(out_c)
ws0, ws1 = ws ws0, ws1 = ws
stride0, stride1 = stride stride0, stride1 = stride
output_val = numpy.zeros(out_shp) output_val = np.zeros(out_shp)
y = pad_img(x) y = pad_img(x)
func = numpy.max func = np.max
if mode == 'sum': if mode == 'sum':
func = numpy.sum func = np.sum
elif mode != 'max': elif mode != 'max':
func = numpy.average func = np.average
inc_pad = mode == 'average_inc_pad' inc_pad = mode == 'average_inc_pad'
for k in numpy.ndindex(*x.shape[:-2]): for k in np.ndindex(*x.shape[:-2]):
for i in range(output_val.shape[-2]): for i in range(output_val.shape[-2]):
ii_stride = i * stride[0] ii_stride = i * stride[0]
ii_end = builtins.min(ii_stride + ws[0], img_rows) ii_end = builtins.min(ii_stride + ws[0], img_rows)
...@@ -160,7 +160,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -160,7 +160,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def pad_img(x): def pad_img(x):
# initialize padded input # initialize padded input
y = numpy.zeros( y = np.zeros(
x.shape[0:-nd] + x.shape[0:-nd] +
tuple(x.shape[-nd + i] + pad[i] * 2 for i in range(nd)), tuple(x.shape[-nd + i] + pad[i] * 2 for i in range(nd)),
dtype=x.dtype) dtype=x.dtype)
...@@ -177,17 +177,17 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -177,17 +177,17 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
padded_size = input.shape[-nd + i] + 2 * pad[i] padded_size = input.shape[-nd + i] + 2 * pad[i]
pad_img_shp.append(padded_size) pad_img_shp.append(padded_size)
out_shp.append((padded_size - ws[i]) // stride[i] + 1) out_shp.append((padded_size - ws[i]) // stride[i] + 1)
output_val = numpy.zeros(out_shp) output_val = np.zeros(out_shp)
padded_input = pad_img(input) padded_input = pad_img(input)
func = numpy.max func = np.max
if mode == 'sum': if mode == 'sum':
func = numpy.sum func = np.sum
elif mode != 'max': elif mode != 'max':
func = numpy.average func = np.average
inc_pad = mode == 'average_inc_pad' inc_pad = mode == 'average_inc_pad'
for l in numpy.ndindex(*input.shape[:-nd]): for l in np.ndindex(*input.shape[:-nd]):
for r in numpy.ndindex(*output_val.shape[-nd:]): for r in np.ndindex(*output_val.shape[-nd:]):
region = [] region = []
for i in range(nd): for i in range(nd):
r_stride = r[i] * stride[i] r_stride = r[i] * stride[i]
...@@ -245,14 +245,14 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -245,14 +245,14 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp.append(out_r) out_shp.append(out_r)
out_shp.append(out_c) out_shp.append(out_c)
func = numpy.max func = np.max
if mode == 'sum': if mode == 'sum':
func = numpy.sum func = np.sum
elif mode != 'max': elif mode != 'max':
func = numpy.average func = np.average
output_val = numpy.zeros(out_shp) output_val = np.zeros(out_shp)
for k in numpy.ndindex(*input.shape[:-2]): for k in np.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]): for i in range(output_val.shape[-2]):
ii_stride = i * stride[0] ii_stride = i * stride[0]
ii_end = builtins.min(ii_stride + ws[0], img_rows) ii_end = builtins.min(ii_stride + ws[0], img_rows)
...@@ -289,15 +289,15 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -289,15 +289,15 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out += 1 out += 1
out_shp.append(out) out_shp.append(out)
func = numpy.max func = np.max
if mode == 'sum': if mode == 'sum':
func = numpy.sum func = np.sum
elif mode != 'max': elif mode != 'max':
func = numpy.average func = np.average
output_val = numpy.zeros(out_shp) output_val = np.zeros(out_shp)
for l in numpy.ndindex(*input.shape[:-nd]): for l in np.ndindex(*input.shape[:-nd]):
for r in numpy.ndindex(*output_val.shape[-nd:]): for r in np.ndindex(*output_val.shape[-nd:]):
region = [] region = []
for i in range(nd): for i in range(nd):
r_stride = r[i] * stride[i] r_stride = r[i] * stride[i]
...@@ -308,7 +308,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -308,7 +308,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
return output_val return output_val
def test_DownsampleFactorMax(self): def test_DownsampleFactorMax(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# maxpool, input size # maxpool, input size
examples = ( examples = (
((2,), (16,)), ((2,), (16,)),
...@@ -361,13 +361,13 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -361,13 +361,13 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
output_shape = Pool.out_shape(imval.shape, maxpoolshp, output_shape = Pool.out_shape(imval.shape, maxpoolshp,
ndim=len(maxpoolshp), ndim=len(maxpoolshp),
ignore_border=ignore_border) ignore_border=ignore_border)
utt.assert_allclose(numpy.asarray(output_shape), numpy_output_val.shape) utt.assert_allclose(np.asarray(output_shape), numpy_output_val.shape)
f = function([], maxpool_op) f = function([], maxpool_op)
output_val = f() output_val = f()
utt.assert_allclose(output_val, numpy_output_val) utt.assert_allclose(output_val, numpy_output_val)
def test_DownsampleFactorMaxStride(self): def test_DownsampleFactorMaxStride(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, ignore_border, input, output sizes # maxpool, stride, ignore_border, input, output sizes
examples = ( examples = (
((1, 1), (1, 1), True, (4, 10, 16, 16), (4, 10, 16, 16)), ((1, 1), (1, 1), True, (4, 10, 16, 16), (4, 10, 16, 16)),
...@@ -426,7 +426,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -426,7 +426,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.assert_allclose(output_val, numpy_output_val) utt.assert_allclose(output_val, numpy_output_val)
def test_DownsampleFactorMaxStrideExtra(self): def test_DownsampleFactorMaxStrideExtra(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((5, 3), (5, 3), (5, 3), (5, 5), (3, 2), (7, 7), (9, 9)) maxpoolshps = ((5, 3), (5, 3), (5, 3), (5, 5), (3, 2), (7, 7), (9, 9))
stridesizes = ((3, 2), (7, 5), (10, 6), (1, 1), stridesizes = ((3, 2), (7, 5), (10, 6), (1, 1),
(2, 3), (10, 10), (1, 1)) (2, 3), (10, 10), (1, 1))
...@@ -438,7 +438,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -438,7 +438,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
(4, 10, 4, 2), (4, 10, 1, 0), (4, 10, 1, 1), (4, 10, 4, 2), (4, 10, 1, 0), (4, 10, 1, 1),
(4, 10, 0, 0), (4, 10, 1, 1)) (4, 10, 0, 0), (4, 10, 1, 1))
images = tensor.dtensor4() images = tensor.dtensor4()
for indx in numpy.arange(len(maxpoolshps)): for indx in np.arange(len(maxpoolshps)):
imvsize = imvsizs[indx] imvsize = imvsizs[indx]
imval = rng.rand(4, 10, imvsize[0], imvsize[1]) imval = rng.rand(4, 10, imvsize[0], imvsize[1])
stride = stridesizes[indx] stride = stridesizes[indx]
...@@ -468,7 +468,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -468,7 +468,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def test_DownsampleFactorMaxPaddingStride(self): def test_DownsampleFactorMaxPaddingStride(self):
ignore_border = True # padding does not support ignore_border=False ignore_border = True # padding does not support ignore_border=False
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, pad, input sizes # maxpool, stride, pad, input sizes
examples = ( examples = (
((3,), (2,), (2,), (5,)), ((3,), (2,), (2,), (5,)),
...@@ -503,7 +503,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -503,7 +503,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.assert_allclose(output_val, numpy_output_val) utt.assert_allclose(output_val, numpy_output_val)
def test_DownsampleFactorMaxPaddingStride_grad(self): def test_DownsampleFactorMaxPaddingStride_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, pad, input sizes # maxpool, stride, pad, input sizes
examples = ( examples = (
((10,), (5,), (3,), (2,)), ((10,), (5,), (3,), (2,)),
...@@ -530,7 +530,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -530,7 +530,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval], rng=rng) utt.verify_grad(mp, [imval], rng=rng)
def test_DownsampleFactorMax_grad(self): def test_DownsampleFactorMax_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# maxpool, input sizes # maxpool, input sizes
examples = ( examples = (
((2,), (3,)), ((2,), (3,)),
...@@ -599,7 +599,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -599,7 +599,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
testcase_func_name=utt.custom_name_func) testcase_func_name=utt.custom_name_func)
def test_DownsampleFactorMax_grad_stride(self, example, ignore_border, mode): def test_DownsampleFactorMax_grad_stride(self, example, ignore_border, mode):
# checks the gradient for the case that stride is used # checks the gradient for the case that stride is used
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
(maxpoolshp, stridesize, inputsize) = example (maxpoolshp, stridesize, inputsize) = example
imval = rng.rand(*inputsize) imval = rng.rand(*inputsize)
...@@ -611,7 +611,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -611,7 +611,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval], rng=rng) utt.verify_grad(mp, [imval], rng=rng)
def test_DownsampleFactorMaxGrad_grad(self): def test_DownsampleFactorMaxGrad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# maxpool, input sizes # maxpool, input sizes
examples = ( examples = (
((2,), (2,)), ((2,), (2,)),
...@@ -649,7 +649,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -649,7 +649,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval, grad_val], rng=rng) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolGrad_grad(self): def test_AveragePoolGrad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# avgpool, input sizes # avgpool, input sizes
examples = ( examples = (
((2,), (2,)), ((2,), (2,)),
...@@ -691,7 +691,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -691,7 +691,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def test_DownsampleFactorMaxGrad_grad_stride(self, example, ignore_border): def test_DownsampleFactorMaxGrad_grad_stride(self, example, ignore_border):
# checks the gradient of the gradient for # checks the gradient of the gradient for
# the case that stride is used # the case that stride is used
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
(maxpoolshp, stride, inputsize) = example (maxpoolshp, stride, inputsize) = example
imval = rng.rand(*inputsize) imval = rng.rand(*inputsize)
grad_shape = Pool.out_shape( grad_shape = Pool.out_shape(
...@@ -699,7 +699,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -699,7 +699,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
ignore_border=ignore_border, stride=stride) ignore_border=ignore_border, stride=stride)
# skip the grad verification when the output is empty # skip the grad verification when the output is empty
if numpy.prod(grad_shape) != 0: if np.prod(grad_shape) != 0:
grad_val = rng.rand(*grad_shape) grad_val = rng.rand(*grad_shape)
def mp(input, grad): def mp(input, grad):
...@@ -722,7 +722,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -722,7 +722,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def test_AveragePoolGrad_grad_stride(self, example, ignore_border, mode): def test_AveragePoolGrad_grad_stride(self, example, ignore_border, mode):
# checks the gradient of the gradient for # checks the gradient of the gradient for
# the case that stride is used # the case that stride is used
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
(avgpoolshp, stride, inputsize) = example (avgpoolshp, stride, inputsize) = example
imval = rng.rand(*inputsize) imval = rng.rand(*inputsize)
grad_shape = Pool.out_shape( grad_shape = Pool.out_shape(
...@@ -731,7 +731,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -731,7 +731,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
ignore_border=ignore_border, stride=stride) ignore_border=ignore_border, stride=stride)
# skip the grad verification when the output is empty # skip the grad verification when the output is empty
if numpy.prod(grad_shape) != 0: if np.prod(grad_shape) != 0:
grad_val = rng.rand(*grad_shape) grad_val = rng.rand(*grad_shape)
def mp(input, grad): def mp(input, grad):
...@@ -744,7 +744,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -744,7 +744,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval, grad_val], rng=rng) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxPaddingStride_grad_grad(self): def test_DownsampleFactorMaxPaddingStride_grad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, pad, input sizes # maxpool, stride, pad, input sizes
examples = ( examples = (
((3,), (2,), (2,), (10,)), ((3,), (2,), (2,), (10,)),
...@@ -781,7 +781,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -781,7 +781,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval, grad_val], rng=rng) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolPaddingStride_grad_grad(self): def test_AveragePoolPaddingStride_grad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# avgpool, stride, pad, input sizes # avgpool, stride, pad, input sizes
examples = ( examples = (
((3,), (2,), (2,), (10,)), ((3,), (2,), (2,), (10,)),
...@@ -831,10 +831,10 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -831,10 +831,10 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
# The value has been manually computed from the theoretical gradient, # The value has been manually computed from the theoretical gradient,
# and confirmed by the implementation. # and confirmed by the implementation.
assert numpy.allclose(fn_hess([1, 2]), [[0., 0.], [0., 982.7667]]) assert np.allclose(fn_hess([1, 2]), [[0., 0.], [0., 982.7667]])
def test_DownsampleFactorMaxGradGrad_grad(self): def test_DownsampleFactorMaxGradGrad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, pad, input sizes # maxpool, stride, pad, input sizes
examples = ( examples = (
((3,), (2,), (2,), (10,)), ((3,), (2,), (2,), (10,)),
...@@ -864,7 +864,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -864,7 +864,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval1, imval2], rng=rng) utt.verify_grad(mp, [imval1, imval2], rng=rng)
def test_max_pool_2d_2D(self): def test_max_pool_2d_2D(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1), (3, 2)) maxpoolshps = ((1, 1), (3, 2))
imval = rng.rand(4, 5) imval = rng.rand(4, 5)
images = tensor.dmatrix() images = tensor.dmatrix()
...@@ -890,7 +890,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -890,7 +890,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval], rng=rng) utt.verify_grad(mp, [imval], rng=rng)
def test_max_pool_3d_3D(self): def test_max_pool_3d_3D(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1, 1), (3, 2, 1)) maxpoolshps = ((1, 1, 1), (3, 2, 1))
imval = rng.rand(4, 5, 6) imval = rng.rand(4, 5, 6)
images = tensor.dtensor3() images = tensor.dtensor3()
...@@ -916,7 +916,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -916,7 +916,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval], rng=rng) utt.verify_grad(mp, [imval], rng=rng)
def test_max_pool_3d_3D_deprecated_interface(self): def test_max_pool_3d_3D_deprecated_interface(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1, 1), (3, 2, 1)) maxpoolshps = ((1, 1, 1), (3, 2, 1))
imval = rng.rand(4, 5, 6) imval = rng.rand(4, 5, 6)
images = tensor.dtensor3() images = tensor.dtensor3()
...@@ -945,12 +945,12 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -945,12 +945,12 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
mode=mode) mode=mode)
def test_max_pool_2d_2D_same_size(self): def test_max_pool_2d_2D_same_size(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
test_input_array = numpy.array([[[ test_input_array = np.array([[[
[1., 2., 3., 4.], [1., 2., 3., 4.],
[5., 6., 7., 8.] [5., 6., 7., 8.]
]]]).astype(theano.config.floatX) ]]]).astype(theano.config.floatX)
test_answer_array = numpy.array([[[ test_answer_array = np.array([[[
[0., 0., 0., 0.], [0., 0., 0., 0.],
[0., 6., 0., 8.] [0., 6., 0., 8.]
]]]).astype(theano.config.floatX) ]]]).astype(theano.config.floatX)
...@@ -965,7 +965,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -965,7 +965,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [test_input_array], rng=rng) utt.verify_grad(mp, [test_input_array], rng=rng)
def test_max_pool_2d_3D(self): def test_max_pool_2d_3D(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = [(1, 2)] maxpoolshps = [(1, 2)]
imval = rng.rand(2, 3, 4) imval = rng.rand(2, 3, 4)
images = tensor.dtensor3() images = tensor.dtensor3()
...@@ -992,7 +992,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -992,7 +992,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
# utt.verify_grad(mp, [imval], rng=rng) # utt.verify_grad(mp, [imval], rng=rng)
def test_max_pool_2d_6D(self): def test_max_pool_2d_6D(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = [(3, 2)] maxpoolshps = [(3, 2)]
imval = rng.rand(2, 1, 1, 1, 3, 4) imval = rng.rand(2, 1, 1, 1, 3, 4)
images = tensor.TensorType('float64', [False] * 6)() images = tensor.TensorType('float64', [False] * 6)()
...@@ -1022,7 +1022,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -1022,7 +1022,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
image = tensor.dtensor4() image = tensor.dtensor4()
maxout = tensor.dtensor4() maxout = tensor.dtensor4()
gz = tensor.dtensor4() gz = tensor.dtensor4()
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3), (3, 2)) maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3), (3, 2))
image_val = rng.rand(4, 6, 7, 9) image_val = rng.rand(4, 6, 7, 9)
...@@ -1078,7 +1078,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -1078,7 +1078,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
window_size = tensor.ivector() window_size = tensor.ivector()
stride = tensor.ivector() stride = tensor.ivector()
padding = tensor.ivector() padding = tensor.ivector()
data = numpy.random.normal(0, 1, (1, 1, 5, 5)).astype('float32') data = np.random.normal(0, 1, (1, 1, 5, 5)).astype('float32')
# checking variable params vs fixed params # checking variable params vs fixed params
for ignore_border in [True, False]: for ignore_border in [True, False]:
...@@ -1110,7 +1110,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -1110,7 +1110,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
window_size = tensor.ivector() window_size = tensor.ivector()
stride = tensor.ivector() stride = tensor.ivector()
padding = tensor.ivector() padding = tensor.ivector()
data = numpy.random.normal(0, 1, (1, 1, 5, 5)).astype('float32') data = np.random.normal(0, 1, (1, 1, 5, 5)).astype('float32')
# checking variable params vs fixed params # checking variable params vs fixed params
for ignore_border in [True, False]: for ignore_border in [True, False]:
...@@ -1172,8 +1172,8 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -1172,8 +1172,8 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
dz_dx = theano.gradient.grad(z.sum(), x) dz_dx = theano.gradient.grad(z.sum(), x)
new_fct = theano.function([x], [y, z, dy_dx, dz_dx]) new_fct = theano.function([x], [y, z, dy_dx, dz_dx])
# 3. Assert that the answer is the same # 3. Assert that the answer is the same
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
image_val = rng.rand(4, 6, 7, 9).astype(numpy.float32) image_val = rng.rand(4, 6, 7, 9).astype(np.float32)
old_out = old_fct(image_val) old_out = old_fct(image_val)
new_out = new_fct(image_val) new_out = new_fct(image_val)
for o, n in zip(old_out, new_out): for o, n in zip(old_out, new_out):
......
theano/tensor/slinalg.py
浏览文件 @ ef9f6efc
...@@ -3,7 +3,7 @@ import logging ...@@ -3,7 +3,7 @@ import logging
import warnings import warnings
from six.moves import xrange from six.moves import xrange
import numpy import numpy as np
try: try:
import scipy.linalg import scipy.linalg
...@@ -145,7 +145,7 @@ class CholeskyGrad(Op): ...@@ -145,7 +145,7 @@ class CholeskyGrad(Op):
dx = outputs[0] dx = outputs[0]
N = x.shape[0] N = x.shape[0]
if self.lower: if self.lower:
F = numpy.tril(dz) F = np.tril(dz)
for k in xrange(N - 1, -1, -1): for k in xrange(N - 1, -1, -1):
for j in xrange(k + 1, N): for j in xrange(k + 1, N):
for i in xrange(j, N): for i in xrange(j, N):
...@@ -156,7 +156,7 @@ class CholeskyGrad(Op): ...@@ -156,7 +156,7 @@ class CholeskyGrad(Op):
F[k, k] -= L[j, k] * F[j, k] F[k, k] -= L[j, k] * F[j, k]
F[k, k] /= (2 * L[k, k]) F[k, k] /= (2 * L[k, k])
else: else:
F = numpy.triu(dz) F = np.triu(dz)
for k in xrange(N - 1, -1, -1): for k in xrange(N - 1, -1, -1):
for j in xrange(k + 1, N): for j in xrange(k + 1, N):
for i in xrange(j, N): for i in xrange(j, N):
...@@ -206,8 +206,8 @@ class Solve(Op): ...@@ -206,8 +206,8 @@ class Solve(Op):
# infer dtype by solving the most simple # infer dtype by solving the most simple
# case with (1, 1) matrices # case with (1, 1) matrices
o_dtype = scipy.linalg.solve( o_dtype = scipy.linalg.solve(
numpy.eye(1).astype(A.dtype), np.eye(1).astype(A.dtype),
numpy.eye(1).astype(b.dtype)).dtype np.eye(1).astype(b.dtype)).dtype
x = tensor.tensor( x = tensor.tensor(
broadcastable=b.broadcastable, broadcastable=b.broadcastable,
dtype=o_dtype) dtype=o_dtype)
...@@ -370,11 +370,11 @@ class EigvalshGrad(Op): ...@@ -370,11 +370,11 @@ class EigvalshGrad(Op):
assert lower in [True, False] assert lower in [True, False]
self.lower = lower self.lower = lower
if lower: if lower:
self.tri0 = numpy.tril self.tri0 = np.tril
self.tri1 = lambda a: numpy.triu(a, 1) self.tri1 = lambda a: np.triu(a, 1)
else: else:
self.tri0 = numpy.triu self.tri0 = np.triu
self.tri1 = lambda a: numpy.tril(a, -1) self.tri1 = lambda a: np.tril(a, -1)
def make_node(self, a, b, gw): def make_node(self, a, b, gw):
assert imported_scipy, ( assert imported_scipy, (
...@@ -394,14 +394,14 @@ class EigvalshGrad(Op): ...@@ -394,14 +394,14 @@ class EigvalshGrad(Op):
def perform(self, node, inputs, outputs): def perform(self, node, inputs, outputs):
(a, b, gw) = inputs (a, b, gw) = inputs
w, v = scipy.linalg.eigh(a, b, lower=self.lower) w, v = scipy.linalg.eigh(a, b, lower=self.lower)
gA = v.dot(numpy.diag(gw).dot(v.T)) gA = v.dot(np.diag(gw).dot(v.T))
gB = - v.dot(numpy.diag(gw * w).dot(v.T)) gB = - v.dot(np.diag(gw * w).dot(v.T))
# See EighGrad comments for an explanation of these lines # See EighGrad comments for an explanation of these lines
out1 = self.tri0(gA) + self.tri1(gA).T out1 = self.tri0(gA) + self.tri1(gA).T
out2 = self.tri0(gB) + self.tri1(gB).T out2 = self.tri0(gB) + self.tri1(gB).T
outputs[0][0] = numpy.asarray(out1, dtype=node.outputs[0].dtype) outputs[0][0] = np.asarray(out1, dtype=node.outputs[0].dtype)
outputs[1][0] = numpy.asarray(out2, dtype=node.outputs[1].dtype) outputs[1][0] = np.asarray(out2, dtype=node.outputs[1].dtype)
def infer_shape(self, node, shapes): def infer_shape(self, node, shapes):
return [shapes[0], shapes[1]] return [shapes[0], shapes[1]]
...@@ -510,13 +510,13 @@ class ExpmGrad(Op): ...@@ -510,13 +510,13 @@ class ExpmGrad(Op):
w, V = scipy.linalg.eig(A, right=True) w, V = scipy.linalg.eig(A, right=True)
U = scipy.linalg.inv(V).T U = scipy.linalg.inv(V).T
exp_w = numpy.exp(w) exp_w = np.exp(w)
X = numpy.subtract.outer(exp_w, exp_w) / numpy.subtract.outer(w, w) X = np.subtract.outer(exp_w, exp_w) / np.subtract.outer(w, w)
numpy.fill_diagonal(X, exp_w) np.fill_diagonal(X, exp_w)
Y = U.dot(V.T.dot(gA).dot(U) * X).dot(V.T) Y = U.dot(V.T.dot(gA).dot(U) * X).dot(V.T)
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.simplefilter("ignore", numpy.ComplexWarning) warnings.simplefilter("ignore", np.ComplexWarning)
out[0] = Y.astype(A.dtype) out[0] = Y.astype(A.dtype)
......
theano/tensor/subtensor.py
浏览文件 @ ef9f6efc
...@@ -4,7 +4,7 @@ from textwrap import dedent ...@@ -4,7 +4,7 @@ from textwrap import dedent
import warnings import warnings
import logging import logging
import numpy import numpy as np
from six import integer_types from six import integer_types
from six.moves import xrange from six.moves import xrange
...@@ -58,7 +58,7 @@ def make_constant(args): ...@@ -58,7 +58,7 @@ def make_constant(args):
return slice(conv(a.start), return slice(conv(a.start),
conv(a.stop), conv(a.stop),
conv(a.step)) conv(a.step))
elif isinstance(a, (integer_types, numpy.integer)): elif isinstance(a, (integer_types, np.integer)):
return scal.ScalarConstant(scal.int64, a) return scal.ScalarConstant(scal.int64, a)
else: else:
return a return a
...@@ -355,11 +355,11 @@ class Subtensor(Op): ...@@ -355,11 +355,11 @@ class Subtensor(Op):
if (isinstance(entry, gof.Variable) and if (isinstance(entry, gof.Variable) and
entry.type in tensor_types and entry.type in tensor_types and
numpy.all(entry.type.broadcastable)): np.all(entry.type.broadcastable)):
return scal.get_scalar_type(entry.type.dtype) return scal.get_scalar_type(entry.type.dtype)
elif (isinstance(entry, gof.Type) and elif (isinstance(entry, gof.Type) and
entry in tensor_types and entry in tensor_types and
numpy.all(entry.broadcastable)): np.all(entry.broadcastable)):
return scal.get_scalar_type(entry.dtype) return scal.get_scalar_type(entry.dtype)
elif slice_ok and isinstance(entry, slice): elif slice_ok and isinstance(entry, slice):
a = entry.start a = entry.start
...@@ -385,7 +385,7 @@ class Subtensor(Op): ...@@ -385,7 +385,7 @@ class Subtensor(Op):
slice_c = None slice_c = None
return slice(slice_a, slice_b, slice_c) return slice(slice_a, slice_b, slice_c)
elif isinstance(entry, (integer_types, numpy.integer)): elif isinstance(entry, (integer_types, np.integer)):
# Disallow the use of python scalars in idx_list # Disallow the use of python scalars in idx_list
raise TypeError("Python scalar in idx_list." raise TypeError("Python scalar in idx_list."
"Please report this error to theano-dev.") "Please report this error to theano-dev.")
...@@ -510,8 +510,8 @@ class Subtensor(Op): ...@@ -510,8 +510,8 @@ class Subtensor(Op):
if start is None: if start is None:
start = 0 start = 0
if (p.stop is None or if (p.stop is None or
(isinstance(p.stop, (integer_types, numpy.integer, (isinstance(p.stop, (integer_types, np.integer,
numpy.ndarray)) and np.ndarray)) and
p.stop > start)): p.stop > start)):
broadcastable.append(True) broadcastable.append(True)
continue continue
...@@ -531,7 +531,7 @@ class Subtensor(Op): ...@@ -531,7 +531,7 @@ class Subtensor(Op):
if len(cdata) == 1: if len(cdata) == 1:
cdata = cdata[0] cdata = cdata[0]
out[0] = numpy.asarray(x.__getitem__(cdata)) out[0] = np.asarray(x.__getitem__(cdata))
def infer_shape(self, node, shapes): def infer_shape(self, node, shapes):
xshp = shapes[0] xshp = shapes[0]
...@@ -681,7 +681,7 @@ class Subtensor(Op): ...@@ -681,7 +681,7 @@ class Subtensor(Op):
return pos[1] return pos[1]
def init_entry(entry, depth=0): def init_entry(entry, depth=0):
if isinstance(entry, (numpy.integer, integer_types)): if isinstance(entry, (np.integer, integer_types)):
init_cmds.append( init_cmds.append(
"subtensor_spec[%i] = %i;" % (spec_pos(), "subtensor_spec[%i] = %i;" % (spec_pos(),
entry)) entry))
...@@ -1390,8 +1390,8 @@ class IncSubtensor(Op): ...@@ -1390,8 +1390,8 @@ class IncSubtensor(Op):
op_is_set = 0 op_is_set = 0
fail = sub['fail'] fail = sub['fail']
view_ndim = (node.inputs[0].ndim - view_ndim = (node.inputs[0].ndim -
numpy.sum([not isinstance(idx, slice) np.sum([not isinstance(idx, slice)
for idx in self.idx_list])) for idx in self.idx_list]))
copy_of_x = self.copy_of_x(x) copy_of_x = self.copy_of_x(x)
...@@ -1712,11 +1712,11 @@ class AdvancedSubtensor1(Op): ...@@ -1712,11 +1712,11 @@ class AdvancedSubtensor1(Op):
# We need to check if values in i can fit in numpy.intp, because # We need to check if values in i can fit in numpy.intp, because
# if they don't, that should be an error (no array can have that # if they don't, that should be an error (no array can have that
# many elements on a 32-bit arch). # many elements on a 32-bit arch).
if i.dtype != numpy.intp: if i.dtype != np.intp:
i_ = theano._asarray(i, dtype=numpy.intp) i_ = theano._asarray(i, dtype=np.intp)
if not numpy.can_cast(i.dtype, numpy.intp): if not np.can_cast(i.dtype, np.intp):
# Check if there was actually an incorrect conversion # Check if there was actually an incorrect conversion
if numpy.any(i != i_): if np.any(i != i_):
raise IndexError( raise IndexError(
'index contains values that are bigger ' 'index contains values that are bigger '
'than the maximum array size on this system.', i) 'than the maximum array size on this system.', i)
...@@ -1946,7 +1946,7 @@ class AdvancedIncSubtensor1(Op): ...@@ -1946,7 +1946,7 @@ class AdvancedIncSubtensor1(Op):
return compile_cutils_code() return compile_cutils_code()
def c_code(self, node, name, input_names, output_names, sub): def c_code(self, node, name, input_names, output_names, sub):
numpy_ver = [int(n) for n in numpy.__version__.split('.')[:2]] numpy_ver = [int(n) for n in np.__version__.split('.')[:2]]
if bool(numpy_ver < [1, 8]): if bool(numpy_ver < [1, 8]):
raise NotImplementedError raise NotImplementedError
x, y, idx = input_names x, y, idx = input_names
...@@ -2113,13 +2113,13 @@ def adv_index_broadcastable_pattern(a, idx): ...@@ -2113,13 +2113,13 @@ def adv_index_broadcastable_pattern(a, idx):
if isinstance(v.type, SliceType): if isinstance(v.type, SliceType):
return slice(None, None) return slice(None, None)
return numpy.zeros((2,) * v.ndim, int) return np.zeros((2,) * v.ndim, int)
newidx = tuple(map(replace_slice, idx)) newidx = tuple(map(replace_slice, idx))
# 2 - True = 1; 2 - False = 2 # 2 - True = 1; 2 - False = 2
fakeshape = [2 - bc for bc in a.broadcastable] fakeshape = [2 - bc for bc in a.broadcastable]
retshape = numpy.empty(fakeshape)[newidx].shape retshape = np.empty(fakeshape)[newidx].shape
return tuple([dim == 1 for dim in retshape]) return tuple([dim == 1 for dim in retshape])
......
theano/tensor/tests/mlp_test.py
浏览文件 @ ef9f6efc
...@@ -129,7 +129,7 @@ class HiddenLayer(object): ...@@ -129,7 +129,7 @@ class HiddenLayer(object):
Hidden unit activation is given by: tanh(dot(input,W) + b) Hidden unit activation is given by: tanh(dot(input,W) + b)
:type rng: np.random.RandomState :type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights :param rng: a random number generator used to initialize weights
:type input: theano.tensor.dmatrix :type input: theano.tensor.dmatrix
...@@ -176,7 +176,7 @@ class MLP(object): ...@@ -176,7 +176,7 @@ class MLP(object):
def __init__(self, rng, input, n_in, n_hidden, n_out): def __init__(self, rng, input, n_in, n_hidden, n_out):
"""Initialize the parameters for the multilayer perceptron """Initialize the parameters for the multilayer perceptron
:type rng: np.random.RandomState :type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights :param rng: a random number generator used to initialize weights
:type input: theano.tensor.TensorType :type input: theano.tensor.TensorType
......
theano/tensor/tests/test_basic.py
浏览文件 @ ef9f6efc
...@@ -164,8 +164,8 @@ def get_numeric_types(with_int=True, with_float=True, with_complex=False, ...@@ -164,8 +164,8 @@ def get_numeric_types(with_int=True, with_float=True, with_complex=False,
# Return True if scalars defined from `cls1` are within the hierarchy # Return True if scalars defined from `cls1` are within the hierarchy
# starting from `cls2`. # starting from `cls2`.
# The third test below is to catch for instance the fact that # The third test below is to catch for instance the fact that
# one can use ``dtype=np.number`` and obtain a float64 scalar, even # one can use ``dtype=numpy.number`` and obtain a float64 scalar, even
# though `np.number` is not under `np.floating` in the class # though `numpy.number` is not under `numpy.floating` in the class
# hierarchy. # hierarchy.
return (cls1 is cls2 or return (cls1 is cls2 or
issubclass(cls1, cls2) or issubclass(cls1, cls2) or
......
theano/tensor/tests/test_blas_c.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import sys import sys
import numpy import numpy as np
from unittest import TestCase from unittest import TestCase
from nose.plugins.skip import SkipTest from nose.plugins.skip import SkipTest
...@@ -44,9 +44,9 @@ class TestCGer(TestCase, TestOptimizationMixin): ...@@ -44,9 +44,9 @@ class TestCGer(TestCase, TestOptimizationMixin):
self.a = tensor.tensor(dtype=dtype, broadcastable=()) self.a = tensor.tensor(dtype=dtype, broadcastable=())
self.x = tensor.tensor(dtype=dtype, broadcastable=(False,)) self.x = tensor.tensor(dtype=dtype, broadcastable=(False,))
self.y = tensor.tensor(dtype=dtype, broadcastable=(False,)) self.y = tensor.tensor(dtype=dtype, broadcastable=(False,))
self.Aval = numpy.ones((2, 3), dtype=dtype) self.Aval = np.ones((2, 3), dtype=dtype)
self.xval = numpy.asarray([1, 2], dtype=dtype) self.xval = np.asarray([1, 2], dtype=dtype)
self.yval = numpy.asarray([1.5, 2.7, 3.9], dtype=dtype) self.yval = np.asarray([1.5, 2.7, 3.9], dtype=dtype)
def function(self, inputs, outputs): def function(self, inputs, outputs):
return theano.function(inputs, outputs, return theano.function(inputs, outputs,
...@@ -59,7 +59,7 @@ class TestCGer(TestCase, TestOptimizationMixin): ...@@ -59,7 +59,7 @@ class TestCGer(TestCase, TestOptimizationMixin):
f(self.Aval[::-1, ::-1], self.xval, self.yval) f(self.Aval[::-1, ::-1], self.xval, self.yval)
def b(self, bval): def b(self, bval):
return tensor.as_tensor_variable(numpy.asarray(bval, dtype=self.dtype)) return tensor.as_tensor_variable(np.asarray(bval, dtype=self.dtype))
def test_eq(self): def test_eq(self):
self.assertTrue(CGer(True) == CGer(True)) self.assertTrue(CGer(True) == CGer(True))
...@@ -127,13 +127,13 @@ class TestCGemv(TestCase, TestOptimizationMixin): ...@@ -127,13 +127,13 @@ class TestCGemv(TestCase, TestOptimizationMixin):
self.mode = theano.compile.get_default_mode().including('fast_run') self.mode = theano.compile.get_default_mode().including('fast_run')
# matrix # matrix
self.A = tensor.tensor(dtype=dtype, broadcastable=(False, False)) self.A = tensor.tensor(dtype=dtype, broadcastable=(False, False))
self.Aval = numpy.ones((2, 3), dtype=dtype) self.Aval = np.ones((2, 3), dtype=dtype)
# vector # vector
self.x = tensor.tensor(dtype=dtype, broadcastable=(False,)) self.x = tensor.tensor(dtype=dtype, broadcastable=(False,))
self.y = tensor.tensor(dtype=dtype, broadcastable=(False,)) self.y = tensor.tensor(dtype=dtype, broadcastable=(False,))
self.xval = numpy.asarray([1, 2], dtype=dtype) self.xval = np.asarray([1, 2], dtype=dtype)
self.yval = numpy.asarray([1.5, 2.7, 3.9], dtype=dtype) self.yval = np.asarray([1.5, 2.7, 3.9], dtype=dtype)
# scalar # scalar
self.a = tensor.tensor(dtype=dtype, broadcastable=()) self.a = tensor.tensor(dtype=dtype, broadcastable=())
...@@ -144,11 +144,11 @@ class TestCGemv(TestCase, TestOptimizationMixin): ...@@ -144,11 +144,11 @@ class TestCGemv(TestCase, TestOptimizationMixin):
f = theano.function([self.A, self.x, self.y, self.a], f = theano.function([self.A, self.x, self.y, self.a],
self.a*self.y + theano.dot(self.A, self.x), self.a*self.y + theano.dot(self.A, self.x),
mode=mode) mode=mode)
Aval = numpy.ones((3, 1), dtype=self.dtype) Aval = np.ones((3, 1), dtype=self.dtype)
xval = numpy.ones((1,), dtype=self.dtype) xval = np.ones((1,), dtype=self.dtype)
yval = float('NaN') * numpy.ones((3,), dtype=self.dtype) yval = float('NaN') * np.ones((3,), dtype=self.dtype)
zval = f(Aval, xval, yval, 0) zval = f(Aval, xval, yval, 0)
assert not numpy.isnan(zval).any() assert not np.isnan(zval).any()
def test_optimizations_vm(self): def test_optimizations_vm(self):
skip_if_blas_ldflags_empty() skip_if_blas_ldflags_empty()
...@@ -165,12 +165,12 @@ class TestCGemv(TestCase, TestOptimizationMixin): ...@@ -165,12 +165,12 @@ class TestCGemv(TestCase, TestOptimizationMixin):
) )
# Assert they produce the same output # Assert they produce the same output
assert numpy.allclose(f(self.xval, self.Aval), assert np.allclose(f(self.xval, self.Aval),
numpy.dot(self.xval, self.Aval)) np.dot(self.xval, self.Aval))
# Test with negative strides on 2 dims # Test with negative strides on 2 dims
assert numpy.allclose(f(self.xval, self.Aval[::-1, ::-1]), assert np.allclose(f(self.xval, self.Aval[::-1, ::-1]),
numpy.dot(self.xval, self.Aval[::-1, ::-1])) np.dot(self.xval, self.Aval[::-1, ::-1]))
def test_optimizations_mv(self): def test_optimizations_mv(self):
skip_if_blas_ldflags_empty() skip_if_blas_ldflags_empty()
...@@ -187,11 +187,11 @@ class TestCGemv(TestCase, TestOptimizationMixin): ...@@ -187,11 +187,11 @@ class TestCGemv(TestCase, TestOptimizationMixin):
) )
# Assert they produce the same output # Assert they produce the same output
assert numpy.allclose(f(self.Aval, self.yval), assert np.allclose(f(self.Aval, self.yval),
numpy.dot(self.Aval, self.yval)) np.dot(self.Aval, self.yval))
# Test with negative strides on 2 dims # Test with negative strides on 2 dims
assert numpy.allclose(f(self.Aval[::-1, ::-1], self.yval), assert np.allclose(f(self.Aval[::-1, ::-1], self.yval),
numpy.dot(self.Aval[::-1, ::-1], self.yval)) np.dot(self.Aval[::-1, ::-1], self.yval))
def test_force_gemv_init(self): def test_force_gemv_init(self):
if check_force_gemv_init(): if check_force_gemv_init():
...@@ -203,20 +203,20 @@ class TestCGemv(TestCase, TestOptimizationMixin): ...@@ -203,20 +203,20 @@ class TestCGemv(TestCase, TestOptimizationMixin):
def t_gemv1(self, m_shp): def t_gemv1(self, m_shp):
''' test vector2 + dot(matrix, vector1) ''' ''' test vector2 + dot(matrix, vector1) '''
rng = numpy.random.RandomState(unittest_tools.fetch_seed()) rng = np.random.RandomState(unittest_tools.fetch_seed())
v1 = theano.shared(numpy.array(rng.uniform(size=(m_shp[1],)), v1 = theano.shared(np.array(rng.uniform(size=(m_shp[1],)),
dtype='float32')) dtype='float32'))
v2_orig = numpy.array(rng.uniform(size=(m_shp[0],)), dtype='float32') v2_orig = np.array(rng.uniform(size=(m_shp[0],)), dtype='float32')
v2 = theano.shared(v2_orig) v2 = theano.shared(v2_orig)
m = theano.shared(numpy.array(rng.uniform(size=m_shp), m = theano.shared(np.array(rng.uniform(size=m_shp),
dtype='float32')) dtype='float32'))
f = theano.function([], v2 + tensor.dot(m, v1), f = theano.function([], v2 + tensor.dot(m, v1),
mode=self.mode) mode=self.mode)
# Assert they produce the same output # Assert they produce the same output
assert numpy.allclose(f(), assert np.allclose(f(),
numpy.dot(m.get_value(), v1.get_value()) + v2_orig) np.dot(m.get_value(), v1.get_value()) + v2_orig)
topo = [n.op for n in f.maker.fgraph.toposort()] topo = [n.op for n in f.maker.fgraph.toposort()]
assert topo == [CGemv(inplace=False)], topo assert topo == [CGemv(inplace=False)], topo
...@@ -227,8 +227,8 @@ class TestCGemv(TestCase, TestOptimizationMixin): ...@@ -227,8 +227,8 @@ class TestCGemv(TestCase, TestOptimizationMixin):
# Assert they produce the same output # Assert they produce the same output
g() g()
assert numpy.allclose(v2.get_value(), assert np.allclose(v2.get_value(),
numpy.dot(m.get_value(), v1.get_value()) + v2_orig) np.dot(m.get_value(), v1.get_value()) + v2_orig)
topo = [n.op for n in g.maker.fgraph.toposort()] topo = [n.op for n in g.maker.fgraph.toposort()]
assert topo == [CGemv(inplace=True)] assert topo == [CGemv(inplace=True)]
...@@ -237,11 +237,11 @@ class TestCGemv(TestCase, TestOptimizationMixin): ...@@ -237,11 +237,11 @@ class TestCGemv(TestCase, TestOptimizationMixin):
m.get_value(borrow=True)[::-1, ::-1], m.get_value(borrow=True)[::-1, ::-1],
borrow=True) borrow=True)
v2.set_value(v2_orig) v2.set_value(v2_orig)
assert numpy.allclose(f(), assert np.allclose(f(),
numpy.dot(m.get_value(), v1.get_value()) + v2_orig) np.dot(m.get_value(), v1.get_value()) + v2_orig)
g() g()
assert numpy.allclose(v2.get_value(), assert np.allclose(v2.get_value(),
numpy.dot(m.get_value(), v1.get_value()) + v2_orig) np.dot(m.get_value(), v1.get_value()) + v2_orig)
def test_gemv1(self): def test_gemv1(self):
skip_if_blas_ldflags_empty() skip_if_blas_ldflags_empty()
...@@ -265,12 +265,12 @@ class TestCGemv(TestCase, TestOptimizationMixin): ...@@ -265,12 +265,12 @@ class TestCGemv(TestCase, TestOptimizationMixin):
mode=self.mode) mode=self.mode)
# Matrix value # Matrix value
A_val = numpy.ones((5, 3), dtype=dtype) A_val = np.ones((5, 3), dtype=dtype)
# Different vector length # Different vector length
ones_3 = numpy.ones(3, dtype=dtype) ones_3 = np.ones(3, dtype=dtype)
ones_4 = numpy.ones(4, dtype=dtype) ones_4 = np.ones(4, dtype=dtype)
ones_5 = numpy.ones(5, dtype=dtype) ones_5 = np.ones(5, dtype=dtype)
ones_6 = numpy.ones(6, dtype=dtype) ones_6 = np.ones(6, dtype=dtype)
f(A_val, ones_3, ones_5) f(A_val, ones_3, ones_5)
f(A_val[::-1, ::-1], ones_3, ones_5) f(A_val[::-1, ::-1], ones_3, ones_5)
...@@ -286,12 +286,12 @@ class TestCGemv(TestCase, TestOptimizationMixin): ...@@ -286,12 +286,12 @@ class TestCGemv(TestCase, TestOptimizationMixin):
f = theano.function([x, y, z], f = theano.function([x, y, z],
[tensor.dot(y, x), tensor.dot(z,x)], [tensor.dot(y, x), tensor.dot(z,x)],
mode=mode_blas_opt) mode=mode_blas_opt)
vx = numpy.random.rand(3, 3) vx = np.random.rand(3, 3)
vy = numpy.random.rand(3) vy = np.random.rand(3)
vz = numpy.random.rand(3) vz = np.random.rand(3)
out = f(vx, vy, vz) out = f(vx, vy, vz)
assert numpy.allclose(out[0], numpy.dot(vy, vx)) assert np.allclose(out[0], np.dot(vy, vx))
assert numpy.allclose(out[1], numpy.dot(vz, vx)) assert np.allclose(out[1], np.dot(vz, vx))
assert len([n for n in f.maker.fgraph.apply_nodes assert len([n for n in f.maker.fgraph.apply_nodes
if isinstance(n.op, tensor.AllocEmpty)]) == 2 if isinstance(n.op, tensor.AllocEmpty)]) == 2
......
theano/tensor/tests/test_casting.py
浏览文件 @ ef9f6efc
...@@ -16,7 +16,7 @@ class test_casting(unittest.TestCase): ...@@ -16,7 +16,7 @@ class test_casting(unittest.TestCase):
x = type_fn() x = type_fn()
f = function([x], op_fn(x)) f = function([x], op_fn(x))
xval = theano._asarray(numpy.random.rand(10) * 10, xval = theano._asarray(np.random.rand(10) * 10,
dtype=type_fn.dtype) dtype=type_fn.dtype)
yval = f(xval) yval = f(xval)
assert (str(yval.dtype) == assert (str(yval.dtype) ==
...@@ -25,7 +25,7 @@ class test_casting(unittest.TestCase): ...@@ -25,7 +25,7 @@ class test_casting(unittest.TestCase):
def test_illegal(self): def test_illegal(self):
try: try:
x = zmatrix() x = zmatrix()
function([x], cast(x, 'float64'))(numpy.ones((2, 3), function([x], cast(x, 'float64'))(np.ones((2, 3),
dtype='complex128')) dtype='complex128'))
except TypeError: except TypeError:
return return
...@@ -44,13 +44,13 @@ class test_casting(unittest.TestCase): ...@@ -44,13 +44,13 @@ class test_casting(unittest.TestCase):
_convert_to_float64]): _convert_to_float64]):
y = converter(x) y = converter(x)
f = function([compile.In(x, strict=True)], y) f = function([compile.In(x, strict=True)], y)
a = numpy.arange(10, dtype=type1) a = np.arange(10, dtype=type1)
b = f(a) b = f(a)
self.assertTrue(numpy.all(b == numpy.arange(10, dtype=type2))) self.assertTrue(np.all(b == np.arange(10, dtype=type2)))
def test_convert_to_complex(self): def test_convert_to_complex(self):
val64 = numpy.ones(3, dtype='complex64') + 0.5j val64 = np.ones(3, dtype='complex64') + 0.5j
val128 = numpy.ones(3, dtype='complex128') + 0.5j val128 = np.ones(3, dtype='complex128') + 0.5j
vec64 = TensorType('complex64', (False, ))() vec64 = TensorType('complex64', (False, ))()
vec128 = TensorType('complex128', (False, ))() vec128 = TensorType('complex128', (False, ))()
...@@ -70,22 +70,22 @@ class test_casting(unittest.TestCase): ...@@ -70,22 +70,22 @@ class test_casting(unittest.TestCase):
# upcasting to complex128 # upcasting to complex128
for t in ['int8', 'int16', 'int32', 'int64', 'float32', 'float64']: for t in ['int8', 'int16', 'int32', 'int64', 'float32', 'float64']:
a = theano.shared(numpy.ones(3, dtype=t)) a = theano.shared(np.ones(3, dtype=t))
b = theano.shared(numpy.ones(3, dtype='complex128')) b = theano.shared(np.ones(3, dtype='complex128'))
f = function([], basic._convert_to_complex128(a)) f = function([], basic._convert_to_complex128(a))
assert a.type.values_eq_approx(b.get_value(), f()) assert a.type.values_eq_approx(b.get_value(), f())
# upcasting to complex64 # upcasting to complex64
for t in ['int8', 'int16', 'int32', 'int64', 'float32']: for t in ['int8', 'int16', 'int32', 'int64', 'float32']:
a = theano.shared(numpy.ones(3, dtype=t)) a = theano.shared(np.ones(3, dtype=t))
b = theano.shared(numpy.ones(3, dtype='complex64')) b = theano.shared(np.ones(3, dtype='complex64'))
f = function([], basic._convert_to_complex64(a)) f = function([], basic._convert_to_complex64(a))
assert a.type.values_eq_approx(b.get_value(), f()) assert a.type.values_eq_approx(b.get_value(), f())
# downcast to complex64 # downcast to complex64
for t in ['float64']: for t in ['float64']:
a = theano.shared(numpy.ones(3, dtype=t)) a = theano.shared(np.ones(3, dtype=t))
b = theano.shared(numpy.ones(3, dtype='complex64')) b = theano.shared(np.ones(3, dtype='complex64'))
f = function([], basic._convert_to_complex64(a)) f = function([], basic._convert_to_complex64(a))
assert a.type.values_eq_approx(b.get_value(), f()) assert a.type.values_eq_approx(b.get_value(), f())
...@@ -96,5 +96,5 @@ class test_casting(unittest.TestCase): ...@@ -96,5 +96,5 @@ class test_casting(unittest.TestCase):
inputs = [v0] inputs = [v0]
outputs = [v1] outputs = [v1]
f = function(inputs, outputs) f = function(inputs, outputs)
i = numpy.zeros((2, 2)) i = np.zeros((2, 2))
assert (f(i) == numpy.zeros((2, 2))).all() assert (f(i) == np.zeros((2, 2))).all()
theano/tensor/tests/test_nlinalg.py
浏览文件 @ ef9f6efc
...@@ -239,8 +239,8 @@ def test_det_shape(): ...@@ -239,8 +239,8 @@ def test_det_shape():
class test_diag(unittest.TestCase): class test_diag(unittest.TestCase):
""" """
Test that linalg.diag has the same behavior as np.diag. Test that linalg.diag has the same behavior as numpy.diag.
np.diag has two behaviors: numpy.diag has two behaviors:
(1) when given a vector, it returns a matrix with that vector as the (1) when given a vector, it returns a matrix with that vector as the
diagonal. diagonal.
(2) when given a matrix, returns a vector which is the diagonal of the (2) when given a matrix, returns a vector which is the diagonal of the
......
theano/tensor/type.py
浏览文件 @ ef9f6efc
...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division ...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import logging import logging
import warnings import warnings
import numpy import numpy as np
import theano import theano
from theano import config from theano import config
...@@ -50,7 +50,7 @@ class TensorType(Type): ...@@ -50,7 +50,7 @@ class TensorType(Type):
self.broadcastable = tuple(bool(b) for b in broadcastable) self.broadcastable = tuple(bool(b) for b in broadcastable)
self.dtype_specs() # error checking is done there self.dtype_specs() # error checking is done there
self.name = name self.name = name
self.numpy_dtype = numpy.dtype(self.dtype) self.numpy_dtype = np.dtype(self.dtype)
self.sparse_grad = sparse_grad self.sparse_grad = sparse_grad
if sparse_grad: if sparse_grad:
warnings.warn( warnings.warn(
...@@ -88,12 +88,12 @@ class TensorType(Type): ...@@ -88,12 +88,12 @@ class TensorType(Type):
'maybe you are trying to call a function on a (possibly ' 'maybe you are trying to call a function on a (possibly '
'shared) variable instead of a numeric array?') 'shared) variable instead of a numeric array?')
if ((type(data) is numpy.ndarray) and if ((type(data) is np.ndarray) and
(data.dtype == self.numpy_dtype)): (data.dtype == self.numpy_dtype)):
if data.dtype.num != self.numpy_dtype.num: if data.dtype.num != self.numpy_dtype.num:
data = theano._asarray(data, dtype=self.dtype) data = theano._asarray(data, dtype=self.dtype)
# -- now fall through to ndim check # -- now fall through to ndim check
elif ((type(data) is numpy.memmap) and elif ((type(data) is np.memmap) and
(data.dtype == self.numpy_dtype)): (data.dtype == self.numpy_dtype)):
# numpy.memmap is a "safe" subclass of ndarray, # numpy.memmap is a "safe" subclass of ndarray,
# so we can use it whereever we expect a base ndarray. # so we can use it whereever we expect a base ndarray.
...@@ -103,7 +103,7 @@ class TensorType(Type): ...@@ -103,7 +103,7 @@ class TensorType(Type):
elif strict: elif strict:
# If any of the two conditions above was not met, # If any of the two conditions above was not met,
# we raise a meaningful TypeError. # we raise a meaningful TypeError.
if not (type(data) is numpy.ndarray): if not (type(data) is np.ndarray):
raise TypeError("%s expected a ndarray object." % self, raise TypeError("%s expected a ndarray object." % self,
data, type(data)) data, type(data))
if data.dtype != self.numpy_dtype: if data.dtype != self.numpy_dtype:
...@@ -118,7 +118,7 @@ class TensorType(Type): ...@@ -118,7 +118,7 @@ class TensorType(Type):
# TODO: consider to pad shape with ones to make it consistent # TODO: consider to pad shape with ones to make it consistent
# with self.broadcastable... like vector->row type thing # with self.broadcastable... like vector->row type thing
else: else:
if isinstance(data, numpy.ndarray): if isinstance(data, np.ndarray):
# Check if self.dtype can accurately represent data # Check if self.dtype can accurately represent data
# (do not try to convert the data) # (do not try to convert the data)
up_dtype = scal.upcast(self.dtype, data.dtype) up_dtype = scal.upcast(self.dtype, data.dtype)
...@@ -150,7 +150,7 @@ class TensorType(Type): ...@@ -150,7 +150,7 @@ class TensorType(Type):
converted_data = theano._asarray(data, self.dtype) converted_data = theano._asarray(data, self.dtype)
# We use the `values_eq` static function from TensorType # We use the `values_eq` static function from TensorType
# to handle NaN values. # to handle NaN values.
if TensorType.values_eq(numpy.asarray(data), if TensorType.values_eq(np.asarray(data),
converted_data, converted_data,
force_same_dtype=False): force_same_dtype=False):
data = converted_data data = converted_data
...@@ -195,7 +195,7 @@ class TensorType(Type): ...@@ -195,7 +195,7 @@ class TensorType(Type):
" dimension.", data.shape, self.broadcastable) " dimension.", data.shape, self.broadcastable)
i += 1 i += 1
if (self.filter_checks_isfinite and if (self.filter_checks_isfinite and
not numpy.all(numpy.isfinite(data))): not np.all(np.isfinite(data))):
raise ValueError("non-finite elements not allowed") raise ValueError("non-finite elements not allowed")
return data return data
...@@ -294,8 +294,8 @@ class TensorType(Type): ...@@ -294,8 +294,8 @@ class TensorType(Type):
@staticmethod @staticmethod
def may_share_memory(a, b): def may_share_memory(a, b):
# This is a method of TensorType, so both a and b should be ndarrays # This is a method of TensorType, so both a and b should be ndarrays
if isinstance(a, numpy.ndarray) and isinstance(b, numpy.ndarray): if isinstance(a, np.ndarray) and isinstance(b, np.ndarray):
return numpy.may_share_memory(a, b) return np.may_share_memory(a, b)
else: else:
return False return False
...@@ -308,14 +308,14 @@ class TensorType(Type): ...@@ -308,14 +308,14 @@ class TensorType(Type):
if force_same_dtype and a.dtype != b.dtype: if force_same_dtype and a.dtype != b.dtype:
return False return False
a_eq_b = (a == b) a_eq_b = (a == b)
r = numpy.all(a_eq_b) r = np.all(a_eq_b)
if r: if r:
return True return True
# maybe the trouble is that there are NaNs # maybe the trouble is that there are NaNs
a_missing = numpy.isnan(a) a_missing = np.isnan(a)
if a_missing.any(): if a_missing.any():
b_missing = numpy.isnan(b) b_missing = np.isnan(b)
return numpy.all(a_eq_b + (a_missing == b_missing)) return np.all(a_eq_b + (a_missing == b_missing))
else: else:
return False return False
...@@ -553,7 +553,7 @@ class TensorType(Type): ...@@ -553,7 +553,7 @@ class TensorType(Type):
Create an numpy ndarray full of 0 values. Create an numpy ndarray full of 0 values.
""" """
return numpy.zeros(shape, dtype=self.dtype) return np.zeros(shape, dtype=self.dtype)
def get_shape_info(self, obj): def get_shape_info(self, obj):
""" """
...@@ -601,9 +601,9 @@ class TensorType(Type): ...@@ -601,9 +601,9 @@ class TensorType(Type):
""" """
if shape_info: if shape_info:
return numpy.prod(shape_info) * numpy.dtype(self.dtype).itemsize return np.prod(shape_info) * np.dtype(self.dtype).itemsize
else: # a scalar else: # a scalar
return numpy.dtype(self.dtype).itemsize return np.dtype(self.dtype).itemsize
theano.compile.ops.expandable_types += (TensorType,) theano.compile.ops.expandable_types += (TensorType,)
...@@ -624,13 +624,13 @@ def values_eq_approx(a, b, allow_remove_inf=False, allow_remove_nan=False, ...@@ -624,13 +624,13 @@ def values_eq_approx(a, b, allow_remove_inf=False, allow_remove_nan=False,
Absolute tolerance, passed to _allclose. Absolute tolerance, passed to _allclose.
""" """
if isinstance(a, numpy.ndarray) and isinstance(b, numpy.ndarray): if isinstance(a, np.ndarray) and isinstance(b, np.ndarray):
if a.shape != b.shape: if a.shape != b.shape:
return False return False
if a.dtype != b.dtype: if a.dtype != b.dtype:
return False return False
if str(a.dtype) not in theano.tensor.continuous_dtypes: if str(a.dtype) not in theano.tensor.continuous_dtypes:
return numpy.all(a == b) return np.all(a == b)
else: else:
cmp = theano.tensor.basic._allclose(a, b, rtol=rtol, atol=atol) cmp = theano.tensor.basic._allclose(a, b, rtol=rtol, atol=atol)
if cmp: if cmp:
...@@ -644,38 +644,38 @@ def values_eq_approx(a, b, allow_remove_inf=False, allow_remove_nan=False, ...@@ -644,38 +644,38 @@ def values_eq_approx(a, b, allow_remove_inf=False, allow_remove_nan=False,
# core recently, so it may not be available to everyone. Thus, # core recently, so it may not be available to everyone. Thus,
# for now we use a home-made recipe, that should probably be # for now we use a home-made recipe, that should probably be
# revisited in the future. # revisited in the future.
a_missing = numpy.isnan(a) a_missing = np.isnan(a)
a_inf = numpy.isinf(a) a_inf = np.isinf(a)
if not (a_missing.any() or (allow_remove_inf and a_inf.any())): if not (a_missing.any() or (allow_remove_inf and a_inf.any())):
# There are no missing values in a, thus this is not the # There are no missing values in a, thus this is not the
# reason why numpy.allclose(a, b) returned False. # reason why numpy.allclose(a, b) returned False.
_logger.info( _logger.info(
'numpy allclose failed for abs_err %f and rel_err %f', 'numpy allclose failed for abs_err %f and rel_err %f',
numpy.max(abs(a - b)), np.max(abs(a - b)),
numpy.max(abs(a - b) / (abs(a) + abs(b)))) np.max(abs(a - b) / (abs(a) + abs(b))))
return False return False
# The following line is what numpy.allclose bases its decision # The following line is what numpy.allclose bases its decision
# upon, according to its documentation. # upon, according to its documentation.
rtol = 1.0000000000000001e-05 rtol = 1.0000000000000001e-05
atol = 1e-8 atol = 1e-8
cmp_elemwise = (numpy.absolute(a - b) <= cmp_elemwise = (np.absolute(a - b) <=
(atol + rtol * numpy.absolute(b))) (atol + rtol * np.absolute(b)))
# Find places where both a and b have missing values. # Find places where both a and b have missing values.
both_missing = a_missing * numpy.isnan(b) both_missing = a_missing * np.isnan(b)
# Find places where both a and b have inf of the same sign. # Find places where both a and b have inf of the same sign.
both_inf = a_inf * numpy.isinf(b) both_inf = a_inf * np.isinf(b)
# cmp_elemwise is weird when we have inf and -inf. # cmp_elemwise is weird when we have inf and -inf.
# set it to False # set it to False
cmp_elemwise = numpy.where( cmp_elemwise = np.where(
both_inf & cmp_elemwise, both_inf & cmp_elemwise,
a == b, a == b,
cmp_elemwise) cmp_elemwise)
# check the sign of the inf # check the sign of the inf
both_inf = numpy.where(both_inf, (a == b), both_inf) both_inf = np.where(both_inf, (a == b), both_inf)
if allow_remove_inf: if allow_remove_inf:
both_inf += a_inf both_inf += a_inf
......
theano/tensor/type_other.py
浏览文件 @ ef9f6efc
...@@ -3,7 +3,7 @@ from __future__ import absolute_import, print_function, division ...@@ -3,7 +3,7 @@ from __future__ import absolute_import, print_function, division
# Slice type and Op. None Type and NoneConst. # Slice type and Op. None Type and NoneConst.
# #
import numpy import numpy as np
import theano import theano
from theano.gof import Apply, Constant, Generic, Op, Type, hashtype from theano.gof import Apply, Constant, Generic, Op, Type, hashtype
...@@ -78,15 +78,15 @@ class SliceConstant(Constant): ...@@ -78,15 +78,15 @@ class SliceConstant(Constant):
def __init__(self, type, data, name=None): def __init__(self, type, data, name=None):
assert isinstance(data, slice) assert isinstance(data, slice)
# Numpy ndarray aren't hashable, so get rid of them. # Numpy ndarray aren't hashable, so get rid of them.
if isinstance(data.start, numpy.ndarray): if isinstance(data.start, np.ndarray):
assert data.start.ndim == 0 assert data.start.ndim == 0
assert str(data.start.dtype) in theano.tensor.integer_dtypes assert str(data.start.dtype) in theano.tensor.integer_dtypes
data = slice(int(data.start), data.stop, data.step) data = slice(int(data.start), data.stop, data.step)
elif isinstance(data.stop, numpy.ndarray): elif isinstance(data.stop, np.ndarray):
assert data.stop.ndim == 0 assert data.stop.ndim == 0
assert str(data.stop.dtype) in theano.tensor.integer_dtypes assert str(data.stop.dtype) in theano.tensor.integer_dtypes
data = slice(data.start, int(data.stop), data.step) data = slice(data.start, int(data.stop), data.step)
elif isinstance(data.step, numpy.ndarray): elif isinstance(data.step, np.ndarray):
assert data.step.ndim == 0 assert data.step.ndim == 0
assert str(data.step.dtype) in theano.tensor.integer_dtypes assert str(data.step.dtype) in theano.tensor.integer_dtypes
data = slice(data.start, int(data.stop), data.step) data = slice(data.start, int(data.stop), data.step)
......
theano/tensor/utils.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
import theano import theano
from theano.compat import izip from theano.compat import izip
...@@ -23,7 +23,7 @@ def hash_from_ndarray(data): ...@@ -23,7 +23,7 @@ def hash_from_ndarray(data):
# too long hash, I call it again on the concatenation of all parts. # too long hash, I call it again on the concatenation of all parts.
if not data.flags["C_CONTIGUOUS"]: if not data.flags["C_CONTIGUOUS"]:
# hash_from_code needs a C-contiguous array. # hash_from_code needs a C-contiguous array.
data = numpy.ascontiguousarray(data) data = np.ascontiguousarray(data)
return hash_from_code(hash_from_code(data) + return hash_from_code(hash_from_code(data) +
hash_from_code(str(data.shape)) + hash_from_code(str(data.shape)) +
hash_from_code(str(data.strides)) + hash_from_code(str(data.strides)) +
......
theano/tensor/var.py
浏览文件 @ ef9f6efc
...@@ -4,7 +4,7 @@ import copy ...@@ -4,7 +4,7 @@ import copy
import traceback as tb import traceback as tb
import warnings import warnings
import numpy import numpy as np
from six import integer_types from six import integer_types
from six.moves import xrange from six.moves import xrange
...@@ -462,7 +462,7 @@ class _tensor_py_operators(object): ...@@ -462,7 +462,7 @@ class _tensor_py_operators(object):
def check_bool(args_el): def check_bool(args_el):
try: try:
if (isinstance(args_el, (numpy.bool_, bool)) or if (isinstance(args_el, (np.bool_, bool)) or
args_el.dtype == 'bool'): args_el.dtype == 'bool'):
raise TypeError('TensorType does not support boolean ' raise TypeError('TensorType does not support boolean '
'mask for indexing such as tensor[x==0]. ' 'mask for indexing such as tensor[x==0]. '
...@@ -495,7 +495,7 @@ class _tensor_py_operators(object): ...@@ -495,7 +495,7 @@ class _tensor_py_operators(object):
elif len(ellipses) == 1: elif len(ellipses) == 1:
new_axes = sum(1 new_axes = sum(1
for index in args for index in args
if index is numpy.newaxis) # numpy.newaxis is None if index is np.newaxis) # numpy.newaxis is None
ellipsis_at = ellipses[0] ellipsis_at = ellipses[0]
args = list(args) args = list(args)
args[ellipsis_at: ellipsis_at + 1] = ( args[ellipsis_at: ellipsis_at + 1] = (
...@@ -503,7 +503,7 @@ class _tensor_py_operators(object): ...@@ -503,7 +503,7 @@ class _tensor_py_operators(object):
# Force input to be int64 datatype if input is an empty list or tuple # Force input to be int64 datatype if input is an empty list or tuple
# Else leave it as is if it is a real number # Else leave it as is if it is a real number
args = tuple([numpy.array(inp, dtype=numpy.int64) args = tuple([np.array(inp, dtype=np.int64)
if(inp == [] or inp == ()) else inp for inp in args]) if(inp == [] or inp == ()) else inp for inp in args])
# Convert python literals to theano constants # Convert python literals to theano constants
args = theano.tensor.subtensor.make_constant(args) args = theano.tensor.subtensor.make_constant(args)
...@@ -515,7 +515,7 @@ class _tensor_py_operators(object): ...@@ -515,7 +515,7 @@ class _tensor_py_operators(object):
axis = None axis = None
for i, arg in enumerate(args): for i, arg in enumerate(args):
try: try:
if arg is not numpy.newaxis: if arg is not np.newaxis:
theano.tensor.subtensor.Subtensor.convert(arg) theano.tensor.subtensor.Subtensor.convert(arg)
except theano.tensor.subtensor.AdvancedIndexingError: except theano.tensor.subtensor.AdvancedIndexingError:
if advanced: if advanced:
...@@ -532,14 +532,14 @@ class _tensor_py_operators(object): ...@@ -532,14 +532,14 @@ class _tensor_py_operators(object):
all(isinstance(a, slice) and all(isinstance(a, slice) and
equal_slices(a, slice(None)) for a in args[axis + 1:]) and equal_slices(a, slice(None)) for a in args[axis + 1:]) and
isinstance(args[axis], isinstance(args[axis],
(numpy.ndarray, list, (np.ndarray, list,
TensorVariable, TensorConstant, TensorVariable, TensorConstant,
theano.tensor.sharedvar.TensorSharedVariable))): theano.tensor.sharedvar.TensorSharedVariable))):
return self.take(args[axis], axis) return self.take(args[axis], axis)
else: else:
return theano.tensor.subtensor.advanced_subtensor(self, *args) return theano.tensor.subtensor.advanced_subtensor(self, *args)
else: else:
if numpy.newaxis in args: if np.newaxis in args:
# None (aka np.newaxis) in numpy indexing means to add a # None (aka np.newaxis) in numpy indexing means to add a
# broadcastable dimension, which theano traditionally did with # broadcastable dimension, which theano traditionally did with
# the dimshuffle op. The following code converts numpy-style # the dimshuffle op. The following code converts numpy-style
...@@ -550,7 +550,7 @@ class _tensor_py_operators(object): ...@@ -550,7 +550,7 @@ class _tensor_py_operators(object):
pattern = [] pattern = []
new_args = [] new_args = []
for arg in args: for arg in args:
if arg == numpy.newaxis: if arg == np.newaxis:
pattern.append('x') pattern.append('x')
new_args.append(slice(None, None, None)) new_args.append(slice(None, None, None))
else: else:
...@@ -642,7 +642,7 @@ class _tensor_py_operators(object): ...@@ -642,7 +642,7 @@ class _tensor_py_operators(object):
def norm(self, L, axis=None, keepdims=False): def norm(self, L, axis=None, keepdims=False):
if L == 0: if L == 0:
raise NotImplementedError() raise NotImplementedError()
if numpy.isinf(L): if np.isinf(L):
raise NotImplementedError() raise NotImplementedError()
# optimizations will/should catch cases like L=1, L=2 # optimizations will/should catch cases like L=1, L=2
y = theano.tensor.basic.pow( y = theano.tensor.basic.pow(
...@@ -862,7 +862,7 @@ class TensorConstantSignature(tuple): ...@@ -862,7 +862,7 @@ class TensorConstantSignature(tuple):
# (note that if there are NaN values in d1, this will return # (note that if there are NaN values in d1, this will return
# False, which is why we do not bother with testing `other.has_nan` # False, which is why we do not bother with testing `other.has_nan`
# here). # here).
return (self.sum == other.sum) and numpy.all(d0 == d1) return (self.sum == other.sum) and np.all(d0 == d1)
def __hash__(self): def __hash__(self):
t, d = self t, d = self
...@@ -880,25 +880,25 @@ class TensorConstantSignature(tuple): ...@@ -880,25 +880,25 @@ class TensorConstantSignature(tuple):
self._sum = self.no_nan.sum() self._sum = self.no_nan.sum()
# The following 2 lines are needede as in Python 3.3 with NumPy # The following 2 lines are needede as in Python 3.3 with NumPy
# 1.7.1, numpy.ndarray and numpy.memmap aren't hashable. # 1.7.1, numpy.ndarray and numpy.memmap aren't hashable.
if type(self._sum) is numpy.memmap: if type(self._sum) is np.memmap:
self._sum = numpy.asarray(self._sum).item() self._sum = np.asarray(self._sum).item()
if self.has_nan and self.no_nan.mask.all(): if self.has_nan and self.no_nan.mask.all():
# In this case the sum is not properly computed by numpy. # In this case the sum is not properly computed by numpy.
self._sum = 0 self._sum = 0
if numpy.isinf(self._sum) or numpy.isnan(self._sum): if np.isinf(self._sum) or np.isnan(self._sum):
# NaN may happen when there are both -inf and +inf values. # NaN may happen when there are both -inf and +inf values.
if self.has_nan: if self.has_nan:
# Filter both NaN and Inf values. # Filter both NaN and Inf values.
mask = self.no_nan.mask + numpy.isinf(self[1]) mask = self.no_nan.mask + np.isinf(self[1])
else: else:
# Filter only Inf values. # Filter only Inf values.
mask = numpy.isinf(self[1]) mask = np.isinf(self[1])
if mask.all(): if mask.all():
self._sum = 0 self._sum = 0
else: else:
self._sum = numpy.ma.masked_array(self[1], mask).sum() self._sum = np.ma.masked_array(self[1], mask).sum()
# At this point there should be no more NaN. # At this point there should be no more NaN.
assert not numpy.isnan(self._sum) assert not np.isnan(self._sum)
return self._sum return self._sum
sum = property(_get_sum) sum = property(_get_sum)
...@@ -906,9 +906,9 @@ class TensorConstantSignature(tuple): ...@@ -906,9 +906,9 @@ class TensorConstantSignature(tuple):
try: try:
return self._no_nan return self._no_nan
except AttributeError: except AttributeError:
nan_mask = numpy.isnan(self[1]) nan_mask = np.isnan(self[1])
if nan_mask.any(): if nan_mask.any():
self._no_nan = numpy.ma.masked_array(self[1], nan_mask) self._no_nan = np.ma.masked_array(self[1], nan_mask)
self.has_nan = True self.has_nan = True
else: else:
self._no_nan = self[1] self._no_nan = self[1]
...@@ -926,7 +926,7 @@ class TensorConstant(_tensor_py_operators, Constant): ...@@ -926,7 +926,7 @@ class TensorConstant(_tensor_py_operators, Constant):
def __init__(self, type, data, name=None): def __init__(self, type, data, name=None):
Constant.__init__(self, type, data, name) Constant.__init__(self, type, data, name)
self.tag.unique_value = None self.tag.unique_value = None
if isinstance(data, numpy.ndarray) and data.ndim > 0: if isinstance(data, np.ndarray) and data.ndim > 0:
flat_data = data.ravel() flat_data = data.ravel()
if flat_data.shape[0]: if flat_data.shape[0]:
if (flat_data == flat_data[0]).all(): if (flat_data == flat_data[0]).all():
...@@ -949,7 +949,7 @@ class TensorConstant(_tensor_py_operators, Constant): ...@@ -949,7 +949,7 @@ class TensorConstant(_tensor_py_operators, Constant):
def equals(self, other): def equals(self, other):
# Override Contant.equals to allow to compare with # Override Contant.equals to allow to compare with
# numpy.ndarray, and python type. # numpy.ndarray, and python type.
if isinstance(other, (numpy.ndarray, int, float)): if isinstance(other, (np.ndarray, int, float)):
# Make a TensorConstant to be able to compare # Make a TensorConstant to be able to compare
other = theano.tensor.basic.constant(other) other = theano.tensor.basic.constant(other)
return (isinstance(other, TensorConstant) and return (isinstance(other, TensorConstant) and
......
theano/tensor/xlogx.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
from theano.tensor.elemwise import Elemwise from theano.tensor.elemwise import Elemwise
from theano import scalar from theano import scalar
...@@ -15,7 +15,7 @@ class XlogX(scalar.UnaryScalarOp): ...@@ -15,7 +15,7 @@ class XlogX(scalar.UnaryScalarOp):
def st_impl(x): def st_impl(x):
if x == 0.0: if x == 0.0:
return 0.0 return 0.0
return x * numpy.log(x) return x * np.log(x)
def impl(self, x): def impl(self, x):
return XlogX.st_impl(x) return XlogX.st_impl(x)
...@@ -48,7 +48,7 @@ class XlogY0(scalar.BinaryScalarOp): ...@@ -48,7 +48,7 @@ class XlogY0(scalar.BinaryScalarOp):
def st_impl(x, y): def st_impl(x, y):
if x == 0.0: if x == 0.0:
return 0.0 return 0.0
return x * numpy.log(y) return x * np.log(y)
def impl(self, x, y): def impl(self, x, y):
return XlogY0.st_impl(x, y) return XlogY0.st_impl(x, y)
......
theano/typed_list/basic.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import numpy import numpy as np
from .type import TypedListType from .type import TypedListType
import theano import theano
...@@ -500,7 +500,7 @@ class Index(Op): ...@@ -500,7 +500,7 @@ class Index(Op):
(out,) = outputs (out,) = outputs
for y in range(len(x)): for y in range(len(x)):
if node.inputs[0].ttype.values_eq(x[y], elem): if node.inputs[0].ttype.values_eq(x[y], elem):
out[0] = numpy.asarray(y, dtype=theano.config.floatX) out[0] = np.asarray(y, dtype=theano.config.floatX)
break break
def __str__(self): def __str__(self):
...@@ -530,7 +530,7 @@ class Count(Op): ...@@ -530,7 +530,7 @@ class Count(Op):
for y in range(len(x)): for y in range(len(x)):
if node.inputs[0].ttype.values_eq(x[y], elem): if node.inputs[0].ttype.values_eq(x[y], elem):
out[0] += 1 out[0] += 1
out[0] = numpy.asarray(out[0], dtype=theano.config.floatX) out[0] = np.asarray(out[0], dtype=theano.config.floatX)
def __str__(self): def __str__(self):
return self.__class__.__name__ return self.__class__.__name__
...@@ -565,7 +565,7 @@ class Length(Op): ...@@ -565,7 +565,7 @@ class Length(Op):
def perform(self, node, x, outputs): def perform(self, node, x, outputs):
(out,) = outputs (out,) = outputs
out[0] = numpy.asarray(len(x[0]), 'int64') out[0] = np.asarray(len(x[0]), 'int64')
def __str__(self): def __str__(self):
return self.__class__.__name__ return self.__class__.__name__
......
theano/typed_list/tests/test_basic.py
浏览文件 @ ef9f6efc
...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division ...@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import unittest import unittest
from nose.plugins.skip import SkipTest from nose.plugins.skip import SkipTest
import numpy import numpy as np
import theano import theano
import theano.typed_list import theano.typed_list
...@@ -24,8 +24,8 @@ except ImportError: ...@@ -24,8 +24,8 @@ except ImportError:
# took from tensors/tests/test_basic.py # took from tensors/tests/test_basic.py
def rand_ranged_matrix(minimum, maximum, shape): def rand_ranged_matrix(minimum, maximum, shape):
return numpy.asarray(numpy.random.rand(*shape) * (maximum - minimum) + return np.asarray(np.random.rand(*shape) * (maximum - minimum) +
minimum, dtype=theano.config.floatX) minimum, dtype=theano.config.floatX)
# took from sparse/tests/test_basic.py # took from sparse/tests/test_basic.py
...@@ -34,8 +34,8 @@ def random_lil(shape, dtype, nnz): ...@@ -34,8 +34,8 @@ def random_lil(shape, dtype, nnz):
huge = 2 ** 30 huge = 2 ** 30
for k in range(nnz): for k in range(nnz):
# set non-zeros in random locations (row x, col y) # set non-zeros in random locations (row x, col y)
idx = numpy.random.randint(1, huge + 1, size=2) % shape idx = np.random.randint(1, huge + 1, size=2) % shape
value = numpy.random.rand() value = np.random.rand()
# if dtype *int*, value will always be zeros! # if dtype *int*, value will always be zeros!
if dtype in theano.tensor.integer_dtypes: if dtype in theano.tensor.integer_dtypes:
value = int(value * 100) value = int(value * 100)
...@@ -68,7 +68,7 @@ class test_get_item(unittest.TestCase): ...@@ -68,7 +68,7 @@ class test_get_item(unittest.TestCase):
x = rand_ranged_matrix(-1000, 1000, [100, 101]) x = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], slice(0, 1, 1)), [x])) self.assertTrue(np.array_equal(f([x], slice(0, 1, 1)), [x]))
def test_sanity_check_single(self): def test_sanity_check_single(self):
...@@ -84,9 +84,9 @@ class test_get_item(unittest.TestCase): ...@@ -84,9 +84,9 @@ class test_get_item(unittest.TestCase):
x = rand_ranged_matrix(-1000, 1000, [100, 101]) x = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], self.assertTrue(np.array_equal(f([x],
numpy.asarray(0, dtype='int64')), np.asarray(0, dtype='int64')),
x)) x))
def test_interface(self): def test_interface(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -100,16 +100,16 @@ class test_get_item(unittest.TestCase): ...@@ -100,16 +100,16 @@ class test_get_item(unittest.TestCase):
x = rand_ranged_matrix(-1000, 1000, [100, 101]) x = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], self.assertTrue(np.array_equal(f([x],
numpy.asarray(0, dtype='int64')), np.asarray(0, dtype='int64')),
x)) x))
z = mySymbolicMatricesList[0] z = mySymbolicMatricesList[0]
f = theano.function([mySymbolicMatricesList], f = theano.function([mySymbolicMatricesList],
z) z)
self.assertTrue(numpy.array_equal(f([x]), x)) self.assertTrue(np.array_equal(f([x]), x))
def test_wrong_input(self): def test_wrong_input(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -130,14 +130,14 @@ class test_get_item(unittest.TestCase): ...@@ -130,14 +130,14 @@ class test_get_item(unittest.TestCase):
x = rand_ranged_matrix(-1000, 1000, [100, 101]) x = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x]), x)) self.assertTrue(np.array_equal(f([x]), x))
z = GetItem()(mySymbolicMatricesList, slice(0, 1, 1)) z = GetItem()(mySymbolicMatricesList, slice(0, 1, 1))
f = theano.function([mySymbolicMatricesList], f = theano.function([mySymbolicMatricesList],
z) z)
self.assertTrue(numpy.array_equal(f([x]), [x])) self.assertTrue(np.array_equal(f([x]), [x]))
class test_append(unittest.TestCase): class test_append(unittest.TestCase):
...@@ -156,7 +156,7 @@ class test_append(unittest.TestCase): ...@@ -156,7 +156,7 @@ class test_append(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], y), [x, y])) self.assertTrue(np.array_equal(f([x], y), [x, y]))
def test_sanity_check(self): def test_sanity_check(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -171,7 +171,7 @@ class test_append(unittest.TestCase): ...@@ -171,7 +171,7 @@ class test_append(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], y), [x, y])) self.assertTrue(np.array_equal(f([x], y), [x, y]))
def test_interfaces(self): def test_interfaces(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -186,7 +186,7 @@ class test_append(unittest.TestCase): ...@@ -186,7 +186,7 @@ class test_append(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], y), [x, y])) self.assertTrue(np.array_equal(f([x], y), [x, y]))
class test_extend(unittest.TestCase): class test_extend(unittest.TestCase):
...@@ -206,7 +206,7 @@ class test_extend(unittest.TestCase): ...@@ -206,7 +206,7 @@ class test_extend(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], [y]), [x, y])) self.assertTrue(np.array_equal(f([x], [y]), [x, y]))
def test_sanity_check(self): def test_sanity_check(self):
mySymbolicMatricesList1 = TypedListType(T.TensorType( mySymbolicMatricesList1 = TypedListType(T.TensorType(
...@@ -223,7 +223,7 @@ class test_extend(unittest.TestCase): ...@@ -223,7 +223,7 @@ class test_extend(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], [y]), [x, y])) self.assertTrue(np.array_equal(f([x], [y]), [x, y]))
def test_interface(self): def test_interface(self):
mySymbolicMatricesList1 = TypedListType(T.TensorType( mySymbolicMatricesList1 = TypedListType(T.TensorType(
...@@ -240,7 +240,7 @@ class test_extend(unittest.TestCase): ...@@ -240,7 +240,7 @@ class test_extend(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], [y]), [x, y])) self.assertTrue(np.array_equal(f([x], [y]), [x, y]))
class test_insert(unittest.TestCase): class test_insert(unittest.TestCase):
...@@ -260,10 +260,10 @@ class test_insert(unittest.TestCase): ...@@ -260,10 +260,10 @@ class test_insert(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], self.assertTrue(np.array_equal(f([x],
numpy.asarray(1, dtype='int64'), np.asarray(1, dtype='int64'),
y), y),
[x, y])) [x, y]))
def test_sanity_check(self): def test_sanity_check(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -279,7 +279,7 @@ class test_insert(unittest.TestCase): ...@@ -279,7 +279,7 @@ class test_insert(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], numpy.asarray(1, self.assertTrue(np.array_equal(f([x], np.asarray(1,
dtype='int64'), y), [x, y])) dtype='int64'), y), [x, y]))
def test_interface(self): def test_interface(self):
...@@ -296,10 +296,10 @@ class test_insert(unittest.TestCase): ...@@ -296,10 +296,10 @@ class test_insert(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], self.assertTrue(np.array_equal(f([x],
numpy.asarray(1, dtype='int64'), np.asarray(1, dtype='int64'),
y), y),
[x, y])) [x, y]))
class test_remove(unittest.TestCase): class test_remove(unittest.TestCase):
...@@ -318,7 +318,7 @@ class test_remove(unittest.TestCase): ...@@ -318,7 +318,7 @@ class test_remove(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x, y], y), [x])) self.assertTrue(np.array_equal(f([x, y], y), [x]))
def test_sanity_check(self): def test_sanity_check(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -333,7 +333,7 @@ class test_remove(unittest.TestCase): ...@@ -333,7 +333,7 @@ class test_remove(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x, y], y), [x])) self.assertTrue(np.array_equal(f([x, y], y), [x]))
def test_interface(self): def test_interface(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -348,7 +348,7 @@ class test_remove(unittest.TestCase): ...@@ -348,7 +348,7 @@ class test_remove(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x, y], y), [x])) self.assertTrue(np.array_equal(f([x, y], y), [x]))
class test_reverse(unittest.TestCase): class test_reverse(unittest.TestCase):
...@@ -366,7 +366,7 @@ class test_reverse(unittest.TestCase): ...@@ -366,7 +366,7 @@ class test_reverse(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x, y]), [y, x])) self.assertTrue(np.array_equal(f([x, y]), [y, x]))
def test_sanity_check(self): def test_sanity_check(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -380,7 +380,7 @@ class test_reverse(unittest.TestCase): ...@@ -380,7 +380,7 @@ class test_reverse(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x, y]), [y, x])) self.assertTrue(np.array_equal(f([x, y]), [y, x]))
def test_interface(self): def test_interface(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -394,7 +394,7 @@ class test_reverse(unittest.TestCase): ...@@ -394,7 +394,7 @@ class test_reverse(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x, y]), [y, x])) self.assertTrue(np.array_equal(f([x, y]), [y, x]))
class test_index(unittest.TestCase): class test_index(unittest.TestCase):
...@@ -570,10 +570,10 @@ class TestMakeList(unittest.TestCase): ...@@ -570,10 +570,10 @@ class TestMakeList(unittest.TestCase):
x = T.tensor3() x = T.tensor3()
y = T.tensor3() y = T.tensor3()
A = numpy.cast[theano.config.floatX](numpy.random.rand(5, 3)) A = np.cast[theano.config.floatX](np.random.rand(5, 3))
B = numpy.cast[theano.config.floatX](numpy.random.rand(7, 2)) B = np.cast[theano.config.floatX](np.random.rand(7, 2))
X = numpy.cast[theano.config.floatX](numpy.random.rand(5, 6, 1)) X = np.cast[theano.config.floatX](np.random.rand(5, 6, 1))
Y = numpy.cast[theano.config.floatX](numpy.random.rand(1, 9, 3)) Y = np.cast[theano.config.floatX](np.random.rand(1, 9, 3))
make_list((3., 4.)) make_list((3., 4.))
c = make_list((a, b)) c = make_list((a, b))
......
theano/typed_list/tests/test_opt.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import unittest import unittest
import numpy import numpy as np
import theano import theano
import theano.typed_list import theano.typed_list
...@@ -14,8 +14,8 @@ from theano import In ...@@ -14,8 +14,8 @@ from theano import In
# took from tensors/tests/test_basic.py # took from tensors/tests/test_basic.py
def rand_ranged_matrix(minimum, maximum, shape): def rand_ranged_matrix(minimum, maximum, shape):
return numpy.asarray(numpy.random.rand(*shape) * (maximum - minimum) + return np.asarray(np.random.rand(*shape) * (maximum - minimum) +
minimum, dtype=theano.config.floatX) minimum, dtype=theano.config.floatX)
class test_inplace(unittest.TestCase): class test_inplace(unittest.TestCase):
...@@ -34,7 +34,7 @@ class test_inplace(unittest.TestCase): ...@@ -34,7 +34,7 @@ class test_inplace(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x, y]), [y, x])) self.assertTrue(np.array_equal(f([x, y]), [y, x]))
def test_append_inplace(self): def test_append_inplace(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -52,7 +52,7 @@ class test_inplace(unittest.TestCase): ...@@ -52,7 +52,7 @@ class test_inplace(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], y), [x, y])) self.assertTrue(np.array_equal(f([x], y), [x, y]))
def test_extend_inplace(self): def test_extend_inplace(self):
mySymbolicMatricesList1 = TypedListType(T.TensorType( mySymbolicMatricesList1 = TypedListType(T.TensorType(
...@@ -72,7 +72,7 @@ class test_inplace(unittest.TestCase): ...@@ -72,7 +72,7 @@ class test_inplace(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], [y]), [x, y])) self.assertTrue(np.array_equal(f([x], [y]), [x, y]))
def test_insert_inplace(self): def test_insert_inplace(self):
mySymbolicMatricesList = TypedListType(T.TensorType( mySymbolicMatricesList = TypedListType(T.TensorType(
...@@ -92,7 +92,7 @@ class test_inplace(unittest.TestCase): ...@@ -92,7 +92,7 @@ class test_inplace(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x], numpy.asarray(1, self.assertTrue(np.array_equal(f([x], np.asarray(1,
dtype='int64'), y), [x, y])) dtype='int64'), y), [x, y]))
def test_remove_inplace(self): def test_remove_inplace(self):
...@@ -110,7 +110,7 @@ class test_inplace(unittest.TestCase): ...@@ -110,7 +110,7 @@ class test_inplace(unittest.TestCase):
y = rand_ranged_matrix(-1000, 1000, [100, 101]) y = rand_ranged_matrix(-1000, 1000, [100, 101])
self.assertTrue(numpy.array_equal(f([x, y], y), [x])) self.assertTrue(np.array_equal(f([x, y], y), [x]))
def test_constant_folding(): def test_constant_folding():
......
theano/typed_list/tests/test_type.py
浏览文件 @ ef9f6efc
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import unittest import unittest
import numpy import numpy as np
import theano import theano
import theano.typed_list import theano.typed_list
...@@ -12,8 +12,8 @@ from theano.tests import unittest_tools as utt ...@@ -12,8 +12,8 @@ from theano.tests import unittest_tools as utt
# took from tensors/tests/test_basic.py # took from tensors/tests/test_basic.py
def rand_ranged_matrix(minimum, maximum, shape): def rand_ranged_matrix(minimum, maximum, shape):
return numpy.asarray(numpy.random.rand(*shape) * (maximum - minimum) + return np.asarray(np.random.rand(*shape) * (maximum - minimum) +
minimum, dtype=theano.config.floatX) minimum, dtype=theano.config.floatX)
class test_typed_list_type(unittest.TestCase): class test_typed_list_type(unittest.TestCase):
...@@ -84,7 +84,7 @@ class test_typed_list_type(unittest.TestCase): ...@@ -84,7 +84,7 @@ class test_typed_list_type(unittest.TestCase):
x = rand_ranged_matrix(-1000, 1000, [100, 100]) x = rand_ranged_matrix(-1000, 1000, [100, 100])
self.assertTrue(numpy.array_equal(myType.filter([x]), [x])) self.assertTrue(np.array_equal(myType.filter([x]), [x]))
def test_intern_filter(self): def test_intern_filter(self):
""" """
...@@ -95,9 +95,9 @@ class test_typed_list_type(unittest.TestCase): ...@@ -95,9 +95,9 @@ class test_typed_list_type(unittest.TestCase):
myType = TypedListType(T.TensorType('float64', myType = TypedListType(T.TensorType('float64',
(False, False))) (False, False)))
x = numpy.asarray([[4, 5], [4, 5]], dtype='float32') x = np.asarray([[4, 5], [4, 5]], dtype='float32')
self.assertTrue(numpy.array_equal(myType.filter([x]), [x])) self.assertTrue(np.array_equal(myType.filter([x]), [x]))
# Will fail for unknown reasons # Will fail for unknown reasons
# under search # under search
...@@ -125,7 +125,7 @@ class test_typed_list_type(unittest.TestCase): ...@@ -125,7 +125,7 @@ class test_typed_list_type(unittest.TestCase):
x = rand_ranged_matrix(-1000, 1000, [100, 100]) x = rand_ranged_matrix(-1000, 1000, [100, 100])
self.assertTrue(numpy.array_equal(myType.filter([[x]]), [[x]])) self.assertTrue(np.array_equal(myType.filter([[x]]), [[x]]))
def test_comparison_different_depth(self): def test_comparison_different_depth(self):
""" """
......
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