def test_setfloatx_correct_values():
# Keep track of the old value
old_floatx = floatx()
# Check correct values
for value in ['float16', 'float32', 'float64']:
set_floatx(value)
assert floatx() == value
# Restore old value
set_floatx(old_floatx)
def test_setfloatx_incorrect_values():
# Keep track of the old value
old_floatx = floatx()
# Try some incorrect values
initial = floatx()
for value in ['', 'beerfloat', 123]:
with pytest.raises(Exception):
set_floatx(value)
assert floatx() == initial
# Restore old value
set_floatx(old_floatx)
def _random_prep(dtype=None, rng=None):
"""Helper function for random functions """
if dtype is None:
dtype = floatx()
if rng is None:
rng = make_rng()
return dtype, rng
def mean(x, axis=None, keepdims=False):
"""Mean of a tensor, alongside the specified axis. """
dtype = None
# bool is available since theano v0.9dev
if 'int' in x.dtype or x.dtype == 'bool':
dtype = floatx()
return T.mean(x, axis=axis, keepdims=keepdims, dtype=dtype)
def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None):
"""Instantiate an input data placeholder variable. """
if dtype is None:
dtype = floatx()
if shape is None and ndim is None:
raise ValueError('Specify either a shape or ndim value.')
if shape is not None:
ndim = len(shape)
broadcast = (False,) * ndim
x = T.TensorType(dtype, broadcast)(name)
return x
def test_set_floatx():
"""
Make sure that changes to the global floatx are effectively
taken into account by the backend.
"""
# Keep track of the old value
old_floatx = floatx()
set_floatx('float16')
var = BTH.variable([10])
check_dtype(var, 'float16')
set_floatx('float64')
var = BTH.variable([10])
check_dtype(var, 'float64')
# Restore old value
set_floatx(old_floatx)
def variable(value, dtype=None, name=None):
"""Instantiates a variable and returns it.
# Arguments
value: Numpy array, initial value of the tensor.
dtype: Tensor type.
name: Optional name string for the tensor.
# Returns
A variable instance .
"""
if dtype is None:
dtype = floatx()
if hasattr(value, 'eval'):
value = value.eval()
return np.asarray(value, dtype=dtype)
def variable(value, dtype=None, name=None):
"""Instantiates a variable and returns it.
# Arguments
value: Numpy array, initial value of the tensor.
dtype: Tensor type.
name: Optional name string for the tensor.
# Returns
A variable instance.
"""
if dtype is None:
dtype = floatx()
if isinstance(value, (theano.tensor.TensorVariable,
theano.tensor.sharedvar.TensorSharedVariable,
theano.tensor.TensorConstant)):
value = value.eval()
value = np.asarray(value, dtype=dtype)
variable = theano.shared(value=value,
name=name,
strict=False)
return variable
def eye(size, dtype=None, name=None):
"""Instantiates an identity matrix. """
if dtype is None:
dtype = floatx()
return variable(np.eye(size), dtype, name)
def ones(shape, dtype=None, name=None):
"""Instantiates an all-ones variable. """
if dtype is None:
dtype = floatx()
return variable(np.ones(shape), dtype, name)