def __call__(self, array):
if len(array.shape) == 1:
fan_in = array.shape[0]
else:
fan_in, fan_out = initializer.get_fans(array.shape)
fill_value = self.scale / np.sqrt(fan_in)
initializers.Constant(fill_value)(array)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype,\
'{} != {}'.format(array.dtype, self.dtype)
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(3. / fan_in)
Uniform(s, rng=self.rng)(array)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, fan_out = initializer.get_fans(array.shape)
if self.criterion == 'glorot':
s = np.sqrt(1. / (fan_in + fan_out))
elif self.criterion == 'he':
s = np.sqrt(1. / fan_in)
else:
raise ValueError('Invalid criterion: ' + self.criterion)
xp = cuda.get_array_module(array)
if xp is not np:
# Only CuPy supports dtype option
if self.dtype == np.float32 or self.dtype == np.float16:
# float16 is not supported in cuRAND
args['dtype'] = np.float32
[a,b,c,d] = array.shape
#### Rayleigh distribution
x_ran = xp.random.uniform(0,1,[a/2,b,c,d])
modulus = s * xp.sqrt(-2*xp.log(x_ran))
phase = xp.random.uniform(low=-np.pi, high=np.pi, size=[a/2,b,c,d])
weight_real = modulus * xp.cos(phase)
weight_imag = modulus * xp.sin(phase)
weight = xp.concatenate([weight_real, weight_imag])
array[...] = weight
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, _ = initializer.get_fans(array.shape)
gain = self._calculate_gain(self._a)
std = gain / np.sqrt(fan_in)
bound = np.sqrt(3.0) * std
Uniform(scale=bound, dtype=self.dtype)(array)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
if len(array.shape) == 1:
Constant(self.scale / numpy.sqrt(array.shape[0]))(array)
else:
fan_in, _ = initializer.get_fans(array.shape)
Constant(self.scale / numpy.sqrt(fan_in))(array)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, fan_out = initializer.get_fans(array.shape)
if self.fan_option == 'fan_in':
s = self.scale * numpy.sqrt(2. / fan_in)
elif self.fan_option == 'fan_out':
s = self.scale * numpy.sqrt(2. / fan_out)
else:
raise ValueError(
'fan_option should be either \'fan_in\' or \'fan_out\'.')
Normal(s)(array)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, fan_out = initializer.get_fans(array.shape)
if self.fan_option == 'fan_in':
s = self.scale * numpy.sqrt(2. / fan_in)
elif self.fan_option == 'fan_out':
s = self.scale * numpy.sqrt(2. / fan_out)
else:
raise ValueError(
'fan_option should be either \'fan_in\' or \'fan_out\'.')
Normal(s)(array)
def __call__(self, array):
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(3. / fan_in)
Uniform(s)(array)
def test_invalid(self):
with self.assertRaises(ValueError):
initializer.get_fans(self.shape)
def test_get_fans(self):
actual = initializer.get_fans(self.shape)
self.assertTupleEqual(self.expect, actual)
def test_invalid(self):
with self.assertRaises(ValueError):
initializer.get_fans(self.shape)
def test_get_fans(self):
actual = initializer.get_fans(self.shape)
self.assertTupleEqual(self.expect, actual)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(2. / fan_in)
Normal(s)(array)
def __call__(self, array):
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(6. / (fan_in + fan_out))
return Uniform(s)(array)
def __call__(self, array):
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(6. / (fan_in + fan_out))
return Uniform(s)(array)
def __call__(self, array):
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(3. / fan_in)
Uniform(s)(array)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(6. / (fan_in + fan_out))
Uniform(s)(array)
def __call__(self, array):
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(2. / fan_in)
return Normal(s)(array)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(6. / (fan_in + fan_out))
Uniform(s)(array)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * np.sqrt(1. / fan_in)
Normal(s)(array)
def __call__(self, array):
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(2. / fan_in)
return Normal(s)(array)
