def random_normal(shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a normal distribution.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
of the normal distribution.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
@{tf.set_random_seed}
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random normal values.
"""
with ops.name_scope(name, "random_normal", [shape, mean, stddev]) as name:
shape_tensor = _ShapeTensor(shape)
mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops._random_standard_normal(
shape_tensor, dtype, seed=seed1, seed2=seed2)
mul = rnd * stddev_tensor
value = math_ops.add(mul, mean_tensor, name=name)
return value
def dropped_x():
rnd = gen_random_ops._random_standard_normal(
(self.store_input, self.output_dim),
dtype=tf.float32,
seed=seed1,
seed2=seed2)
mul = rnd * self.alpha
si = math_ops.add(mul,
tf.ones((self.store_input, self.output_dim)),
name='dropped')
wi = si * self.theta
b = K.dot(x, wi)
return (b)
