def random_normal_variable(shape, mean, scale, dtype=None, name=None,
seed=None):
"""Instantiates a variable with values drawn from a normal distribution.
Arguments:
shape: Tuple of integers, shape of returned Keras variable.
mean: Float, mean of the normal distribution.
scale: Float, standard deviation of the normal distribution.
dtype: String, dtype of returned Keras variable.
name: String, name of returned Keras variable.
seed: Integer, random seed.
Returns:
A Keras variable, filled with drawn samples.
Example:
```python
# TensorFlow example
>>> kvar = K.random_normal_variable((2,3), 0, 1)
>>> kvar
<tensorflow.python.ops.variables.Variable object at 0x10ab12dd0>
>>> K.eval(kvar)
array([[ 1.19591331, 0.68685907, -0.63814116],
[ 0.92629528, 0.28055015, 1.70484698]], dtype=float32)
"""
if dtype is None:
dtype = floatx()
shape = tuple(map(int, shape))
tf_dtype = _convert_string_dtype(dtype)
if seed is None:
# ensure that randomness is conditioned by the Numpy RNG
seed = np.random.randint(10e8)
value = init_ops.random_normal_initializer(
mean, scale, dtype=tf_dtype, seed=seed)(shape)
return variable(value, dtype=dtype, name=name)```
