def random_uniform_variable(shape, low, high, dtype=None, name=None, seed=None):
"""Instantiates a variable with values drawn from a uniform distribution.
Arguments:
shape: Tuple of integers, shape of returned Keras variable.
low: Float, lower boundary of the output interval.
high: Float, upper boundary of the output interval.
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_uniform_variable((2,3), 0, 1)
>>> kvar
<tensorflow.python.ops.variables.Variable object at 0x10ab40b10>
>>> K.eval(kvar)
array([[ 0.10940075, 0.10047495, 0.476143 ],
[ 0.66137183, 0.00869417, 0.89220798]], 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_uniform_initializer(
low, high, dtype=tf_dtype, seed=seed)(shape)
return variable(value, dtype=dtype, name=name)```
