7c84229f350b83ce397b8c65e52e333e99b6d19d,keras/backend/tensorflow_backend.py,,random_uniform_variable,#Any#Any#Any#Any#Any#Any#,853

Before Change

    
    if dtype is None:
        dtype = floatx()
    tf_dtype = tf.as_dtype(dtype)
    if seed is None:
        // ensure that randomness is conditioned by the Numpy RNG
        seed = np.random.randint(10e8)
    value = tf.random_uniform_initializer(

After Change

    if seed is None:
        // ensure that randomness is conditioned by the Numpy RNG
        seed = np.random.randint(10e8)
    with tf_ops.init_scope():
        value = tf.random_uniform_initializer(
            low, high, dtype=dtype, seed=seed)(shape)
        return variable(value, dtype=dtype, name=name)


def random_normal_variable(shape, mean, scale, dtype=None,
                           name=None, seed=None):
    Instantiates a variable with values drawn from a normal distribution.

Instances