def dropout(x, level, seed=None):
    '''Sets entries in `x` to zero at random,
    while scaling the entire tensor.

    # Arguments
        x: tensor
        level: fraction of the entries in the tensor
            that will be set to 0
        seed: random seed to ensure determinism.
    '''
    retain_prob = 1. - level
    if seed is None:
        seed = np.random.randint(10e6)
    # the dummy 1. works around a TF bug
    # (float32_ref vs. float32 incomptability)
    return tf.nn.dropout(x * 1., retain_prob, seed=seed)
dataset/ETHPy150Open fchollet/keras/keras/backend/tensorflow_backend.py/dropout/original